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ubowang

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cite-llm-multi-cite-eval

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[ "<|reference_start|> Membership Inference Attacks From First Principles: A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training dataset. These attacks are currently evaluated using average-case \"accuracy\" metrics that fail to characterize whether the attack can confidently identify any members of the training set. We argue that attacks should instead be evaluated by computing their true-positive rate at low (e.g., <0.1%) false-positive rates, and find most prior attacks perform poorly when evaluated in this way. To address this we develop a Likelihood Ratio Attack (LiRA) that carefully combines multiple ideas from the literature. Our attack is 10x more powerful at low false-positive rates, and also strictly dominates prior attacks on existing metrics. <|reference_end|>", "<|reference_start|> Deep Learning with Differential Privacy: Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality. <|reference_end|>", "<|reference_start|> Privacy Amplification via Random Check-Ins: Differentially Private Stochastic Gradient Descent (DP-SGD) forms a fundamental building block in many applications for learning over sensitive data. Two standard approaches, privacy amplification by subsampling, and privacy amplification by shuffling, permit adding lower noise in DP-SGD than via na\\\"{\\i}ve schemes. A key assumption in both these approaches is that the elements in the data set can be uniformly sampled, or be uniformly permuted -- constraints that may become prohibitive when the data is processed in a decentralized or distributed fashion. In this paper, we focus on conducting iterative methods like DP-SGD in the setting of federated learning (FL) wherein the data is distributed among many devices (clients). Our main contribution is the \\emph{random check-in} distributed protocol, which crucially relies only on randomized participation decisions made locally and independently by each client. It has privacy/accuracy trade-offs similar to privacy amplification by subsampling/shuffling. However, our method does not require server-initiated communication, or even knowledge of the population size. To our knowledge, this is the first privacy amplification tailored for a distributed learning framework, and it may have broader applicability beyond FL. Along the way, we extend privacy amplification by shuffling to incorporate $(\\epsilon,\\delta)$-DP local randomizers, and exponentially improve its guarantees. In practical regimes, this improvement allows for similar privacy and utility using data from an order of magnitude fewer users. <|reference_end|>", "<|reference_start|> {Calibrating Uncertainty Models for Steering Angle Estimation: Various approaches to end-to-end vehicle control using deep neural networks have been proposed recently, examining various architectures to predict steering angles based on raw sensor data. However, most of these approaches are only used as black boxes, which work well in most scenarios and drive vehicles in real traffic, but it is unclear when they will fail. In order to use such models in larger architectures used in autonomous vehicles, they need to reason about decisions or at least provide an additional measure of confidence that captures the uncertainty of the model. In this paper, we introduce and motivate different uncertainty models, comparing Monte Carlo dropout with network architectures based on the bootstrap ensembling method and a Gaussian mixture for the task of end-to-end vehicle control. Furthermore, we evaluate the presented uncertainty models regarding their driving performance as well as their model uncertainty calibration. The model calibration can be regarded as a measure of how well an uncertainty estimates fits to the expected performance. Well calibrated uncertainty estimates are crucial when embedding deep learning models into probabilistic models. <|reference_end|>" ]

[ 6, 18, 24, 39 ]

[ "<|reference_start|> Graph R-CNN for Scene Graph Generation: We propose a novel scene graph generation model called Graph R-CNN, that is both effective and efficient at detecting objects and their relations in images. Our model contains a Relation Proposal Network (RePN) that efficiently deals with the quadratic number of potential relations between objects in an image. We also propose an attentional Graph Convolutional Network (aGCN) that effectively captures contextual information between objects and relations. Finally, we introduce a new evaluation metric that is more holistic and realistic than existing metrics. We report state-of-the-art performance on scene graph generation as evaluated using both existing and our proposed metrics. <|reference_end|>", "<|reference_start|> Importance Weighted Autoencoders: The variational autoencoder (VAE; Kingma, Welling (2014)) is a recently proposed generative model pairing a top-down generative network with a bottom-up recognition network which approximates posterior inference. It typically makes strong assumptions about posterior inference, for instance that the posterior distribution is approximately factorial, and that its parameters can be approximated with nonlinear regression from the observations. As we show empirically, the VAE objective can lead to overly simplified representations which fail to use the network's entire modeling capacity. We present the importance weighted autoencoder (IWAE), a generative model with the same architecture as the VAE, but which uses a strictly tighter log-likelihood lower bound derived from importance weighting. In the IWAE, the recognition network uses multiple samples to approximate the posterior, giving it increased flexibility to model complex posteriors which do not fit the VAE modeling assumptions. We show empirically that IWAEs learn richer latent space representations than VAEs, leading to improved test log-likelihood on density estimation benchmarks. <|reference_end|>", "<|reference_start|> Categorical Reparameterization with Gumbel-Softmax: Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification. <|reference_end|>", "<|reference_start|> The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables: The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack useful reparameterizations due to the discontinuous nature of discrete states. In this work we introduce Concrete random variables---continuous relaxations of discrete random variables. The Concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a one-hot bit representation that is treated continuously, Concrete stochastic nodes can be used with automatic differentiation to produce low-variance biased gradients of objectives (including objectives that depend on the log-probability of latent stochastic nodes) on the corresponding discrete graph. We demonstrate the effectiveness of Concrete relaxations on density estimation and structured prediction tasks using neural networks. <|reference_end|>" ]

[ 0, 4, 5, 9 ]

[ "<|reference_start|> PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications: PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efficiently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications. <|reference_end|>", "<|reference_start|> Wasserstein Generative Adversarial Networks: We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to different distances between distributions. <|reference_end|>", "<|reference_start|> Autoencoding beyond pixels using a learned similarity metric: We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic. <|reference_end|>", "<|reference_start|> How good is my GAN?: Generative adversarial networks (GANs) are one of the most popular methods for generating images today. While impressive results have been validated by visual inspection, a number of quantitative criteria have emerged only recently. We argue here that the existing ones are insufficient and need to be in adequation with the task at hand. In this paper we introduce two measures based on image classification---GAN-train and GAN-test, which approximate the recall (diversity) and precision (quality of the image) of GANs respectively. We evaluate a number of recent GAN approaches based on these two measures and demonstrate a clear difference in performance. Furthermore, we observe that the increasing difficulty of the dataset, from CIFAR10 over CIFAR100 to ImageNet, shows an inverse correlation with the quality of the GANs, as clearly evident from our measures. <|reference_end|>" ]

[ 3, 7, 12, 13 ]

[ "<|reference_start|> High‐dimensional Statistics: A Non‐asymptotic Viewpoint, Martin J.Wainwright, Cambridge University Press, 2019, xvii 552 pages, £57.99, hardback ISBN: 978‐1‐1084‐9802‐9: <|reference_end|>", "<|reference_start|> Learning with convolution and pooling operations in kernel methods: Recent empirical work has shown that hierarchical convolutional kernels inspired by convolutional neural networks (CNNs) significantly improve the performance of kernel methods in image classification tasks. A widely accepted explanation for their success is that these architectures encode hypothesis classes that are suitable for natural images. However, understanding the precise interplay between approximation and generalization in convolutional architectures remains a challenge. In this paper, we consider the stylized setting of covariates (image pixels) uniformly distributed on the hypercube, and characterize exactly the RKHS of kernels composed of single layers of convolution, pooling, and downsampling operations. We use this characterization to compute sharp asymptotics of the generalization error for any given function in high-dimension. In particular, we quantify the gain in sample complexity brought by enforcing locality with the convolution operation and approximate translation invariance with average pooling. Notably, these results provide a precise description of how convolution and pooling operations trade off approximation with generalization power in one layer convolutional kernels. <|reference_end|>", "<|reference_start|> On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels: We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels. We prove that, with normalized multi-channel input and ReLU activation, the eigenfunctions of these kernels with the uniform measure are formed by products of spherical harmonics, defined over the channels of the different pixels. We next use hierarchical factorizable kernels to bound their respective eigenvalues. We show that the eigenvalues decay polynomially, quantify the rate of decay, and derive measures that reflect the composition of hierarchical features in these networks. Our results provide concrete quantitative characterization of over-parameterized convolutional network architectures. <|reference_end|>", "<|reference_start|> The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks: It is currently known how to characterize functions that neural networks can learn with SGD for two extremal parameterizations: neural networks in the linear regime, and neural networks with no structural constraints. However, for the main parametrization of interest (non-linear but regular networks) no tight characterization has yet been achieved, despite significant developments. We take a step in this direction by considering depth-2 neural networks trained by SGD in the mean-field regime. We consider functions on binary inputs that depend on a latent low-dimensional subspace (i.e., small number of coordinates). This regime is of interest since it is poorly understood how neural networks routinely tackle high-dimensional datasets and adapt to latent low-dimensional structure without suffering from the curse of dimensionality. Accordingly, we study SGD-learnability with $O(d)$ sample complexity in a large ambient dimension $d$. Our main results characterize a hierarchical property, the \"merged-staircase property\", that is both necessary and nearly sufficient for learning in this setting. We further show that non-linear training is necessary: for this class of functions, linear methods on any feature map (e.g., the NTK) are not capable of learning efficiently. The key tools are a new \"dimension-free\" dynamics approximation result that applies to functions defined on a latent space of low-dimension, a proof of global convergence based on polynomial identity testing, and an improvement of lower bounds against linear methods for non-almost orthogonal functions. <|reference_end|>" ]

[ 0, 29, 33, 37 ]

[ "<|reference_start|> Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation: The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-driven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported. <|reference_end|>", "<|reference_start|> IoT--Assets Taxonomy, Threats Assessment and Potential Solutions: Internet of Things (IoT) is a system of interconnected devices and networks that provides autonomous functioning capability. Increasing expansion rate of IoT system in diverse set of domains has resulted in inclination of associated risks as well. This research paper presents classification of assets which are part of IoT system, identify platform-independent threats, and consolidate potential solutions to secure the IoT system from threats. Various researchers have conducted studies which are focused on specific industry/domain. Therefore, there is an inevitable necessity to present generalized assessment of IoT system and associated threats, irrespective of the industry or domain. In addition, this research presents prioritization, in terms of criticality, of IoT assets and threats that will enable stakeholders to identify the items that are crucial and items that can be ignored considering the low priority. This would enable researchers and stakeholders of IoT system (such as policy makers, end-users, manufacturers, and security experts etc. to have a deeper understanding of the implications and have an insight on solutions/recommendations in a broad-spectrum. Survey findings showed that DDoS (distributed denial of service), privacy attack, and information modification are the top three threats in terms of criticality ranking. A generalized (industry-independent) set of recommendations have been consolidated based on literature analysis and survey findings in three categories, which are related to policies, organizational measures, and technical measures. <|reference_end|>", "<|reference_start|> Cyber-Resilience, Principles, and Practices: <|reference_end|>", "<|reference_start|> Effect of Heating, Ventilation, and Air Conditioning (HVAC) system on indoor air quality in a medical facility: Indoor Air Quality (IAQ) refers to the stationary air within an inhabited or occupied structure. Previously, there were fewer studies on indoor air quality in medical facilities in Malaysia especially in Terengganu. Most indoor air quality issues are caused by insufficient Heating, Ventilation, and Air Conditioning (HVAC) systems, which regulate three parameters. The purpose of this study is to assess the indoor air quality of a medical facility and determine if it complies with the Industry Code of Practice 2010 (ICOP 2010) and ASHRAE 170-2017. In this investigation, a total of 3 locations namely Administration Office, Surgical Outpatient Department (SOPD) waiting area and Ophthalmology Consultation Room in Hospital Sultanah Nur Zahirah (HSNZ) were evaluated. Walkthrough inspections were done at the locations before data collection to determine the IAQ. Two IAQ meters, notably VelociCalc and Testo, were used to collect data to assess the temperature, relative humidity, and air flow of the selected locations. Samples were taken every 2 hours for each location from 8 a.m. to 5 p.m. The data then were analysed. All three locations' temperatures were lower than ICOP 2010's acceptable limit (23-26°C), but still within ASHRAE 170-207's 21-24°C range, except for the SOPD waiting room. All three locations met ICOP 2010 and ASHRAE 170-207 relative humidity standards. Meanwhile, only the SOPD waiting room had an appropriate air flow of 0.16-0.17m/s per ICOP 2010. The study also revealed that there was a correlation between the number of occupancies and the performance of HVAC system with the indoor air quality level. <|reference_end|>" ]

[ 0, 2, 3, 4 ]

[ "<|reference_start|> Combining human computing and machine learning to make sense of big (aerial) data for disaster response: Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission's Joint Research Center (JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap ). The results suggest that the platform we have developed to combine crowdsourcing and machine learning to make sense of large volumes of aerial images can be used for disaster response. <|reference_end|>", "<|reference_start|> Comparison of damage assessment maps derived from very high spatial resolution satellite and aerial imagery produced for the Haiti 2010 earthquake: Following the devastating M7.2 earthquake that affected Haiti on 12 January 2010 two types of building damage assessment maps were produced: 1) area-based damage assessments using pre- and post-event satellite imagery and 2) detailed building-by-building damage assessments using post-event aerial photography. In this paper, we compare the reliability and the usability of area-based damage assessment maps from satellite imagery with respect to the detailed damage assessment from aerial data. The main objective is to better understand how cooperative rapid mapping can steer the more detailed assessments that are typical in determining postdisaster recovery and reconstruction efforts. The results of these experiments indicate that damage assessment maps based on satellite data are capable of capturing the damage pattern, mainly in areas with a high level of damaged and many collapsed structures. However, these maps cannot provide the level of information needed for the quantification of damage intensity. <|reference_end|>", "<|reference_start|> Damage patterns from satellite images of the 2003 Bam, Iran, earthquake: High-resolution (0.6m) commercial satellite images contain a wealth of information for mapping earthquake damage. Satellite images of the city of Bam, acquired on 30 September 2003 (pre-earthquake) and 03 January 2004 (post-earthquake), were obtained and used to distinguish damage patterns across the city. Comparisons between pre- and post-earthquake images clearly show structural damage and collapse. Using spectral (color) and textural information from the post-earthquake image, regions of damage were identified using a semi-automated computer-based algorithm. This analysis indicates that the damage within the city of Bam was concentrated in the eastern sections of the city. The extent of damage in some sections of the city reached 100%. The results from this study not only provide information regarding damage patterns for the city of Bam, but they also illustrate the potential for using satellite images to understand and document earthquake effects during future earthquakes. <|reference_end|>", "<|reference_start|> xView: Objects in Context in Overhead Imagery: We introduce a new large-scale dataset for the advancement of object detection techniques and overhead object detection research. This satellite imagery dataset enables research progress pertaining to four key computer vision frontiers. We utilize a novel process for geospatial category detection and bounding box annotation with three stages of quality control. Our data is collected from WorldView-3 satellites at 0.3m ground sample distance, providing higher resolution imagery than most public satellite imagery datasets. We compare xView to other object detection datasets in both natural and overhead imagery domains and then provide a baseline analysis using the Single Shot MultiBox Detector. xView is one of the largest and most diverse publicly available object-detection datasets to date, with over 1 million objects across 60 classes in over 1,400 km^2 of imagery. <|reference_end|>" ]

[ 9, 10, 19, 24 ]

[ "<|reference_start|> Explaining and Harnessing Adversarial Examples: Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset. <|reference_end|>", "<|reference_start|> Robustness Against Adversarial Attacks Using Dimensionality: <|reference_end|>", "<|reference_start|> DAP: A Dynamic Adversarial Patch for Evading Person Detectors: Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However, their conspicuous and easily detectable nature challenge their practicality in real-world setting. To address this, recent work has proposed using Generative Adversarial Networks (GANs) to generate naturalistic patches that may not attract human attention. However, such approaches suffer from a limited latent space making it challenging to produce a patch that is efficient, stealthy, and robust to multiple real-world transformations. This paper introduces a novel approach that produces a Dynamic Adversarial Patch (DAP) designed to overcome these limitations. DAP maintains a naturalistic appearance while optimizing attack efficiency and robustness to real-world transformations. The approach involves redefining the optimization problem and introducing a novel objective function that incorporates a similarity metric to guide the patch's creation. Unlike GAN-based techniques, the DAP directly modifies pixel values within the patch, providing increased flexibility and adaptability to multiple transformations. Furthermore, most clothing-based physical attacks assume static objects and ignore the possible transformations caused by non-rigid deformation due to changes in a person's pose. To address this limitation, a 'Creases Transformation' (CT) block is introduced, enhancing the patch's resilience to a variety of real-world distortions. Experimental results demonstrate that the proposed approach outperforms state-of-the-art attacks, achieving a success rate of up to 82.28% in the digital world when targeting the YOLOv7 detector and 65% in the physical world when targeting YOLOv3tiny detector deployed in edge-based smart cameras. <|reference_end|>", "<|reference_start|> Local Gradients Smoothing: Defense against localized adversarial attacks: Deep neural networks (DNNs) have shown vulnerability to adversarial attacks, i.e., carefully perturbed inputs designed to mislead the network at inference time. Recently introduced localized attacks, Localized and Visible Adversarial Noise (LaVAN) and Adversarial patch, pose a new challenge to deep learning security by adding adversarial noise only within a specific region without affecting the salient objects in an image. Driven by the observation that such attacks introduce concentrated high-frequency changes at a particular image location, we have developed an effective method to estimate noise location in gradient domain and transform those high activation regions caused by adversarial noise in image domain while having minimal effect on the salient object that is important for correct classification. Our proposed Local Gradients Smoothing (LGS) scheme achieves this by regularizing gradients in the estimated noisy region before feeding the image to DNN for inference. We have shown the effectiveness of our method in comparison to other defense methods including Digital Watermarking, JPEG compression, Total Variance Minimization (TVM) and Feature squeezing on ImageNet dataset. In addition, we systematically study the robustness of the proposed defense mechanism against Back Pass Differentiable Approximation (BPDA), a state of the art attack recently developed to break defenses that transform an input sample to minimize the adversarial effect. Compared to other defense mechanisms, LGS is by far the most resistant to BPDA in localized adversarial attack setting. <|reference_end|>" ]

[ 3, 5, 6, 16 ]

[ "<|reference_start|> Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets: Today’s scientific simulations require a significant reduction of the data size because of extremely large volumes of data they produce and the limitation of storage bandwidth and space. If the compression is set to reach a high compression ratio, however, the reconstructed data are often distorted too much to tolerate. In this paper, we explore a new compression strategy that can effectively control the data distortion when significantly reducing the data size. The contribution is threefold. (1) We propose an adaptive compression framework to select either our improved Lorenzo prediction method or our optimized linear regression method dynamically in different regions of the dataset. (2) We explore how to select them accurately based on the data features in each block to obtain the best compression quality. (3) We analyze the effectiveness of our solution in details using four real-world scientific datasets with 100+ fields. Evaluation results confirm that our new adaptive solution can significantly improve the rate distortion for the lossy compression with fairly high compression ratios. The compression ratio of our compressor is 1.5X~8X as high as that of two other leading lossy compressors (SZ and ZFP) with the same peak single-to-noise ratio (PSNR), in the high-compression cases. Parallel experiments with 8,192 cores and 24 TB of data shows that our solution obtains 1.86X dumping performance and 1.95X loading performance compared with the second-best lossy compressor, respectively. <|reference_end|>", "<|reference_start|> Optimizing Lossy Compression with Adjacent Snapshots for N-body Simulation Data: Today’s N-body simulations are producing extremely large amounts of data. The Hardware/Hybrid Accelerated Cosmology Code (HACC), for example, may simulate trillions of particles, producing tens of petabytes of data to store in a parallel file system, according to the HACC users. In this paper, we design and implement an efficient, in situ error-bounded lossy compressor to significantly reduce the data size for N-body simulations. Not only can our compressor save significant storage space for N-body simulation researchers, but it can also improve the I/O performance considerably with limited memory and computation overhead. Our contribution is threefold. (1) We propose an efficient data compression model by leveraging the consecutiveness of the cosmological data in both space and time dimensions as well as the physical correlation across different fields. (2) We propose a lightweight, efficient alignment mechanism to align the disordered particles across adjacent snapshots in the simulation, which is a fundamental step in the whole compression procedure. We also optimize the compression quality by exploring best-fit data prediction strategies and optimizing the frequencies of the space-based compression vs. time-based compression. (3) We evaluate our compressor using both a cosmological simulation package and molecular dynamics simulation data—two major categories in the N-body simulation domain. Experiments show that under the same distortion of data, our solution produces up to 43% higher compression ratios on the velocity field and up to 300% higher on the position field than do other state-of-the-art compressors (including SZ, ZFP, NUMARCK, and decimation). With our compressor, the overall I/O time on HACC data is reduced by up to 20% compared with the second-best compressor. <|reference_end|>", "<|reference_start|> Memory-Efficient Quantum Circuit Simulation by Using Lossy Data Compression: In order to evaluate, validate, and refine the design of new quantum algorithms or quantum computers, researchers and developers need methods to assess their correctness and fidelity. This requires the capabilities of quantum circuit simulations. However, the number of quantum state amplitudes increases exponentially with the number of qubits, leading to the exponential growth of the memory requirement for the simulations. In this work, we present our memory-efficient quantum circuit simulation by using lossy data compression. Our empirical data shows that we reduce the memory requirement to 16.5% and 2.24E-06 of the original requirement for QFT and Grover's search, respectively. This finding further suggests that we can simulate deep quantum circuits up to 63 qubits with 0.8 petabytes memory. <|reference_end|>", "<|reference_start|> Exploration of lossy compression for application-level checkpoint/restart: The scale of high performance computing (HPC) systems is exponentially growing, potentially causing prohibitive shrinkage of mean time between failures (MTBF) while the overall increase in the I/O performance of parallel file systems will be far behind the increase in scale. As such, there have been various attempts to decrease the checkpoint overhead, one of which is to employ compression techniques to the checkpoint files. While most of the existing techniques focus on lossless compression, their compression rates and thus effectiveness remain rather limited. Instead, we propose a loss compression technique based on wavelet transformation for checkpoints, and explore its impact to application results. Experimental application of our loss compression technique to a production climate application, NICAM, shows that the overall checkpoint time including compression is reduced by 81%, while relative error remains fairly constant at approximately 1.2% on overall average of all variables of compressed physical quantities compared to original checkpoint without compression. <|reference_end|>" ]

[ 2, 3, 5, 17 ]

[ "<|reference_start|> IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks: The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks. The environment is designed to advance reinforcement learning from simple toy tasks to complex tasks requiring both long-term planning and sophisticated low-level control. Our environment supports over 80 different furniture models, Sawyer and Baxter robot simulation, and domain randomization. The IKEA Furniture Assembly Environment is a testbed for methods aiming to solve complex manipulation tasks. The environment is publicly available at https://clvrai.com/furniture <|reference_end|>", "<|reference_start|> Equivariant descriptor fields: Se (3)-equivariant energy-based models for end-to-end visual robotic manipulation learning: End-to-end learning for visual robotic manipulation is known to suffer from sample inefficiency, requiring large numbers of demonstrations. The spatial roto-translation equivariance, or the SE(3)-equivariance can be exploited to improve the sample efficiency for learning robotic manipulation. In this paper, we present SE(3)-equivariant models for visual robotic manipulation from point clouds that can be trained fully end-to-end. By utilizing the representation theory of the Lie group, we construct novel SE(3)-equivariant energy-based models that allow highly sample efficient end-to-end learning. We show that our models can learn from scratch without prior knowledge and yet are highly sample efficient (5~10 demonstrations are enough). Furthermore, we show that our models can generalize to tasks with (i) previously unseen target object poses, (ii) previously unseen target object instances of the category, and (iii) previously unseen visual distractors. We experiment with 6-DoF robotic manipulation tasks to validate our models' sample efficiency and generalizability. Codes are available at: https://github.com/tomato1mule/edf <|reference_end|>", "<|reference_start|> Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors: Learning to autonomously assemble shapes is a crucial skill for many robotic applications. While the majority of existing part assembly methods focus on correctly posing semantic parts to recreate a whole object, we interpret assembly more literally: as mating geometric parts together to achieve a snug fit. By focusing on shape alignment rather than semantic cues, we can achieve across-category generalization. In this paper, we introduce a novel task, pairwise 3D geometric shape mating, and propose Neural Shape Mating (NSM) to tackle this problem. Given the point clouds of two object parts of an unknown category, NSM learns to reason about the fit of the two parts and predict a pair of 3D poses that tightly mate them together. We couple the training of NSM with an implicit shape reconstruction task to make NSM more robust to imperfect point cloud observations. To train NSM, we present a self-supervised data collection pipeline that generates pairwise shape mating data with ground truth by randomly cutting an object mesh into two parts, resulting in a dataset that consists of 200K shape mating pairs from numerous object meshes with diverse cut types. We train NSM on the collected dataset and compare it with several point cloud registration methods and one part assembly baseline. Extensive experimental results and ablation studies under various settings demonstrate the effectiveness of the proposed algorithm. Additional material is available at: https://neural-shape-mating.github.io/ <|reference_end|>", "<|reference_start|> Generative 3D Part Assembly via Dynamic Graph Learning: Autonomous part assembly is a challenging yet crucial task in 3D computer vision and robotics. Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation. In this paper, we focus on the pose estimation subproblem from the vision side involving geometric and relational reasoning over the input part geometry. Essentially, the task of generative 3D part assembly is to predict a 6-DoF part pose, including a rigid rotation and translation, for each input part that assembles a single 3D shape as the final output. To tackle this problem, we propose an assembly-oriented dynamic graph learning framework that leverages an iterative graph neural network as a backbone. It explicitly conducts sequential part assembly refinements in a coarse-to-fine manner, exploits a pair of part relation reasoning module and part aggregation module for dynamically adjusting both part features and their relations in the part graph. We conduct extensive experiments and quantitative comparisons to three strong baseline methods, demonstrating the effectiveness of the proposed approach. <|reference_end|>" ]

[ 0, 26, 30, 41 ]

[ "<|reference_start|> Generative {{Adversarial Nets}}: CNN and RNN are classifiers for image and speech recognition, and are used in many computer vision. However, this model alone does not produce images... <|reference_end|>", "<|reference_start|> Towards Pose Invariant Face Recognition in the Wild: Pose variation is one key challenge in face recognition. As opposed to current techniques for pose invariant face recognition, which either directly extract pose invariant features for recognition, or first normalize profile face images to frontal pose before feature extraction, we argue that it is more desirable to perform both tasks jointly to allow them to benefit from each other. To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties. First, PIM is a novel and unified deep architecture, containing a Face Frontalization sub-Net (FFN) and a Discriminative Learning sub-Net (DLN), which are jointly learned from end to end. Second, FFN is a well-designed dual-path Generative Adversarial Network (GAN) which simultaneously perceives global structures and local details, incorporated with an unsupervised cross-domain adversarial training and a \"learning to learn\" strategy for high-fidelity and identity-preserving frontal view synthesis. Third, DLN is a generic Convolutional Neural Network (CNN) for face recognition with our enforced cross-entropy optimization strategy for learning discriminative yet generalized feature representation. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of the proposed model over the state-of-the-arts. <|reference_end|>", "<|reference_start|> Multimodal Unsupervised Image-to-Image Translation: Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to the state-of-the-art approaches further demonstrates the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at https://github.com/nvlabs/MUNIT <|reference_end|>", "<|reference_start|> Multimodal Unsupervised Image-to-Image Translation: Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any pairs of corresponding images. While this conditional distribution is inherently multimodal, existing approaches make an overly simplified assumption, modeling it as a deterministic one-to-one mapping. As a result, they fail to generate diverse outputs from a given source domain image. To address this limitation, we propose a Multimodal Unsupervised Image-to-image Translation (MUNIT) framework. We assume that the image representation can be decomposed into a content code that is domain-invariant, and a style code that captures domain-specific properties. To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain. We analyze the proposed framework and establish several theoretical results. Extensive experiments with comparisons to the state-of-the-art approaches further demonstrates the advantage of the proposed framework. Moreover, our framework allows users to control the style of translation outputs by providing an example style image. Code and pretrained models are available at https://github.com/nvlabs/MUNIT <|reference_end|>" ]

[ 6, 12, 26, 36 ]

[ "<|reference_start|> 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications: <|reference_end|>", "<|reference_start|> 2013 Conference on Lasers and Electro-Optics Europe and International Quantum Electronics Conference CLEO EUROPE/IQEC: <|reference_end|>", "<|reference_start|> {Vertical-Cavity Surface-Emitting Lasers for Data Communication and Sensing: Vertical-cavity surface-emitting lasers (VCSELs) are the ideal optical sources for data communication and sensing. In data communication, large data rates combined with excellent energy efficiency and temperature stability have been achieved based on advanced device design and modulation formats. VCSELs are also promising sources for photonic integrated circuits due to their small footprint and low power consumption. Also, VCSELs are commonly used for a wide variety of applications in the consumer electronics market. These applications range from laser mice to three-dimensional (3D) sensing and imaging, including various 3D movement detections, such as gesture recognition or face recognition. Novel VCSEL types will include metastructures, exhibiting additional unique properties, of largest importance for next-generation data communication, sensing, and photonic integrated circuits. <|reference_end|>", "<|reference_start|> ICNIRP Guidelines on Limits of Exposure to Laser Radiation of Wavelengths between 180 nm and 1,000 μm.: Since the publication of the ICNIRP Revision of the Guidelines on Limits of Exposure to Laser Radiation (), further research supports amending the retinal thermal exposure limits in terms of spot size dependence, pulse duration dependence for short pulses and wavelength dependence between 1,200 nm and 1,400 nm. A detailed discussion of the rational for the changes is presented in the Appendix of these Guidelines (Rationale for updating the Guidelines). <|reference_end|>" ]

[ 13, 18, 24, 31 ]

[ "<|reference_start|> Australian prawn farming manual: health management for profit: This manual is an easy to read guide to best management practises, with a focus on health management for Australian prawn farmers. Using the combined knowledge of Australia's leading scientists, prawn health specialists, prawn farmers and extensionists this manual captures what is known about managing prawn health and maximizing the farm's productivity and profitability. \n \nFunded by the Australian Center for International Agricultural Research and developed in collaboration with the Australian Prawn Farmers' Association, The Queensland Department of Primary Industries and Fisheries and the \nNew South Wales Department of Primary Industries, the manual draws on extensive research conducted across the Australasia region. The contents reflect the knowledge of a wide array of internationally recognized researchers and the wisdom and research gained through the efforts of the Australian prawn farming industry. <|reference_end|>", "<|reference_start|> AquaFarm: simulation and decision support for aquaculture facility design and management planning: <|reference_end|>", "<|reference_start|> Sequence to Sequence Learning with Neural Networks: Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier. <|reference_end|>", "<|reference_start|> Attention Is All You Need: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. <|reference_end|>" ]

[ 2, 10, 13, 16 ]

[ "<|reference_start|> {You-Do, I-Learn: Discovering Task Relevant Objects and their Modes of Interaction from Multi-User Egocentric Video: We present a fully unsupervised approach for the discovery of i) task relevant objects and ii) how these objects have been used. A Task Relevant Object (TRO) is an object, or part of an object, with which a person interacts during task performance. Given egocentric video from multiple operators, the approach can discover objects with which the users interact, both static objects such as a coffee machine as well as movable ones such as a cup. Importantly, we also introduce the term Mode of Interaction (MOI) to refer to the different ways in which TROs are used. Say, a cup can be lifted, washed, or poured into. When harvesting interactions with the same object from multiple operators, common MOIs can be found. Setup and Dataset: Using a wearable camera and gaze tracker (Mobile Eye-XG from ASL), egocentric video is collected of users performing tasks, along with their gaze in pixel coordinates. Six locations were chosen: kitchen, workspace, laser printer, corridor with a locked door, cardiac gym and weight-lifting machine. The Bristol Egocentric Object Interactions Dataset is publically available . <|reference_end|>", "<|reference_start|> Robot-centric activity prediction from first-person videos: What will they do to me?: In this paper, we present a core technology to enable robot recognition of human activities during human-robot interactions. In particular, we propose a methodology for early recognition of activities from robot-centric videos (i.e., first-person videos) obtained from a robot's viewpoint during its interaction with humans. Early recognition, which is also known as activity prediction, is an ability to infer an ongoing activity at its early stage. We present an algorithm to recognize human activities targeting the camera from streaming videos, enabling the robot to predict intended activities of the interacting person as early as possible and take fast reactions to such activities (e.g., avoiding harmful events targeting itself before they actually occur). We introduce the novel concept of'onset' that efficiently summarizes pre-activity observations, and design a recognition approach to consider event history in addition to visual features from first-person videos. We propose to represent an onset using a cascade histogram of time series gradients, and we describe a novel algorithmic setup to take advantage of such onset for early recognition of activities. The experimental results clearly illustrate that the proposed concept of onset enables better/earlier recognition of human activities from first-person videos collected with a robot. Categories and Subject Descriptors I.2.10 [Artificial Intelligence]: Vision and Scene Understanding–video analysis; I.4.8 [Image Processing and Computer Vision]: Scene Analysis-motion; I.2.9 [Artificial Intelligence]: Robotics–sensors <|reference_end|>", "<|reference_start|> Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks: State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. <|reference_end|>", "<|reference_start|> Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks: State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. <|reference_end|>" ]

[ 2, 20, 29, 34 ]

[ "<|reference_start|> {Multimodal data fusion: an overview of methods, challenges, and prospects: In various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term “modality” for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or “challenges,” are common to multiple domains. This paper deals with two key issues: “why we need data fusion” and “how we perform it.” The first issue is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second issue, “diversity” is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the data sets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects, and the opportunities that it holds. <|reference_end|>", "<|reference_start|> Are Sixteen Heads Really Better than One?: Attention is a powerful and ubiquitous mechanism for allowing neural models to focus on particular salient pieces of information by taking their weighted average when making predictions. In particular, multi-headed attention is a driving force behind many recent state-of-the-art NLP models such as Transformer-based MT models and BERT. These models apply multiple attention mechanisms in parallel, with each attention \"head\" potentially focusing on different parts of the input, which makes it possible to express sophisticated functions beyond the simple weighted average. In this paper we make the surprising observation that even if models have been trained using multiple heads, in practice, a large percentage of attention heads can be removed at test time without significantly impacting performance. In fact, some layers can even be reduced to a single head. We further examine greedy algorithms for pruning down models, and the potential speed, memory efficiency, and accuracy improvements obtainable therefrom. Finally, we analyze the results with respect to which parts of the model are more reliant on having multiple heads, and provide precursory evidence that training dynamics play a role in the gains provided by multi-head attention. <|reference_end|>", "<|reference_start|> Deep Multimodal Fusion for Persuasiveness Prediction: Persuasiveness is a high-level personality trait that quantifies the influence a speaker has on the beliefs, attitudes, intentions, motivations, and behavior of the audience. With social multimedia becoming an important channel in propagating ideas and opinions, analyzing persuasiveness is very important. In this work, we use the publicly available Persuasive Opinion Multimedia (POM) dataset to study persuasion. One of the challenges associated with this problem is the limited amount of annotated data. To tackle this challenge, we present a deep multimodal fusion architecture which is able to leverage complementary information from individual modalities for predicting persuasiveness. Our methods show significant improvement in performance over previous approaches. <|reference_end|>", "<|reference_start|> DeepCU: Integrating both Common and Unique Latent Information for Multimodal Sentiment Analysis: Multimodal sentiment analysis combines information available from visual, textual, and acoustic representations for sentiment prediction. The recent multimodal fusion schemes combine multiple modalities as a tensor and obtain either; the common information by utilizing neural networks, or the unique information by modeling low-rank representation of the tensor. However, both of these information are essential as they render inter-modal and intra-modal relationships of the data. In this research, we first propose a novel deep architecture to extract the common information from the multi-mode representations. Furthermore, we propose unique networks to obtain the modality-specific information that enhances the generalization performance of our multimodal system. Finally, we integrate these two aspects of information via a fusion layer and propose a novel multimodal data fusion architecture, which we call DeepCU (Deep network with both Common and Unique latent information). The proposed DeepCU consolidates the two networks for joint utilization and discovery of all-important latent information. Comprehensive experiments are conducted to demonstrate the effectiveness of utilizing both common and unique information discovered by DeepCU on multiple real-world datasets. The source code of proposed DeepCU is available at https://github.com/sverma88/DeepCU-IJCAI19. <|reference_end|>" ]

[ 0, 9, 12, 22 ]

[ "<|reference_start|> Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification: This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first \"patch-wise\" network acts as an auto-encoder that extracts the most salient features of image patches while the second \"image-wise\" network performs classification of the whole image. The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields 95 % accuracy on the validation set compared to previously reported 77 % accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018 <|reference_end|>", "<|reference_start|> The whole slide breast histopathology image detection based on a fused model and heatmaps: <|reference_end|>", "<|reference_start|> Data-efficient and weakly supervised computational pathology on whole-slide images: <|reference_end|>", "<|reference_start|> Deep learning for semantic segmentation vs. classification in computational pathology: application to mitosis analysis in breast cancer grading: Existing computational approaches have not yet resulted in effective and efficient computer-aided tools that are used in pathologists' daily practice. Focusing on a computer-based qualification for breast cancer diagnosis, the present study proposes two deep learning architectures to efficiently and effectively detect and classify mitosis in a histopathological tissue sample. The first method consists of two parts, entailing a preprocessing of the digital histological image and a free-handcrafted-feature Convolutional Neural Network (CNN) used for binary classification. Results show that the methodology proposed can achieve 95% accuracy in testing, with an F1-score of 94.35%. This result is higher than the results using classical image processing techniques and also higher than the approaches combining CCNs with handcrafted features. The second approach is an end-to-end methodology using semantic segmentation. Results showed that this algorithm can achieve an accuracy higher than 95% in testing and an average Dice index of 0.6, higher than the existing results using CNNs (0.9 F1-score). Additionally, due to the semantic properties of the deep learning approach, an end-to-end deep learning framework is viable to perform both tasks: detection and classification of mitosis. The results show the potential of deep learning in the analysis of Whole Slide Images (WSI) and its integration to computer-aided systems. The extension of this work to whole slide images is also addressed in the last sections; as well as, some computational key points that are useful when constructing a computer-aided-system inspired by the proposed technology. <|reference_end|>" ]

[ 0, 7, 8, 24 ]

[ "<|reference_start|> The Class Imbalance Problem: A Systematic Study: In machine learning problems, differences in prior class probabilities -- or class imbalances -- have been reported to hinder the performance of some standard classifiers, such as decision trees. This paper presents a systematic study aimed at answering three different questions. First, we attempt to understand the nature of the class imbalance problem by establishing a relationship between concept complexity, size of the training set and class imbalance level. Second, we discuss several basic re-sampling or cost-modifying methods previously proposed to deal with the class imbalance problem and compare their effectiveness. The results obtained by such methods on artificial domains are linked to results in real-world domains. Finally, we investigate the assumption that the class imbalance problem does not only affect decision tree systems but also affects other classification systems such as Neural Networks and Support Vector Machines. <|reference_end|>", "<|reference_start|> Rethinking the Value of Labels for Improving Class-Imbalanced Learning: Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models. We identify a persisting dilemma on the value of labels in the context of imbalanced learning: on the one hand, supervision from labels typically leads to better results than its unsupervised counterparts; on the other hand, heavily imbalanced data naturally incurs \"label bias\" in the classifier, where the decision boundary can be drastically altered by the majority classes. In this work, we systematically investigate these two facets of labels. We demonstrate, theoretically and empirically, that class-imbalanced learning can significantly benefit in both semi-supervised and self-supervised manners. Specifically, we confirm that (1) positively, imbalanced labels are valuable: given more unlabeled data, the original labels can be leveraged with the extra data to reduce label bias in a semi-supervised manner, which greatly improves the final classifier; (2) negatively however, we argue that imbalanced labels are not useful always: classifiers that are first pre-trained in a self-supervised manner consistently outperform their corresponding baselines. Extensive experiments on large-scale imbalanced datasets verify our theoretically grounded strategies, showing superior performance over previous state-of-the-arts. Our intriguing findings highlight the need to rethink the usage of imbalanced labels in realistic long-tailed tasks. Code is available at https://github.com/YyzHarry/imbalanced-semi-self. <|reference_end|>", "<|reference_start|> ADASYN: Adaptive synthetic sampling approach for imbalanced learning: This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics. <|reference_end|>", "<|reference_start|> Balanced Meta-Softmax for Long-Tailed Visual Recognition: Deep classifiers have achieved great success in visual recognition. However, real-world data is long-tailed by nature, leading to the mismatch between training and testing distributions. In this paper, we show that the Softmax function, though used in most classification tasks, gives a biased gradient estimation under the long-tailed setup. This paper presents Balanced Softmax, an elegant unbiased extension of Softmax, to accommodate the label distribution shift between training and testing. Theoretically, we derive the generalization bound for multiclass Softmax regression and show our loss minimizes the bound. In addition, we introduce Balanced Meta-Softmax, applying a complementary Meta Sampler to estimate the optimal class sample rate and further improve long-tailed learning. In our experiments, we demonstrate that Balanced Meta-Softmax outperforms state-of-the-art long-tailed classification solutions on both visual recognition and instance segmentation tasks. <|reference_end|>" ]

[ 8, 14, 25, 39 ]

[ "<|reference_start|> Risk and uncertainty communication: This review briefly examines the vast range of techniques used to communicate risk assessments arising from statistical analysis. After discussing essential psychological and sociological issues, I focus on individual health risks and relevant research on communicating numbers, verbal expressions, graphics, and conveying deeper uncertainty. I then consider practice in a selection of diverse case studies, including gambling, the benefits and risks of pharmaceuticals, weather forecasting, natural hazards, climate change, environmental exposures, security and intelligence, industrial reliability, and catastrophic national and global risks. There are some tentative final conclusions, but the primary message is to acknowledge expert guidance, be clear about objectives, and work closely with intended audiences. <|reference_end|>", "<|reference_start|> Communicating uncertainty about facts, numbers and science: Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combining a statistical and psychological perspective. This informs a framework for uncertainty communication in which we identify three objects of uncertainty—facts, numbers and science—and two levels of uncertainty: direct and indirect. An examination of current practices provides a scale of nine expressions of direct uncertainty. We discuss attempts to codify indirect uncertainty in terms of quality of the underlying evidence. We review the limited literature about the effects of communicating epistemic uncertainty on cognition, affect, trust and decision-making. While there is some evidence that communicating epistemic uncertainty does not necessarily affect audiences negatively, impact can vary between individuals and communication formats. Case studies in economic statistics and climate change illustrate our framework in action. We conclude with advice to guide both communicators and future researchers in this important but so far rather neglected field. <|reference_end|>", "<|reference_start|> Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models: <|reference_end|>", "<|reference_start|> Second-line Glucose-Lowering Therapy in Type 2 Diabetes Mellitus: <|reference_end|>" ]

[ 0, 1, 2, 4 ]

[ "<|reference_start|> Intelligent Traffic Light Controlling Algorithms Using Vehicular Networks: In this paper, we propose an intelligent traffic light controlling (ITLC) algorithm. ITLC is intended to schedule the phases of each isolated traffic light efficiently. This algorithm considers the real-time traffic characteristics of the competing traffic flows at the signalized road intersection. Moreover, we have adopted the ITLC algorithm to design a traffic scheduling algorithm for an arterial street scenario; we have thus proposed an arterial traffic light (ATL) controlling algorithm. In the ATL controlling algorithm, the intelligent traffic lights installed at each road intersection coordinate with each other to generate an efficient traffic schedule for the entire road network. We report on the performance of ITLC and ATL algorithms for several scenarios using NS-2. From the experimental results, we infer that the ITLC algorithm reduces, at each isolated traffic light, the queuing delay and increases the traffic fluency by 30% compared with the online algorithm (OAF) traffic light scheduling algorithm. The latter algorithm achieved the best performance when compared with the OAF traffic light scheduling algorithm. On the other hand, the ATL controlling algorithm increases the traffic fluency of traveling vehicles at arterial street coordinations by 70% more than the random and separate traffic light scheduling system. Furthermore, compared with the previously introduced traffic scheduling ART-SYS, the ATL controlling algorithm decreases the average delay at each traffic light by 10%. <|reference_end|>", "<|reference_start|> Analyses of a heterogeneous lattice hydrodynamic model with low and high-sensitivity vehicles: <|reference_end|>", "<|reference_start|> CAR-FOLLOWING MODEL BASED ON FUZZY INFERENCE SYSTEM: Car-following theory has been receiving renewed attention for its use in the analysis of traffic flow characteristics and vehicle separation control under the IVHS. A car-following model that uses the fuzzy inference system, which consists of many straightforward natural language-based driving rules, is proposed. It predicts the reaction of the driver of the following vehicle (acceleration-deceleration rates) given the action of the leading vehicle. A range of possible reaction is predicted and expressed by the fuzzy membership function. The model is applied to the analysis of traffic stability and speed-density relationship. For traffic stability, the results are compared with those derived from the deterministic approach. The speed-density relationship derived from the model is compared with a set of actual flow data. The predicted range is found to be reasonable. The proposed fuzzy approach helps explain the scatter of the actual data as possibility rather than random variation. <|reference_end|>", "<|reference_start|> A New Microscopic Traffic Model Using a Spring-Mass-Damper-Clutch System: Microscopic traffic models describe how cars interact with their neighbors in an uninterrupted traffic flow and are frequently used for reference in advanced vehicle control design. In this paper, we propose a novel mechanical system inspired microscopic traffic model using a mass-spring-damper-clutch system. This model naturally captures the ego vehicle's resistance to large relative speed and deviation from a (driver and speed dependent) desired relative distance when following the lead vehicle. Comparing to existing car following (CF) models, this model offers physically interpretable insights on the underlying CF dynamics, and is able to characterize the impact of the ego vehicle on the lead vehicle, which is neglected in existing CF models. Thanks to the nonlinear wave propagation analysis techniques for mechanical systems, the proposed model therefore has great scalability so that multiple mass-spring-damper-clutch system can be chained to study the macroscopic traffic flow. We investigate the stability of the proposed model on the system parameters and the time delay using spectral element method. We also develop a parallel recursive least square with inverse QR decomposition (PRLS-IQR) algorithm to identify the model parameters online. These real-time estimated parameters can be used to predict the driving trajectory that can be incorporated in advanced vehicle longitudinal control systems for improved safety and fuel efficiency. The PRLS-IQR is computationally efficient and numerically stable so it is suitable for online implementation. The traffic model and the parameter identification algorithm are validated on both simulations and naturalistic driving data from multiple drivers. Promising performance is demonstrated. <|reference_end|>" ]

[ 10, 19, 36, 44 ]

[ "<|reference_start|> More Effective Distributed ML via a Stale Synchronous Parallel Parameter\nServer: We propose a parameter server system for distributed ML, which follows a Stale Synchronous Parallel (SSP) model of computation that maximizes the time computational workers spend doing useful work on ML algorithms, while still providing correctness guarantees. The parameter server provides an easy-to-use shared interface for read/write access to an ML model's values (parameters and variables), and the SSP model allows distributed workers to read older, stale versions of these values from a local cache, instead of waiting to get them from a central storage. This significantly increases the proportion of time workers spend computing, as opposed to waiting. Furthermore, the SSP model ensures ML algorithm correctness by limiting the maximum age of the stale values. We provide a proof of correctness under SSP, as well as empirical results demonstrating that the SSP model achieves faster algorithm convergence on several different ML problems, compared to fully-synchronous and asynchronous schemes. <|reference_end|>", "<|reference_start|> Decoupled Parallel Backpropagation with Convergence Guarantee: Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way back to the input layer. The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully leveraging the computing resources. Recently, several algorithms have been proposed for breaking the backward locking. However, their performances degrade seriously when networks are deep. In this paper, we propose decoupled parallel backpropagation algorithm for deep learning optimization with convergence guarantee. Firstly, we decouple the backpropagation algorithm using delayed gradients, and show that the backward locking is removed when we split the networks into multiple modules. Then, we utilize decoupled parallel backpropagation in two stochastic methods and prove that our method guarantees convergence to critical points for the non-convex problem. Finally, we perform experiments for training deep convolutional neural networks on benchmark datasets. The experimental results not only confirm our theoretical analysis, but also demonstrate that the proposed method can achieve significant speedup without loss of accuracy. <|reference_end|>", "<|reference_start|> GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism: Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks. In many cases, increasing model capacity beyond the memory limit of a single accelerator has required developing special algorithms or infrastructure. These solutions are often architecture-specific and do not transfer to other tasks. To address the need for efficient and task-independent model parallelism, we introduce GPipe, a pipeline parallelism library that allows scaling any network that can be expressed as a sequence of layers. By pipelining different sub-sequences of layers on separate accelerators, GPipe provides the flexibility of scaling a variety of different networks to gigantic sizes efficiently. Moreover, GPipe utilizes a novel batch-splitting pipelining algorithm, resulting in almost linear speedup when a model is partitioned across multiple accelerators. We demonstrate the advantages of GPipe by training large-scale neural networks on two different tasks with distinct network architectures: (i) Image Classification: We train a 557-million-parameter AmoebaNet model and attain a top-1 accuracy of 84.4% on ImageNet-2012, (ii) Multilingual Neural Machine Translation: We train a single 6-billion-parameter, 128-layer Transformer model on a corpus spanning over 100 languages and achieve better quality than all bilingual models. <|reference_end|>", "<|reference_start|> PipeDream: Generalized Pipeline Parallelism for DNN Training: DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available workers, but suffer from diminishing returns at higher worker counts. We present PipeDream, a system that adds inter-batch pipelining to intra-batch parallelism to further improve parallel training throughput, helping to better overlap computation with communication and reduce the amount of communication when possible. Unlike traditional pipelining, DNN training is bi-directional, where a forward pass through the computation graph is followed by a backward pass that uses state and intermediate data computed during the forward pass. Naïve pipelining can thus result in mismatches in state versions used in the forward and backward passes, or excessive pipeline flushes and lower hardware efficiency. To address these challenges, PipeDream versions model parameters for numerically correct gradient computations, and schedules forward and backward passes of different minibatches concurrently on different workers with minimal pipeline stalls. PipeDream also automatically partitions DNN layers among workers to balance work and minimize communication. Extensive experimentation with a range of DNN tasks, models, and hardware configurations shows that PipeDream trains models to high accuracy up to 5.3X faster than commonly used intra-batch parallelism techniques. <|reference_end|>" ]

[ 1, 2, 7, 8 ]

[ "<|reference_start|> Associative Hierarchical CRFs for Object Class Image Segmentation: Most methods for object class segmentation are formulated as a labelling problem over a single choice of quantisation of an image space - pixels, segments or group of segments. It is well known that each quantisation has its fair share of pros and cons; and the existence of a common optimal quantisation level suitable for all object categories is highly unlikely. Motivated by this observation, we propose a hierarchical random field model, that allows integration of features computed at different levels of the quantisation hierarchy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalises much of the previous work based on pixels or segments. We evaluate its efficiency on some of the most challenging data-sets for object class segmentation, and show it obtains state-of-the-art results. <|reference_end|>", "<|reference_start|> International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge: <|reference_end|>", "<|reference_start|> Indoor Segmentation and Support Inference from RGBD Images: <|reference_end|>", "<|reference_start|> Rethinking Atrous Convolution for Semantic Image Segmentation: In this work, we revisit atrous convolution, a powerful tool to explicitly adjust filter's field-of-view as well as control the resolution of feature responses computed by Deep Convolutional Neural Networks, in the application of semantic image segmentation. To handle the problem of segmenting objects at multiple scales, we design modules which employ atrous convolution in cascade or in parallel to capture multi-scale context by adopting multiple atrous rates. Furthermore, we propose to augment our previously proposed Atrous Spatial Pyramid Pooling module, which probes convolutional features at multiple scales, with image-level features encoding global context and further boost performance. We also elaborate on implementation details and share our experience on training our system. The proposed `DeepLabv3' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark. <|reference_end|>" ]

[ 4, 5, 10, 17 ]

[ "<|reference_start|> Deep neural Networks for Youtube Recommendations: YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact. <|reference_end|>", "<|reference_start|> {Factorizing Personalized Markov Chains for Next-basket Recommendation: Recommender systems are an important component of many websites. Two of the most popular approaches are based on matrix factorization (MF) and Markov chains (MC). MF methods learn the general taste of a user by factorizing the matrix over observed user-item preferences. On the other hand, MC methods model sequential behavior by learning a transition graph over items that is used to predict the next action based on the recent actions of a user. In this paper, we present a method bringing both approaches together. Our method is based on personalized transition graphs over underlying Markov chains. That means for each user an own transition matrix is learned - thus in total the method uses a transition cube. As the observations for estimating the transitions are usually very limited, our method factorizes the transition cube with a pairwise interaction model which is a special case of the Tucker Decomposition. We show that our factorized personalized MC (FPMC) model subsumes both a common Markov chain and the normal matrix factorization model. For learning the model parameters, we introduce an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data. Empirically, we show that our FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization. <|reference_end|>", "<|reference_start|> Ensemble methods in Machine Learning: Ensemble methods are learning algorithms that construct a set of classiiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging , but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classiier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overrt rapidly. <|reference_end|>", "<|reference_start|> Ensembling neural networks: Many could be better than all: <|reference_end|>" ]

[ 13, 37, 40, 41 ]

[ "<|reference_start|> A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.: How do people know as much as they do with as little information as they get? The problem takes many forms; learning vocabulary from text is an especially dramatic and convenient case for research. A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena. By inducing global knowledge indirectly from local co-occurrence data in a large body of representative text, LSA acquired knowledge about the full vocabulary of English at a comparable rate to schoolchildren. LSA uses no prior linguistic or perceptual similarity knowledge; it is based solely on a general mathematical learning method that achieves powerful inductive effects by extracting the right number of dimensions (e.g., 300) to represent objects and contexts. Relations to other theories, phenomena, and problems are sketched. <|reference_end|>", "<|reference_start|> Wordnet: An Electronic Lexical Database: Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and implementation of the WordNet lexical database and searching software, Randee I. Tengi. Part 2: automated discovery of WordNet relations, Marti A. Hearst representing verb alterations in WordNet, Karen T. Kohl et al the formalization of WordNet by methods of relational concept analysis, Uta E. Priss. Part 3 Applications of WordNet: building semantic concordances, Shari Landes et al performance and confidence in a semantic annotation task, Christiane Fellbaum et al WordNet and class-based probabilities, Philip Resnik combining local context and WordNet similarity for word sense identification, Claudia Leacock and Martin Chodorow using WordNet for text retrieval, Ellen M. Voorhees lexical chains as representations of context for the detection and correction of malapropisms, Graeme Hirst and David St-Onge temporal indexing through lexical chaining, Reem Al-Halimi and Rick Kazman COLOR-X - using knowledge from WordNet for conceptual modelling, J.F.M. Burg and R.P. van de Riet knowledge processing on an extended WordNet, Sanda M. Harabagiu and Dan I Moldovan appendix - obtaining and using WordNet. <|reference_end|>", "<|reference_start|> Fast Training of Support Vector Machines Using Sequential Minimal Optimization: This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because large matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while a standard projected conjugate gradient (PCG) chunking algorithm scales somewhere between linear and cubic in the training set size. SMO's computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. For the MNIST database, SMO is as fast as PCG chunking; while for the UCI Adult database and linear SVMs, SMO can be more than 1000 times faster than the PCG chunking algorithm. <|reference_end|>", "<|reference_start|> Semeval-2007 task 04: Classification of semantic relations between nominals: The NLP community has shown a renewed interest in deeper semantic analyses, among them automatic recognition of relations between pairs of words in a text. We present an evaluation task designed to provide a framework for comparing different approaches to classifying semantic relations between nominals in a sentence. This is part of SemEval, the 4th edition of the semantic evaluation event previously known as SensEval. We define the task, describe the training/test data and their creation, list the participating systems and discuss their results. There were 14 teams who submitted 15 systems. <|reference_end|>" ]

[ 0, 3, 5, 7 ]

[ "<|reference_start|> Using scada data for wind turbine condition monitoring: A systematic literature review: Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years. <|reference_end|>", "<|reference_start|> Wind turbine fault diagnosis by the approach of scada alarms analysis: Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault. <|reference_end|>", "<|reference_start|> Offshore wind turbine fault alarm prediction: <|reference_end|>", "<|reference_start|> Process alarm prediction using deep learning and word embedding methods.: <|reference_end|>" ]

[ 1, 4, 5, 7 ]

[ "<|reference_start|> Relevance feedback in information retrieval: <|reference_end|>", "<|reference_start|> Multimodal fusion for multimedia analysis: a survey: <|reference_end|>", "<|reference_start|> Information mining in remote sensing image archives: System concepts: In this paper, we demonstrate the concepts of a prototype of a knowledge-driven content-based information mining system produced to manage and explore large volumes of remote sensing image data. The system consists of a computationally intensive offline part and an online interface. The offline part aims at the extraction of primitive image features, their compression, and data reduction, the generation of a completely unsupervised image content-index, and the ingestion of the catalogue entry in the database management system. Then, the user's interests-semantic interpretations of the image content-are linked with Bayesian networks to the content-index. Since this calculation is only based on a few training samples, the link can be computed online, and the complete image archive can be searched for images that contain the defined cover type. Practical applications exemplified with different remote sensing datasets show the potential of the system. <|reference_end|>", "<|reference_start|> Design and evaluation of a content--based image retrieval system: The growth in size and accessibility of multimedia databases have changed our approach to information retrieval. Classical text-based systems show their limitations in the context of multimedia retrieval. In this chapter, we address the problem of conceiving and evaluating a content-based image retrieval system. First, we investigate the use of the query-by-example (QBE) paradigm as a base paradigm for the development of a content-based image retrieval system (CBIRS). We show that it should be considered as a complement to the classical textual-based paradigms. We then evaluate the capabilities of the most up-to-date computer vision techniques in contributing to the realisation of such a system. Further, beyond the necessity of accurate image understanding techniques, we show that the amount of data involved by the process of describing image content should also be considered as an important issue. This aspect of our study is largely based on the experience acquired by the text retrieval (TR) community, which we adapt to the context of CBIR. Similarly, the text retrieval community has also developed a significant experience in evaluating retrieval systems, where judgements include subjectivity and context dependency. Extending this experience, we study a coherent framework for performing the evaluation of a CBIRS. As a practical example, we use our Viper CBIR system, using a novel communication protocol called MRML to pinpoint the importance of the sharing of resource in facilitating the evaluation and therefore the development of CBIRS. <|reference_end|>" ]

[ 2, 3, 5, 11 ]

[ "<|reference_start|> Handbook of Graph Grammars and Computing by Graph Transformations,\nVolume 1: Foundations: A graph program consists of declarations of conditional graph transformation rules G. Rozenberg, editors: Handbook of Graph Grammars and Computing. We introduce s-graph grammars, a new grammar formalism for computing Handbook of Graph Grammars and Computing by Graph Transformation, pp. The double-pushout approach to graph transformation, which was invented in the early 1970's, is Handbook of Graph Grammars and Computing by Graph. <|reference_end|>", "<|reference_start|> Handbook of graph grammars and computing by graph transformation: vol. 3: concurrency, parallelism, and distribution: <|reference_end|>", "<|reference_start|> Term Graph Rewriting: <|reference_end|>", "<|reference_start|> Term rewriting and all that: Preface 1. Motivating examples 2. Abstract reduction systems 3. Universal algebra 4. Equational problems 5. Termination 6. Confluence 7. Completion 8. Grobner bases and Buchberger's algorithm 9. Combination problems 10. Equational unification 11. Extensions Appendix 1. Ordered sets Appendix 2. A bluffer's guide to ML Bibliography Index. <|reference_end|>" ]

[ 0, 2, 4, 6 ]

[ "<|reference_start|> {CDS in a learning health care system: Identifying physicians' reasons for rejection of best-practice recommendations in pneumonia through computerized clinical decision support: Abstract Background Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process. Objectives This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations. Methods We implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis. Results Among 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection. Conclusion While adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system. <|reference_end|>", "<|reference_start|> Clinical impact of an electronic dashboard and alert system for sedation minimization and ventilator liberation: a before-after study: Supplemental Digital Content is available in the text. <|reference_end|>", "<|reference_start|> Mimic-iv: 目的 鉴于脓毒症的高发病率和高病死率，早期识别高风险患者并及时干预至关重要，而现有死亡风险预测模型在操作、适用性和预测长期预后等方面均存在不足。本研究旨在探讨脓毒症患者死亡的危险因素，构建近期和远期死亡风险预测模型。 方法 从美国重症监护医学信息数据库IV(Medical Information Mart for Intensive Care-IV，MIMIC-IV)中选取符合脓毒症3.0诊断标准的人群，按7꞉3的比例随机分为建模组和验证组，分析患者的基线资料。采用单因素Cox回归分析和全子集回归确定脓毒症患者死亡的危险因素并筛选出构建预测模型的变量。分别用时间依赖性曲线下面积(area under the curve，AUC)、校准曲线和决策曲线评估模型的区分度、校准度和临床实用性。 结果 共纳入14 240例脓毒症患者，28 d和1年病死率分别为21.45%(3 054例)和36.50%(5 198例)。高龄、女性、高感染相关器官衰竭评分(sepsis-related organ failure assessment，SOFA)、高简明急性生理学评分(simplified acute physiology score II，SAPS II)、心率快、呼吸频率快、脓毒症休克、充血性心力衰竭、慢性阻塞性肺疾病、肝脏疾病、肾脏疾病、糖尿病、恶性肿瘤、高白细胞计数(white blood cell count，WBC)、长凝血酶原时间(prothrombin time，PT)、高血肌酐(serum creatinine，SCr)水平均为脓毒症死亡的危险因素(均P<0.05)。由PT、呼吸频率、体温、合并恶性肿瘤、合并肝脏疾病、脓毒症休克、SAPS II及年龄8个变量构建的模型，其28 d和1年生存的AUC分别为0.717(95% CI 0.710~0.724)和0.716(95% CI 0.707~0.725)。校准曲线和决策曲线表明该模型具有良好的校准度及较好的临床应用价值。 结论 基于MIMIC-IV建立的脓毒症患者近期和远期死亡风险预测模型有较好的识别能力，对患者预后风险评估及干预治疗具有一定的临床参考意义。 <|reference_end|>", "<|reference_start|> Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit: Objective:To determine whether automated identification with physician notification of the systemic inflammatory response syndrome in medical intensive care unit patients expedites early administration of new antibiotics or improvement of other patient outcomes in patients with sepsis. Design:A prospective randomized, controlled, single center study. Setting:Medical intensive care unit of an academic, tertiary care medical center. Patients:Four hundred forty-two consecutive patients admitted over a 4-month period who met modified systemic inflammatory response syndrome criteria in a medical intensive care unit. Intervention:Patients were randomized to monitoring by an electronic “Listening Application” to detect modified (systemic inflammatory response syndrome) criteria vs. usual care. The listening application notified physicians in real time when modified systemic inflammatory response syndrome criteria were detected, but did not provide management recommendations. Measurements and Main Results:The median time to new antibiotics was similar between the intervention and usual care groups when comparing among all patients (6.0 hr vs. 6.1 hr, p = .95), patients with sepsis (5.3 hr vs. 5.1 hr; p = .90), patients on antibiotics at enrollment (5.2 hr vs. 7.0 hr, p = .27), or patients not on antibiotics at enrollment (5.2 hr vs. 5.1 hr, p = .85). The amount of fluid administered following detection of modified systemic inflammatory response syndrome criteria was similar between groups whether comparing all patients or only patients who were hypotensive at enrollment. Other clinical outcomes including intensive care unit length of stay, hospital length of stay, and mortality were not shown to be different between patients in the intervention and control groups. Conclusions:Realtime alerts of modified systemic inflammatory response syndrome criteria to physicians in one tertiary care medical intensive care unit were feasible and safe but did not influence measured therapeutic interventions for sepsis or significantly alter clinical outcomes. <|reference_end|>" ]

[ 3, 6, 17, 24 ]

[ "<|reference_start|> An experimental approach to linguistic representation: Abstract Within the cognitive sciences, most researchers assume that it is the job of linguists to investigate how language is represented, and that they do so largely by building theories based on explicit judgments about patterns of acceptability – whereas it is the task of psychologists to determine how language is processed, and that in doing so, they do not typically question the linguists' representational assumptions. We challenge this division of labor by arguing that structural priming provides an implicit method of investigating linguistic representations that should end the current reliance on acceptability judgments. Moreover, structural priming has now reached sufficient methodological maturity to provide substantial evidence about such representations. We argue that evidence from speakers' tendency to repeat their own and others' structural choices supports a linguistic architecture involving a single shallow level of syntax connected to a semantic level containing information about quantification, thematic relations, and information structure, as well as to a phonological level. Many of the linguistic distinctions often used to support complex (or multilevel) syntactic structure are instead captured by semantics; however, the syntactic level includes some specification of “missing” elements that are not realized at the phonological level. We also show that structural priming provides evidence about the consistency of representations across languages and about language development. In sum, we propose that structural priming provides a new basis for understanding the nature of language. <|reference_end|>", "<|reference_start|> An experimental approach to linguistic representation: Abstract Within the cognitive sciences, most researchers assume that it is the job of linguists to investigate how language is represented, and that they do so largely by building theories based on explicit judgments about patterns of acceptability – whereas it is the task of psychologists to determine how language is processed, and that in doing so, they do not typically question the linguists' representational assumptions. We challenge this division of labor by arguing that structural priming provides an implicit method of investigating linguistic representations that should end the current reliance on acceptability judgments. Moreover, structural priming has now reached sufficient methodological maturity to provide substantial evidence about such representations. We argue that evidence from speakers' tendency to repeat their own and others' structural choices supports a linguistic architecture involving a single shallow level of syntax connected to a semantic level containing information about quantification, thematic relations, and information structure, as well as to a phonological level. Many of the linguistic distinctions often used to support complex (or multilevel) syntactic structure are instead captured by semantics; however, the syntactic level includes some specification of “missing” elements that are not realized at the phonological level. We also show that structural priming provides evidence about the consistency of representations across languages and about language development. In sum, we propose that structural priming provides a new basis for understanding the nature of language. <|reference_end|>", "<|reference_start|> Total word count : 1104 The logic of syntactic priming and acceptability judgments: word count: 53 Main text word count: 993 References word count: 58 Total word count: 1104 The logic of syntactic priming and acceptability judgments Phoebe Gaston, Nick Huang, and Colin Phillips University of Maryland Mailing address: 1401 Marie Mount Hall, College Park, MD 20742 Phone: +1 301 405 3082 Emails: [email protected], [email protected], [email protected] Homepage URLs: https://phoebegaston.wordpress.com/, http://ling.umd.edu/~znhuang, http://colinphillips.net <|reference_end|>", "<|reference_start|> Becoming syntactic.: Psycholinguistic research has shown that the influence of abstract syntactic knowledge on performance is shaped by particular sentences that have been experienced. To explore this idea, the authors applied a connectionist model of sentence production to the development and use of abstract syntax. The model makes use of (a) error-based learning to acquire and adapt sequencing mechanisms and (b) meaning-form mappings to derive syntactic representations. The model is able to account for most of what is known about structural priming in adult speakers, as well as key findings in preferential looking and elicited production studies of language acquisition. The model suggests how abstract knowledge and concrete experience are balanced in the development and use of syntax. <|reference_end|>" ]

[ 0, 2, 3, 17 ]

[ "<|reference_start|> Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks: When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequently, this choice can be considered as an implicit regularization for the training of over-parametrized models. In this work, we push this idea further by studying the discrete gradient dynamics of the training of a two-layer linear network with the least-squares loss. Using a time rescaling, we show that, with a vanishing initialization and a small enough step size, this dynamics sequentially learns the solutions of a reduced-rank regression with a gradually increasing rank. <|reference_end|>", "<|reference_start|> Learning internal representations by error propagation: <|reference_end|>", "<|reference_start|> Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks: When optimizing over-parameterized models, such as deep neural networks, a large set of parameters can achieve zero training error. In such cases, the choice of the optimization algorithm and its respective hyper-parameters introduces biases that will lead to convergence to specific minimizers of the objective. Consequently, this choice can be considered as an implicit regularization for the training of over-parametrized models. In this work, we push this idea further by studying the discrete gradient dynamics of the training of a two-layer linear network with the least-squares loss. Using a time rescaling, we show that, with a vanishing initialization and a small enough step size, this dynamics sequentially learns the solutions of a reduced-rank regression with a gradually increasing rank. <|reference_end|>", "<|reference_start|> From Variational to Deterministic Autoencoders: Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models. However, learning a VAE from data poses still unanswered theoretical questions and considerable practical challenges. In this work, we propose an alternative framework for generative modeling that is simpler, easier to train, and deterministic, yet has many of the advantages of VAEs. We observe that sampling a stochastic encoder in a Gaussian VAE can be interpreted as simply injecting noise into the input of a deterministic decoder. We investigate how substituting this kind of stochasticity, with other explicit and implicit regularization schemes, can lead to an equally smooth and meaningful latent space without forcing it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism to sample new data, we introduce an ex-post density estimation step that can be readily applied also to existing VAEs, improving their sample quality. We show, in a rigorous empirical study, that the proposed regularized deterministic autoencoders are able to generate samples that are comparable to, or better than, those of VAEs and more powerful alternatives when applied to images as well as to structured data such as molecules. \\footnote{An implementation is available at: \\url{https://github.com/ParthaEth/Regularized_autoencoders-RAE-}} <|reference_end|>" ]

[ 4, 8, 19, 26 ]

[ "<|reference_start|> Are aligned neural networks adversarially aligned?: Large language models are now tuned to align with the goals of their creators, namely to be \"helpful and harmless.\" These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study adversarial alignment, and ask to what extent these models remain aligned when interacting with an adversarial user who constructs worst-case inputs (adversarial examples). These inputs are designed to cause the model to emit harmful content that would otherwise be prohibited. We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force. As a result, the failure of current attacks should not be seen as proof that aligned text models remain aligned under adversarial inputs. However the recent trend in large-scale ML models is multimodal models that allow users to provide images that influence the text that is generated. We show these models can be easily attacked, i.e., induced to perform arbitrary un-aligned behavior through adversarial perturbation of the input image. We conjecture that improved NLP attacks may demonstrate this same level of adversarial control over text-only models. <|reference_end|>", "<|reference_start|> Are aligned neural networks adversarially aligned?: Large language models are now tuned to align with the goals of their creators, namely to be \"helpful and harmless.\" These models should respond helpfully to user questions, but refuse to answer requests that could cause harm. However, adversarial users can construct inputs which circumvent attempts at alignment. In this work, we study adversarial alignment, and ask to what extent these models remain aligned when interacting with an adversarial user who constructs worst-case inputs (adversarial examples). These inputs are designed to cause the model to emit harmful content that would otherwise be prohibited. We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models: even when current NLP-based attacks fail, we can find adversarial inputs with brute force. As a result, the failure of current attacks should not be seen as proof that aligned text models remain aligned under adversarial inputs. However the recent trend in large-scale ML models is multimodal models that allow users to provide images that influence the text that is generated. We show these models can be easily attacked, i.e., induced to perform arbitrary un-aligned behavior through adversarial perturbation of the input image. We conjecture that improved NLP attacks may demonstrate this same level of adversarial control over text-only models. <|reference_end|>", "<|reference_start|> Query-Relevant Images Jailbreak Large Multi-Modal Models: Warning: This paper contains examples of harmful language and images, and reader discretion is recommended. The security concerns surrounding Large Language Models (LLMs) have been extensively explored, yet the safety of Large Multi-Modal Models (LMMs) remains understudied. In our study, we present a novel visual prompt attack that exploits query-relevant images to jailbreak the open-source LMMs. Our method creates a composite image from one image generated by diffusion models and another that displays the text as typography, based on keywords extracted from a malicious query. We show LLMs can be easily at-tacked by our approach, even if the employed Large Language Models are safely aligned. To evaluate the extent of this vulnerability in open-source LMMs, we have compiled a substantial dataset encompassing 13 scenarios with a total of 5,040 text-image pairs, using our presented attack technique. Our evaluation of 12 cutting-edge LMMs using this dataset shows the vulnerability of existing multi-modal models on adversarial attacks. This finding under-scores the need for a concerted effort to strengthen and enhance the safety measures of open-source LMMs against potential malicious exploits. The resource is available at https://github.com/isXinLiu/MM-SafetyBench. <|reference_end|>", "<|reference_start|> Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned.: We describe our early efforts to red team language models in order to simultaneously discover, measure, and attempt to reduce their potentially harmful outputs. We make three main contributions. First, we investigate scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B parameters) and 4 model types: a plain language model (LM); an LM prompted to be helpful, honest, and harmless; an LM with rejection sampling; and a model trained to be helpful and harmless using reinforcement learning from human feedback (RLHF). We find that the RLHF models are increasingly difficult to red team as they scale, and we find a flat trend with scale for the other model types. Second, we release our dataset of 38,961 red team attacks for others to analyze and learn from. We provide our own analysis of the data and find a variety of harmful outputs, which range from offensive language to more subtly harmful non-violent unethical outputs. Third, we exhaustively describe our instructions, processes, statistical methodologies, and uncertainty about red teaming. We hope that this transparency accelerates our ability to work together as a community in order to develop shared norms, practices, and technical standards for how to red team language models. <|reference_end|>" ]

[ 3, 7, 18, 25 ]

[ "<|reference_start|> {{ROS: 小鼠体内发育的囊胚、扩张囊胚和从1-细胞期取出在CzB培养液中培养的囊胚、扩张囊胚的染色体数目分别是39.00,38.43和42.47,39.56.经统计分析,体内与体外CzB培养的染色体数目无显著性差异.体外CZB培养的不同时期添加外源性H2O2,发育至囊胚、扩张囊胚阶段,制作胚胎标本,观察胚胎的染色体数目.结果显示:扩张囊胚的0～72 h组的染色体数目与对照组的染色体数目有显著性差异(P＜0.05),而其它各发育阶段的各处理组染色体数目之间均无显著性差异.说明经H2O2处理之后存活下来的胚胎与正常体外发育的胚胎染色体数目没有差异. <|reference_end|>", "<|reference_start|> RO-LOAM: 3D Reference Object-based Trajectory and Map Optimization in LiDAR Odometry and Mapping: We propose an extension to the LiDAR Odometry and Mapping framework (LOAM) that enables reference object-based trajectory and map optimization. Our approach assumes that the location and geometry of a large reference object are known, e.g., as a CAD model from Building Information Modeling (BIM) or a previously captured dense point cloud model. We do not expect the reference object to be present in every LiDAR scan. Our approach uses the poses of the LOAM algorithm as an initial guess to refine them with scan-to-model alignment. To evaluate if the alignment was accurate, an EKF-based motion prior filtering step is employed. Subsequently, the past trajectory is optimized by adding the model-aligned pose as a pose graph constraint and the map of the LOAM algorithm is corrected to improve future localization and mapping. We evaluate our approach with data captured in a visual airplane inspection scenario inside an aircraft hangar. A 3D LiDAR sensor is mounted via a gimbal on an Unmanned Aerial Vehicle (UAV) and is continuously actuated. We compare the localization accuracy of the LOAM and R-LOAM algorithms when enabling or disabling our proposed reference object-based trajectory and map optimization extension. For three recorded datasets, enabling the proposed extension yields a reduction in Absolute Pose Error compared to conventional LOAM and R-LOAM, while being able to run online. This reduces drift and improves map quality. <|reference_end|>", "<|reference_start|> Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM: For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect. <|reference_end|>", "<|reference_start|> Navigation without localisation: reliable teach and repeat based on the convergence theorem: We present a novel concept for teach-and-repeat visual navigation. The proposed concept is based on a mathematical model, which indicates that in teach-and-repeat navigation scenarios, mobile robots do not need to perform explicit localisation. Rather than that, a mobile robot which repeats a previously taught path can simply `replay' the learned velocities, while using its camera information only to correct its heading relative to the intended path. To support our claim, we establish a position error model of a robot, which traverses a taught path by only correcting its heading. Then, we outline a mathematical proof which shows that this position error does not diverge over time. Based on the insights from the model, we present a simple monocular teach-and-repeat navigation method. The method is computationally efficient, it does not require camera calibration, and it can learn and autonomously traverse arbitrarily-shaped paths. In a series of experiments, we demonstrate that the method can reliably guide mobile robots in realistic indoor and outdoor conditions, and can cope with imperfect odometry, landmark deficiency, illumination variations and naturally-occurring environment changes. Furthermore, we provide the navigation system and the datasets gathered at http://www.github.com/gestom/stroll_bearnav. <|reference_end|>" ]

[ 0, 5, 6, 13 ]

[ "<|reference_start|> An evaluation of AmazonÕs grid computing services: EC2, S3 and SQS: Amazon.com’s Elastic Compute Cloud (EC2), Simple Storage Service (S3) and Simple Queue Service (SQS) offer enterprise-class computing, storage and coordination facilities to any organization or individual in the world with a valid credit card. This paper details our experience working with these commodity grid computing services between November 2006 and May 2007, including an analysis of the overall system’s API and ease-of-use; an analysis of EC2’s management and security facilities; an end-to-end performance analysis of S3’s throughput and latency as observed from Amazon’s EC2 cluster and other locations on the Internet; and an analysis of the SQS operation and performance. We conclude with a report of our experience moving a large-scale research application from dedicated hardware to the Amazon offering. We find that this collection of AmazonWeb Services (AWS) has great promise but are hobbled by service consistency problems, the lack of a Service Level Agreement (SLA), and a problematic Web Services Licensing Agreement (WSLA). <|reference_end|>", "<|reference_start|> Stout: an adaptive interface to scalable cloud storage: Many of today's applications are delivered as scalable, multi-tier services deployed in large data centers. These services frequently leverage shared, scale-out, key-value storage layers that can deliver low latency under light workloads, but may exhibit significant queuing delay and even dropped requests under high load. \n \nStout is a system that helps these applications adapt to variation in storage-layer performance by treating scalable key-value storage as a shared resource requiring congestion control. Under light workloads, applications using Stout send requests to the store immediately, minimizing delay. Under heavy workloads, Stout automatically batches the application's requests together before sending them to the store, resulting in higher throughput and preventing queuing delay. We show experimentally that Stout's adaptation algorithm converges to an appropriate batch size for workloads that require the batch size to vary by over two orders of magnitude. Compared to a non-adaptive strategy optimized for throughput, Stout delivers over 34× lower latency under light workloads; compared to a non-adaptive strategy optimized for latency, Stout can scale to over 3× as many requests. <|reference_end|>", "<|reference_start|> On the Delay of Network Coding over Line Networks: We analyze a simple network where a source and a receiver are connected by a line of erasure channels of different reliabilities. Recent prior work has shown that random linear network coding can achieve the min-cut capacity and therefore the asymptotic rate is determined by the worst link of the line network. In this paper we investigate the delay for transmitting a batch of packets, which is a function of all the erasure probabilities and the number of packets in the batch. We show a monotonicity result on the delay function and derive simple expressions which characterize the expected delay behavior of line networks. Further, we use a martingale bounded differences argument to show that the actual delay is tightly concentrated around its expectation. <|reference_end|>", "<|reference_start|> Codes Can Reduce Queueing Delay in Data Centers: In this paper, we quantify how much codes can reduce the data retrieval latency in storage systems. By combining a simple linear code with a novel request scheduling algorithm, which we call Blocking-one Scheduling (BoS), we show analytically that it is possible to reduce data retrieval delay by up to 17% over currently popular replication-based strategies. Although in this work we focus on a simplified setting where the storage system stores a single content, the methodology developed can be applied to more general settings with multiple contents. The results also offer insightful guidance to the design of storage systems in data centers and content distribution networks. <|reference_end|>" ]

[ 9, 10, 18, 23 ]

[ "<|reference_start|> Object location using path separators: We study a novel separator property called <i>k-path separable</i>. Roughly speaking, a <i>k-path separable</i> graph can be recursively separated into smaller components by sequentially removing <i>k</i> shortest paths. Our main result is that every minor free weighted graph is <i>k</i>-path separable. We then show that <i>k</i>-path separable graphs can be used to solve several object location problems: (1) a small-worldization with an average poly-logarithmic number of hops; (2) an (1 + ε)-approximate distance labeling scheme with <i>O</i>(log <i>n</i>) space labels; (3) a stretch-(1 + ε) compact routing scheme with tables of poly-logarithmic space; (4) an (1 + ε)-approximate distance oracle with <i>O</i>(<i>n</i> log <i>n</i>) space and <i>O</i>(log <i>n</i>) query time. Our results generalizes to much wider classes of weighted graphs, namely to bounded-dimension isometric sparable graphs. <|reference_end|>", "<|reference_start|> Approximate nearest neighbor search in metrics of planar graphs: We investigate the problem of approximate Nearest-Neighbor Search (NNS) in graphical metrics: The task is to preprocess an edge-weighted graph G=(V,E) on m vertices and a small \"dataset\" D \\subset V of size n << m, so that given a query point q \\in V, one can quickly approximate dist(q,D) (the distance from q to its closest vertex in D) and find a vertex a \\in D within this approximated distance. We assume the query algorithm has access to a distance oracle, that quickly evaluates the exact distance between any pair of vertices. \n \nFor planar graphs G with maximum degree Delta, we show how to efficiently construct a compact data structure -- of size ~O(n(Delta+1/epsilon)) -- that answers (1+epsilon)-NNS queries in time ~O(Delta+1/epsilon). Thus, as far as NNS applications are concerned, metrics derived from bounded-degree planar graphs behave as low-dimensional metrics, even though planar metrics do not necessarily have a low doubling dimension, nor can they be embedded with low distortion into l_2. We complement our algorithmic result by lower bounds showing that the access to an exact distance oracle (rather than an approximate one) and the dependency on Delta (in query time) are both essential. <|reference_end|>", "<|reference_start|> A face cover perspective to $\\ell_1$ embeddings of planar graphs: It was conjectured by Gupta et al. [Combinatorica04] that every planar graph can be embedded into $\\ell_1$ with constant distortion. However, given an $n$-vertex weighted planar graph, the best upper bound on the distortion is only $O(\\sqrt{\\log n})$, by Rao [SoCG99]. In this paper we study the case where there is a set $K$ of terminals, and the goal is to embed only the terminals into $\\ell_1$ with low distortion. In a seminal paper, Okamura and Seymour [J.Comb.Theory81] showed that if all the terminals lie on a single face, they can be embedded isometrically into $\\ell_1$. The more general case, where the set of terminals can be covered by $\\gamma$ faces, was studied by Lee and Sidiropoulos [STOC09] and Chekuri et al. [J.Comb.Theory13]. The state of the art is an upper bound of $O(\\log \\gamma)$ by Krauthgamer, Lee and Rika [SODA19]. Our contribution is a further improvement on the upper bound to $O(\\sqrt{\\log\\gamma})$. Since every planar graph has at most $O(n)$ faces, any further improvement on this result, will be a major breakthrough, directly improving upon Rao's long standing upper bound. Moreover, it is well known that the flow-cut gap equals to the distortion of the best embedding into $\\ell_1$. Therefore, our result provides a polynomial time $O(\\sqrt{\\log \\gamma})$-approximation to the sparsest cut problem on planar graphs, for the case where all the demand pairs can be covered by $\\gamma$ faces. <|reference_end|>", "<|reference_start|> A face cover perspective to $\\ell_1$ embeddings of planar graphs: It was conjectured by Gupta et al. [Combinatorica04] that every planar graph can be embedded into $\\ell_1$ with constant distortion. However, given an $n$-vertex weighted planar graph, the best upper bound on the distortion is only $O(\\sqrt{\\log n})$, by Rao [SoCG99]. In this paper we study the case where there is a set $K$ of terminals, and the goal is to embed only the terminals into $\\ell_1$ with low distortion. In a seminal paper, Okamura and Seymour [J.Comb.Theory81] showed that if all the terminals lie on a single face, they can be embedded isometrically into $\\ell_1$. The more general case, where the set of terminals can be covered by $\\gamma$ faces, was studied by Lee and Sidiropoulos [STOC09] and Chekuri et al. [J.Comb.Theory13]. The state of the art is an upper bound of $O(\\log \\gamma)$ by Krauthgamer, Lee and Rika [SODA19]. Our contribution is a further improvement on the upper bound to $O(\\sqrt{\\log\\gamma})$. Since every planar graph has at most $O(n)$ faces, any further improvement on this result, will be a major breakthrough, directly improving upon Rao's long standing upper bound. Moreover, it is well known that the flow-cut gap equals to the distortion of the best embedding into $\\ell_1$. Therefore, our result provides a polynomial time $O(\\sqrt{\\log \\gamma})$-approximation to the sparsest cut problem on planar graphs, for the case where all the demand pairs can be covered by $\\gamma$ faces. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Static pricing for multi-unit prophet inequalities: We study a pricing problem where a seller has $k$ identical copies of a product, buyers arrive sequentially, and the seller prices the items aiming to maximize social welfare. When $k=1$, this is the so called \"prophet inequality\" problem for which there is a simple pricing scheme achieving a competitive ratio of $1/2$. On the other end of the spectrum, as $k$ goes to infinity, the asymptotic performance of both static and adaptive pricing is well understood. We provide a static pricing scheme for the small-supply regime: where $k$ is small but larger than $1$. Prior to our work, the best competitive ratio known for this setting was the $1/2$ that follows from the single-unit prophet inequality. Our pricing scheme is easy to describe as well as practical -- it is anonymous, non-adaptive, and order-oblivious. We pick a single price that equalizes the expected fraction of items sold and the probability that the supply does not sell out before all customers are served; this price is then offered to each customer while supply lasts. This extends an approach introduced by Samuel-Cahn for the case of $k=1$. This pricing scheme achieves a competitive ratio that increases gradually with the supply. Subsequent work by Jiang, Ma, and Zhang shows that our pricing scheme is the optimal static pricing for every value of $k$. <|reference_end|>", "<|reference_start|> A Constant Factor Prophet Inequality for Online Combinatorial Auctions: In online combinatorial auctions m indivisible items are to be allocated to n agents who arrive online. Agents have random valuations for the different subsets of items and the goal is to allocate the items on the fly so as to maximize the total value of the assignment. A prophet inequality in this setting refers to the existence of an online algorithm guaranteed to obtain, in expectation, a certain fraction of the expected value obtained by an optimal solution in hindsight. The study of prophet inequalities for online combinatorial auctions has been an intensive area of research in recent years, and constant factor prophet inequalities are known when the agents’ valuation functions are submodular or fractionally subadditive. Despite many efforts, for the more general case of subadditive valuations, the best known prophet inequality has an approximation guarantee of O(loglogm). In this paper, we prove the existence of a constant factor prophet inequality for the subadditive case, resolving a central open problem in the area. Our prophet inequality is achieved by a novel, but elementary, sampling idea which we call the Mirror Lemma. This lemma is essentially concerned with understanding online algorithms for which the set of items that are allocated and those that are not, distribute equally. The other main ingredient is a nonstandard application of Kakutani’s fixed point theorem. Finally, we note that our prophet inequality works against an almighty adversary and even can be implemented in an incentive compatible way. <|reference_end|>", "<|reference_start|> Prophet Inequalities with Limited Information: In the classical prophet inequality, a gambler observes a sequence of stochastic rewards $V_1,...,V_n$ and must decide, for each reward $V_i$, whether to keep it and stop the game or to forfeit the reward forever and reveal the next value $V_i$. The gambler's goal is to obtain a constant fraction of the expected reward that the optimal offline algorithm would get. Recently, prophet inequalities have been generalized to settings where the gambler can choose $k$ items, and, more generally, where he can choose any independent set in a matroid. However, all the existing algorithms require the gambler to know the distribution from which the rewards $V_1,...,V_n$ are drawn. The assumption that the gambler knows the distribution from which $V_1,...,V_n$ are drawn is very strong. Instead, we work with the much simpler assumption that the gambler only knows a few samples from this distribution. We construct the first single-sample prophet inequalities for many settings of interest, whose guarantees all match the best possible asymptotically, \\emph{even with full knowledge of the distribution}. Specifically, we provide a novel single-sample algorithm when the gambler can choose any $k$ elements whose analysis is based on random walks with limited correlation. In addition, we provide a black-box method for converting specific types of solutions to the related \\emph{secretary problem} to single-sample prophet inequalities, and apply it to several existing algorithms. Finally, we provide a constant-sample prophet inequality for constant-degree bipartite matchings. We apply these results to design the first posted-price and multi-dimensional auction mechanisms with limited information in settings with asymmetric bidders. <|reference_end|>", "<|reference_start|> Prophet Inequalities with Limited Information: In the classical prophet inequality, a gambler observes a sequence of stochastic rewards $V_1,...,V_n$ and must decide, for each reward $V_i$, whether to keep it and stop the game or to forfeit the reward forever and reveal the next value $V_i$. The gambler's goal is to obtain a constant fraction of the expected reward that the optimal offline algorithm would get. Recently, prophet inequalities have been generalized to settings where the gambler can choose $k$ items, and, more generally, where he can choose any independent set in a matroid. However, all the existing algorithms require the gambler to know the distribution from which the rewards $V_1,...,V_n$ are drawn. The assumption that the gambler knows the distribution from which $V_1,...,V_n$ are drawn is very strong. Instead, we work with the much simpler assumption that the gambler only knows a few samples from this distribution. We construct the first single-sample prophet inequalities for many settings of interest, whose guarantees all match the best possible asymptotically, \\emph{even with full knowledge of the distribution}. Specifically, we provide a novel single-sample algorithm when the gambler can choose any $k$ elements whose analysis is based on random walks with limited correlation. In addition, we provide a black-box method for converting specific types of solutions to the related \\emph{secretary problem} to single-sample prophet inequalities, and apply it to several existing algorithms. Finally, we provide a constant-sample prophet inequality for constant-degree bipartite matchings. We apply these results to design the first posted-price and multi-dimensional auction mechanisms with limited information in settings with asymmetric bidders. <|reference_end|>" ]

[ 24, 31, 33, 35 ]

[ "<|reference_start|> A novel EPT autonomous motion control framework for an off-axle hitching tractor-trailer system with drawbar: A tractor-trailer system is an underactuated system that is subject to nonholonomic constraints and exhibits extremely nonlinear behaviors. Its trajectory planning and control issues remain widely researched. In this paper, on the basis of establishing a precise kinematic model, a novel method is proposed to realize autonomous motion control of an off-axle hitching tractor-trailer system with a drawbar. The proposed method is roughly divided into three steps: EXTRACTING (E) a virtual subsystem, trajectory PLANNING (P) for the original full system, and trajectory TRACKING (T) by feedback control techniques, thus the “EPT method”. In the E step, two alternative strategies are considered to construct a virtual subsystem by extracting a part of an original towed aircraft system. Then, in the P step, a two-layer optimal control-based method is developed to generate a reference trajectory. A trajectory for the virtual subsystem is generated in the first layer, where the Reeds-Shepp curve with artificial expertise in selecting intermediate nodes is introduced to provide initial guesses. The results obtained in the first layer are utilized to initialize partial state and control variables in the trajectory planning for the original full system in the second layer. Finally, in the T step, a receding horizon controller is designed to drive the carrier aircraft to track the reference trajectory under various external disturbances. Numerical simulations demonstrate that the EPT method is applicable and highly efficient. The proposed method is also applicable for generalized tractor-trailer systems and other chain-link systems. <|reference_end|>", "<|reference_start|> Data-driven identification of vehicle dynamics using koopman operator: This paper presents the results of identification of vehicle dynamics using the Koopman operator. The basic idea is to transform the state space of a nonlinear system (a car in our case) to a higher-dimensional space, using so-called basis functions, where the system dynamics is linear. The selection of basis functions is crucial and there is no general approach on how to select them, this paper gives some discussion on this topic. Two distinct approaches for selecting the basis functions are presented. The first approach, based on Extended Dynamic Mode Decomposition, relies heavily on expert basis selection and is completely data-driven. The second approach utilizes the knowledge of the nonlinear dynamics, which is used to construct eigenfunctions of the Koopman operator which are known by definition to evolve linearly along the nonlinear system trajectory. The eigenfunctions are then used as basis functions for prediction. Each approach is presented with a numerical example and discussion on the feasibility of the approach for a nonlinear vehicle system. <|reference_end|>", "<|reference_start|> Bilinearization, reachability, and optimal control of control-affine nonlinear systems: A Koopman spectral approach: This article considers the problem of bilinearization and optimal control of a control-affine nonlinear system by projecting the system dynamics onto the Koopman eigenspace. Although there are linearization techniques like Carleman linearization for embedding a finite-dimensional nonlinear system into an infinite-dimensional space, they depend on the analytic property of the vector fields and work only on polynomial space. The proposed method utilizes the Koopman canonical transform, specifically the Koopman eigenfunctions of the drift vector field, to transform the dynamics into a bilinear system under certain assumptions. While the bilinearization is exact, if there exists a Koopman-invariant finite-dimensional subspace for the drift vector field, sometimes this condition is too conservative. An approximate approach is to minimize an $\\mathcal {L}^2$ norm on the truncated state-space. The approximation can be carried out from time-series data without explicit knowledge of the drift vector field. Controllability of the bilinear system is analyzed using the Myhill semigroup method and Lie algebraic structures. Pontryagin’s principle is applied to the bilinear system to yield a two-point boundary-value problem for the optimal control design. A single shooting method solves the boundary value problem in order to determine the control signal. Alternatively, a gradient-based method is also outlined to find the optimal control which exploits the bilinear structure. Several examples of control-affine nonlinear systems numerically illustrate the bilinearization and optimal control design, assuming a cost function quadratic in the states and control input. <|reference_end|>", "<|reference_start|> Advantages of Bilinear Koopman Realizations for the Modeling and Control of Systems with Unknown Dynamics: Nonlinear dynamical systems can be made easier to control by lifting them into the space of observable functions, where their evolution is described by the linear Koopman operator. This paper describes how the Koopman operator can be used to generate approximate linear, bilinear, and nonlinear model realizations from data, and argues in favor of bilinear realizations for characterizing systems with unknown dynamics. Necessary and sufficient conditions for a dynamical system to have a valid linear or bilinear realization over a given set of observable functions are presented and used to show that every control-affine system admits an infinite-dimensional bilinear realization, but does not necessarily admit a linear one. Therefore, approximate bilinear realizations constructed from generic sets of basis functions tend to improve as the number of basis functions increases, whereas approximate linear realizations may not. To demonstrate the advantages of bilinear Koopman realizations for control, a linear, bilinear, and nonlinear Koopman model realization of a simulated robot arm are constructed from data. In a trajectory following task, the bilinear realization exceeds the prediction accuracy of the linear realization and the computational efficiency of the nonlinear realization when incorporated into a model predictive control framework. <|reference_end|>" ]

[ 1, 9, 12, 13 ]

[ "<|reference_start|> Diffusion Models in Vision: A Survey: Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research. <|reference_end|>", "<|reference_start|> A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices: Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches have recently drawn attention because of their good generalization capability. In a typical situation, BP-NN-based models are iteratively optimized in server machines with input data gathered from edge devices. However, (1) the iterative optimization often requires significant efforts to follow changes in the distribution of normal data (i.e., concept drift), and (2) data transfers between edge and server impose additional latency and energy consumption. To address these issues, we propose ONLAD and its IP core, named ONLAD Core. ONLAD is highly optimized to perform fast sequential learning to follow concept drift in less than one millisecond. ONLAD Core realizes on-device learning for edge devices at low power consumption, which realizes standalone execution where data transfers between edge and server are not required. Experiments show that ONLAD has favorable anomaly detection capability in an environment that simulates concept drift. Evaluations of ONLAD Core confirm that the training latency is 1.95x~6.58x faster than the other software implementations. Also, the runtime power consumption of ONLAD Core implemented on PYNQ-Z1 board, a small FPGA/CPU SoC platform, is 5.0x~25.4x lower than them. <|reference_end|>", "<|reference_start|> Neural Architecture Search with Reinforcement Learning: Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214. <|reference_end|>", "<|reference_start|> Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN: Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (LCNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed LCNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real world surveillance video streams. The experimental study has validated the design of LCNN and shown it is a promising approach to computing intensive applications at the edge. <|reference_end|>" ]

[ 1, 6, 20, 28 ]

[ "<|reference_start|> Almost sure convergence to the Quicksort process: <|reference_end|>", "<|reference_start|> Recursive self-similarity for random trees, random triangulations and Brownian excursion: Recursive self-similarity for a random object is the property of being de-composable into independent rescaled copies of the original object. Certain random combinatorial objects-trees and triangulations-possess approximate versions of recursive self-similarity, and then their continuous limits possess exact recursive self-similarity. In particular, since the limit continuum random tree can be identified with Brownian excursion, we get a nonobvious recursive self-similarity property for Brownian excursion. <|reference_end|>", "<|reference_start|> Noncrossing trees are almost conditioned Galton--Watson trees: A noncrossing tree (NC‐tree) is a tree drawn on the plane having as vertices a set of points on the boundary of a circle, and whose edges are straight line segments that do not cross. In this article, we show that NC‐trees with size n are conditioned Galton–Watson trees. As corollaries, we give the limit law of depth‐first traversal processes and the limit profile of NC‐trees. © 2002 John Wiley & Sons, Inc. Random Struct. Alg., 20, 115–125, 2002 <|reference_end|>", "<|reference_start|> Random stable laminations of the disk: We study large random dissections of polygons. We consider random dissections of a regular polygon with n sides, which are chosen according to Boltzmann weights in the domain of attraction of a stable law of index 2 (1;2]. As n goes to infinity, we prove that these random dissections converge in distribution towards a random compact set, called the random stable lamination. If = 2, we recover Aldous’ Brownian triangulation. However, if 2 (1;2), large faces remain in the limit and a dierent random compact set appears. We show that the random stable lamination can be coded by the continuous-time height function associated to the normalized excursion of a strictly stable spectrally positive Levy process of index . Using this coding, we establish that the Hausdor dimension of the stable random lamination is almost surely 2 1= . <|reference_end|>" ]

[ 1, 4, 9, 10 ]

[ "<|reference_start|> Automatic Chain of Thought Prompting in Large Language Models: Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting. CoT prompting has two major paradigms. One leverages a simple prompt like \"Let's think step by step\" to facilitate step-by-step thinking before answering a question. The other uses a few manual demonstrations one by one, each composed of a question and a reasoning chain that leads to an answer. The superior performance of the second paradigm hinges on the hand-crafting of task-specific demonstrations one by one. We show that such manual efforts may be eliminated by leveraging LLMs with the \"Let's think step by step\" prompt to generate reasoning chains for demonstrations one by one, i.e., let's think not just step by step, but also one by one. However, these generated chains often come with mistakes. To mitigate the effect of such mistakes, we find that diversity matters for automatically constructing demonstrations. We propose an automatic CoT prompting method: Auto-CoT. It samples questions with diversity and generates reasoning chains to construct demonstrations. On ten public benchmark reasoning tasks with GPT-3, Auto-CoT consistently matches or exceeds the performance of the CoT paradigm that requires manual designs of demonstrations. Code is available at https://github.com/amazon-research/auto-cot <|reference_end|>", "<|reference_start|> Active {{Learning Literature Survey}}: The most time consuming and expensive task in machine learning is the gathering of labeled data to train the model or to estimate its parameters. In the real-world scenario, the availability of labeled data is scarce and we have limited resources to label the abundantly available unlabeled data. Hence it makes sense to pick only the most informative instances from the unlabeled data and request an expert to provide the label for that instance. Active learning algorithms aim at minimizing the amount of labeled data required to achieve the goal of the machine learning task in hand by strategically selecting the data instance to be labeled by the expert. A lot of research has been conducted in this area over the past two decades leading to great improvements in performance of several existing machine learning algorithms and has also been applied to diverse fields like text classification, information retrieval, computer vision and bioinformatics, to name a few. This survey aims at providing an insight into the research in this area and categorizes the diverse algorithms proposed based on main characteristics. We also provides a desk where different active learning algorithms can be compared by evaluation on benchmark datasets. <|reference_end|>", "<|reference_start|> Multi-task Active Learning for Pre-trained Transformer-based Models: Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predictions when the tasks are inter-related. This technique, however, requires annotating the same text with multiple annotation schemes which may be costly and laborious. Active learning (AL) has been demonstrated to optimize annotation processes by iteratively selecting unlabeled examples whose annotation is most valuable for the NLP model. Yet, multi-task active learning (MT-AL) has not been applied to state-of-the-art pre-trained Transformer-based NLP models. This paper aims to close this gap. We explore various multi-task selection criteria in three realistic multi-task scenarios, reflecting different relations between the participating tasks, and demonstrate the effectiveness of multi-task compared to single-task selection. Our results suggest that MT-AL can be effectively used in order to minimize annotation efforts for multi-task NLP models. <|reference_end|>", "<|reference_start|> Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers: Active learning is the iterative construction of a classification model through targeted labeling, enabling significant labeling cost savings. As most research on active learning has been carried out before transformer-based language models (\"transformers\") became popular, despite its practical importance, comparably few papers have investigated how transformers can be combined with active learning to date. This can be attributed to the fact that using state-of-the-art query strategies for transformers induces a prohibitive runtime overhead, which effectively nullifies, or even outweighs the desired cost savings. For this reason, we revisit uncertainty-based query strategies, which had been largely outperformed before, but are particularly suited in the context of fine-tuning transformers. In an extensive evaluation, we connect transformers to experiments from previous research, assessing their performance on five widely used text classification benchmarks. For active learning with transformers, several other uncertainty-based approaches outperform the well-known prediction entropy query strategy, thereby challenging its status as most popular uncertainty baseline in active learning for text classification. <|reference_end|>" ]

[ 2, 5, 6, 7 ]

[ "<|reference_start|> A Vision and GPS-Based Real-Time Trajectory Planning for a MAV in Unknown and Low-Sunlight Environments: <|reference_end|>", "<|reference_start|> A vision and GPS-based real-time trajectory planning for MAV in unknown urban environments: This paper addresses the issue of real-time optimal trajectory generation of a micro Air Vehicle (MAV) in unknown urban environments. The MAV is required to navigate from an initial and outdoor position to a final position inside a building. To achieve this objective, we develop a safe path planning method using the information provided by the GPS and a consumer depth camera. With the purpose to develop a safe path planning with obstacle avoidance capabilities, a model predictive control approach is developed, which uses the environment information acquired by the navigation system. <|reference_end|>", "<|reference_start|> A sensor fusion methodology for obstacle avoidance robot: Obstacle detection and navigation of dynamic environments is a challenge in mobile robotics. To address this challenge, this paper presents an efficient sensor fusion methodology to detect the size and location of obstacles and navigate the mobile robot with high accuracy. This is done by leveraging upon the unique advantages of accuracy in both ultrasonic sensor and a Kinect sensor for near-field and far-fields respectively. Further, an efficient Kalman filter is implemented to reduce the systematic errors in encoder data to track robot pose of the robot in real-time and reach the destination with high accuracy. Implemented on differential drive-based mobile robot, the proposed system has been validated with a high efficiency of detecting obstacles and reaching the destination with an accuracy of 5cm. <|reference_end|>", "<|reference_start|> Detect and avoid system based on multi sensor fusion for uav: With the rapid growth of the personal drone market, manufacturers of unmanned aerial vehicles(UAV) mainly used in the existing military field are expanding its application in various fields such as leisure, industrial, and public as well. In addition, the autonomous flight of UAV is being developed through combination with various sensors and signal processing technologies. Obstacle detect and avoidance is the most important function to ensure safety of autonomous flight mode. In this paper, we propose an obstacle detect and avoid technique that combines a 2D LiDAR with a stereo camera for safe navigation in autonomous flight of a multi-copter UAV. <|reference_end|>" ]

[ 0, 1, 8, 9 ]

[ "<|reference_start|> Evinets: Neural networks for combining evidence signals for factoid question answering: A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate. This paper proposes EviNets: a novel neural network architecture for factoid question answering. EviNets scores candidate answer entities by combining the available supporting evidence, e.g., structured knowledge bases and unstructured text documents. EviNets represents each piece of evidence with a dense embeddings vector, scores their relevance to the question, and aggregates the support for each candidate to predict their final scores. Each of the components is generic and allows plugging in a variety of models for semantic similarity scoring and information aggregation. We demonstrate the effectiveness of EviNets in experiments on the existing TREC QA and WikiMovies benchmarks, and on the new Yahoo! Answers dataset introduced in this paper. EviNets can be extended to other information types and could facilitate future work on combining evidence signals for joint reasoning in question answering. <|reference_end|>", "<|reference_start|> Key-Value Memory Networks for Directly Reading Documents: Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restrictive, as the schema cannot support certain types of answers, and too sparse, e.g. Wikipedia contains much more information than Freebase. In this work we introduce a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation. To compare using KBs, information extraction or Wikipedia documents directly in a single framework we construct an analysis tool, WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in the domain of movies. Our method reduces the gap between all three settings. It also achieves state-of-the-art results on the existing WikiQA benchmark. <|reference_end|>", "<|reference_start|> Gated self-matching networks for reading comprehension and question answering: In this paper, we present the gated self-matching networks for reading comprehension style question answering, which aims to answer questions from a given passage. We first match the question and passage with gated attention-based recurrent networks to obtain the question-aware passage representation. Then we propose a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. We finally employ the pointer networks to locate the positions of answers from the passages. We conduct extensive experiments on the SQuAD dataset. The single model achieves 71.3% on the evaluation metrics of exact match on the hidden test set, while the ensemble model further boosts the results to 75.9%. At the time of submission of the paper, our model holds the first place on the SQuAD leaderboard for both single and ensemble model. <|reference_end|>", "<|reference_start|> Neural Programmer: Inducing Latent Programs with Gradient Descent: Deep neural networks have achieved impressive supervised classification performance in many tasks including image recognition, speech recognition, and sequence to sequence learning. However, this success has not been translated to applications like question answering that may involve complex arithmetic and logic reasoning. A major limitation of these models is in their inability to learn even simple arithmetic and logic operations. For example, it has been shown that neural networks fail to learn to add two binary numbers reliably. In this work, we propose Neural Programmer, an end-to-end differentiable neural network augmented with a small set of basic arithmetic and logic operations. Neural Programmer can call these augmented operations over several steps, thereby inducing compositional programs that are more complex than the built-in operations. The model learns from a weak supervision signal which is the result of execution of the correct program, hence it does not require expensive annotation of the correct program itself. The decisions of what operations to call, and what data segments to apply to are inferred by Neural Programmer. Such decisions, during training, are done in a differentiable fashion so that the entire network can be trained jointly by gradient descent. We find that training the model is difficult, but it can be greatly improved by adding random noise to the gradient. On a fairly complex synthetic table-comprehension dataset, traditional recurrent networks and attentional models perform poorly while Neural Programmer typically obtains nearly perfect accuracy. <|reference_end|>" ]

[ 3, 9, 10, 31 ]

[ "<|reference_start|> BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension: We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance. <|reference_end|>", "<|reference_start|> FreeLB: Enhanced Adversarial Training for Natural Language Understanding: Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In this work, we propose a novel adversarial training algorithm, FreeLB, that promotes higher invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples. To validate the effectiveness of the proposed approach, we apply it to Transformer-based models for natural language understanding and commonsense reasoning tasks. Experiments on the GLUE benchmark show that when applied only to the finetuning stage, it is able to improve the overall test scores of BERT-base model from 78.3 to 79.4, and RoBERTa-large model from 88.5 to 88.8. In addition, the proposed approach achieves state-of-the-art single-model test accuracies of 85.44\\% and 67.75\\% on ARC-Easy and ARC-Challenge. Experiments on CommonsenseQA benchmark further demonstrate that FreeLB can be generalized and boost the performance of RoBERTa-large model on other tasks as well. Code is available at \\url{https://github.com/zhuchen03/FreeLB . <|reference_end|>", "<|reference_start|> Adversarial Training for Free!: Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters. Our \"free\" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at https://github.com/ashafahi/free_adv_train. <|reference_end|>", "<|reference_start|> A Co-regularization approach to semi-supervised learning with multiple views: The Co-Training algorithm uses unlabeled examples in multiple views to bootstrap classifiers in each view, typically in a greedy manner, and operating under assumptions of view-independence and compatibility. In this paper, we propose a Co-Regularization framework where classifiers are learnt in each view through forms of multi-view regularization. We propose algorithms within this framework that are based on optimizing measures of agreement and smoothness over labeled and unlabeled examples. These algorithms naturally extend standard regularization methods like Support Vector Machines (SVM) and Regularized Least squares (RLS) for multi-view semi-supervised learning, and inherit their benefits and applicability to high-dimensional classification problems. An empirical investigation is presented that confirms the promise of this approach. <|reference_end|>" ]

[ 6, 9, 22, 25 ]

[ "<|reference_start|> The Secrecy Graph and Some of its Properties: A new random geometric graph model, the so-called secrecy graph, is introduced and studied. The graph represents a wireless network and includes only edges over which secure communication in the presence of eavesdroppers is possible. The underlying point process models considered are lattices and Poisson point processes. In the lattice case, analogies to standard bond and site percolation can be exploited to determine percolation thresholds. In the Poisson case, the node degrees are determined and percolation is studied using analytical bounds and simulations. It turns out that a small density of eavesdroppers already has a drastic impact on the connectivity of the secrecy graph. <|reference_end|>", "<|reference_start|> Physical-layer security in stochastic wireless networks: Motivated by recent developments in physical-layer security and stochastic geometry, we aim to characterize the fundamental limits of secure communication in wireless networks. Based on a general model in which legitimate nodes and potential eavesdroppers are randomly scattered in space, we define the secure communication graph (s-graph) from the point of view of information-theoretic security. For the Poisson s-graph, we provide conclusive results for: (a) the in-degree and out-degree of a node; (b) the isolation probability; and (c) the secrecy capacity between a node and each of its neighbours. Our analysis reveals the innate connections between information-theoretic security and the spatial densities of legitimate and eavesdropper nodes. <|reference_end|>", "<|reference_start|> On transmission secrecy outage of a multi-antenna system with randomly located eavesdroppers: This letter studies the physical-layer security of a multi-antenna transmission system in the presence of Poisson distributed eavesdroppers. The transmission secrecy outage probability (TSOP) is adopted to evaluate the security. We derive an accurate integral expression as well as a closed-form upper bound on TSOP for the noncolluding eavesdroppers' case and a closed-form solution for the colluding eavesdroppers' case, respectively. Based on these, we define a novel concept of security region to intuitively illustrate the security from a spatial perspective. We further analyze the impacts of various factors on the security, such as the number of transmit antennas, the node intensity, and the target secrecy rate. <|reference_end|>", "<|reference_start|> Securing visible light communications via friendly jamming: Despite offering higher security than radio frequency (RF) channels, the broadcast nature of the visible light communication (VLC) channel makes VLC links inherently susceptible to eavesdropping by unauthorized users. In this work, we consider the physical-layer security of VLC links aided by friendly jamming. The jammer has multiple light sources, but does not have access to the data transmitted. The eavesdropper's reception is degraded by a jamming signal that causes no interference to the legitimate receiver. Due to the limited dynamic range of typical light-emitting diodes (LEDs), both the data and jamming signals are subject to amplitude constraints. Therefore, we begin with deriving a closed-form secrecy rate expression for the corresponding wiretap channel, and adopt secrecy rate as the performance measure. Then, we formulate a linear programming problem to maximize the secrecy rate when the eavesdropper's channel is accurately known to the jammer. Finally, we consider robust beamforming to maximize the worst-case secrecy rate when information about the eavesdropper's channel is uncertain due to location uncertainty. The robust scheme makes use of simple linear programming, making real-time implementation feasible in a variety of real-world scenarios. <|reference_end|>" ]

[ 1, 2, 9, 13 ]

[ "<|reference_start|> Deep Learning and the Information Bottleneck Principle: Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual information between the layers and the input and output variables. Using this representation we can calculate the optimal information theoretic limits of the DNN and obtain finite sample generalization bounds. The advantage of getting closer to the theoretical limit is quantifiable both by the generalization bound and by the network's simplicity. We argue that both the optimal architecture, number of layers and features/connections at each layer, are related to the bifurcation points of the information bottleneck tradeoff, namely, relevant compression of the input layer with respect to the output layer. The hierarchical representations at the layered network naturally correspond to the structural phase transitions along the information curve. We believe that this new insight can lead to new optimality bounds and deep learning algorithms. <|reference_end|>", "<|reference_start|> VarGrad: A Low-Variance Gradient Estimator for Variational Inference: We analyse the properties of an unbiased gradient estimator of the ELBO for variational inference, based on the score function method with leave-one-out control variates. We show that this gradient estimator can be obtained using a new loss, defined as the variance of the log-ratio between the exact posterior and the variational approximation, which we call the $\\textit{log-variance loss}$. Under certain conditions, the gradient of the log-variance loss equals the gradient of the (negative) ELBO. We show theoretically that this gradient estimator, which we call $\\textit{VarGrad}$ due to its connection to the log-variance loss, exhibits lower variance than the score function method in certain settings, and that the leave-one-out control variate coefficients are close to the optimal ones. We empirically demonstrate that VarGrad offers a favourable variance versus computation trade-off compared to other state-of-the-art estimators on a discrete VAE. <|reference_end|>", "<|reference_start|> A Simple Framework for Contrastive Learning of Visual Representations: This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels. <|reference_end|>", "<|reference_start|> Momentum Contrast for Unsupervised Visual Representation Learning: We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks. <|reference_end|>" ]

[ 9, 23, 28, 29 ]

[ "<|reference_start|> Sandstone HPC: A Domain-General Gateway for New HPC Users: The complexity of high-performance computing (HPC) resources poses many challenges to new users. A number of science gateways have been developed to increase the productivity of novice users by hiding the underlying infrastructure, however these solutions tend not to teach HPC skills that transfer easily outside of the gateway. In this paper we introduce a domain-general gateway, Sandstone HPC, that represents the HPC environment more naturally to novice users by abstracting the command-line interface and providing contextual help. We assess the degree to which Sandstone HPC improves upon the usability of the command-line interface by analyzing the results of a usability study conducted on both environments. We will also detail how the architecture promotes long-term sustainability and a community-development model. <|reference_end|>", "<|reference_start|> Vizic: A Jupyter-based interactive visualization tool for astronomical catalogs: <|reference_end|>", "<|reference_start|> Sandstone HPC: A Domain-General Gateway for New HPC Users: The complexity of high-performance computing (HPC) resources poses many challenges to new users. A number of science gateways have been developed to increase the productivity of novice users by hiding the underlying infrastructure, however these solutions tend not to teach HPC skills that transfer easily outside of the gateway. In this paper we introduce a domain-general gateway, Sandstone HPC, that represents the HPC environment more naturally to novice users by abstracting the command-line interface and providing contextual help. We assess the degree to which Sandstone HPC improves upon the usability of the command-line interface by analyzing the results of a usability study conducted on both environments. We will also detail how the architecture promotes long-term sustainability and a community-development model. <|reference_end|>", "<|reference_start|> Incorporating interactive compute environments into web-based training materials using the Cornell job runner service: Online training materials, such as the Cornell Virtual WorkshopSM have many advantages, the foremost being that they are always available as a 24x7 option for learners who want to study a topic on demand and at their own pace. It can be challenging to create online materials that are engaging and provide a realistic learning environment. Traditionally, training materials and compute environments have been separate entities. Even in the HPC environment, students learn from online materials in one window, then log into a new machine or session to try out new skills or concepts. Accessing this second environment can impose obstacles such as gaining access to the appropriate computer and learning to navigate a computer-specific login environment and file system. In an effort to circumvent these obstacles, the Cornell University Center for Advanced Computing (CAC) developed the Cornell Job Runner ServiceSM (CJRS), along with a general-purpose toolkit for using the CJRS to embed a computing environment directly into web pages, backed by real or virtual compute resources. This implementation provides the learner immediate access to a compute environment that looks and feels like a typical HPC login node or batch job, allowing incorporation of on-demand learning experiences interspersed with general training content. With CJRS, students can try out commands and run jobs without obtaining an account or leaving the learning environment to sign in to a remote machine. This paper explores the use of the CJRS toolkit to provide three different interactive modes for learners: a Linux console configured as a general login node, a form element that launches a pre-defined SLURM job, and a guided session which allows the user to walk through pre-planned steps of compiling, fixing, and running MPI code. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Delayed Impact of Fair Machine Learning: Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs. <|reference_end|>", "<|reference_start|> Delayed Impact of Fair Machine Learning: Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs. <|reference_end|>", "<|reference_start|> Towards Long-term Fairness in Recommendation: As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static or one-shot setting, where the protected groups of items are fixed, and the model provides a one-time fairness solution based on fairness-constrained optimization. This fails to consider the dynamic nature of the recommender systems, where attributes such as item popularity may change over time due to the recommendation policy and user engagement. For example, products that were once popular may become no longer popular, and vice versa. As a result, the system that aims to maintain long-term fairness on the item exposure in different popularity groups must accommodate this change in a timely fashion. Novel to this work, we explore the problem of long-term fairness in recommendation and accomplish the problem through dynamic fairness learning. We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process. We tackle this problem by proposing a fairness-constrained reinforcement learning algorithm for recommendation, which models the recommendation problem as a Constrained Markov Decision Process (CMDP), so that the model can dynamically adjust its recommendation policy to make sure the fairness requirement is always satisfied when the environment changes. Experiments on several real-world datasets verify our framework's superiority in terms of recommendation performance, short-term fairness, and long-term fairness. <|reference_end|>", "<|reference_start|> Achieving Long-Term Fairness in Sequential Decision Making: In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimization (RRM) is used for model training, and the convergence of RRM is theoretically analyzed. The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets. <|reference_end|>" ]

[ 4, 5, 6, 7 ]

[ "<|reference_start|> Joint topology control and routing for multi-radio multi-channel WMNs under SINR model using bio-inspired techniques: <|reference_end|>", "<|reference_start|> Traffic engineering in cognitive mesh networks: Joint link-channel selection and power allocation: <|reference_end|>", "<|reference_start|> Cross-layer design and performance analysis for maximizing the network utilization of wireless mesh networks in cloud computing: <|reference_end|>", "<|reference_start|> PLASMA: A new routing paradigm for wireless multihop networks: In this paper we present a new routing paradigm for wireless multihop networks. In plasma routing, each packet is delivered over the best available path to one of the gateways. The choice of the path and gateway for each packet is not made beforehand by the source node, but rather on-the-fly by the mesh routers as the packet traverses the network. We propose a distributed routing algorithm to jointly optimize the transmission rate and the set of gateways each node should use. A load balancing technique is also proposed to disperse the network traffic among multiple gateways. We validate our proposal with simulations and show that plasma routing outperforms the state-of-the-art multirate anypath routing paradigm, with a 98% throughput gain and a 2.2x delay decrease. Finally, we also show that the load can be evenly distributed among gateways with a similar routing cost, resulting in a further 63% throughput gain. <|reference_end|>" ]

[ 8, 18, 19, 21 ]

[ "<|reference_start|> Are Large-Scale 3D models really necessary for accurate visual localization?: Accurate visual localization is a key technology for autonomous navigation. 3D structure-based methods employ 3D models of the scene to estimate the full 6DOF pose of a camera very accurately. However, constructing (and extending) large-scale 3D models is still a significant challenge. In contrast, 2D image retrieval-based methods only require a database of geo-tagged images, which is trivial to construct and to maintain. They are often considered inaccurate since they only approximate the positions of the cameras. Yet, the exact camera pose can theoretically be recovered when enough relevant database images are retrieved. In this paper, we demonstrate experimentally that large-scale 3D models are not strictly necessary for accurate visual localization. We create reference poses for a large and challenging urban dataset. Using these poses, we show that combining image-based methods with local reconstructions results in a pose accuracy similar to the state-of-the-art structure-based methods. Our results suggest that we might want to reconsider the current approach for accurate large-scale localization. <|reference_end|>", "<|reference_start|> VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change: <|reference_end|>", "<|reference_start|> Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization: This paper tackles the problem of large-scale image-based localization (IBL) where the spatial location of a query image is determined by finding out the most similar reference images in a large database. For solving this problem, a critical task is to learn discriminative image representation that captures informative information relevant for localization. We propose a novel representation learning method having higher location-discriminating power. It provides the following contributions: 1) we represent a place (location) as a set of exemplar images depicting the same landmarks and aim to maximize similarities among intra-place images while minimizing similarities among inter-place images; 2) we model a similarity measure as a probability distribution on L_2-metric distances between intra-place and inter-place image representations; 3) we propose a new Stochastic Attraction and Repulsion Embedding (SARE) loss function minimizing the KL divergence between the learned and the actual probability distributions; 4) we give theoretical comparisons between SARE, triplet ranking and contrastive losses. It provides insights into why SARE is better by analyzing gradients. Our SARE loss is easy to implement and pluggable to any CNN. Experiments show that our proposed method improves the localization performance on standard benchmarks by a large margin. Demonstrating the broad applicability of our method, we obtained the third place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our code and model are available at https://github.com/Liumouliu/deepIBL. <|reference_end|>", "<|reference_start|> Attentional pyramid pooling of salient visual residuals for place recognition: The core of visual place recognition (VPR) lies in how to identify task-relevant visual cues and embed them into dis- criminative representations. Focusing on these two points, we propose a novel encoding strategy named Attentional Pyramid Pooling of Salient Visual Residuals (APPSVR). It incorporates three types of attention modules to model the saliency of local features in individual, spatial and cluster dimensions respectively. (1) To inhibit task-irrelevant local features, a semantic-reinforced local weighting scheme is employed for local feature refinement; (2) To leverage the spatial context, an attentional pyramid structure is constructed to adaptively encode regional features according to their relative spatial saliency; (3) To distinguish the different importance of visual clusters to the task, a parametric normalization is proposed to adjust their contribution to image descriptor generation. Experiments demonstrate APPSVR outperforms the existing techniques and achieves a new state-of-the-art performance on VPR benchmark datasets. The visualization shows the saliency map learned in a weakly supervised manner is largely consistent with human cognition. <|reference_end|>" ]

[ 5, 6, 14, 33 ]

[ "<|reference_start|> MedAL: Accurate and robust deep active learning for medical image analysis: Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance. However, such large labeled datasets are costly to acquire. Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space. We then extend our sampling method to define a better initial training set, without the need for a trained model, by using Oriented FAST and Rotated BRIEF (ORB) feature descriptors. We validate MedAL on 3 medical image datasets and show that our method is robust to different dataset properties. MedAL is also efficient, achieving 80% accuracy on the task of Diabetic Retinopathy detection using only 425 labeled images, corresponding to a 32% reduction in the number of required labeled examples compared to the standard uncertainty sampling technique, and a 40% reduction compared to random sampling. <|reference_end|>", "<|reference_start|> {Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks: Automatic segmentation of abdominal anatomy on computed tomography (CT) images can support diagnosis, treatment planning, and treatment delivery workflows. Segmentation methods using statistical models and multi-atlas label fusion (MALF) require inter-subject image registrations, which are challenging for abdominal images, but alternative methods without registration have not yet achieved higher accuracy for most abdominal organs. We present a registration-free deep-learning-based segmentation algorithm for eight organs that are relevant for navigation in endoscopic pancreatic and biliary procedures, including the pancreas, the gastrointestinal tract (esophagus, stomach, and duodenum) and surrounding organs (liver, spleen, left kidney, and gallbladder). We directly compared the segmentation accuracy of the proposed method to the existing deep learning and MALF methods in a cross-validation on a multi-centre data set with 90 subjects. The proposed method yielded significantly higher Dice scores for all organs and lower mean absolute distances for most organs, including Dice scores of 0.78 versus 0.71, 0.74, and 0.74 for the pancreas, 0.90 versus 0.85, 0.87, and 0.83 for the stomach, and 0.76 versus 0.68, 0.69, and 0.66 for the esophagus. We conclude that the deep-learning-based segmentation represents a registration-free method for multi-organ abdominal CT segmentation whose accuracy can surpass current methods, potentially supporting image-guided navigation in gastrointestinal endoscopy procedures. <|reference_end|>", "<|reference_start|> RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning: Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Rather than designing a \"fast\" reinforcement learning algorithm, we propose to represent it as a recurrent neural network (RNN) and learn it from data. In our proposed method, RL$^2$, the algorithm is encoded in the weights of the RNN, which are learned slowly through a general-purpose (\"slow\") RL algorithm. The RNN receives all information a typical RL algorithm would receive, including observations, actions, rewards, and termination flags; and it retains its state across episodes in a given Markov Decision Process (MDP). The activations of the RNN store the state of the \"fast\" RL algorithm on the current (previously unseen) MDP. We evaluate RL$^2$ experimentally on both small-scale and large-scale problems. On the small-scale side, we train it to solve randomly generated multi-arm bandit problems and finite MDPs. After RL$^2$ is trained, its performance on new MDPs is close to human-designed algorithms with optimality guarantees. On the large-scale side, we test RL$^2$ on a vision-based navigation task and show that it scales up to high-dimensional problems. <|reference_end|>", "<|reference_start|> Learning how to Active Learn: A Deep Reinforcement Learning Approach: Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limited and moreover, the performance of heuristics varies between datasets. To address these shortcomings, we introduce a novel formulation by reframing the active learning as a reinforcement learning problem and explicitly learning a data selection policy, where the policy takes the role of the active learning heuristic. Importantly, our method allows the selection policy learned using simulation on one language to be transferred to other languages. We demonstrate our method using cross-lingual named entity recognition, observing uniform improvements over traditional active learning. <|reference_end|>" ]

[ 15, 24, 27, 36 ]

[ "<|reference_start|> {The magical number seven, plus or minus two: Some limits on our capacity for processing information.: First, the span of absolute judgment and the span of immediate memory impose severe limitations on the amount of information that we are able to receive, process, and remember. By organizing the stimulus input simultaneously into several dimensions and successively into a sequence or chunks, we manage to break (or at least stretch) this informational bottleneck. Second, the process of recoding is a very important one in human psychology and deserves much more explicit attention than it has received. In particular, the kind of linguistic recoding that people do seems to me to be the very lifeblood of the thought processes. Recoding procedures are a constant concern to clinicians, social psychologists, linguists, and anthropologists and yet, probably because recoding is less accessible to experimental manipulation than nonsense syllables or T mazes, the traditional experimental psychologist has contributed little or nothing to their analysis. Nevertheless, experimental techniques can be used, methods of recoding can be specified, behavioral indicants can be found. And I anticipate that we will find a very orderly set of relations describing what now seems an uncharted wilderness of individual differences. Third, the concepts and measures provided by the theory of information provide a quantitative way of getting at some of these questions. The theory provides us with a yardstick for calibrating our stimulus materials and for measuring the performance of our subjects. In the interests of communication I have suppressed the technical details of information measurement and have tried to express the ideas in more familiar terms; I hope this paraphrase will not lead you to think they are not useful in research. Informational concepts have already proved valuable in the study of discrimination and of language; they promise a great deal in the study of learning and memory; and it has even been proposed that they can be useful in the study of concept formation. A lot of questions that seemed fruitless twenty or thirty years ago may now be worth another look. In fact, I feel that my story here must stop just as it begins to get really interesting. And finally, what about the magical number seven? What about the seven wonders of the world, the seven seas, the seven deadly sins, the seven daughters of Atlas in the Pleiades, the seven ages of man, the seven levels of hell, the seven primary colors, the seven notes of the musical scale, and the seven days of the week? What about the seven-point rating scale, the seven categories for absolute judgment, the seven objects in the span of attention, and the seven digits in the span of immediate memory? For the present I propose to withhold judgment. Perhaps there is something deep and profound behind all these sevens, something just calling out for us to discover it. But I suspect that it is only a pernicious, Pythagorean coincidence. <|reference_end|>", "<|reference_start|> The magical number 4 in short-term memory: A reconsideration of mental storage capacity: Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described. Four basic conditions in which chunks can be identified and capacity limits can accordingly be observed are: (1) when information overload limits chunks to individual stimulus items, (2) when other steps are taken specifically to block the recoding of stimulus items into larger chunks, (3) in performance discontinuities caused by the capacity limit, and (4) in various indirect effects of the capacity limit. Under these conditions, rehearsal and long-term memory cannot be used to combine stimulus items into chunks of an unknown size; nor can storage mechanisms that are not capacity-limited, such as sensory memory, allow the capacity-limited storage mechanism to be refilled during recall. A single, central capacity limit averaging about four chunks is implicated along with other, noncapacity-limited sources. The pure STM capacity limit expressed in chunks is distinguished from compound STM limits obtained when the number of separately held chunks is unclear. Reasons why pure capacity estimates fall within a narrow range are discussed and a capacity limit for the focus of attention is proposed. <|reference_end|>", "<|reference_start|> Bayesian modeling of human concept learning: I consider the problem of learning concepts from small numbers of positive examples, a feat which humans perform routinely but which computers are rarely capable of. Bridging machine learning and cognitive science perspectives, I present both theoretical analysis and an empirical study with human subjects for the simple task oflearning concepts corresponding to axis-aligned rectangles in a multidimensional feature space. Existing learning models, when applied to this task, cannot explain how subjects generalize from only a few examples of the concept. I propose a principled Bayesian model based on the assumption that the examples are a random sample from the concept to be learned. The model gives precise fits to human behavior on this simple task and provides qualitative insights into more complex, realistic cases of concept learning. <|reference_end|>", "<|reference_start|> Interactive Concept Bottleneck Models: Concept bottleneck models (CBMs) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions. We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate that a simple policy combining concept prediction uncertainty and influence of the concept on the final prediction achieves strong performance and outperforms static approaches as well as active feature acquisition methods proposed in the literature. We show that the interactive CBM can achieve accuracy gains of 5-10% with only 5 interactions over competitive baselines on the Caltech-UCSD Birds, CheXpert and OAI datasets. <|reference_end|>" ]

[ 0, 2, 4, 7 ]

[ "<|reference_start|> New results on ordered statistics and analysis of minimum-selection generalized selection combining (GSC): Diversity combining techniques improve the performance of wireless communication systems at the cost of increased power consumption. Minimum-selection generalized selection combining (MS-GSC) scheme has been proposed as a power saving implementation of conventional generalized selection combining (GSC) scheme. In this paper, noting that previous analytical results on the error rate of MS-GSC are approximate, we carry out a thorough and exact analysis for MS-GSC. In particular, based on a new result on order statistics, we obtain the statistics of the combined SNR with MS-GSC and we then apply these results to analyze the performance of MS-GSC over fading channels. We derive the closed-form expressions of important performance measures, including outage probability and average error rate, for the Rayleigh fading scenario. In addition, we investigate the average number of active MRC branches with MS-GSC, as a quantification of the power saving <|reference_end|>", "<|reference_start|> Analysis of hybrid selection/maximal-ratio diversity combiners with Gaussian errors: The paper examines the impact of Gaussian distributed weighting errors (in the channel gain estimates used for coherent combination) on both the output statistics of a hybrid selection/maximal-ratio (SC/MRC) receiver and the degradation of the average symbol-error rate (ASER) performance as compared with the ideal case. New expressions are derived for the probability density function, cumulative distribution function and moment generating function (MGF) of the coherent hybrid SC/MRC combiner output signal-to-noise ratio (SNR). The MGF is then used to derive exact, closed-form, ASER expressions for binary and M-ary modulations in conjunction a nonideal hybrid SC/MRC receiver in a Rayleigh fading environment. Results for both selection combining (SC) and maximal-ratio combining (MRC) are obtained as limiting cases. Additionally, the effect of the weighting errors on both the outage rate of error probability and the average combined SNR is investigated. These analytical results provide insights into the tradeoff between diversity gain and combination losses, in concert with increasing orders of diversity branches in an energy-sharing communication system. <|reference_end|>", "<|reference_start|> Finger replacement method for RAKE receivers in the soft handover region: We propose and analyze a new finger replacement technique that is applicable for RAKE receivers in the soft handover (SHO) region. More specifically, the receiver uses in the SHO region by default the strongest paths from the serving base station (BS) and only when the combined signal-to-noise ratio falls below a certain pre-determined threshold, the receiver uses more resolvable paths from the target BS to improve the performance. Instead of changing the configuration for all fingers, the receiver just compares the sum of the weakest paths out of the currently connected paths from the serving BS with the sum of the strongest paths from the target BS and selects the better group. Using accurate statistical analysis, we investigate in this letter the tradeoff between error performance, average number of required path comparisons, and SHO overhead offered by this newly proposed scheme. <|reference_end|>", "<|reference_start|> Adaptive modulation and diversity combining based on output-threshold {MRC}: In this paper, we propose a combined adaptive modulation transmission and output-threshold MRC diversity reception scheme for high spectral and power efficient wireless transceiver design. The modulation constellation size and the number of combined diversity paths are jointly determined based on the fading channel conditions and the required error rate performance. We derive the statistics of the resulting output signal-to-noise ratio (SNR) and then use it to analyze the proposed system in terms of performance, complexity, and spectral efficiency. Some selected numerical examples are finally presented to illustrate the mathematical formulism <|reference_end|>" ]

[ 4, 11, 16, 19 ]

[ "<|reference_start|> Optimal Linear Codes with a Local-Error-Correction Property: Motivated by applications to distributed storage, Gopalan \\textit{et al} recently introduced the interesting notion of information-symbol locality in a linear code. By this it is meant that each message symbol appears in a parity-check equation associated with small Hamming weight, thereby enabling recovery of the message symbol by examining a small number of other code symbols. This notion is expanded to the case when all code symbols, not just the message symbols, are covered by such \"local\" parity. In this paper, we extend the results of Gopalan et. al. so as to permit recovery of an erased code symbol even in the presence of errors in local parity symbols. We present tight bounds on the minimum distance of such codes and exhibit codes that are optimal with respect to the local error-correction property. As a corollary, we obtain an upper bound on the minimum distance of a concatenated code. <|reference_end|>", "<|reference_start|> Optimal Locally Repairable Codes via Rank-Metric Codes: This paper presents a new explicit construction for locally repairable codes (LRCs) for distributed storage systems which possess all-symbols locality and maximal possible minimum distance, or equivalently, can tolerate the maximal number of node failures. This construction, based on maximum rank distance (MRD) Gabidulin codes, provides new optimal vector and scalar LRCs. In addition, the paper also discusses mechanisms by which codes obtained using this construction can be used to construct LRCs with efficient repair of failed nodes by combination of LRC with regenerating codes. <|reference_end|>", "<|reference_start|> A family of optimal locally recoverable codes: A code over a finite alphabet is called locally recoverable (LRC) if every symbol in the encoding is a function of a small number (at most $r$) other symbols. We present a family of LRC codes that attain the maximum possible value of the distance for a given locality parameter and code cardinality. The codewords are obtained as evaluations of specially constructed polynomials over a finite field, and reduce to a Reed-Solomon code if the locality parameter $r$ is set to be equal to the code dimension. The size of the code alphabet for most parameters is only slightly greater than the code length. The recovery procedure is performed by polynomial interpolation over $r$ points. We also construct codes with several disjoint recovering sets for every symbol. This construction enables the system to conduct several independent and simultaneous recovery processes of a specific symbol by accessing different parts of the codeword. This property enables high availability of frequently accessed data (\"hot data\"). <|reference_end|>", "<|reference_start|> Bounds and Constructions of Codes with Multiple Localities: This paper studies bounds and constructions of locally repairable codes (LRCs) with multiple localities so-called multiple-locality LRCs (ML-LRCs). In the simplest case of two localities some code symbols of an ML-LRC have a certain locality while the remaining code symbols have another one. We extend two bounds, the Singleton and the alphabet-dependent upper bound on the dimension of Cadambe--Mazumdar for LRCs, to the case of ML-LRCs with more than two localities. Furthermore, we construct Singleton-optimal ML-LRCs as well as codes that achieve the extended alphabet-dependent bound. We give a family of binary ML-LRCs based on generalized code concatenation that is optimal with respect to the alphabet-dependent bound. <|reference_end|>" ]

[ 1, 2, 8, 14 ]

[ "<|reference_start|> Multi-model fusion metric learning for image set classification: <|reference_end|>", "<|reference_start|> VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge: This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge was to evaluate how well state-of-the-art speaker recognition systems can diarise and recognise speakers from speech obtained \"in the wild\". The challenge consisted of: (i) the provision of publicly available speaker recognition and diarisation data from YouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a public challenge and hybrid workshop held at INTERSPEECH 2022. We describe the four tracks of our challenge along with the baselines, methods, and results. We conclude with a discussion on the new domain-transfer focus of VoxSRC-22, and on the progression of the challenge from the previous three editions. <|reference_end|>", "<|reference_start|> The SpeakIn System for VoxCeleb Speaker Recognition Challange 2021: This report describes our submission to the track 1 and track 2 of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC 2021). Both track 1 and track 2 share the same speaker verification system, which only uses VoxCeleb2-dev as our training set. This report explores several parts, including data augmentation, network structures, domain-based large margin fine-tuning, and back-end refinement. Our system is a fusion of 9 models and achieves first place in these two tracks of VoxSRC 2021. The minDCF of our submission is 0.1034, and the corresponding EER is 1.8460%. <|reference_end|>", "<|reference_start|> A survey on ensemble learning: <|reference_end|>" ]

[ 13, 17, 19, 28 ]

[ "<|reference_start|> Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs: We present a deep convolutional decoder architecture that can generate volumetric 3D outputs in a compute- and memory-efficient manner by using an octree representation. The network learns to predict both the structure of the octree, and the occupancy values of individual cells. This makes it a particularly valuable technique for generating 3D shapes. In contrast to standard decoders acting on regular voxel grids, the architecture does not have cubic complexity. This allows representing much higher resolution outputs with a limited memory budget. We demonstrate this in several application domains, including 3D convolutional autoencoders, generation of objects and whole scenes from high-level representations, and shape from a single image. <|reference_end|>", "<|reference_start|> Customers' joining behavior in an unobservable GI/Geo/m queue: This paper studies the equilibrium balking strategies of impatient customers in a discrete-time multi-server renewal input queue with identical servers. Arriving customers are unaware of the number of customers in the queue before making a decision whether to join or balk the queue. We model the decision-making process as a non-cooperative symmetric game and derive the Nash equilibrium mixed strategy and optimal social strategies. The stationary system-length distributions at different observation epochs under the equilibrium structure are obtained using the roots method. Finally, some numerical examples are presented to show the effect of the information level together with system parameters on the equilibrium and social behavior of impatient customers. <|reference_end|>", "<|reference_start|> Deep Level Sets: Implicit Surface Representations for 3D Shape Inference: Existing 3D surface representation approaches are unable to accurately classify pixels and their orientation lying on the boundary of an object. Thus resulting in coarse representations which usually require post-processing steps to extract 3D surface meshes. To overcome this limitation, we propose an end-to-end trainable model that directly predicts implicit surface representations of arbitrary topology by optimising a novel geometric loss function. Specifically, we propose to represent the output as an oriented level set of a continuous embedding function, and incorporate this in a deep end-to-end learning framework by introducing a variational shape inference formulation. We investigate the benefits of our approach on the task of 3D surface prediction and demonstrate its ability to produce a more accurate reconstruction compared to voxel-based representations. We further show that our model is flexible and can be applied to a variety of shape inference problems. <|reference_end|>", "<|reference_start|> FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation: Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and segmentation. In this work, a novel end-to-end deep auto-encoder is proposed to address unsupervised learning challenges on point clouds. On the encoder side, a graph-based enhancement is enforced to promote local structures on top of PointNet. Then, a novel folding-based decoder deforms a canonical 2D grid onto the underlying 3D object surface of a point cloud, achieving low reconstruction errors even for objects with delicate structures. The proposed decoder only uses about 7% parameters of a decoder with fully-connected neural networks, yet leads to a more discriminative representation that achieves higher linear SVM classification accuracy than the benchmark. In addition, the proposed decoder structure is shown, in theory, to be a generic architecture that is able to reconstruct an arbitrary point cloud from a 2D grid. Our code is available at http://www.merl.com/research/license#FoldingNet <|reference_end|>" ]

[ 3, 7, 29, 32 ]

[ "<|reference_start|> Learning Quadrupedal Locomotion over Challenging Terrain: Some of the most challenging environments on our planet are accessible to quadrupedal animals but remain out of reach for autonomous machines. Legged locomotion can dramatically expand the operational domains of robotics. However, conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes. These designs have escalated in complexity while falling short of the generality and robustness of animal locomotion. Here we present a radically robust controller for legged locomotion in challenging natural environments. We present a novel solution to incorporating proprioceptive feedback in locomotion control and demonstrate remarkable zero-shot generalization from simulation to natural environments. The controller is trained by reinforcement learning in simulation. It is based on a neural network that acts on a stream of proprioceptive signals. The trained controller has taken two generations of quadrupedal ANYmal robots to a variety of natural environments that are beyond the reach of prior published work in legged locomotion. The controller retains its robustness under conditions that have never been encountered during training: deformable terrain such as mud and snow, dynamic footholds such as rubble, and overground impediments such as thick vegetation and gushing water. The presented work opens new frontiers for robotics and indicates that radical robustness in natural environments can be achieved by training in much simpler domains. <|reference_end|>", "<|reference_start|> Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning: In this work, we present and study a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU. We analyze and discuss the impact of different training algorithm components in the massively parallel regime on the final policy performance and training times. In addition, we present a novel game-inspired curriculum that is well suited for training with thousands of simulated robots in parallel. We evaluate the approach by training the quadrupedal robot ANYmal to walk on challenging terrain. The parallel approach allows training policies for flat terrain in under four minutes, and in twenty minutes for uneven terrain. This represents a speedup of multiple orders of magnitude compared to previous work. Finally, we transfer the policies to the real robot to validate the approach. We open-source our training code to help accelerate further research in the field of learned legged locomotion. <|reference_end|>", "<|reference_start|> Instance based Generalization in Reinforcement Learning: Agents trained via deep reinforcement learning (RL) routinely fail to generalize to unseen environments, even when these share the same underlying dynamics as the training levels. Understanding the generalization properties of RL is one of the challenges of modern machine learning. Towards this goal, we analyze policy learning in the context of Partially Observable Markov Decision Processes (POMDPs) and formalize the dynamics of training levels as instances. We prove that, independently of the exploration strategy, reusing instances introduces significant changes on the effective Markov dynamics the agent observes during training. Maximizing expected rewards impacts the learned belief state of the agent by inducing undesired instance specific speedrunning policies instead of generalizeable ones, which are suboptimal on the training set. We provide generalization bounds to the value gap in train and test environments based on the number of training instances, and use insights based on these to improve performance on unseen levels. We propose training a shared belief representation over an ensemble of specialized policies, from which we compute a consensus policy that is used for data collection, disallowing instance specific exploitation. We experimentally validate our theory, observations, and the proposed computational solution over the CoinRun benchmark. <|reference_end|>", "<|reference_start|> OpenRooms: An open framework for photorealistic indoor scene datasets: We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, transforming scans into photorealistic datasets with high-quality ground truth for appearance, layout, semantic labels, high quality spatially-varying BRDF and complex lighting, including direct, indirect and visibility components. This enables important applications in inverse rendering, scene understanding and robotics. We show that deep networks trained on the proposed dataset achieve competitive performance for shape, material and lighting estimation on real images, enabling photorealistic augmented reality applications, such as object insertion and material editing. We also show our semantic labels may be used for segmentation and multi-task learning. Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes. The dataset and all the tools to create such datasets will be made publicly available.1 <|reference_end|>" ]

[ 1, 2, 3, 4 ]

[ "<|reference_start|> Algorithmic game theory: We give an introduction to the micro-economic field of Mechanism Design slightly biased towards a computer-scientist’s point of view. <|reference_end|>", "<|reference_start|> Playing Large Games Using Simple Strategies: We prove the existence of ε-Nash equilibrium strategies with support logarithmic in the number of pure strategies. We also show that the payoffs to all players in any (exact) Nash equilibrium can be ε-approximated by the payoffs to the players in some such logarithmic support ε-Nash equilibrium. These strategies are also uniform on a multiset of logarithmic size and therefore this leads to a quasi-polynomial algorithm for computing an ε-Nash equilibrium. To our knowledge this is the first subexponential algorithm for finding an ε-Nash equilibrium. Our results hold for any multiple-player game as long as the number of players is a constant (i.e., it is independent of the number of pure strategies). A similar argument also proves that for a fixed number of players m, the payoffs to all players in any m-tuple of mixed strategies can be ε-approximated by the payoffs in some m-tuple of constant support strategies.We also prove that if the payoff matrices of a two person game have low rank then the game has an exact Nash equilibrium with small support. This implies that if the payoff matrices can be well approximated by low rank matrices, the game has an ε-equilibrium with small support. It also implies that if the payoff matrices have constant rank we can compute an exact Nash equilibrium in polynomial time. <|reference_end|>", "<|reference_start|> The Approximate Rank of a Matrix and Its Algorithmic Applications: Approximate Rank: We study the ε-rank of a real matrix A, defined for any ε > 0 as the minimum rank over matrices that approximate every entry of A to within an additive ε. This parameter is connected to other notions of approximate rank and is motivated by problems from various topics including communication complexity, combinatorial optimization, game theory, computational geometry and learning theory. Here we give bounds on the ε-rank and use them for algorithmic applications. Our main algorithmic results are (a) polynomial-time additive approximation schemes for Nash equilibria for 2-player games when the payoff matrices are positive semidefinite or have logarithmic rank and (b) an additive PTAS for the densest subgraph problem for similar classes of weighted graphs. We use combinatorial, geometric and spectral techniques; our main new tool is an algorithm for efficiently covering a convex body with translates of another convex body. <|reference_end|>", "<|reference_start|> The Approximate Rank of a Matrix and Its Algorithmic Applications: Approximate Rank: We study the ε-rank of a real matrix A, defined for any ε > 0 as the minimum rank over matrices that approximate every entry of A to within an additive ε. This parameter is connected to other notions of approximate rank and is motivated by problems from various topics including communication complexity, combinatorial optimization, game theory, computational geometry and learning theory. Here we give bounds on the ε-rank and use them for algorithmic applications. Our main algorithmic results are (a) polynomial-time additive approximation schemes for Nash equilibria for 2-player games when the payoff matrices are positive semidefinite or have logarithmic rank and (b) an additive PTAS for the densest subgraph problem for similar classes of weighted graphs. We use combinatorial, geometric and spectral techniques; our main new tool is an algorithm for efficiently covering a convex body with translates of another convex body. <|reference_end|>" ]

[ 3, 4, 40, 41 ]

[ "<|reference_start|> Deep Convolutional Networks as shallow Gaussian Processes: We show that the output of a (residual) convolutional neural network (CNN) with an appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike \"deep kernels\", has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer. The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GPs with a comparable number of parameters. <|reference_end|>", "<|reference_start|> Exact solutions to the nonlinear dynamics of learning in deep linear neural networks: Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning. Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent, delay in learning speed relative to shallow networks. We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot. We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times. We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos. <|reference_end|>", "<|reference_start|> Random Neural Networks in the Infinite Width Limit as Gaussian Processes: This article gives a new proof that fully connected neural networks with random weights and biases converge to Gaussian processes in the regime where the input dimension, output dimension, and depth are kept fixed, while the hidden layer widths tend to infinity. Unlike prior work, convergence is shown assuming only moment conditions for the distribution of weights and for quite general non-linearities. <|reference_end|>", "<|reference_start|> Beyond IID weights: sparse and low-rank deep Neural Networks are also Gaussian Processes: The infinitely wide neural network has been proven a useful and manageable mathematical model that enables the understanding of many phenomena appearing in deep learning. One example is the convergence of random deep networks to Gaussian processes that allows a rigorous analysis of the way the choice of activation function and network weights impacts the training dynamics. In this paper, we extend the seminal proof of Matthews et al. (2018) to a larger class of initial weight distributions (which we call PSEUDO-IID), including the established cases of IID and orthogonal weights, as well as the emerging low-rank and structured sparse settings celebrated for their computational speed-up benefits. We show that fully-connected and convolutional networks initialized with PSEUDO-IID distributions are all effectively equivalent up to their variance. Using our results, one can identify the Edge-of-Chaos for a broader class of neural networks and tune them at criticality in order to enhance their training. Moreover, they enable the posterior distribution of Bayesian Neural Networks to be tractable across these various initialization schemes. <|reference_end|>" ]

[ 3, 13, 31, 32 ]

[ "<|reference_start|> Long-term Recurrent Convolutional Networks for Visual Recognition and Description: Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or \"temporally deep\", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are \"doubly deep\"' in that they can be compositional in spatial and temporal \"layers\". Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized. <|reference_end|>", "<|reference_start|> Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering: Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multi-modal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multi-modal feature fusion, here we develop a Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi-modal features, which results in superior performance for VQA compared with other bilinear pooling approaches. For fine-grained image and question representation, we develop a co-attention mechanism using an end-to-end deep network architecture to jointly learn both the image and question attentions. Combining the proposed MFB approach with co-attention learning in a new network architecture provides a unified model for VQA. Our experimental results demonstrate that the single MFB with co-attention model achieves new state-of-the-art performance on the real-world VQA dataset. Code available at https://github.com/yuzcccc/mfb. <|reference_end|>", "<|reference_start|> Dual Attention Networks for Multimodal Reasoning and Matching: We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend to specific regions in images and words in text through multiple steps and gather essential information from both modalities. Based on this framework, we introduce two types of DANs for multimodal reasoning and matching, respectively. The reasoning model allows visual and textual attentions to steer each other during collaborative inference, which is useful for tasks such as Visual Question Answering (VQA). In addition, the matching model exploits the two attention mechanisms to estimate the similarity between images and sentences by focusing on their shared semantics. Our extensive experiments validate the effectiveness of DANs in combining vision and language, achieving the state-of-the-art performance on public benchmarks for VQA and image-text matching. <|reference_end|>", "<|reference_start|> MovieQA: Understanding Stories in Movies through Question-Answering: We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from simpler \"Who\" did \"What\" to \"Whom\", to \"Why\" and \"How\" certain events occurred. Each question comes with a set of five possible answers; a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information -- video clips, plots, subtitles, scripts, and DVS. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data set public along with an evaluation benchmark to encourage inspiring work in this challenging domain. <|reference_end|>" ]

[ 0, 9, 11, 31 ]

[ "<|reference_start|> NExT-GPT: Any-to-Any Multimodal LLM: While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without the ability to produce content in multiple modalities. As we humans always perceive the world and communicate with people through various modalities, developing any-to-any MM-LLMs capable of accepting and delivering content in any modality becomes essential to human-level AI. To fill the gap, we present an end-to-end general-purpose any-to-any MM-LLM system, NExT-GPT. We connect an LLM with multimodal adaptors and different diffusion decoders, enabling NExT-GPT to perceive inputs and generate outputs in arbitrary combinations of text, images, videos, and audio. By leveraging the existing well-trained highly-performing encoders and decoders, NExT-GPT is tuned with only a small amount of parameter (1%) of certain projection layers, which not only benefits low-cost training and also facilitates convenient expansion to more potential modalities. Moreover, we introduce a modality-switching instruction tuning (MosIT) and manually curate a high-quality dataset for MosIT, based on which NExT-GPT is empowered with complex cross-modal semantic understanding and content generation. Overall, our research showcases the promising possibility of building an AI agent capable of modeling universal modalities, paving the way for more human-like AI research in the community. Project page: https://next-gpt.github.io/ <|reference_end|>", "<|reference_start|> Learning Unseen Modality Interaction: Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. In this paper, we challenge this modality-complete assumption for multimodal learning and instead strive for generalization to unseen modality combinations during inference. We pose the problem of unseen modality interaction and introduce a first solution. It exploits a module that projects the multidimensional features of different modalities into a common space with rich information preserved. This allows the information to be accumulated with a simple summation operation across available modalities. To reduce overfitting to less discriminative modality combinations during training, we further improve the model learning with pseudo-supervision indicating the reliability of a modality's prediction. We demonstrate that our approach is effective for diverse tasks and modalities by evaluating it for multimodal video classification, robot state regression, and multimedia retrieval. Project website: https://xiaobai1217.github.io/Unseen-Modality-Interaction/. <|reference_end|>", "<|reference_start|> Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation: The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. To alleviate the learning difficulties caused by modality-specific appearance discrepancy, we first present an Image Alignment Module (IAM) to narrow the appearance gap between assistant and target modality data.We then propose a novel Mutual Knowledge Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge to facilitate the target-modality segmentation. To be specific, we formulate our framework as an integration of two individual segmentors. Each segmentor not only explicitly extracts one modality knowledge from corresponding annotations, but also implicitly explores another modality knowledge from its counterpart in mutual-guided manner. The ensemble of two segmentors would further integrate the knowledge from both modalities and generate reliable segmentation results on target modality. Experimental results on the public multi-class cardiac segmentation data, i.e., MMWHS 2017, show that our method achieves large improvements on CT segmentation by utilizing additional MRI data and outperforms other state-of-the-art multi-modality learning methods. <|reference_end|>", "<|reference_start|> ESC: Dataset for Environmental Sound Classification: One of the obstacles in research activities concentrating on environmental sound classification is the scarcity of suitable and publicly available datasets. This paper tries to address that issue by presenting a new annotated collection of 2000 short clips comprising 50 classes of various common sound events, and an abundant unified compilation of 250000 unlabeled auditory excerpts extracted from recordings available through the Freesound project. The paper also provides an evaluation of human accuracy in classifying environmental sounds and compares it to the performance of selected baseline classifiers using features derived from mel-frequency cepstral coefficients and zero-crossing rate. <|reference_end|>" ]

[ 2, 3, 16, 18 ]

[ "<|reference_start|> Supporting Online Material for Reducing the Dimensionality of Data with Neural Networks: <|reference_end|>", "<|reference_start|> ImageNet Large Scale Visual Recognition Challenge: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements. <|reference_end|>", "<|reference_start|> Large-scale nanophotonic phased array: <|reference_end|>", "<|reference_start|> Fast bistable all-optical switch and memory on a silicon photonic crystal on-chip: We demonstrate extremely low-power all-optical bistability by utilizing silicon photonic crystal nanocavities, based on the plasma effect of carriers generated by two-photon absorption. Owing to the high quality factor and the small volume of the nanocavities, the photon density inside the cavity becomes extremely high, which leads to a large reduction in operation power. Optical bistable operation in a single nanocavity permits optical read-write memory operation, which opens the possibility of an integrated optical logic circuit on a single chip, based on photonic crystals. The demonstrated bistable threshold power is 0.4 mW with a set pulse energy of 74 fJ, at a switching speed of <100 ps. <|reference_end|>" ]

[ 2, 6, 26, 28 ]

[ "<|reference_start|> Estimation of the causal effects of time-varying exposures: This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying , microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Longitudinal data analysis / editors, Garrett Fitzmaurice ... [et al.]. p. cm.-(Chapman and Hall/CRC series of handbooks of modern statistical methods) Includes bibliographical references and index. <|reference_end|>", "<|reference_start|> Comparison of Chebyshev ’ s Inequality and Non-parametric B-Basis to Estimate Failure Strength of Composite Open Hole Tension Tests: B-basis failure strength represents the lower 10 percentile with 95% confidence level. In many risk averse applications the true statistical distribution is unknown, and the B-basis is calculated using a non-parametric formulation. Chebyshev’s inequality makes no assumption about the statistical distribution and can be used to bound the 10 percentile. It is possible to improve these bounds by restricting Chebyshev’s inequality to a class of statistical distributions. B-basis failure strengths are compared using these methods on a collection of composite open hole tension tests. <|reference_end|>", "<|reference_start|> Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations: Identifying when to give treatments to patients and how to select among multiple treatments over time are important medical problems with a few existing solutions. In this paper, we introduce the Counterfactual Recurrent Network (CRN), a novel sequence-to-sequence model that leverages the increasingly available patient observational data to estimate treatment effects over time and answer such medical questions. To handle the bias from time-varying confounders, covariates affecting the treatment assignment policy in the observational data, CRN uses domain adversarial training to build balancing representations of the patient history. At each timestep, CRN constructs a treatment invariant representation which removes the association between patient history and treatment assignments and thus can be reliably used for making counterfactual predictions. On a simulated model of tumour growth, with varying degree of time-dependent confounding, we show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment than current state-of-the-art methods. <|reference_end|>", "<|reference_start|> {CPTR: 일반적으로 카세그레인 오프셋 복 반사경(Cassegrain Dual Offset Reflector)은 위성 통신용 안테나로 사용되지만, 여기서는 CPTR(Compact Payload Test Range)을 위한 반사경 시스템으로 해석하였다. 시험 영역의 근접 전계는 물리 광학법(Physical Optics)을 적용하여 계산하였다. CPTR은 균일한 평면파 제공을 목적으로 하며, 이를 위해 최소한의 진폭과 위상 리플(ripple)을 가져야 하며, 교차 편파 또한 작아야 한다. 따라서 본 논문에서는 반사경 구조 및 시험 영역의 위치에 따른 근접 전계 패턴을 구하여 전계의 리플, 테이퍼와 교차 편파를 고찰하였다. 특히 통신용 반사경 안테나에서는 나타나지 않는 안테나 축방향의 교차 편파 성분을 고찰하였다. <|reference_end|>" ]

[ 27, 28, 35, 43 ]

[ "<|reference_start|> Intelligent Surface-Aided Transmitter Architectures for Millimeter-Wave Ultra Massive MIMO Systems: In this article, we study two novel massive multiple-input multiple-output (MIMO) transmitter architectures for millimeter wave (mmWave) communications which comprise few active antennas, each equipped with a dedicated radio frequency (RF) chain, that illuminate a nearby large intelligent reflecting/transmitting surface (IRS/ITS). The IRS (ITS) consists of a large number of low-cost and energy-efficient passive antenna elements which are able to reflect (transmit) a phase-shifted version of the incident electromagnetic field. Similar to lens array (LA) antennas, IRS/ITS-aided antenna architectures are energy efficient due to the almost lossless over-the-air connection between the active antennas and the intelligent surface. However, unlike for LA antennas, for which the number of active antennas has to linearly grow with the number of passive elements (i.e., the lens aperture) due to the non-reconfigurablility (i.e., non-intelligence) of the lens, for IRS/ITS-aided antennas, the reconfigurablility of the IRS/ITS facilitates scaling up the number of radiating passive elements without increasing the number of costly and bulky active antennas. We show that the constraints that the precoders for IRS/ITS-aided antennas have to meet differ from those of conventional MIMO architectures. Taking these constraints into account and exploiting the sparsity of mmWave channels, we design two efficient precoders; one based on maximizing the mutual information and one based on approximating the optimal unconstrained fully digital (FD) precoder via the orthogonal matching pursuit algorithm. Furthermore, we develop a power consumption model for IRS/ITS-aided antennas that takes into account the impacts of the IRS/ITS imperfections, namely the spillover loss, taper loss, aperture loss, and phase shifter loss. Moreover, we study the effect that the various system parameters have on the achievable rate and show that a proper positioning of the active antennas with respect to the IRS/ITS leads to a considerable performance improvement. Our simulation results reveal that unlike conventional MIMO architectures, IRS/ITS-aided antennas are both highly energy efficient and fully scalable in terms of the number of transmitting (passive) antennas. Therefore, IRS/ITS-aided antennas are promising candidates for realizing the potential of mmWave ultra massive MIMO communications in practice. <|reference_end|>", "<|reference_start|> Beyond Diagonal Reconfigurable Intelligent Surfaces: From Transmitting and Reflecting Modes to Single-, Group-, and Fully-Connected Architectures: Reconfigurable intelligent surfaces (RISs) are envisioned as a promising technology for future wireless communications. With various hardware realizations, RISs can work under different modes (reflective/transmissive/hybrid) or have different architectures (single/group/fully-connected). However, most existing research focused on single-connected reflective RISs, mathematically characterized by diagonal phase shift matrices, while there is a lack of a comprehensive study for RISs unifying different modes/architectures. In this paper, we solve this issue by analyzing and proposing a general RIS-aided communication model. Specifically, we establish an RIS model not limited to diagonal phase shift matrices, a novel branch referred to as beyond diagonal RIS (BD-RIS), unifying modes and architectures. With the proposed model, we develop efficient algorithms to jointly design transmit precoder and BDRIS matrix to maximize the sum-rate for RIS-aided systems. We also provide simulation results to compare the performance of BD-RISs with different modes/architectures. Simulation results show that under the same mode, fully- and group-connected RIS can effectively increase the sum-rate performance compared with single-connected RIS, and that hybrid RIS outperforms reflective/transmissive RIS with the same architecture. <|reference_end|>", "<|reference_start|> Intelligent Surface-Aided Transmitter Architectures for Millimeter-Wave Ultra Massive MIMO Systems: In this article, we study two novel massive multiple-input multiple-output (MIMO) transmitter architectures for millimeter wave (mmWave) communications which comprise few active antennas, each equipped with a dedicated radio frequency (RF) chain, that illuminate a nearby large intelligent reflecting/transmitting surface (IRS/ITS). The IRS (ITS) consists of a large number of low-cost and energy-efficient passive antenna elements which are able to reflect (transmit) a phase-shifted version of the incident electromagnetic field. Similar to lens array (LA) antennas, IRS/ITS-aided antenna architectures are energy efficient due to the almost lossless over-the-air connection between the active antennas and the intelligent surface. However, unlike for LA antennas, for which the number of active antennas has to linearly grow with the number of passive elements (i.e., the lens aperture) due to the non-reconfigurablility (i.e., non-intelligence) of the lens, for IRS/ITS-aided antennas, the reconfigurablility of the IRS/ITS facilitates scaling up the number of radiating passive elements without increasing the number of costly and bulky active antennas. We show that the constraints that the precoders for IRS/ITS-aided antennas have to meet differ from those of conventional MIMO architectures. Taking these constraints into account and exploiting the sparsity of mmWave channels, we design two efficient precoders; one based on maximizing the mutual information and one based on approximating the optimal unconstrained fully digital (FD) precoder via the orthogonal matching pursuit algorithm. Furthermore, we develop a power consumption model for IRS/ITS-aided antennas that takes into account the impacts of the IRS/ITS imperfections, namely the spillover loss, taper loss, aperture loss, and phase shifter loss. Moreover, we study the effect that the various system parameters have on the achievable rate and show that a proper positioning of the active antennas with respect to the IRS/ITS leads to a considerable performance improvement. Our simulation results reveal that unlike conventional MIMO architectures, IRS/ITS-aided antennas are both highly energy efficient and fully scalable in terms of the number of transmitting (passive) antennas. Therefore, IRS/ITS-aided antennas are promising candidates for realizing the potential of mmWave ultra massive MIMO communications in practice. <|reference_end|>", "<|reference_start|> Hybrid Beamforming Design for ITS-Assisted Wireless Networks: This letter proposes a hybrid beamforming design for an intelligent transmissive surface (ITS)-assisted transmitter wireless network. We aim to suppress the sidelobes and optimize the mainlobes of the transmit beams by minimizing the proposed cost function based on the least squares (LS) for the digital beamforming vector of the base station (BS) and the phase shifts of the ITS. To solve the minimization problem, we adopt an efficient algorithm based on the alternating optimization (AO) method to design the digital beamforming vector and the phase shifts of the ITS in an alternating manner. In particular, the alternating direction method of multipliers (ADMM) algorithm is utilized to obtain the optimal phase shifts of the ITS. Finally, we verify the improvement achieved by the proposed algorithm in terms of the beam response compared to the benchmark schemes by the simulation results. <|reference_end|>" ]

[ 1, 6, 10, 11 ]

[ "<|reference_start|> RANDOM-MATRIX THEORIES IN QUANTUM PHYSICS : COMMON CONCEPTS: <|reference_end|>", "<|reference_start|> Limit laws for Random matrices and free products: <|reference_end|>", "<|reference_start|> Asymptotic Behaviour of Random Vandermonde Matrices with Entries on the Unit Circle: Analytical methods for finding moments of random Vandermonde matrices with entries on the unit circle are developed. Vandermonde Matrices play an important role in signal processing and wireless applications such as direction of arrival estimation, precoding, and sparse sampling theory, just to name a few. Within this framework, we extend classical freeness results on random matrices with independent, identically distributed (i.i.d.) entries and show that Vandermonde structured matrices can be treated in the same vein with different tools. We focus on various types of matrices, such as Vandermonde matrices with and without uniform phase distributions, as well as generalized Vandermonde matrices. In each case, we provide explicit expressions of the moments of the associated Gram matrix, as well as more advanced models involving the Vandermonde matrix. Comparisons with classical i.i.d. random matrix theory are provided, and deconvolution results are discussed. We review some applications of the results to the fields of signal processing and wireless communications. <|reference_end|>", "<|reference_start|> A note on averages over random matrix ensembles: Abstract. In this work we find a closed form expression for matrix averages over the Gaussian ensemble. More precisely, given an n × n Hermitian matrix A and a continuous function f(x) we find a closed form expression for the expectation E(Tr(f(XAX∗))) where X is a Gaussian n × n matrix with complex independent and identically distributed entries of zero mean and variance 1. Taking f(x) = log(1+x) this gives us another formula for the capacity of the MIMO communication channel and taking f(x) = (1 + x) gives us the minimum MMSE achieved by a linear receiver. <|reference_end|>" ]

[ 2, 4, 14, 21 ]

[ "<|reference_start|> Hypergraph Neural Networks: In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods. <|reference_end|>", "<|reference_start|> GVCNN: Group-view convolutional neural networks for 3D shape recognition: 3D shape recognition has attracted much attention recently. Its recent advances advocate the usage of deep features and achieve the state-of-the-art performance. However, existing deep features for 3D shape recognition are restricted to a view-to-shape setting, which learns the shape descriptor from the view-level feature directly. Despite the exciting progress on view-based 3D shape description, the intrinsic hierarchical correlation and discriminability among views have not been well exploited, which is important for 3D shape representation. To tackle this issue, in this paper, we propose a group-view convolutional neural network (GVCNN) framework for hierarchical correlation modeling towards discriminative 3D shape description. The proposed GVCNN framework is composed of a hierarchical view-group-shape architecture, i.e., from the view level, the group level and the shape level, which are organized using a grouping strategy. Concretely, we first use an expanded CNN to extract a view level descriptor. Then, a grouping module is introduced to estimate the content discrimination of each view, based on which all views can be splitted into different groups according to their discriminative level. A group level description can be further generated by pooling from view descriptors. Finally, all group level descriptors are combined into the shape level descriptor according to their discriminative weights. Experimental results and comparison with state-of-the-art methods show that our proposed GVCNN method can achieve a significant performance gain on both the 3D shape classification and retrieval tasks. <|reference_end|>", "<|reference_start|> HGNN+: General Hypergraph Neural Networks: Graph Neural Networks have attracted increasing attention in recent years. However, existing GNN frameworks are deployed based upon simple graphs, which limits their applications in dealing with complex data correlation of multi-modal/multi-type data in practice. A few hypergraph-based methods have recently been proposed to address the problem of multi-modal/multi-type data correlation by directly concatenating the hypergraphs constructed from each single individual modality/type, which is difficult to learn an adaptive weight for each modality/type. In this paper, we extend the original conference version HGNN, and introduce a general high-order multi-modal/multi-type data correlation modeling framework called HGNN<inline-formula><tex-math notation=\"LaTeX\">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"feng-ieq1-3182052.gif\"/></alternatives></inline-formula> to learn an optimal representation in a single hypergraph based framework. It is achieved by bridging multi-modal/multi-type data and hyperedge with hyperedge groups. Specifically, in our method, hyperedge groups are first constructed to represent latent high-order correlations in each specific modality/type with explicit or implicit graph structures. An adaptive hyperedge group fusion strategy is then used to effectively fuse the correlations from different modalities/types in a unified hypergraph. After that a new hypergraph convolution scheme performed in spatial domain is used to learn a general data representation for various tasks. We have evaluated this framework on several popular datasets and compared it with recent state-of-the-art methods. The comprehensive evaluations indicate that the proposed HGNN<inline-formula><tex-math notation=\"LaTeX\">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"feng-ieq2-3182052.gif\"/></alternatives></inline-formula> framework can consistently outperform existing methods with a significant margin, especially when modeling implicit data correlations. We also release a toolbox called THU-DeepHypergraph for the proposed framework, which can be used for various of applications, such as data classification, retrieval and recommendation. <|reference_end|>", "<|reference_start|> Multi-View 3D Shape Recognition via Correspondence-Aware Deep Learning: In recent years, multi-view learning has emerged as a promising approach for 3D shape recognition, which identifies a 3D shape based on its 2D views taken from different viewpoints. Usually, the correspondences inside a view or across different views encode the spatial arrangement of object parts and the symmetry of the object, which provide useful geometric cues for recognition. However, such view correspondences have not been explicitly and fully exploited in existing work. In this paper, we propose a correspondence-aware representation (CAR) module, which explicitly finds potential intra-view correspondences and cross-view correspondences via $k$ NN search in semantic space and then aggregates the shape features from the correspondences via learned transforms. Particularly, the spatial relations of correspondences in terms of their viewpoint positions and intra-view locations are taken into account for learning correspondence-aware features. Incorporating the CAR module into a ResNet-18 backbone, we propose an effective deep model called CAR-Net for 3D shape classification and retrieval. Extensive experiments have demonstrated the effectiveness of the CAR module as well as the excellent performance of the CAR-Net. <|reference_end|>" ]

[ 16, 27, 45, 51 ]

[ "<|reference_start|> Superpixel lattices: Unsupervised over-segmentation of an image into superpixels is a common preprocessing step for image parsing algorithms. Ideally, every pixel within each superpixel region will belong to the same real-world object. Existing algorithms generate superpixels that forfeit many useful properties of the regular topology of the original pixels: for example, the nth superpixel has no consistent position or relationship with its neighbors. We propose a novel algorithm that produces superpixels that are forced to conform to a grid (a regular superpixel lattice). Despite this added topological constraint, our algorithm is comparable in terms of speed and accuracy to alternative segmentation approaches. To demonstrate this, we use evaluation metrics based on (i) image reconstruction (ii) comparison to human-segmented images and (iii) stability of segmentation over subsequent frames of video sequences. <|reference_end|>", "<|reference_start|> Superpixel Convolutional Networks using Bilateral Inceptions: In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception module between the last CNN (1x1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time. <|reference_end|>", "<|reference_start|> Interpretable Structure-Evolving LSTM: This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over the pre-fixed structures, we propose to further learn the intermediate interpretable multi-level graph structures in a progressive and stochastic way from data during the LSTM network optimization. We thus call this model the structure-evolving LSTM. In particular, starting with an initial element-level graph representation where each node is a small data element, the structure-evolving LSTM gradually evolves the multi-level graph representations by stochastically merging the graph nodes with high compatibilities along the stacked LSTM layers. In each LSTM layer, we estimate the compatibility of two connected nodes from their corresponding LSTM gate outputs, which is used to generate a merging probability. The candidate graph structures are accordingly generated where the nodes are grouped into cliques with their merging probabilities. We then produce the new graph structure with a Metropolis-Hasting algorithm, which alleviates the risk of getting stuck in local optimums by stochastic sampling with an acceptance probability. Once a graph structure is accepted, a higher-level graph is then constructed by taking the partitioned cliques as its nodes. During the evolving process, representation becomes more abstracted in higher-levels where redundant information is filtered out, allowing more efficient propagation of long-range data dependencies. We evaluate the effectiveness of structure-evolving LSTM in the application of semantic object parsing and demonstrate its advantage over state-of-the-art LSTM models on standard benchmarks. <|reference_end|>", "<|reference_start|> Adaptive pyramid context network for semantic segmentation: Recent studies witnessed that context features can significantly improve the performance of deep semantic segmentation networks. Current context based segmentation methods differ with each other in how to construct context features and perform differently in practice. This paper firstly introduces three desirable properties of context features in segmentation task. Specially, we find that Global-guided Local Affinity (GLA) can play a vital role in constructing effective context features, while this property has been largely ignored in previous works. Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet) for semantic segmentation. APCNet adaptively constructs multi-scale contextual representations with multiple well-designed Adaptive Context Modules (ACMs). Specifically, each ACM leverages a global image representation as a guidance to estimate the local affinity coefficients for each sub-region, and then calculates a context vector with these affinities. We empirically evaluate our APCNet on three semantic segmentation and scene parsing datasets, including PASCAL VOC 2012, Pascal-Context, and ADE20K dataset. Experimental results show that APCNet achieves state-of-the-art performance on all three benchmarks, and obtains a new record 84.2% on PASCAL VOC 2012 test set without MS COCO pre-trained and any post-processing. <|reference_end|>" ]

[ 13, 35, 39, 51 ]

[ "<|reference_start|> Learning to Interpret Natural Language Navigation Instructions from\nObservations: The ability to understand natural-language instructions is critical to building intelligent agents that interact with humans. We present a system that learns to transform natural-language navigation instructions into executable formal plans. Given no prior linguistic knowledge, the system learns by simply observing how humans follow navigation instructions. The system is evaluated in three complex virtual indoor environments with numerous objects and landmarks. A previously collected realistic corpus of complex English navigation instructions for these environments is used for training and testing data. By using a learned lexicon to refine inferred plans and a supervised learner to induce a semantic parser, the system is able to automatically learnto correctly interpret a reasonable fraction of the complex instructions in this corpus. <|reference_end|>", "<|reference_start|> Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation: We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model's performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction. <|reference_end|>", "<|reference_start|> Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications.: A method includes enabling a robot to learn a mapping between English language commands and Linear Temporal Logic (LTL) expressions, wherein neural sequence-to-sequence learning models are employed to infer a LTL sequence corresponding to a given natural language command. <|reference_end|>", "<|reference_start|> Transferable Representation Learning in Vision-and-Language Navigation: Vision-and-Language Navigation (VLN) tasks such as Room-to-Room (R2R) require machine agents to interpret natural language instructions and learn to act in visually realistic environments to achieve navigation goals. The overall task requires competence in several perception problems: successful agents combine spatio-temporal, vision and language understanding to produce appropriate action sequences. Our approach adapts pre-trained vision and language representations to relevant in-domain tasks making them more effective for VLN. Specifically, the representations are adapted to solve both a cross-modal sequence alignment and sequence coherence task. In the sequence alignment task, the model determines whether an instruction corresponds to a sequence of visual frames. In the sequence coherence task, the model determines whether the perceptual sequences are predictive sequentially in the instruction-conditioned latent space. By transferring the domain-adapted representations, we improve competitive agents in R2R as measured by the success rate weighted by path length (SPL) metric. <|reference_end|>" ]

[ 2, 3, 5, 9 ]

[ "<|reference_start|> Defects4J: a database of existing faults to enable controlled testing studies for Java programs: Empirical studies in software testing research may not be comparable, reproducible, or characteristic of practice. One reason is that real bugs are too infrequently used in software testing research. Extracting and reproducing real bugs is challenging and as a result hand-seeded faults or mutants are commonly used as a substitute. This paper presents Defects4J, a database and extensible framework providing real bugs to enable reproducible studies in software testing research. The initial version of Defects4J contains 357 real bugs from 5 real-world open source pro- grams. Each real bug is accompanied by a comprehensive test suite that can expose (demonstrate) that bug. Defects4J is extensible and builds on top of each program’s version con- trol system. Once a program is configured in Defects4J, new bugs can be added to the database with little or no effort. Defects4J features a framework to easily access faulty and fixed program versions and corresponding test suites. This framework also provides a high-level interface to common tasks in software testing research, making it easy to con- duct and reproduce empirical studies. Defects4J is publicly available at http://defects4j.org. <|reference_end|>", "<|reference_start|> {Search-based software test data generation: a\nsurvey: The use of metaheuristic search techniques for the automatic generation of test data has been a burgeoning interest for many researchers in recent years. Previous attempts to automate the test generation process have been limited, having been constrained by the size and complexity of software, and the basic fact that, in general, test data generation is an undecidable problem. Metaheuristic search techniques offer much promise in regard to these problems. Metaheuristic search techniques are high‐level frameworks, which utilize heuristics to seek solutions for combinatorial problems at a reasonable computational cost. To date, metaheuristic search techniques have been applied to automate test data generation for structural and functional testing; the testing of grey‐box properties, for example safety constraints; and also non‐functional properties, such as worst‐case execution time. This paper surveys some of the work undertaken in this field, discussing possible new future directions of research for each of its different individual areas. Copyright © 2004 John Wiley & Sons, Ltd. <|reference_end|>", "<|reference_start|> {Modeling Readability to Improve Unit Tests: Writing good unit tests can be tedious and error prone, but even once they are written, the job is not done: Developers need to reason about unit tests throughout software development and evolution, in order to diagnose test failures, maintain the tests, and to understand code written by other developers. Unreadable tests are more difficult to maintain and lose some of their value to developers. To overcome this problem, we propose a domain-specific model of unit test readability based on human judgements, and use this model to augment automated unit test generation. The resulting approach can automatically generate test suites with both high coverage and also improved readability. In human studies users prefer our improved tests and are able to answer maintenance questions about them 14% more quickly at the same level of accuracy. <|reference_end|>", "<|reference_start|> TOGA: A Neural Method for Test Oracle Generation: Testing is widely recognized as an important stage of the software development lifecycle. Effective software testing can provide benefits such as bug finding, preventing regressions, and documentation. In terms of documentation, unit tests express a unit's intended functionality, as conceived by the developer. A test oracle, typically expressed as an condition, documents the intended behavior of a unit under a given test prefix. Synthesizing a functional test oracle is a challenging problem, as it must capture the intended functionality rather than the implemented functionality. In this paper, we propose TOGA (a neural method for Test Oracle GenerAtion), a unified transformer-based neural approach to infer both exceptional and assertion test oracles based on the context of the focal method. Our approach can handle units with ambiguous or missing documentation, and even units with a missing implementation. We evaluate our approach on both oracle inference accuracy and functional bug-finding. Our technique improves accuracy by 33\\% over existing oracle inference approaches, achieving 96\\% overall accuracy on a held out test dataset. Furthermore, we show that when integrated with a automated test generation tool (EvoSuite), our approach finds 57 real world bugs in large-scale Java programs, including 30 bugs that are not found by any other automated testing method in our evaluation. <|reference_end|>" ]

[ 7, 9, 13, 26 ]

[ "<|reference_start|> LT codes: We introduce LT codes, the first rateless erasure codes that are very efficient as the data length grows. <|reference_end|>", "<|reference_start|> Raptor Codes: A Fountain code is a code of fixed dimension and a limitless block-length. This is a class of codes with many interesting properties and applications. In this talk I will introduce several classes of probabilistic Fountain codes, including LT-and Raptor codes, show tools for their design and analysis, and discuss how they are used today to solve various data transmission problems on heterogenous unreliable networks. I will also talk about the theory of these codes when transmission takes place over non-erasure channels, and low-complexity algorithms are used for their decoding. <|reference_end|>", "<|reference_start|> Finite length analysis of LT codes: This paper provides an efficient method for analyzing the error probability of the belief propagation (BP) decoder applied to LT Codes. Each output symbol is generated independently by sampling from a distribution and adding the input symbols corresponding to the support of the sampled vector. <|reference_end|>", "<|reference_start|> Raptor Codes: A Fountain code is a code of fixed dimension and a limitless block-length. This is a class of codes with many interesting properties and applications. In this talk I will introduce several classes of probabilistic Fountain codes, including LT-and Raptor codes, show tools for their design and analysis, and discuss how they are used today to solve various data transmission problems on heterogenous unreliable networks. I will also talk about the theory of these codes when transmission takes place over non-erasure channels, and low-complexity algorithms are used for their decoding. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Automatic Detection of Myocontrol Failures Based upon Situational Context Information: Myoelectric control systems for assistive devices are still unreliable. The user's input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat. Hence, such controllers need to be constantly updated and heavily rely on user feedback. In this paper, we present an automatic failure detection method which learns when plausible predictions become unreliable and model updates are necessary. Our key insight is to enhance the control system with a set of generative models that learn sensible behaviour for a desired task from human demonstration. We illustrate our approach on a grasping scenario in Virtual Reality, in which the user is asked to grasp a bottle on a table. From demonstration our model learns the reach-to-grasp motion from a resting position to two grasps (power grasp and tridigital grasp) and how to predict the most adequate grasp from local context, e.g. tridigital grasp on the bottle cap or around the bottleneck. By measuring the error between new grasp attempts and the model prediction, the system can effectively detect which input commands do not reflect the user's intention. We evaluated our model in two cases: i) with both position and rotation information of the wrist pose, and ii) with only rotational information. Our results show that our approach detects statistically highly significant differences in error distributions with p < 0.001 between successful and failed grasp attempts in both cases. <|reference_end|>", "<|reference_start|> Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods: <|reference_end|>", "<|reference_start|> WQT and DG-YOLO: towards domain generalization in underwater object detection: A General Underwater Object Detector (GUOD) should perform well on most of underwater circumstances. However, with limited underwater dataset, conventional object detection methods suffer from domain shift severely. This paper aims to build a GUOD with small underwater dataset with limited types of water quality. First, we propose a data augmentation method Water Quality Transfer (WQT) to increase domain diversity of the original small dataset. Second, for mining the semantic information from data generated by WQT, DG-YOLO is proposed, which consists of three parts: YOLOv3, DIM and IRM penalty. Finally, experiments on original and synthetic URPC2019 dataset prove that WQT+DG-YOLO achieves promising performance of domain generalization in underwater object detection. <|reference_end|>", "<|reference_start|> Detection of marine animals in a new underwater dataset with varying visibility: The increasing demand for marine monitoring calls for robust automated systems to support researchers in gathering information from marine ecosystems. This includes computer vision based marine organism detection and species classification systems. Current state-of-the-art marine vision systems are based on CNNs, which in nature require a relatively large amount of varied training data. In this paper we present a new publicly available underwater dataset with annotated image sequences of fish, crabs, and starfish captured in brackish water with varying visibility. The dataset is called the Brackish Dataset and it is the first part of a planned long term monitoring of the marine species visiting the strait where the cameras are permanently mounted. To the best of our knowledge, this is the first annotated underwater image dataset captured in temperate brackish waters. In order to obtain a baseline performance for future reference, the YOLOv2 and YOLOv3 CNNs were fine-tuned and tested on the Brackish Dataset. <|reference_end|>" ]

[ 13, 18, 22, 23 ]

[ "<|reference_start|> Memory Fusion Network for Multi-view Sequential Learning: Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the existing multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for these multi-view datasets. <|reference_end|>", "<|reference_start|> Long Short-Term Memory-Networks for Machine Reading: In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a machine reading simulator which processes text incrementally from left to right and performs shallow reasoning with memory and attention. The reader extends the Long Short-Term Memory architecture with a memory network in place of a single memory cell. This enables adaptive memory usage during recurrence with neural attention, offering a way to weakly induce relations among tokens. The system is initially designed to process a single sequence but we also demonstrate how to integrate it with an encoder-decoder architecture. Experiments on language modeling, sentiment analysis, and natural language inference show that our model matches or outperforms the state of the art. <|reference_end|>", "<|reference_start|> Key-Value Memory Networks for Directly Reading Documents: Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restrictive, as the schema cannot support certain types of answers, and too sparse, e.g. Wikipedia contains much more information than Freebase. In this work we introduce a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation. To compare using KBs, information extraction or Wikipedia documents directly in a single framework we construct an analysis tool, WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in the domain of movies. Our method reduces the gap between all three settings. It also achieves state-of-the-art results on the existing WikiQA benchmark. <|reference_end|>", "<|reference_start|> Multimodal Attention for Neural Machine Translation: The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simultaneously focus over an image and its natural language description for generating a description in another language. We train several variants of our proposed attention mechanism on the Multi30k multilingual image captioning dataset. We show that a dedicated attention for each modality achieves up to 1.6 points in BLEU and METEOR compared to a textual NMT baseline. <|reference_end|>" ]

[ 17, 32, 37, 53 ]

[ "<|reference_start|> Voronoi Game on Graphs: \\textit{Voronoi game} is a geometric model of competitive facility location problem played between two players. Users are generally modeled as points uniformly distributed on a given underlying space. Each player chooses a set of points in the underlying space to place their facilities. Each user avails service from its nearest facility. Service zone of a facility consists of the set of users which are closer to it than any other facility. Payoff of each player is defined by the quantity of users served by all of its facilities. The objective of each player is to maximize their respective payoff. In this paper we consider the two players {\\it Voronoi game} where the underlying space is a road network modeled by a graph. In this framework we consider the problem of finding $k$ optimal facility locations of Player 2 given any placement of $m$ facilities by Player 1. Our main result is a dynamic programming based polynomial time algorithm for this problem on tree network. On the other hand, we show that the problem is strongly $\\mathcal{NP}$-complete for graphs. This proves that finding a winning strategy of P2 is $\\mathcal{NP}$-complete. Consequently, we design an $1-\\frac{1}{e}$ factor approximation algorithm, where $e \\approx 2.718$. <|reference_end|>", "<|reference_start|> The Competitive Facility Location Problem in a Duopoly: Connections to the 1-Median Problem: <|reference_end|>", "<|reference_start|> Balancing graph voronoi diagrams: Many facility location problems are concerned with minimizing operation and transportation costs by partitioning territory into regions of similar size, each of which is served by a facility. For many optimization problems, the overall cost can be reduced by means ofa partitioning into balanced subsets, especially in those cases where the cost associated with a subset is superlinear in its size.In this paper, we consider the problem of generating a Voronoi partition of a discrete graph so as to achieve balance conditions on the region sizes.Through experimentation, we first establishthat the region sizes of randomly-generated graph Voronoi diagrams vary greatly in practice. We then show how to achieve a balanced partition of a graph via Voronoi site resampling. For bounded-degree graphs, where each of the $n$ nodes has degree at most $d$, and for an initial randomly-chosen set of $s$ Voronoi nodes,we prove that, by extending the set of Voronoi nodes using an algorithm by Thorup and Zwick, each Voronoi region has size at most $4dn/s+1$ nodes, and that the expected size of the extended set of Voronoi nodes is at most $2s\\log n$. <|reference_end|>", "<|reference_start|> On the second eigenvalue of random regular graphs: The following is an extended abstract for two papers, one written by Kahn and Szemeredi, the other written by Friedman, which have been combined at the request of the STOC committee. The introduction was written jointly, the second section by Kahn and Szemeredi, and the third by Friedman, Let G be a d-regular (i.e. each vertex has degree d) undirected graph on n nodes. It’s adjacency matrix is symmetric, and therefore has real eigenvalues Ar = d 2 x2 >_ *-. >_ X, with IX,] 5 d. Graphs for which X2 and <|reference_end|>" ]

[ 0, 5, 12, 17 ]

[ "<|reference_start|> XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models: The latest breakthroughs in large vision-language models, such as Bard and GPT-4, have showcased extraordinary abilities in performing a wide range of tasks. Such models are trained on massive datasets comprising billions of public image-text pairs with diverse tasks. However, their performance on task-specific domains, such as radiology, is still under-investigated and potentially limited due to a lack of sophistication in understanding biomedical images. On the other hand, conversational medical models have exhibited remarkable success but have mainly focused on text-based analysis. In this paper, we introduce XrayGPT, a novel conversational medical vision-language model that can analyze and answer open-ended questions about chest radiographs. Specifically, we align both medical visual encoder (MedClip) with a fine-tuned large language model (Vicuna), using a simple linear transformation. This alignment enables our model to possess exceptional visual conversation abilities, grounded in a deep understanding of radiographs and medical domain knowledge. To enhance the performance of LLMs in the medical context, we generate ~217k interactive and high-quality summaries from free-text radiology reports. These summaries serve to enhance the performance of LLMs through the fine-tuning process. Our approach opens up new avenues the research for advancing the automated analysis of chest radiographs. Our open-source demos, models, and instruction sets are available at: https://github.com/mbzuai-oryx/XrayGPT. <|reference_end|>", "<|reference_start|> RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback: Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but gathering high-quality preference labels is expensive. RL from AI Feedback (RLAIF), introduced in Bai et al., offers a promising alternative that trains the reward model (RM) on preferences generated by an off-the-shelf LLM. Across the tasks of summarization, helpful dialogue generation, and harmless dialogue generation, we show that RLAIF achieves comparable performance to RLHF. Furthermore, we take a step towards\"self-improvement\"by demonstrating that RLAIF can outperform a supervised fine-tuned baseline even when the AI labeler is the same size as the policy, or even the exact same checkpoint as the initial policy. Finally, we introduce direct-RLAIF (d-RLAIF) - a technique that circumvents RM training by obtaining rewards directly from an off-the-shelf LLM during RL, which achieves superior performance to canonical RLAIF. Our results suggest that RLAIF can achieve performance on-par with using human feedback, offering a potential solution to the scalability limitations of RLHF. <|reference_end|>", "<|reference_start|> G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment: The quality of texts generated by natural language generation (NLG) systems is hard to measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE, have been shown to have relatively low correlation with human judgments, especially for tasks that require creativity and diversity. Recent studies suggest using large language models (LLMs) as reference-free metrics for NLG evaluation, which have the benefit of being applicable to new tasks that lack human references. However, these LLM-based evaluators still have lower human correspondence than medium-size neural evaluators. In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs. We experiment with two generation tasks, text summarization and dialogue generation. We show that G-Eval with GPT-4 as the backbone model achieves a Spearman correlation of 0.514 with human on summarization task, outperforming all previous methods by a large margin. We also propose preliminary analysis on the behavior of LLM-based evaluators, and highlight the potential issue of LLM-based evaluators having a bias towards the LLM-generated texts. The code is at https://github.com/nlpyang/geval <|reference_end|>", "<|reference_start|> MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports: <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> HuatuoGPT, towards Taming Language Model to Be a Doctor: In this paper, we present HuatuoGPT, a large language model (LLM) for medical consultation. The core recipe of HuatuoGPT is to leverage both \\textit{distilled data from ChatGPT} and \\textit{real-world data from doctors} in the supervised fine-tuned stage. The responses of ChatGPT are usually detailed, well-presented and informative while it cannot perform like a doctor in many aspects, e.g. for integrative diagnosis. We argue that real-world data from doctors would be complementary to distilled data in the sense the former could tame a distilled language model to perform like doctors. To better leverage the strengths of both data, we train a reward model to align the language model with the merits that both data bring, following an RLAIF (reinforced learning from AI feedback) fashion. To evaluate and benchmark the models, we propose a comprehensive evaluation scheme (including automatic and manual metrics). Experimental results demonstrate that HuatuoGPT achieves state-of-the-art results in performing medical consultation among open-source LLMs in GPT-4 evaluation, human evaluation, and medical benchmark datasets. It is worth noting that by using additional real-world data and RLAIF, the distilled language model (i.e., HuatuoGPT) outperforms its teacher model ChatGPT in most cases. Our code, data, and models are publicly available at \\url{https://github.com/FreedomIntelligence/HuatuoGPT}. The online demo is available at \\url{https://www.HuatuoGPT.cn/}. <|reference_end|>", "<|reference_start|> Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease: The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed. <|reference_end|>", "<|reference_start|> Review of Alzheimer's disease scales: is there a need for a new multi-domain scale for therapy evaluation in medical practice?: <|reference_end|>", "<|reference_start|> Human Gait Analysis in Neurodegenerative Diseases: a Review.: This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegenerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined. <|reference_end|>" ]

[ 1, 10, 18, 31 ]

[ "<|reference_start|> Implicit proofs: Abstract. We describe a general method how to construct from a prepositional proof system P a possibly much stronger proof system iP. The system iP operates with exponentially long P-proofs described “implicitly” by polynomial size circuits. As an example we prove that proof system iEF, implicit EF, corresponds to bounded arithmetic theory and hence, in particular, polynomially simulates the quantified prepositional calculus G and the -consequences of proved with one use of exponentiation. Furthermore, the soundness of iEF is not provable in . An iteration of the construction yields a proof system corresponding to T2 + Exp and, in principle, to much stronger theories. <|reference_end|>", "<|reference_start|> Implicit proofs: Abstract. We describe a general method how to construct from a prepositional proof system P a possibly much stronger proof system iP. The system iP operates with exponentially long P-proofs described “implicitly” by polynomial size circuits. As an example we prove that proof system iEF, implicit EF, corresponds to bounded arithmetic theory and hence, in particular, polynomially simulates the quantified prepositional calculus G and the -consequences of proved with one use of exponentiation. Furthermore, the soundness of iEF is not provable in . An iteration of the construction yields a proof system corresponding to T2 + Exp and, in principle, to much stronger theories. <|reference_end|>", "<|reference_start|> The Intractability of Resolution: <|reference_end|>", "<|reference_start|> Short proofs are narrow---resolution made simple: The widthof a Resolution proof is defined to be the maximal number of literals in any clause of the proof. In this paper, we relate proof width to proof length (=size), in both general Resolution, and its tree-like variant. The following consequences of these relations reveal width as a crucial “resource” of Resolution proofs.\nIn one direction, the relations allow us to give simple, unified proofs for almost all known exponential lower bounds on size of resolution proofs, as well as several interesting new ones. They all follow from width lower bounds, and we show how these follow from natural expansion property of clauses of the input tautology.\nIn the other direction, the width-size relations naturally suggest a simple dynamic programming procedure for automated theorem proving—one which simply searches for small width proofs. This relation guarantees that the runnuing time (and thus the size of the produced proof) is at most quasi-polynomial in the smallest tree-like proof. This algorithm is never much worse than any of the recursive automated provers (such as DLL) used in practice. In contrast, we present a family of tautologies on which it is exponentially faster. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> {The spread of true and false news online: Lies spread faster than the truth There is worldwide concern over false news and the possibility that it can influence political, economic, and social well-being. To understand how false news spreads, Vosoughi et al. used a data set of rumor cascades on Twitter from 2006 to 2017. About 126,000 rumors were spread by ∼3 million people. False news reached more people than the truth; the top 1% of false news cascades diffused to between 1000 and 100,000 people, whereas the truth rarely diffused to more than 1000 people. Falsehood also diffused faster than the truth. The degree of novelty and the emotional reactions of recipients may be responsible for the differences observed. Science, this issue p. 1146 A large-scale analysis of tweets reveals that false rumors spread further and faster than the truth. We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it. <|reference_end|>", "<|reference_start|> Through the Google Goggles: Sociopolitical Bias in Search Engine Design: <|reference_end|>", "<|reference_start|> {The spread of true and false news online: Lies spread faster than the truth There is worldwide concern over false news and the possibility that it can influence political, economic, and social well-being. To understand how false news spreads, Vosoughi et al. used a data set of rumor cascades on Twitter from 2006 to 2017. About 126,000 rumors were spread by ∼3 million people. False news reached more people than the truth; the top 1% of false news cascades diffused to between 1000 and 100,000 people, whereas the truth rarely diffused to more than 1000 people. Falsehood also diffused faster than the truth. The degree of novelty and the emotional reactions of recipients may be responsible for the differences observed. Science, this issue p. 1146 A large-scale analysis of tweets reveals that false rumors spread further and faster than the truth. We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it. <|reference_end|>", "<|reference_start|> Topical interests and the mitigation of search engine bias: Search engines have become key media for our scientific, economic, and social activities by enabling people to access information on the web despite its size and complexity. On the down side, search engines bias the traffic of users according to their page ranking strategies, and it has been argued that they create a vicious cycle that amplifies the dominance of established and already popular sites. This bias could lead to a dangerous monopoly of information. We show that, contrary to intuition, empirical data do not support this conclusion; popular sites receive far less traffic than predicted. We discuss a model that accurately predicts traffic data patterns by taking into consideration the topical interests of users and their searching behavior in addition to the way search engines rank pages. The heterogeneity of user interests explains the observed mitigation of search engines’ popularity bias. <|reference_end|>" ]

[ 0, 2, 24, 32 ]

[ "<|reference_start|> Fast Reed-Solomon Interactive Oracle Proofs of Proximity: The family of Reed-Solomon (RS) codes plays a prominent role in the construction of quasilinear probabilistically checkable proofs (PCPs) and interactive oracle proofs (IOPs) with perfect zero knowledge and polylogarithmic verifiers. The large concrete computational complexity required to prove membership in RS codes is one of the biggest obstacles to deploying such PCP/IOP systems in practice.\nTo advance on this problem we present a new interactive oracle proof of proximity (IOPP) for RS codes; we call it the Fast RS IOPP (FRI) because (i) it resembles the ubiquitous Fast Fourier Transform (FFT) and (ii) the arithmetic complexity of its prover is strictly linear and that of the verifier is strictly logarithmic (in comparison, FFT arithmetic complexity is quasi-linear but not strictly linear). Prior RS IOPPs and PCPs of proximity (PCPPs) required super-linear proving time even for polynomially large query complexity.\nFor codes of block-length N, the arithmetic complexity of the (interactive) FRI prover is less than 6 * N, while the (interactive) FRI verifier has arithmetic complexity <= 21 * log N, query complexity 2 * log N and constant soundness - words that are delta-far from the code are rejected with probability min{delta * (1-o(1)),delta_0} where delta_0 is a positive constant that depends mainly on the code rate. The particular combination of query complexity and soundness obtained by FRI is better than that of the quasilinear PCPP of [Ben-Sasson and Sudan, SICOMP 2008], even with the tighter soundness analysis of [Ben-Sasson et al., STOC 2013; ECCC 2016]; consequently, FRI is likely to facilitate better concretely efficient zero knowledge proof and argument systems.\nPrevious concretely efficient PCPPs and IOPPs suffered a constant multiplicative factor loss in soundness with each round of \"proof composition\" and thus used at most O(log log N) rounds. We show that when delta is smaller than the unique decoding radius of the code, FRI suffers only a negligible additive loss in soundness. This observation allows us to increase the number of \"proof composition\" rounds to Theta(log N) and thereby reduce prover and verifier running time for fixed soundness. <|reference_end|>", "<|reference_start|> Succinct Arguments in the Quantum Random Oracle Model: <|reference_end|>", "<|reference_start|> A note on efficient zero-knowledge proofs and arguments (extended abstract): In this note, we present new zero-knowledge interactive proofs and arguments for languages in <italic>NP</italic>. To show that <italic>x ε L</italic>, with an error probability of at most 2<supscrpt>-<italic>k</italic></supscrpt>, our zero-knowledge proof system requires <italic>O</italic>(|<italic>x</italic>|<supscrpt><italic>c</italic><subscrpt>1</subscrpt></supscrpt>)+<italic>O</italic>(lg<supscrpt><italic>c</italic><subscrpt>2</subscrpt></supscrpt>|<italic>x</italic>|)<italic>k</italic> ideal bit commitments, where <italic>c</italic><subscrpt>1</subscrpt> and <italic>c</italic><subscrpt>2</subscrpt> depend only on <italic>L</italic>. This construction is the first in the ideal bit commitment model that achieves large values of <italic>k</italic> more efficiently than by running <italic>k</italic> independent iterations of the base interactive proof system. Under suitable complexity assumptions, we exhibit zero knowledge arguments that require <italic>O</italic>(lg<supscrpt>c</supscrpt>|<italic>x</italic>|<italic>kl</italic> bits of communication, where <italic>c</italic> depends only on <italic>L</italic>, and <italic>l</italic> is the security parameter for the prover. This is the first construction in which the total amount of communication can be less than that needed to transmit the <italic>NP</italic> witness. Our protocols are based on efficiently checkable proofs for <italic>NP</italic>[4]. <|reference_end|>", "<|reference_start|> COMPUTATIONALLY SOUND PROOFS *: This paper puts forward a new notion of a proof based on computational complexity and explores its implications for computation at large. \nComputationally sound proofs provide, in a novel and meaningful framework, answers to old and new questions in complexity theory. In particular, given a random oracle or a new complexity assumption, they enable us to \nprove that verifying is easier than deciding for all theorems; provide a quite effective way to prove membership in computationally hard languages (such as ${\\cal C}o$-$\\cal N \\cal P$-complete ones); and show that every computation possesses a short certificate vouching its correctness. \n \nFinally, if a special type of computationally sound proof exists, we show that Blum's notion of program checking can be meaningfully broadened so as to prove that $\\cal N \\cal P$-complete languages are checkable. <|reference_end|>" ]

[ 4, 10, 15, 18 ]

[ "<|reference_start|> Zufälligkeit und Wahrscheinlichkeit: <|reference_end|>", "<|reference_start|> Feasible Analysis, Randomness, and Base Invariance: <|reference_end|>", "<|reference_start|> A polynomial-time algorithm for computing absolutely normal numbers: <|reference_end|>", "<|reference_start|> Endliche Automaten und Zufallsfolgen: <|reference_end|>" ]

[ 1, 3, 4, 8 ]

[ "<|reference_start|> ImageNet Large Scale Visual Recognition Challenge: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements. <|reference_end|>", "<|reference_start|> Deep learning for fixed model reuse: Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building a model from scratch. It is helpful to reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices and videos. There are several studies have the sense of model reuse, by trying to reuse pre-trained deep networks architectures or deep model features to train a new deep model. They, however, neglect the fact that there are many other fixed models or features available. In this paper, we propose a more thorough model reuse scheme, FMR (Fixed Model Reuse). FMR utilizes the learning power of deep models to implicitly grab the useful discriminative information from fixed model/features that have been widely used in general tasks. We firstly arrange the convolution layers of a deep network and the provided fixed model/features in parallel, fully connecting to the output layer nodes. Then, the dependencies between the output layer nodes and the fixed model/features are knockdown such that only the raw feature inputs are needed when the model is being used for testing, though the helpful information in the fixed model/features have already been incorporated into the model. On one hand, by the FMR scheme, the required amount of training data can be significantly reduced because of the reuse of fixed model/features. On the other hand, the fixed model/features are not explicitly used in testing, and thus, the scheme can be quite useful in applications where the fixed model/features are protected by patents or commercial secrets. Experiments on five real-world datasets validate the effectiveness of FMR compared with state-of-the-art deep methods. <|reference_end|>", "<|reference_start|> Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution: Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-art methods. <|reference_end|>", "<|reference_start|> Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval: Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, let alone the unsupervised retrieval task. We propose the Selective Convolutional Descriptor Aggregation (SCDA) method. SCDA firstly localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and dimensionality reduced into a short feature vector using the best practices we found. SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained datasets confirm the effectiveness of SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high mean average precision in fine-grained retrieval. Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches. <|reference_end|>" ]

[ 1, 2, 4, 6 ]

[ "<|reference_start|> Gaussian process optimization with failures: classification and convergence proof: <|reference_end|>", "<|reference_start|> Evolutionary Search in Lethal Environments: In Natural evolution, a mutation may be lethal, causing an abrupt end to an evolving lineage. This fact has a tendency to cause evolution to \"prefer\" mutationally robust solutions (which can in turn slow innovation), an effect that has been studied previously, especially in the context of evolution on neutral plateaux. Here, we tackle related issues but from the perspective of a practical optimization scenario. We wish to evolve a finite population of entities quickly (i.e. improve them), but when a lethal solution (modelled here as one below a certain fitness threshold) is evaluated, it is immediately removed from the population and the population size is reduced by one. This models certain closed-loop evolution scenarios that may be encountered, for example, when evolving nano-technologies or autonomous robots. We motivate this scenario, and find that evolutionary search performs best in a lethal environment when limiting randomness in the solution generation process, e.g. by using elitism, above-average selection pressure, a less random mutating operator, and no or little crossover. For NKa landscapes, these strategies turn out to be particularly important on rugged and non-homogeneous landscapes (i.e. for large K and α). <|reference_end|>", "<|reference_start|> Evolutionary Search in Lethal Environments: In Natural evolution, a mutation may be lethal, causing an abrupt end to an evolving lineage. This fact has a tendency to cause evolution to \"prefer\" mutationally robust solutions (which can in turn slow innovation), an effect that has been studied previously, especially in the context of evolution on neutral plateaux. Here, we tackle related issues but from the perspective of a practical optimization scenario. We wish to evolve a finite population of entities quickly (i.e. improve them), but when a lethal solution (modelled here as one below a certain fitness threshold) is evaluated, it is immediately removed from the population and the population size is reduced by one. This models certain closed-loop evolution scenarios that may be encountered, for example, when evolving nano-technologies or autonomous robots. We motivate this scenario, and find that evolutionary search performs best in a lethal environment when limiting randomness in the solution generation process, e.g. by using elitism, above-average selection pressure, a less random mutating operator, and no or little crossover. For NKa landscapes, these strategies turn out to be particularly important on rugged and non-homogeneous landscapes (i.e. for large K and α). <|reference_end|>", "<|reference_start|> Safe controller optimization for quadrotors with Gaussian processes: One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention. <|reference_end|>" ]

[ 8, 12, 19, 25 ]

[ "<|reference_start|> Coordinating cognition: The costs and benefits of shared gaze during collaborative search: <|reference_end|>", "<|reference_start|> Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze: Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the network to identify and pay attention to the humans in the crowd that are most critical to navigation. We propose a novel network utilizing a graph representation to learn the policy. We first train a graph convolutional network based on human gaze data that accurately predicts human attention to different agents in the crowd. Then we incorporate the learned attention into a graph-based reinforcement learning architecture. The proposed attention mechanism enables the assignment of meaningful weightings to the neighbors of the robot, and has the additional benefit of interpretability. Experiments on real-world dense pedestrian datasets with various crowd sizes demonstrate that our model outperforms state-of-art methods by 18.4% in task accomplishment and by 16.4% in time efficiency. <|reference_end|>", "<|reference_start|> Recognizing behavior in hand-eye coordination patterns: Modeling human behavior is important for the design of robots as well as human-computer interfaces that use humanoid avatars. Constructive models have been built, but they have not captured all of the detailed structure of human behavior such as the moment-to-moment deployment and coordination of hand, head and eye gaze used in complex tasks. We show how this data from human subjects performing a task can be used to program a dynamic Bayes network (DBN) which in turn can be used to recognize new performance instances. As a specific demonstration we show that the steps in a complex activity such as sandwich making can be recognized by a DBN in real time. <|reference_end|>", "<|reference_start|> Adapting the coordination of eyes and head to differences in task and environment during fully-mobile visual exploration: How are eyes and head adapted to meet the demands of visual exploration in different tasks and environments? In two studies, we measured the horizontal movements of the eyes (using mobile eye tracking in Studies 1 and 2) and the head (using inertial sensors in Study 2) while participants completed a walking task and a search and retrieval task in a large, outdoor environment. We found that the spread of visual exploration was greater while searching compared with walking, and this was primarily driven by increased movement of the head as opposed to the eyes. The contributions of the head to gaze shifts of different eccentricities was greater when searching compared to when walking. Findings are discussed with respect to understanding visual exploration as a motor action with multiple degrees of freedom. <|reference_end|>" ]

[ 8, 9, 11, 19 ]

[ "<|reference_start|> Consonant-vowel sequences as subword units for code-mixed languages: In this research work, we develop a state-of-art model for identifying sentiment in Hindi-English code-mixed language. We introduce new phonemic sub-word units for Hindi-English code-mixed text along with a hierarchical deep learning model which uses these sub-word units for predicting sentiment. The results indicate that the model yields a significant increase in accuracy as compared to other models. <|reference_end|>", "<|reference_start|> Overview for the first shared task on language identification in code-switched data: We present an overview of the first shared task on language identification on code-switched data. The shared task included code-switched data from four language pairs: Modern Standard Arabic-Dialectal Arabic (MSA-DA), Mandarin-English (MAN-EN), Nepali-English (NEP-EN), and Spanish-English (SPA-EN). A total of seven teams participated in the task and submitted 42 system runs. The evaluation showed that language identification at the token level is more difficult when the languages present are closely related, as in the case of MSA-DA, where the prediction performance was the lowest among all language pairs. In contrast, the language pairs with the higest F-measure where SPA-EN and NEP-EN. The task made evident that language identification in code-switched data is still far from solved and warrants further research. <|reference_end|>", "<|reference_start|> Part-of-speech tagging for code-mixed English-Hindi Twitter and Facebook chat messages: The paper reports work on collecting and annotating code-mixed English-Hindi social media text (Twitter and Facebook messages), and experiments on automatic tagging of these corpora, using both a coarse-grained and a fine-grained part-ofspeech tag set. We compare the performance of a combination of language specific taggers to that of applying four machine learning algorithms to the task (Conditional Random Fields, Sequential Minimal Optimization, Naive Bayes and Random Forests), using a range of different features based on word context and wordinternal information. <|reference_end|>", "<|reference_start|> Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM: Analyzing people’s opinions and sentiments towards certain aspects is an important task of natural language understanding. In this paper, we propose a novel solution to targeted aspect-based sentiment analysis, which tackles the challenges of both aspect-based sentiment analysis and targeted sentiment analysis by exploiting commonsense knowledge. We augment the long short-term memory (LSTM) network with a hierarchical attention mechanism consisting of a target-level attention and a sentence-level attention. Commonsense knowledge of sentiment-related concepts is incorporated into the end-to-end training of a deep neural network for sentiment classification. In order to tightly integrate the commonsense knowledge into the recurrent encoder, we propose an extension of LSTM, termed Sentic LSTM. We conduct experiments on two publicly released datasets, which show that the combination of the proposed attention architecture and Sentic LSTM can outperform state-of-the-art methods in targeted aspect sentiment tasks. <|reference_end|>" ]

[ 3, 5, 19, 27 ]

[ "<|reference_start|> Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level: <|reference_end|>", "<|reference_start|> Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review: <|reference_end|>", "<|reference_start|> Functional brain networks in schizophrenia: a review: Functional magnetic resonance imaging (fMRI) has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event-related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA) which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large-scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their inter-relationships with fMRI has great potential to improve our understanding of schizophrenia. <|reference_end|>", "<|reference_start|> Connecting the dots: R ising capacity to measure extensive arrays of biological parameters has ushered in an era of biomedical big data. As massive datasets from large cohorts become the norm, the discipline of data science has emerged to tackle data-driven problems at the intersection of biomedical research and patient care. We introduce several sources of cardiovascular big data and discuss the importance of maximizing participation in data-driven knowledge production models. <|reference_end|>" ]

[ 2, 5, 15, 16 ]

[ "<|reference_start|> Distributed Networked Learning with Correlated Data: We consider a distributed estimation method in a setting with heterogeneous streams of correlated data distributed across nodes in a network. In the considered approach, linear models are estimated locally (i.e., with only local data) subject to a network regularization term that penalizes a local model that differs from neighboring models. We analyze computation dynamics (associated with stochastic gradient updates) and information exchange (associated with exchanging current models with neighboring nodes). We provide a finite-time characterization of convergence of the weighted ensemble average estimate and compare this result to federated learning, an alternative approach to estimation wherein a single model is updated by locally generated gradient updates. This comparison highlights the trade-off between speed vs precision: while model updates take place at a faster rate in federated learning, the proposed networked approach to estimation enables the identification of models with higher precision. We illustrate the method's general applicability in two examples: estimating a Markov random field using wireless sensor networks and modeling prey escape behavior of flocking birds based on a publicly available dataset. <|reference_end|>", "<|reference_start|> Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization: Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations and the asynchronous parallel mechanism. To fill the gaps in theory and provide theoretical supports, this paper studies two asynchronous parallel implementations of SG: one is on the computer network and the other is on the shared memory system. We establish an ergodic convergence rate $O(1/\\sqrt{K})$ for both algorithms and prove that the linear speedup is achievable if the number of workers is bounded by $\\sqrt{K}$ ($K$ is the total number of iterations). Our results generalize and improve existing analysis for convex minimization. <|reference_end|>", "<|reference_start|> Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization: Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations and the asynchronous parallel mechanism. To fill the gaps in theory and provide theoretical supports, this paper studies two asynchronous parallel implementations of SG: one is on the computer network and the other is on the shared memory system. We establish an ergodic convergence rate $O(1/\\sqrt{K})$ for both algorithms and prove that the linear speedup is achievable if the number of workers is bounded by $\\sqrt{K}$ ($K$ is the total number of iterations). Our results generalize and improve existing analysis for convex minimization. <|reference_end|>", "<|reference_start|> Extra: An exact first- order algorithm for decentralized consensus optimization: Recently, there has been growing interest in solving consensus optimization problems in a multiagent network. In this paper, we develop a decentralized algorithm for the consensus optimization problem $\\mathrm{minimize}_{x\\in\\mathbb{R}^p}~\\bar{f}(x)=\\frac{1}{n}\\sum_{i=1}^n f_i(x),$ which is defined over a connected network of $n$ agents, where each function $f_i$ is held privately by agent $i$ and encodes the agent's data and objective. All the agents shall collaboratively find the minimizer while each agent can only communicate with its neighbors. Such a computation scheme avoids a data fusion center or long-distance communication and offers better load balance to the network. This paper proposes a novel decentralized exact first-order algorithm (abbreviated as EXTRA) to solve the consensus optimization problem. “Exact” means that it can converge to the exact solution. EXTRA uses a fixed, large step size, which can be determined independently of the network size or topology. The local variable of every a... <|reference_end|>" ]

[ 4, 12, 18, 19 ]

[ "<|reference_start|> The variational multiscale method—a paradigm for computational mechanics: <|reference_end|>", "<|reference_start|> Stabilization arising from PGEM: A review and further developments: <|reference_end|>", "<|reference_start|> Localization of Elliptic Multiscale Problems: This paper constructs a local generalized finite element basis for elliptic problems with heterogeneous and highly varying coefficients. The basis functions are solutions of local problems on vertex patches. The error of the corresponding generalized finite element method decays exponentially with respect to the number of layers of elements in the patches. Hence, on a uniform mesh of size $ H$, patches of diameter $ H\\log (1/H)$ are sufficient to preserve a linear rate of convergence in $ H$ without pre-asymptotic or resonance effects. The analysis does not rely on regularity of the solution or scale separation in the coefficient. This result motivates new and justifies old classes of variational multiscale methods. - See more at: http://www.ams.org/journals/mcom/2014-83-290/S0025-5718-2014-02868-8/#sthash.z2CCFXIg.dpuf <|reference_end|>", "<|reference_start|> A weak Galerkin generalized multiscale finite element method: <|reference_end|>" ]

[ 3, 13, 17, 21 ]

[ "<|reference_start|> Probably approximately correct learning with beta-mixing input sequences: In this paper, we study the behaviour of PAC learning algorithms when the input sequence is not i.i.d., but is β-mixing instead. A meta-theorem is proved, showing that if an algorithm is (i) PAC when the inputs are i.i.d., and (ii) ‘sub-additive’ in a sense defined in the paper, then the same algorithm continues to be PAC even with β-mixing inputs. It is shown that if a function family is distribution-free learnable or consistently learnable with i.i.d. inputs, then every consistent algorithm is PAC even when the input sequence is β-mixing. Explicit quantitative estimates are derived for the learning rates with β-mixing inputs, in terms of the learning rates with i.i.d. inputs and the β-mixing coefficients of the input sequence. Finally, it is shown that a large of Markov chains have the β-mixing property. Hence the results derived here have wide applicability. <|reference_end|>", "<|reference_start|> Nonparametric Time Series Prediction Through Adaptive Model Selection: <|reference_end|>", "<|reference_start|> Prediction, learning, and games: Empirical evidence to lend proper credence, however, continues to elude the quality literature. This hardly vexes Taguchi (or most of those who produce the corpus of the discipline), but it is importunate to the reviewer. In many settings, the loss function is unlikely to be symmetric with respect to the target and, furthermore, the behavior on either side of the target is not necessarily the same. Such seemingly obvious deviations have not deterred the vast majority from proclaiming the ubiquity of the function. The current book offers no new insights here. The treatment of experimental design is fairly strong. Taguchi’s use of outer arrays is one of his greatest contributions (and one that has caught the ire of a few academics). The book elucidates design adequately and illuminates Taguchi’s advances. Anyone who is well versed in design will be able to skip the introductions and go straight to the discussion of orthogonal arrays. In this reviewer’s opinion, this is the major strength of the book. Another strength is the extensive set of case studies that cover each topic from the previous chapters. Applications include robust engineering in polymer chemistry, material design in automatic transmissions, improvements in omelet taste, and the use of Mahalanobis distance to measure drug efficacy. The sheer range of topical coverage in the cases will doubtlessly find appeal for virtually any practitioner regardless of specific field. There is the obligatory mention of Six Sigma as it relates to Taguchi’s work. Given the scope of Six Sigma in the current landscape, finding your place therein is necessary. A glaring omission is the lack of a similar consideration of ISO and QS certifications (as is given in Juran). Do not assume that the reviewer sees this as a negative. It is hoped here that Taguchi sees these quality certifications as largely specious and unworthy of a reference. Overall, it is hard not to be impressed with the utter volume of Taguchi’s output. The expanse of coverage is not to be dismissed. As a vehicle for presenting his prolific production, the handbook succeeds. The book may appear to be somewhat self-indulgent (as if 1600+ pages about your previous work could appear otherwise!). No doubt an ambitious undertaking, the authors nevertheless generally hit their mark. One would be hard-pressed not to at least enjoy most of the ride. What is positive (negative) about the book is largely what one perceives to be positive (negative) about Taguchi. The aforementioned lack of scholarly references is unsurprising, because Taguchi largely practiced beyond the boundaries of academia. Many academics have tended to reciprocate with less attention to his work than is probably deserved. What can safely be said is that if you are a fan of Taguchi’s work, this is definitely for you. If you need a single reference for his work or simply desire a “complete quality library,” you cannot go wrong here. Otherwise, it is unlikely that you would be interested. But in the event that you are a practitioner itching to get acquainted with Taguchi and have $150 burning a hole in your wallet or Visa, this one’s a winner. <|reference_end|>", "<|reference_start|> Online learning: Random averages, combinatorial parameters, and learnability: We develop a theory of online learning by defining several complexity measures. Among them are analogues of Rademacher complexity, covering numbers and fat-shattering dimension from statistical learning theory. Relationship among these complexity measures, their connection to online learning, and tools for bounding them are provided. We apply these results to various learning problems. We provide a complete characterization of online learnability in the supervised setting. <|reference_end|>" ]

[ 0, 1, 2, 3 ]

[ "<|reference_start|> Learning to summarize with human feedback: <|reference_end|>", "<|reference_start|> Better Aligning Text-to-Image Models with Human Preference: Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human aesthetic preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using the HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human aesthetic preferences. Our experiments show that the HPS outperforms CLIP in predicting human choices and has good generalization capability towards images generated from other models. By tuning Stable Diffusion with the guidance of the HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align sd web/. <|reference_end|>", "<|reference_start|> Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned.: We describe our early efforts to red team language models in order to simultaneously discover, measure, and attempt to reduce their potentially harmful outputs. We make three main contributions. First, we investigate scaling behaviors for red teaming across 3 model sizes (2.7B, 13B, and 52B parameters) and 4 model types: a plain language model (LM); an LM prompted to be helpful, honest, and harmless; an LM with rejection sampling; and a model trained to be helpful and harmless using reinforcement learning from human feedback (RLHF). We find that the RLHF models are increasingly difficult to red team as they scale, and we find a flat trend with scale for the other model types. Second, we release our dataset of 38,961 red team attacks for others to analyze and learn from. We provide our own analysis of the data and find a variety of harmful outputs, which range from offensive language to more subtly harmful non-violent unethical outputs. Third, we exhaustively describe our instructions, processes, statistical methodologies, and uncertainty about red teaming. We hope that this transparency accelerates our ability to work together as a community in order to develop shared norms, practices, and technical standards for how to red team language models. <|reference_end|>", "<|reference_start|> Do the rewards justify the means? measuring trade-offs between rewards and ethical behavior in the MACHIAVELLI benchmark.: Artificial agents have traditionally been trained to maximize reward, which may incentivize power-seeking and deception, analogous to how next-token prediction in language models (LMs) may incentivize toxicity. So do agents naturally learn to be Machiavellian? And how do we measure these behaviors in general-purpose models such as GPT-4? Towards answering these questions, we introduce MACHIAVELLI, a benchmark of 134 Choose-Your-Own-Adventure games containing over half a million rich, diverse scenarios that center on social decision-making. Scenario labeling is automated with LMs, which are more performant than human annotators. We mathematize dozens of harmful behaviors and use our annotations to evaluate agents' tendencies to be power-seeking, cause disutility, and commit ethical violations. We observe some tension between maximizing reward and behaving ethically. To improve this trade-off, we investigate LM-based methods to steer agents' towards less harmful behaviors. Our results show that agents can both act competently and morally, so concrete progress can currently be made in machine ethics--designing agents that are Pareto improvements in both safety and capabilities. <|reference_end|>" ]

[ 11, 27, 44, 56 ]

[ "<|reference_start|> HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization: Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \\emph{inaccurate} labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders \\cite{devlin:2018:arxiv}, we propose {\\sc Hibert} (as shorthand for {\\bf HI}erachical {\\bf B}idirectional {\\bf E}ncoder {\\bf R}epresentations from {\\bf T}ransformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained {\\sc Hibert} to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets. <|reference_end|>", "<|reference_start|> ETC: encoding long and structured data in transformers: Transformer-based models have pushed the state of the art in many natural language processing tasks. However, one of their main limitations is the quadratic computational and memory cost of the standard attention mechanism. In this paper, we present a new family of Transformer models, which we call the Extended Transformer Construction (ETC), that allows for significant increases in input sequence length by introducing a new global-local attention mechanism between a global memory and the standard input tokens. We also show that combining global-local attention with relative position encodings allows ETC to handle structured data with ease. Empirical results on the Natural Questions data set show the promise of the approach. <|reference_end|>", "<|reference_start|> AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization: Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step. Previous work shows the efficacy of jointly scoring and selecting sentences with neural sequence generation models. It is, however, not well-understood if the gain is due to better encoding techniques or better redundancy reduction approaches. Similarly, the contribution of salience versus diversity components on the created summary is not studied well. Building on the state-of-the-art encoding methods for summarization, we present two adaptive learning models: AREDSUM-SEQ that jointly considers salience and novelty during sentence selection; and a two-step AREDSUM-CTX that scores salience first, then learns to balance salience and redundancy, enabling the measurement of the impact of each aspect. Empirical results on CNN/DailyMail and NYT50 datasets show that by modeling diversity explicitly in a separate step, AREDSUM-CTX achieves significantly better performance than AREDSUM-SEQ as well as state-of-the-art extractive summarization baselines. <|reference_end|>", "<|reference_start|> The Use of MMR, Diversity-Based Reranking for Reordering Documents\nand Producing Summaries: This paper presents a method for combining\nquery-relevance with information-novelty in the context\nof text retrieval and summarization. The Maximal\nMarginal Relevance (MMR) criterion strives to reduce\nredundancy while maintaining query relevance in\nre-ranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results\nindicate some benefits for MMR diversity ranking\nin document retrieval and in single document summarization.\nThe latter are borne out by the recent results of the\nSUMMAC conference in the evaluation of summarization\nsystems. However, the clearest advantage is demonstrated\nin constructing non-redundant multi-document\nsummaries, where MMR results are clearly superior to\nnon-MMR passage selection. <|reference_end|>" ]

[ 8, 27, 38, 44 ]

[ "<|reference_start|> Racial Bias in Hate Speech and Abusive Language Detection Datasets: Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect. <|reference_end|>", "<|reference_start|> {A Survey of Offensive Language Detection for the Arabic Language: The use of offensive language in user-generated content is a serious problem that needs to be addressed with the latest technology. The field of Natural Language Processing (NLP) can support the automatic detection of offensive language. In this survey, we review previous NLP studies that cover Arabic offensive language detection. This survey investigates the state-of-the-art in offensive language detection for the Arabic language, providing a structured overview of previous approaches, including core techniques, tools, resources, methods, and main features used. This work also discusses the limitations and gaps of the previous studies. Findings from this survey emphasize the importance of investing further effort in detecting Arabic offensive language, including the development of benchmark resources and the invention of novel preprocessing and feature extraction techniques. <|reference_end|>", "<|reference_start|> Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate: Detecting online hate is a complex task, and low-performing models have harmful consequences when used for sensitive applications such as content moderation. Emoji-based hate is an emerging challenge for automated detection. We present HatemojiCheck, a test suite of 3,930 short-form statements that allows us to evaluate performance on hateful language expressed with emoji. Using the test suite, we expose weaknesses in existing hate detection models. To address these weaknesses, we create the HatemojiBuild dataset using a human-and-model-in-the-loop approach. Models built with these 5,912 adversarial examples perform substantially better at detecting emoji-based hate, while retaining strong performance on text-only hate. Both HatemojiCheck and HatemojiBuild are made publicly available. See our Github Repository (https://github.com/HannahKirk/Hatemoji). HatemojiCheck, HatemojiBuild, and the final Hatemoji Model are also available on HuggingFace (https://huggingface.co/datasets/HannahRoseKirk/). <|reference_end|>", "<|reference_start|> Grass-Mud Horses to Victory: The Phonological Constraints of Subversive Puns: In 2008, Chinese netizens began creating subversive puns. These puns, including the well known “grass -mud horse,” were designed to engag e in a satirical online movement against internet censorship of vulgar or politically sensitive words. By examining online subversive puns’ birth and development , this paper presents a phonological analysis of the growing Chinese internet lexicon. First, the relevant phonological features of the puns are identified, which underscore how the game plays with the inherent characteristics of Mandarin. Next, a series of rules or constraints are identified; these highlight both the formulaic nature of subversive puns as well as the flexibility of the language. Finally, using Optimality Theory as a descriptive tool, this paper explores the interaction of universal constraints with several possible new language game constraints. Through this examination, this paper identifies implications for Mandarin lexical access and Mandarin word form encoding. 1. Intr oduction The Chinese language has a rich history of word play. Language game research dates back to Chao’s (1931) preliminary study in which he outlined a series of 反切语 fanqieyu ‘secret languages’ that made use of the syllable onset and fixed rime spelling system. Branner (2010) has suggested that these games are, in fact, rooted in an even earlier military fanqie cipher dating back to the 16 th century. Furthermore, these Chinese language games are not restricted to one ‘regional dialect’ or 方言 fangyan. In addition to the secret languages and games Chao cites, research into Taiwanese (Li 1985), Hakka (Branner 2010), Shanxi dialect (Hou 1988), and Cantonese (Bolton and Hutton 1995) has underscored the ubiquitous creativity and metaphor inherent in speakers throughout China. Language games and secret languages are by no means restricted to Chinese. Laycock (1972) first coined the term ludling by combining the Latin word for ‘game’ 1 Several people have shared their time and suggestions in order to improve this paper. Rebecca Morley was especially helpful with regards to the OT framework. Heather Inwood was equally invaluable, reviewing earlier drafts and offering encouraging feedback on the Chinese internet. Any errors that remain are those of the author. <|reference_end|>" ]

[ 0, 5, 11, 12 ]

[ "<|reference_start|> Deep Residual Learning for Image Recognition: Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. <|reference_end|>", "<|reference_start|> Kurator: Using the crowd to help families with personal curation tasks: People capture photos, audio recordings, video, and more on a daily basis, but organizing all these digital artifacts quickly becomes a daunting task. Automated solutions struggle to help us manage this data because they cannot understand its meaning. In this paper, we introduce Kurator, a hybrid intelligence system leveraging mixed-expertise crowds to help families curate their personal digital content. Kurator produces a refined set of content via a combination of automated systems able to scale to large data sets and human crowds able to understand the data. Our results with 5 families show that Kurator can reduce the amount of effort needed to find meaningful memories within a large collection. This work also suggests that crowdsourcing can be used effectively even in domains where personal preference is key to accurately solving the task. <|reference_end|>", "<|reference_start|> Best of Both Worlds: Human-Machine Collaboration for Object Annotation: The long-standing goal of localizing every object in an image remains elusive. Manually annotating objects is quite expensive despite crowd engineering innovations. Current state-of-the-art automatic object detectors can accurately detect at most a few objects per image. This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images. The input to the system is an image to annotate and a set of annotation constraints: desired precision, utility and/or human cost of the labeling. The output is a set of object annotations, informed by human feedback and computer vision. Our model seamlessly integrates multiple computer vision models with multiple sources of human input in a Markov Decision Process. We empirically validate the effectiveness of our human-in-the-loop labeling approach on the ILSVRC2014 object detection dataset. <|reference_end|>", "<|reference_start|> Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing: Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to sequentially infer intermediate concepts associated with the final task. To acquire training data in desired quantities with ground truth 3D shape and relevant concepts, we render 3D object CAD models to generate large-scale synthetic data and simulate challenging occlusion configurations between objects. We train the network only on synthetic data and demonstrate state-of-the-art performances on real image benchmarks including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and IKEA for 2D and 3D keypoint localization and instance segmentation. The empirical results substantiate the utility of our deep supervision scheme by demonstrating effective transfer of knowledge from synthetic data to real images, resulting in less overfitting compared to standard end-to-end training. <|reference_end|>" ]

[ 0, 2, 4, 12 ]

[ "<|reference_start|> Vector S-Parametric Analysis of Signal Phase Dynamic Radio Images: <|reference_end|>", "<|reference_start|> A New Technique for Verifying the Consistency of Distributed R-Trees: The ever-increasing of the large spatial datasets and the widely application of the complex computation have motivated the emergence of distributed algorithms to process spatial operations efficiently. The R-tree index is broadly used by researches as a distributed spatial structure for indexing and retrieval of spatial objects. However, a big challenge has arisen, that is, how to check the consistency of distributed R-Trees.  In the past few years researches have been published on both distributed R-Tree and verification of distributed systems. Though none of them has proposed a technique to check the consistency of distributed R-Trees. This article presents a new approach for verifying the consistency of distributed R-Trees, which is called RConsistency.  It allows collect information about the distributed R-Tree once it has been created.  RConsistency also collects information about the distribute R-Tree and can helps to reduce the overlapping and dead area. It can be used with any index similar to R-Tree, since the RConsistency algorithm uses the nodes organization of the R-Tree to collect consistency information.  The algorithm was used on DistGeo, a platform to process distributed spatial operations.  A graphic tool, named RConsistency Visualizer, was developed to show the output of the RConsistency algorithm. <|reference_end|>", "<|reference_start|> Concepts as Semantic Pointers: A Framework and Computational Model: The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts with a more finely grained taxonomy of mental representations. In this paper, we describe an alternative approach involving a single class of mental representations called \"semantic pointers.\" Semantic pointers are symbol-like representations that result from the compression and recursive binding of perceptual, lexical, and motor representations, effectively integrating traditional connectionist and symbolic approaches. We present a computational model using semantic pointers that replicates experimental data from categorization studies involving each prior paradigm. We argue that a framework involving semantic pointers can provide a unified account of conceptual phenomena, and we compare our framework to existing alternatives in accounting for the scope, content, recursive combination, and neural implementation of concepts. <|reference_end|>", "<|reference_start|> The Magical Number Seven, Plus or Minus Two: Some Limits On Our Capacity For Processing Information: First, the span of absolute judgment and the span of immediate memory impose severe limitations on the amount of information that we are able to receive, process, and remember. By organizing the stimulus input simultaneously into several dimensions and successively into a sequence or chunks, we manage to break (or at least stretch) this informational bottleneck. Second, the process of recoding is a very important one in human psychology and deserves much more explicit attention than it has received. In particular, the kind of linguistic recoding that people do seems to me to be the very lifeblood of the thought processes. Recoding procedures are a constant concern to clinicians, social psychologists, linguists, and anthropologists and yet, probably because recoding is less accessible to experimental manipulation than nonsense syllables or T mazes, the traditional experimental psychologist has contributed little or nothing to their analysis. Nevertheless, experimental techniques can be used, methods of recoding can be specified, behavioral indicants can be found. And I anticipate that we will find a very orderly set of relations describing what now seems an uncharted wilderness of individual differences. Third, the concepts and measures provided by the theory of information provide a quantitative way of getting at some of these questions. The theory provides us with a yardstick for calibrating our stimulus materials and for measuring the performance of our subjects. In the interests of communication I have suppressed the technical details of information measurement and have tried to express the ideas in more familiar terms; I hope this paraphrase will not lead you to think they are not useful in research. Informational concepts have already proved valuable in the study of discrimination and of language; they promise a great deal in the study of learning and memory; and it has even been proposed that they can be useful in the study of concept formation. A lot of questions that seemed fruitless twenty or thirty years ago may now be worth another look. In fact, I feel that my story here must stop just as it begins to get really interesting. And finally, what about the magical number seven? What about the seven wonders of the world, the seven seas, the seven deadly sins, the seven daughters of Atlas in the Pleiades, the seven ages of man, the seven levels of hell, the seven primary colors, the seven notes of the musical scale, and the seven days of the week? What about the seven-point rating scale, the seven categories for absolute judgment, the seven objects in the span of attention, and the seven digits in the span of immediate memory? For the present I propose to withhold judgment. Perhaps there is something deep and profound behind all these sevens, something just calling out for us to discover it. But I suspect that it is only a pernicious, Pythagorean coincidence. <|reference_end|>" ]

[ 1, 3, 9, 18 ]

[ "<|reference_start|> {Adversarial Optimization for Dictionary Attacks on Speaker Verification: In this paper, we assess vulnerability of speaker verification systems to dictionary attacks. We seek master voices, i.e., adversarial utterances optimized to match against a large number of users by pure chance. First, we perform menagerie analysis to identify utterances which intrinsically hold this property. Then, we propose an adversarial optimization approach for generating master voices synthetically. Our experiments show that, even in the most secure configuration, on average, a master voice can match approx. 20% of females and 10% of males without any knowledge about the population. We demonstrate that dictionary attacks should be considered as a feasible threat model for sensitive and high-stakes deployments of speaker verification. <|reference_end|>", "<|reference_start|> Speaker recognition based on deep learning: An overview: <|reference_end|>", "<|reference_start|> Generalized End-to-End Loss for Speaker Verification: In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection. With these properties, our model with the new loss function decreases speaker verification EER by more than 10%, while reducing the training time by 60% at the same time. We also introduce the MultiReader technique, which allows us to do domain adaptation - training a more accurate model that supports multiple keywords (i.e. \"OK Google\" and \"Hey Google\") as well as multiple dialects. <|reference_end|>", "<|reference_start|> Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems: Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown that finger-vein- and fingerprint-based authentication methods are susceptible to wolf attacks, in which a wolf sample matches many enrolled user templates. In this work, we demonstrated that wolf (generic) faces, which we call \"master faces,\" can also compromise face recognition systems and that the master face concept can be generalized in some cases. Motivated by recent similar work in the fingerprint domain, we generated high-quality master faces by using the state-of-the-art face generator StyleGAN in a process called latent variable evolution. Experiments demonstrated that even attackers with limited resources using only pre-trained models available on the Internet can initiate master face attacks. The results, in addition to demonstrating performance from the attacker's point of view, can also be used to clarify and improve the performance of face recognition systems and harden face authentication systems. <|reference_end|>" ]

[ 21, 23, 35, 58 ]

[ "<|reference_start|> Algorithmic Aspects of Dominator Colorings in Graphs: <|reference_end|>", "<|reference_start|> On some domination colorings of graphs: <|reference_end|>", "<|reference_start|> On some domination colorings of graphs: <|reference_end|>", "<|reference_start|> Dominator colorings and safe clique partitions: Given a graph G, the dominator coloring problem seeks a proper coloring of G with the additional property that every vertex in the graph dominates an entire color class. The safe clique partition problem seeks a partition of the vertices of a graph into cliques with the additional property that for each vertex v, there is a clique that has no element in the open neighborhood of v. We typically seek to minimize the number of color classes or cliques used, respectively. In this paper, we study these two problems and consider the relationship between them. <|reference_end|>" ]

[ 18, 21, 25, 27 ]

[ "<|reference_start|> Rate region of the quadratic gaussian ceo problem: In the so-called CEO problem, a hidden source random process is of interest to a central unit or the \"CEO\". But this process cannot be observed directly. L sensors or agents observe independently corrupted versions of the source. They encode their observations without cooperating with one another and send through rate constrained noiseless channels to the CEO. The problem was first studied by T. Berger et al. (1996) in the context of discrete memoryless sources. The quadratic Gaussian version of the problem was studied. The best result known to date is the characterization of the sum-rate when all the agents have the same quality of observations. Here we characterize the rate region for any number of agents without assuming that their quality of observations is the same. This is one of the few examples of multiterminal lossy source coding problems in which the rate region can be characterized completely. <|reference_end|>", "<|reference_start|> Secure Multiterminal Source Coding With Actions: This paper studies the secure multiterminal source coding problem with actions. In particular, one main encoder observes an independent and identically distributed (i.i.d.) source Xn and wishes to compress this source lossyly to the decoder. Another encoder observes the source Yn and wants to compress this source losslessly to the decoder. A passive eavesdropper having access to the side information Zn can observe the information bits sent by the main encoder. In this scenario, the decoder is allowed to choose actions affecting the correlated source Yn and the side information Zn. For this problem, we characterize the optimal rate-distortion-cost-leakage region for a discrete memoryless setting. <|reference_end|>", "<|reference_start|> The generalized stochastic likelihood decoder: Random coding and expurgated bounds: The likelihood decoder is a stochastic decoder that selects the decoded message at random, using the posterior distribution of the true underlying message given the channel output. In this paper, we study a generalized version of this decoder, where the posterior is proportional to a general function that depends only on the joint empirical distribution of the output vector and the code word. This framework allows both mismatched versions and universal versions of the likelihood decoder, as well as the corresponding ordinary deterministic decoders, among many others. We provide a direct analysis method that yields the exact random coding exponent (as opposed to separate upper bounds and lower bounds that turn out to be compatible, which were derived earlier by Scarlett et al.). We also extend the result from pure channel coding to combined source and channel coding (random binning followed by random channel coding) with side information available to the decoder. Finally, returning to pure channel coding, we derive also an expurgated exponent for the stochastic likelihood decoder, which turns out to be at least as tight (and in some cases, strictly so) as the classical expurgated exponent of the maximum likelihood decoder, even though the stochastic likelihood decoder is suboptimal. <|reference_end|>", "<|reference_start|> Quasi Linear Codes: Application to point-to-point and multi-terminal source coding: A new ensemble of structured codes is introduced. These codes are called Quasi Linear Codes (QLC). The QLC's are constructed by taking subsets of linear codes. They have a looser structure compared to linear codes and are not closed under addition. We argue that these codes provide gains in terms of achievable Rate-Distortions (RD) in different multi-terminal source coding problems. We derive the necessary covering bounds for analyzing the performance of QLC's. We then consider the Multiple-Descriptions (MD) problem, and prove through an example that the application of QLC's gives an improved achievable RD region for this problem. Finally, we derive an inner bound to the achievable RD region for the general MD problem which strictly contains all of the previous known achievable regions. <|reference_end|>" ]

[ 3, 11, 33, 34 ]

[ "<|reference_start|> Benchmarking and validation of cascading failure analysis tools: Cascading failure in electric power systems is a complicated problem for which a variety of models, software tools, and analytical tools have been proposed but are difficult to verify. Benchmarking and validation are necessary to understand how closely a particular modeling method corresponds to reality, what engineering conclusions may be drawn from a particular tool, and what improvements need to be made to the tool in order to reach valid conclusions. The community needs to develop the test cases tailored to cascading that are central to practical benchmarking and validation. In this paper, the IEEE PES working group on cascading failure reviews and synthesizes how benchmarking and validation can be done for cascading failure analysis, summarizes and reviews the cascading test cases that are available to the international community, and makes recommendations for improving the state of the art. <|reference_end|>", "<|reference_start|> A ``Random Chemistry'' Algorithm for Identifying Collections of Multiple Contingencies That Initiate Cascading Failure: This paper describes a stochastic “Random Chemistry” (RC) algorithm to identify large collections of multiple (n-k) contingencies that initiate large cascading failures in a simulated power system. The method requires only O(log (n)) simulations per contingency identified, which is orders of magnitude faster than random search of this combinatorial space. We applied the method to a model of cascading failure in a power network with n=2896 branches and identify 148243 unique, minimal n-k branch contingencies (2 ≤ k ≤ 5) that cause large cascades, many of which would be missed by using pre-contingency flows, linearized line outage distribution factors, or performance indices as screening factors. Within each n-k collection, the frequency with which individual branches appear follows a power-law (or nearly so) distribution, indicating that a relatively small number of components contribute disproportionately to system vulnerability. The paper discusses various ways that RC generated collections of dangerous contingencies could be used in power systems planning and operations. <|reference_end|>", "<|reference_start|> {A Multistage Game in Smart Grid Security: A Reinforcement Learning Solution: Existing smart grid security research investigates different attack techniques and cascading failures from the attackers’ viewpoints, while the defenders’ or the operators’ protection strategies are somehow neglected. Game theoretic methods are applied for the attacker–defender games in the smart grid security area. Yet, most of the existing works only use the one-shot game and do not consider the dynamic process of the electric power grid. In this paper, we propose a new solution for a multistage game (also called a dynamic game) between the attacker and the defender based on reinforcement learning to identify the optimal attack sequences given certain objectives (e.g., transmission line outages or generation loss). Different from a one-shot game, the attacker here learns a sequence of attack actions applying for the transmission lines and the defender protects a set of selected lines. After each time step, the cascading failure will be measured, and the line outage (and/or generation loss) will be used as the feedback for the attacker to generate the next action. The performance is evaluated on W&W 6-bus and IEEE 39-bus systems. A comparison between a multistage attack and a one-shot attack is conducted to show the significance of the multistage attack. Furthermore, different protection strategies are evaluated in simulation, which shows that the proposed reinforcement learning solution can identify optimal attack sequences under several attack objectives. It also indicates that attacker’s learned information helps the defender to enhance the security of the system. <|reference_end|>", "<|reference_start|> Robust optimization for transmission defense against multi-period attacks with uncertainties: <|reference_end|>" ]

[ 1, 5, 15, 16 ]

[ "<|reference_start|> Random Tensors and Planted Cliques: The r-parity tensor of a graph is a generalization of the adjacency matrix, where the tensor's entries denote the parity of the number of edges in subgraphs induced by r distinct vertices. For r=2, it is the adjacency matrix with 1's for edges and -1's for nonedges. It is well-known that the 2-norm of the adjacency matrix of a random graph is O(\\sqrt{n}). Here we show that the 2-norm of the r-parity tensor is at most f(r)\\sqrt{n}\\log^{O(r)}n, answering a question of Frieze and Kannan who proved this for r=3. As a consequence, we get a tight connection between the planted clique problem and the problem of finding a vector that approximates the 2-norm of the r-parity tensor of a random graph. Our proof method is based on an inductive application of concentration of measure. <|reference_end|>", "<|reference_start|> Stochastic Block Models and Reconstruction: The planted partition model (also known as the stochastic blockmodel) is a classical cluster-exhibiting random graph model that has been extensively studied in statistics, physics, and computer science. In its simplest form, the planted partition model is a model for random graphs on $n$ nodes with two equal-sized clusters, with an between-class edge probability of $q$ and a within-class edge probability of $p$. Although most of the literature on this model has focused on the case of increasing degrees (ie.\\ $pn, qn \\to \\infty$ as $n \\to \\infty$), the sparse case $p, q = O(1/n)$ is interesting both from a mathematical and an applied point of view. \nA striking conjecture of Decelle, Krzkala, Moore and Zdeborov\\'a based on deep, non-rigorous ideas from statistical physics gave a precise prediction for the algorithmic threshold of clustering in the sparse planted partition model. In particular, if $p = a/n$ and $q = b/n$, then Decelle et al.\\ conjectured that it is possible to cluster in a way correlated with the true partition if $(a - b)^2 > 2(a + b)$, and impossible if $(a - b)^2 C (a + b)$ for some sufficiently large $C$. \nWe prove half of their prediction, showing that it is indeed impossible to cluster if $(a - b)^2 2(a + b)$. Following Decelle et al, our work establishes a rigorous connection between the clustering problem, spin-glass models on the Bethe lattice and the so called reconstruction problem. This connection points to fascinating applications and open problems. <|reference_end|>", "<|reference_start|> Stochastic Variational Inference: We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets. <|reference_end|>", "<|reference_start|> Scalable inference of overlapping communities: We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel (MMSB). It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60,000 nodes. It converges several orders of magnitude faster than the state-of-the-art algorithm for MMSB, finds hundreds of communities in large real-world networks, and detects the true communities in 280 benchmark networks with equal or better accuracy compared to other scalable algorithms. <|reference_end|>" ]

[ 2, 4, 5, 7 ]

[ "<|reference_start|> Efficient Kernel UCB for Contextual Bandits: In this paper, we tackle the computational efficiency of kernelized UCB algorithms in contextual bandits. While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems. Specifically, our method relies on incremental Nystrom approximations of the joint kernel embedding of contexts and actions. This allows us to achieve a complexity of O(CTm^2) where m is the number of Nystrom points. To recover the same regret as the standard kernelized UCB algorithm, m needs to be of order of the effective dimension of the problem, which is at most O(\\sqrt(T)) and nearly constant in some cases. <|reference_end|>", "<|reference_start|> A Survey on Causal Inference: Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well known causal inference framework. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods. <|reference_end|>", "<|reference_start|> Causal Bandits: Learning Good Interventions via Causal Inference: We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm that exploits the causal feedback and prove a bound on its simple regret that is strictly better (in all quantities) than algorithms that do not use the additional causal information. <|reference_end|>", "<|reference_start|> Finite-Time Analysis of Kernelised Contextual Bandits: We tackle the problem of online reward maximisation over a large finite set of actions described by their contexts. We focus on the case when the number of actions is too big to sample all of them even once. However we assume that we have access to the similarities between actions' contexts and that the expected reward is an arbitrary linear function of the contexts' images in the related reproducing kernel Hilbert space (RKHS). We propose KernelUCB, a kernelised UCB algorithm, and give a cumulative regret bound through a frequentist analysis. For contextual bandits, the related algorithm GP-UCB turns out to be a special case of our algorithm, and our finite-time analysis improves the regret bound of GP-UCB for the agnostic case, both in the terms of the kernel-dependent quantity and the RKHS norm of the reward function. Moreover, for the linear kernel, our regret bound matches the lower bound for contextual linear bandits. <|reference_end|>" ]

[ 10, 13, 17, 32 ]

[ "<|reference_start|> The mathematics of cancer: integrating quantitative models: <|reference_end|>", "<|reference_start|> The dynamics of drug resistance: a mathematical perspective.: <|reference_end|>", "<|reference_start|> Mathematical modeling as a tool for planning anticancer therapy.: <|reference_end|>", "<|reference_start|> Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer: With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among individual patients’ parameter values, particularly those relating to treatment response; thus, exploiting the use of these various metrics for model calibration can be helpful to infer such patient-specific parameters both accurately and early, so that treatment protocols can be adjusted mid-course for maximum efficacy. However, taking measurements can be costly and invasive, limiting clinicians to a sparse collection schedule. As such, the determination of optimal times and metrics for which to collect data in order to best inform proper treatment protocols could be of great assistance to clinicians. In this investigation, we employ a Bayesian information-theoretic calibration protocol for experimental design in order to identify the optimal times at which to collect data for informing treatment parameters. Within this procedure, data collection times are chosen sequentially to maximize the reduction in parameter uncertainty with each added measurement, ensuring that a budget of n high-fidelity experimental measurements results in maximum information gain about the low-fidelity model parameter values. In addition to investigating the optimal temporal pattern for data collection, we also develop a framework for deciding which metrics should be utilized at each data collection point. We illustrate this framework with a variety of toy examples, each utilizing a radiotherapy treatment regimen. For each scenario, we analyze the dependence of the predictive power of the low-fidelity model upon the measurement budget. <|reference_end|>" ]

[ 0, 1, 2, 12 ]

[ "<|reference_start|> NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction: In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature fusing at every layer from different tasks. This is in contrast with the most widely used MTL CNN structures which empirically or heuristically share features on some specific layers (e.g., share all the features except the last convolutional layer). The proposed layerwise feature fusing scheme is formulated by combining existing CNN components in a novel way, with clear mathematical interpretability as discriminative dimensionality reduction, which is referred to as Neural Discriminative Dimensionality Reduction (NDDR). Specifically, we first concatenate features with the same spatial resolution from different tasks according to their channel dimension. Then, we show that the discriminative dimensionality reduction can be fulfilled by 1x1 Convolution, Batch Normalization, and Weight Decay in one CNN. The use of existing CNN components ensures the end-to-end training and the extensibility of the proposed NDDR layer to various state-of-the-art CNN architectures in a \"plug-and-play\" manner. The detailed ablation analysis shows that the proposed NDDR layer is easy to train and also robust to different hyperparameters. Experiments on different task sets with various base network architectures demonstrate the promising performance and desirable generalizability of our proposed method. The code of our paper is available at https://github.com/ethanygao/NDDR-CNN. <|reference_end|>", "<|reference_start|> SSD: Single Shot MultiBox Detector: We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For $300\\times 300$ input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for $500\\times 500$ input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model. Code is available at https://github.com/weiliu89/caffe/tree/ssd . <|reference_end|>", "<|reference_start|> End-to-End Multi-Task Learning with Attention: We propose a novel multi-task learning architecture, which allows learning of task-specific feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with a soft-attention module for each task. These modules allow for learning of task-specific features from the global features, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be trained end-to-end and can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. We evaluate our approach on a variety of datasets, across both image-to-image predictions and image classification tasks. We show that our architecture is state-of-the-art in multi-task learning compared to existing methods, and is also less sensitive to various weighting schemes in the multi-task loss function. Code is available at https://github.com/lorenmt/mtan. <|reference_end|>", "<|reference_start|> 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): <|reference_end|>" ]

[ 7, 12, 18, 32 ]

[ "<|reference_start|> Edinburgh Research Explorer Computational effects and operations: an overview: We overview a programme to provide a unified semantics for computational effects based upon the notion of a countable enriched Lawvere theory. We define the notion of countable enriched Lawvere theory, show how the various leading examples of computational effects, except for continuations, give rise to them, and we compare the definition with that of a strong monad. We outline how one may use the notion to model three natural ways in which to combine computational effects: by their sum, by their commutative combination, and by distributivity. We also outline a unified account of operational semantics. We present results we have already shown, some partial results, and our plans for further development of the programme. <|reference_end|>", "<|reference_start|> Tensor topology: <|reference_end|>", "<|reference_start|> Space in Monoidal Categories: The category of Hilbert modules may be interpreted as a naive quantum field theory over a base space. Open subsets of the base space are recovered as idempotent subunits, which form a meet-semilattice in any firm braided monoidal category. There is an operation of restriction to an idempotent subunit: it is a graded monad on the category, and has the universal property of algebraic localisation. Spacetime structure on the base space induces a closure operator on the idempotent subunits. Restriction is then interpreted as spacetime propagation. This lets us study relativistic quantum information theory using methods entirely internal to monoidal categories. As a proof of concept, we show that quantum teleportation is only successfully supported on the intersection of Alice and Bob's causal future. <|reference_end|>", "<|reference_start|> Tensor-restriction categories: Restriction categories were established to handle maps that are partially defined with respect to composition. Tensor topology realises that monoidal categories have an intrinsic notion of space, and deals with objects and maps that are partially defined with respect to this spatial structure. We introduce a construction that turns a firm monoidal category into a restriction category and axiomatise the monoidal restriction categories that arise this way, called tensor-restriction categories. <|reference_end|>" ]

[ 0, 11, 13, 14 ]

[ "<|reference_start|> Network Properties in Transitions of Consciousness during Propofol-induced Sedation: <|reference_end|>", "<|reference_start|> Natural speech reveals the semantic maps that tile human cerebral cortex: <|reference_end|>", "<|reference_start|> Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials: Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for any domain-specific knowledge or calibration data. We report across subject mean accuracy of approximately 80% (chance being 8.3%) and show this is substantially better than current state-of-the-art hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we analyze our Compact-CNN to examine the underlying feature representation, discovering that the deep learner extracts additional phase and amplitude related features associated with the structure of the dataset. We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex. <|reference_end|>", "<|reference_start|> Soft Computing-Based EEG Classification by Optimal Feature Selection and Neural Networks: Brain computer interface translates electroencephalogram (EEG) signals into control commands so that paralyzed people can control assistive devices. This human thought translation is a very challenging process as EEG signals contain noise. For noise removal, a bandpass filter or a filter bank is used. However, these techniques also remove useful information from the signal. Furthermore, after feature extraction, there are such features which do not play any significant role in effective classification. Thus, soft computing-based EEG classification followed by extraction and then selection of optimal features can produce better results. In this paper, subband common spatial patterns using sequential backward floating selection is being proposed in order to classify motor-imagery-based EEG signals. The signal is decomposed into subband using a filter bank having overlapped frequency cutoffs. Linear discriminant analysis followed by common spatial pattern is applied to the output of each filter for features extraction. Then, sequential backward floating selection is applied for selection of optimal features to train radial basis function neural networks. Two different datasets have been used for evaluation of results, i.e., Open BCI dataset and EEG signals acquired by Emotiv Epoc. The proposed system shows an overall accuracy of 93.05% and 85.00% for both datasets, respectively. The results show that the proposed optimal feature selection and neural network-based classification approach with overlapped frequency bands is an effective method for EEG classification as compared to previous techniques. <|reference_end|>" ]

[ 9, 12, 26, 27 ]

[ "<|reference_start|> Politeness Transfer: A Tag and Generate Approach: This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. We also provide a dataset of more than 1.39 instances automatically labeled for politeness to encourage benchmark evaluations on this new task. We design a tag and generate pipeline that identifies stylistic attributes and subsequently generates a sentence in the target style while preserving most of the source content. For politeness as well as five other transfer tasks, our model outperforms the state-of-the-art methods on automatic metrics for content preservation, with a comparable or better performance on style transfer accuracy. Additionally, our model surpasses existing methods on human evaluations for grammaticality, meaning preservation and transfer accuracy across all the six style transfer tasks. The data and code is located at https://github.com/tag-and-generate. <|reference_end|>", "<|reference_start|> Deep Learning for Text Style Transfer: A Survey: Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_Survey <|reference_end|>", "<|reference_start|> More than reflections: empathy in motivational interviewing includes language style synchrony between therapist and client.: <|reference_end|>", "<|reference_start|> Counseling-style reflection generation using generative pretrained transformers with augmented context: We introduce a counseling dialogue system that seeks to assist counselors while they are learning and refining their counseling skills. The system generates counselors’reflections – i.e., responses that reflect back on what the client has said given the dialogue history. Our method builds upon the new generative pretrained transformer architecture and enhances it with context augmentation techniques inspired by traditional strategies used during counselor training. Through a set of comparative experiments, we show that the system that incorporates these strategies performs better in the reflection generation task than a system that is just fine-tuned with counseling conversations. To confirm our findings, we present a human evaluation study that shows that our system generates naturally-looking reflections that are also stylistically and grammatically correct. <|reference_end|>" ]

[ 8, 9, 13, 18 ]

[ "<|reference_start|> Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study: Large language models (LLMs) have demonstrated a powerful ability to answer various queries as a general-purpose assistant. The continuous multi-modal large language models (MLLM) empower LLMs with the ability to perceive visual signals. The launch of GPT-4 (Generative Pre-trained Transformers) has generated significant interest in the research communities. GPT-4V(ison) has demonstrated significant power in both academia and industry fields, as a focal point in a new artificial intelligence generation. Though significant success was achieved by GPT-4V, exploring MLLMs in domain-specific analysis (e.g., marine analysis) that required domain-specific knowledge and expertise has gained less attention. In this study, we carry out the preliminary and comprehensive case study of utilizing GPT-4V for marine analysis. This report conducts a systematic evaluation of existing GPT-4V, assessing the performance of GPT-4V on marine research and also setting a new standard for future developments in MLLMs. The experimental results of GPT-4V show that the responses generated by GPT-4V are still far away from satisfying the domain-specific requirements of the marine professions. All images and prompts used in this study will be available at https://github.com/hkust-vgd/Marine_GPT-4V_Eval <|reference_end|>", "<|reference_start|> SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer: There has been growing interest in parameter-efficient methods to apply pre-trained language models to downstream tasks. Building on the Prompt Tuning approach of Lester et al. (2021), which learns task-specific soft prompts to condition a frozen pre-trained model to perform different tasks, we propose a novel prompt-based transfer learning approach called SPoT: Soft Prompt Transfer. SPoT first learns a prompt on one or more source tasks and then uses it to initialize the prompt for a target task. We show that SPoT significantly boosts the performance of Prompt Tuning across many tasks. More remarkably, across all model sizes, SPoT matches or outperforms standard Model Tuning (which fine-tunes all model parameters) on the SuperGLUE benchmark, while using up to 27,000x fewer task-specific parameters. To understand where SPoT is most effective, we conduct a large-scale study on task transferability with 26 NLP tasks in 160 combinations, and demonstrate that many tasks can benefit each other via prompt transfer. Finally, we propose an efficient retrieval approach that interprets task prompts as task embeddings to identify similar tasks and predict the most transferable source tasks for a novel target task. <|reference_end|>", "<|reference_start|> ClipCap: CLIP Prefix for Image Captioning: Image captioning is a fundamental task in vision-language understanding, where the model predicts a textual informative caption to a given input image. In this paper, we present a simple approach to address this task. We use CLIP encoding as a prefix to the caption, by employing a simple mapping network, and then fine-tunes a language model to generate the image captions. The recently proposed CLIP model contains rich semantic features which were trained with textual context, making it best for vision-language perception. Our key idea is that together with a pre-trained language model (GPT2), we obtain a wide understanding of both visual and textual data. Hence, our approach only requires rather quick training to produce a competent captioning model. Without additional annotations or pre-training, it efficiently generates meaningful captions for large-scale and diverse datasets. Surprisingly, our method works well even when only the mapping network is trained, while both CLIP and the language model remain frozen, allowing a lighter architecture with less trainable parameters. Through quantitative evaluation, we demonstrate our model achieves comparable results to state-of-the-art methods on the challenging Conceptual Captions and nocaps datasets, while it is simpler, faster, and lighter. Our code is available in https://github.com/rmokady/CLIP_prefix_caption. <|reference_end|>", "<|reference_start|> mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration: Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily focus on enhancing multi-modal capabilities. In this work, we introduce a versatile multi-modal large language model, mPLUG-Owl2, which effectively leverages modality collaboration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design, with the language decoder acting as a universal interface for managing different modalities. Specifically, mPLUG-Owl2 incorporates shared functional modules to facilitate modality collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experiments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks and achieving state-of-the-art performances with a single generic model. Notably, mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios, setting a pioneering path in the development of future multi-modal foundation models. <|reference_end|>" ]

[ 12, 28, 31, 39 ]