Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2409.10889	Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes	Real-time deepfake, a type of generative AI, is capable of "creating" non-existing contents (e.g., swapping one's face with another) in a video. It has been, very unfortunately, misused to produce deepfake videos (during web conferences, video calls, and identity authentication) for malicious purposes, including financ...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	488,916
2006.08658	ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation	While fully-supervised deep learning yields good models for urban scene semantic segmentation, these models struggle to generalize to new environments with different lighting or weather conditions for instance. In addition, producing the extensive pixel-level annotations that the task requires comes at a great cost. Un...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	182,250
2212.07563	Explainable Machine Learning for Hydrocarbon Prospect Risking	Hydrocarbon prospect risking is a critical application in geophysics predicting well outcomes from a variety of data including geological, geophysical, and other information modalities. Traditional routines require interpreters to go through a long process to arrive at the probability of success of specific outcomes. A...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	336,440
2408.09786	Cross-composition Feature Disentanglement for Compositional Zero-shot Learning	Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the feature divergence of an attribute (resp. object) when combined with different objects (resp. attributes), it is challenging to learn disentangled...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,589
2010.15217	Away from Trolley Problems and Toward Risk Management	As automated vehicles receive more attention from the media, there has been an equivalent increase in the coverage of the ethical choices a vehicle may be forced to make in certain crash situations with no clear safe outcome. Much of this coverage has focused on a philosophical thought experiment known as the "trolley ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	203,694
1512.06682	$K$ Users Caching Two Files: An Improved Achievable Rate	Caching is an approach to smoothen the variability of traffic over time. Recently it has been proved that the local memories at the users can be exploited for reducing the peak traffic in a much more efficient way than previously believed. In this work we improve upon the existing results and introduce a novel caching ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	50,343
1511.01214	Quantification of observed prior and likelihood information in parametric Bayesian modeling	Two data-dependent information metrics are developed to quantify the information of the prior and likelihood functions within a parametric Bayesian model, one of which is closely related to the reference priors from Berger, Bernardo, and Sun, and information measure introduced by Lindley. A combination of theoretical, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,480
2311.15000	Satellite-based feature extraction and multivariate time-series prediction of biotoxin contamination in shellfish	Shellfish production constitutes an important sector for the economy of many Portuguese coastal regions, yet the challenge of shellfish biotoxin contamination poses both public health concerns and significant economic risks. Thus, predicting shellfish contamination levels holds great potential for enhancing production ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,344
2007.13231	CoV-ABM: A stochastic discrete-event agent-based framework to simulate spatiotemporal dynamics of COVID-19	The paper develops a stochastic Agent-Based Model (ABM) mimicking the spread of infectious diseases in geographical domains. The model is designed to simulate the spatiotemporal spread of SARS-CoV2 disease, known as COVID-19. Our SARS-CoV2-based ABM framework (CoV-ABM) simulates the spread at any geographical scale, ra...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	189,060
2012.09501	A Hierarchical Feature Constraint to Camouflage Medical Adversarial Attacks	Deep neural networks (DNNs) for medical images are extremely vulnerable to adversarial examples (AEs), which poses security concerns on clinical decision making. Luckily, medical AEs are also easy to detect in hierarchical feature space per our study herein. To better understand this phenomenon, we thoroughly investiga...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,097
2311.07349	Vehicle-to-grid for car sharing -- A simulation study for 2030	The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these systems can play a pivotal role in bolstering grid stability through the provision of ancillary services via vehicle-to-grid (V2G) technologie...	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	407,291
2112.13650	Multiagent Transition Systems for Composing Fault-Resilient Protocol Stacks	We present a novel mathematical framework for the specification and analysis of fault-resilient distributed protocols and their implementations, with the following components: 1. Transition systems that allow the specification and analysis of computations with safety and liveness faults and their fault resilience. 2. N...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	273,320
2310.04007	Robust Safety for Mixed-Autonomy Traffic with Delays and Disturbances	Various control strategies and field experiments have been designed for connected and automated vehicles (CAVs) to stabilize mixed traffic that contains both CAVs and Human-driven Vehicles (HVs). The effect of these stabilizing CAV control strategies on traffic safety is still under investigation. In an effort to prior...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	397,507
1707.03334	Recommendation with k-anonymized Ratings	Recommender systems are widely used to predict personalized preferences of goods or services using users' past activities, such as item ratings or purchase histories. If collections of such personal activities were made publicly available, they could be used to personalize a diverse range of services, including targete...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	76,845
2403.13129	Better Call SAL: Towards Learning to Segment Anything in Lidar	We propose the SAL (Segment Anything in Lidar) method consisting of a text-promptable zero-shot model for segmenting and classifying any object in Lidar, and a pseudo-labeling engine that facilitates model training without manual supervision. While the established paradigm for Lidar Panoptic Segmentation (LPS) relies o...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	439,484
2404.09316	Numerical Discretization Methods for the Extended Linear Quadratic Control Problem	In this study, we introduce numerical methods for discretizing continuous-time linear-quadratic optimal control problems (LQ-OCPs). The discretization of continuous-time LQ-OCPs is formulated into differential equation systems, and we can obtain the discrete equivalent by solving these systems. We present the ordinary ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	446,622
1809.03994	Efficient Road Lane Marking Detection with Deep Learning	Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge to reduce system complexity and maintain high accuracy at the same time. In this paper, we propose a Lane Marking Detector (LMD) using a dee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,445
2105.14380	Transmission Delay Minimization via Joint Power Control and Caching in Wireless HetNets	A fundamental challenge in wireless heterogeneous networks (HetNets) is to effectively utilize the limited transmission and storage resources in the presence of increasing deployment density and backhaul capacity constraints. To alleviate bottlenecks and reduce resource consumption, we design optimal caching and power ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	237,649
2304.13394	Robust One-Step Estimation of Impulsive Time Series	The paper deals with the estimation of a signal model in the form of the output of a continuous linear time-invariant system driven by a sequence of instantaneous impulses, i.e. an impulsive time series. This modeling concept arises in, e.g., endocrinology when episodic hormone secretion events and elimination rates ar...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	360,561
2410.20533	Guiding Through Complexity: What Makes Good Supervision for Hard Reasoning Tasks?	How can "weak teacher models" such as average human annotators or existing AI systems, effectively supervise LLMs to improve performance on hard reasoning tasks, especially those that challenge and requires expertise or daily practice from the teacher models? In this paper, we seek for empirical answers to this questio...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	502,851
2404.19475	TwinDiffusion: Enhancing Coherence and Efficiency in Panoramic Image Generation with Diffusion Models	Diffusion models have emerged as effective tools for generating diverse and high-quality content. However, their capability in high-resolution image generation, particularly for panoramic images, still faces challenges such as visible seams and incoherent transitions. In this paper, we propose TwinDiffusion, an optimiz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,657
2203.02576	Machine Learning Simulates Agent-Based Model Towards Policy	Public Policies are not intrinsically positive or negative. Rather, policies provide varying levels of effects across different recipients. Methodologically, computational modeling enables the application of multiple influences on empirical data, thus allowing for heterogeneous response to policies. We use a random for...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	283,785
1206.3286	New Techniques for Algorithm Portfolio Design	We present and evaluate new techniques for designing algorithm portfolios. In our view, the problem has both a scheduling aspect and a machine learning aspect. Prior work has largely addressed one of the two aspects in isolation. Building on recent work on the scheduling aspect of the problem, we present a technique th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	16,543
2412.15973	Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support	We present Legommenders, a unique library designed for content-based recommendation that enables the joint training of content encoders alongside behavior and interaction modules, thereby facilitating the seamless integration of content understanding directly into the recommendation pipeline. Legommenders allows resear...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	519,325
1105.5451	The Automatic Inference of State Invariants in TIM	As planning is applied to larger and richer domains the effort involved in constructing domain descriptions increases and becomes a significant burden on the human application designer. If general planners are to be applied successfully to large and complex domains it is necessary to provide the domain designer with so...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,527
1210.3634	Quick Summary	Quick Summary is an innovate implementation of an automatic document summarizer that inputs a document in the English language and evaluates each sentence. The scanner or evaluator determines criteria based on its grammatical structure and place in the paragraph. The program then asks the user to specify the number of ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	19,092
2308.04798	Enhancing Mobile Privacy and Security: A Face Skin Patch-Based Anti-Spoofing Approach	As Facial Recognition System(FRS) is widely applied in areas such as access control and mobile payments due to its convenience and high accuracy. The security of facial recognition is also highly regarded. The Face anti-spoofing system(FAS) for face recognition is an important component used to enhance the security of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,563
2302.07612	Towards Optimal Compression: Joint Pruning and Quantization	Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware. The prevailing methods for network compression, namely quantization and pruning, have been shown to enhance efficiency at the cost of performance. Determining the most effective quantization and pruning strat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,784
cs/0508095	Capacity of Ultra Wide Band Wireless Ad Hoc Networks	Throughput capacity is a critical parameter for the design and evaluation of ad-hoc wireless networks. Consider n identical randomly located nodes, on a unit area, forming an ad-hoc wireless network. Assuming a fixed per node transmission capability of T bits per second at a fixed range, it has been shown that the unif...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	538,905
2312.14502	ViStripformer: A Token-Efficient Transformer for Versatile Video Restoration	Video restoration is a low-level vision task that seeks to restore clean, sharp videos from quality-degraded frames. One would use the temporal information from adjacent frames to make video restoration successful. Recently, the success of the Transformer has raised awareness in the computer-vision community. However, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	417,664
2205.15941	Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images	In recent years, 3D convolutional neural networks have become the dominant approach for volumetric medical image segmentation. However, compared to their 2D counterparts, 3D networks introduce substantially more training parameters and higher requirement for the GPU memory. This has become a major limiting factor for d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	299,925
2401.13197	Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques	Mitral Transcatheter Edge-to-Edge Repair (mTEER) is a medical procedure utilized for the treatment of mitral valve disorders. However, predicting the outcome of the procedure poses a significant challenge. This paper makes the first attempt to harness classical machine learning (ML) and deep learning (DL) techniques fo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,642
2212.10562	Character-Aware Models Improve Visual Text Rendering	Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word's visual makeup as a series of glyphs. To quantify this effect, we cond...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	337,528
1911.09572	Automatically Generating Macro Research Reports from a Piece of News	Automatically generating macro research reports from economic news is an important yet challenging task. As we all know, it requires the macro analysts to write such reports within a short period of time after the important economic news are released. This motivates our work, i.e., using AI techniques to save manual co...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	154,560
2403.05811	Statistical Efficiency of Distributional Temporal Difference Learning and Freedman's Inequality in Hilbert Spaces	Distributional reinforcement learning (DRL) has achieved empirical success in various domains. One core task in DRL is distributional policy evaluation, which involves estimating the return distribution $\eta^\pi$ for a given policy $\pi$. Distributional temporal difference learning has been accordingly proposed, which...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	436,169
2502.02195	EFKAN: A KAN-Integrated Neural Operator For Efficient Magnetotelluric Forward Modeling	Magnetotelluric (MT) forward modeling is fundamental for improving the accuracy and efficiency of MT inversion. Neural operators (NOs) have been effectively used for rapid MT forward modeling, demonstrating their promising performance in solving the MT forward modeling-related partial differential equations (PDEs). Par...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,213
1907.05632	Laplacian-regularized graph bandits: Algorithms and theoretical analysis	We consider a stochastic linear bandit problem with multiple users, where the relationship between users is captured by an underlying graph and user preferences are represented as smooth signals on the graph. We introduce a novel bandit algorithm where the smoothness prior is imposed via the random-walk graph Laplacian...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,416
1305.0842	Time Invariant Error Bounds for Modified-CS based Sparse Signal Sequence Recovery	In this work, we obtain performance guarantees for modified-CS and for its improved version, modified-CS-Add-LS-Del, for recursive reconstruction of a time sequence of sparse signals from a reduced set of noisy measurements available at each time. Under mild assumptions, we show that the support recovery error of both ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,378
1806.05382	PCAS: Pruning Channels with Attention Statistics for Deep Network Compression	Compression techniques for deep neural networks are important for implementing them on small embedded devices. In particular, channel-pruning is a useful technique for realizing compact networks. However, many conventional methods require manual setting of compression ratios in each layer. It is difficult to analyze th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	100,456
2409.01465	Terminal Soft Landing Guidance Law Using Analytic Gravity Turn Trajectory	This paper presents an innovative terminal landing guidance law that utilizes an analytic solution derived from the gravity turn trajectory. The characteristics of the derived solution are thoroughly investigated, and the solution is employed to generate a reference velocity vector that satisfies terminal landing condi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	485,344
2209.10489	Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data	The process of obtaining high-resolution images from single or multiple low-resolution images of the same scene is of great interest for real-world image and signal processing applications. This study is about exploring the potential usage of deep learning based image super-resolution algorithms on thermal data for pro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	318,880
2204.11326	Beyond the Quadratic Approximation: the Multiscale Structure of Neural Network Loss Landscapes	A quadratic approximation of neural network loss landscapes has been extensively used to study the optimization process of these networks. Though, it usually holds in a very small neighborhood of the minimum, it cannot explain many phenomena observed during the optimization process. In this work, we study the structure...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,106
2311.00676	Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games	Algorithms based on regret matching, specifically regret matching$^+$ (RM$^+$), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice. Unlike algorithms such as optimistic gradient descent ascent, which have strong last-iterate and ergodic convergence properties ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	404,732
2401.11394	Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset	In this paper, we propose leveraging causal generative learning as an interpretable tool for explaining image classifiers. Specifically, we present a generative counterfactual inference approach to study the influence of visual features (i.e., pixels) as well as causal factors through generative learning. To this end, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	422,979
2007.00197	Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions	We develop an algorithm to improve the performance of a pre-trained model under concept shift without retraining the model from scratch when only unannotated samples of initial concepts are accessible. We model this problem as a domain adaptation problem, where the source domain data is inaccessible during model adapta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,041
2010.13121	FAPE: a Constraint-based Planner for Generative and Hierarchical Temporal Planning	Temporal planning offers numerous advantages when based on an expressive representation. Timelines have been known to provide the required expressiveness but at the cost of search efficiency. We propose here a temporal planner, called FAPE, which supports many of the expressive temporal features of the ANML modeling la...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	203,015
2305.05154	Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation	Weakly supervised semantic segmentation (WSSS) models relying on class activation maps (CAMs) have achieved desirable performance comparing to the non-CAMs-based counterparts. However, to guarantee WSSS task feasible, we need to generate pseudo labels by expanding the seeds from CAMs which is complex and time-consuming...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,030
1907.10421	A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training	A scalable graphical method is presented for selecting, and partitioning datasets for the training phase of a classification task. For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself. This step is proceeded by construction of an information gra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	139,620
2405.16411	Tensor Attention Training: Provably Efficient Learning of Higher-order Transformers	Tensor Attention, a multi-view attention that is able to capture high-order correlations among multiple modalities, can overcome the representational limitations of classical matrix attention. However, the $O(n^3)$ time complexity of tensor attention poses a significant obstacle to its utilization in transformers, wher...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	457,406
2003.00790	Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS -- a collection of Technical Notes Part 2	This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	166,420
1907.01979	Toward Real-Time Wireless Control of Mobile Platforms for Future Industrial Systems	The use of mobile platforms (MPs) is particularly attractive for various industrial applications. This demonstration highlights the importance of remote control of MPs and shows its viability over a high-performance wireless solution designed for closed-loop control. Further, it shows the viability of formation control...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	137,489
2405.10743	Occupancy-SLAM: Simultaneously Optimizing Robot Poses and Continuous Occupancy Map	In this paper, we propose an optimization based SLAM approach to simultaneously optimize the robot trajectory and the occupancy map using 2D laser scans (and odometry) information. The key novelty is that the robot poses and the occupancy map are optimized together, which is significantly different from existing occupa...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	454,874
2401.12513	Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR	Purpose: The capacity to isolate and recognize individual characters from facsimile images of papyrus manuscripts yields rich opportunities for digital analysis. For this reason the `ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri' was held as part of the 17th International Conference on ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	423,411
1805.05654	The Essential Guide to Realizing 5G-Connected UAVs with Massive MIMO	What will it take for drones -- and the whole associated ecosystem -- to take off? Arguably, infallible command and control (C&C) channels for safe and autonomous flying, and high-throughput links for multi-purpose live video streaming. And indeed, meeting these aspirations may entail a full cellular support, provided ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,471
1909.11022	Reservoir Topology in Deep Echo State Networks	Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	146,690
2006.12830	Extension of Direct Feedback Alignment to Convolutional and Recurrent Neural Network for Bio-plausible Deep Learning	Throughout this paper, we focus on the improvement of the direct feedback alignment (DFA) algorithm and extend the usage of the DFA to convolutional and recurrent neural networks (CNNs and RNNs). Even though the DFA algorithm is biologically plausible and has a potential of high-speed training, it has not been consider...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	183,725
1111.7108	Joint Relay and Jammer Selection for Secure Two-Way Relay Networks	In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with the constraints of physical layer security. Specifically, the proposed algorithms select two or three intermediate nodes to enhance securi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,243
2103.00083	Flexible Model Aggregation for Quantile Regression	Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being overly reductive. For instance, epidemiological forecasts, cost estimates, and revenue predictions all benefit from being able to quantify the r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,140
2005.03059	CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image	Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	176,039
1804.07000	Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences	Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individual...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	95,425
2210.10625	HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding	Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturing hierarchical relations. To this end, we present a novel framework that introduces hyperbolic embed...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	324,992
2201.06776	Pruning-aware Sparse Regularization for Network Pruning	Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy. To reduce the degradation of performance after pruning, many methods utilize the loss with sparse regularization to produce str...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	275,828
2010.04891	Online Optimal Control with Affine Constraints	This paper considers online optimal control with affine constraints on the states and actions under linear dynamics with bounded random disturbances. The system dynamics and constraints are assumed to be known and time-invariant but the convex stage cost functions change adversarially. To solve this problem, we propose...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	199,905
2501.15659	AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability	Inertial odometry (IO) using only Inertial Measurement Units (IMUs) offers a lightweight and cost-effective solution for Unmanned Aerial Vehicle (UAV) applications, yet existing learning-based IO models often fail to generalize to UAVs due to the highly dynamic and non-linear-flight patterns that differ from pedestrian...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	527,654
2402.13518	RITFIS: Robust input testing framework for LLMs-based intelligent software	The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testing. Current testing methods focus solely on the robustness of LLM-based software to prompts. Given the complexity and diversity of real-world...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	431,284
2306.15498	Using Large Language Models to Provide Explanatory Feedback to Human Tutors	Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning. However, providing learners real-time explanatory feedback often presents challenges related to classification accuracy, particularly in domain-specific environments, cont...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	376,039
2009.09687	Contrastive Clustering	In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected in...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,659
1705.02973	Community Detection in Hypergraphs, Spiked Tensor Models, and Sum-of-Squares	We study the problem of community detection in hypergraphs under a stochastic block model. Similarly to how the stochastic block model in graphs suggests studying spiked random matrices, our model motivates investigating statistical and computational limits of exact recovery in a certain spiked tensor model. In contras...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	73,099
1707.09602	Robust stability conditions for feedback interconnections of distributed-parameter negative imaginary systems	Sufficient and necessary conditions for the stability of positive feedback interconnections of negative imaginary systems are derived via an integral quadratic constraint (IQC) approach. The IQC framework accommodates distributed-parameter systems with irrational transfer function representations, while generalising ex...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	78,034
1905.01562	A Similarity Measure for Material Appearance	We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced ex...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	129,748
1507.02444	Non-Asymptotic Achievable Rates for Energy-Harvesting Channels using Save-and-Transmit	This paper investigates the information-theoretic limits of energy-harvesting (EH) channels in the finite blocklength regime. The EH process is characterized by a sequence of i.i.d. random variables with finite variances. We use the save-and-transmit strategy proposed by Ozel and Ulukus (2012) together with Shannon's n...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,987
1705.05940	Subregular Complexity and Deep Learning	This paper argues that the judicial use of formal language theory and grammatical inference are invaluable tools in understanding how deep neural networks can and cannot represent and learn long-term dependencies in temporal sequences. Learning experiments were conducted with two types of Recurrent Neural Networks (RNN...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	73,569
2109.14151	Multi-frame Joint Enhancement for Early Interlaced Videos	Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality. Although the high-definition reconstruction technology for early videos has made great progress in recent years, related research on deinterlacing is still lacking. Traditio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	257,868
2308.11223	LDP-Feat: Image Features with Local Differential Privacy	Modern computer vision services often require users to share raw feature descriptors with an untrusted server. This presents an inherent privacy risk, as raw descriptors may be used to recover the source images from which they were extracted. To address this issue, researchers recently proposed privatizing image featur...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	387,054
1705.03875	Coded convolution for parallel and distributed computing within a deadline	We consider the problem of computing the convolution of two long vectors using parallel processing units in the presence of "stragglers". Stragglers refer to the small fraction of faulty or slow processors that delays the entire computation in time-critical distributed systems. We first show that splitting the vectors ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,246
1603.05200	Multi-Vehicle Collision Avoidance via Hamilton-Jacobi Reachability and Mixed Integer Programming	Multi-agent differential games are important and useful tools for analyzing many practical problems. With the recent surge of interest in using UAVs for civil purposes, the importance and urgency of developing tractable multi-agent analysis techniques that provide safety and performance guarantees is at an all-time hig...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	53,332
1402.4645	A Survey on Semi-Supervised Learning Techniques	Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,983
2204.13061	Can deep learning match the efficiency of human visual long-term memory in storing object details?	Humans have a remarkably large capacity to store detailed visual information in long-term memory even after a single exposure, as demonstrated by classic experiments in psychology. For example, Standing (1973) showed that humans could recognize with high accuracy thousands of pictures that they had seen only once a few...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	293,692
1702.08656	Stepping Forward with Exoskeletons: Team IHMC's Design and Approach in the 2016 Cybathlon	Exoskeletons are a promising technology that enables individuals with mobility limitations to walk again. As the 2016 Cybathlon illustrated, however, the community has a considerable way to go before exoskeletons have the necessary capabilities to be incorporated into daily life. While most exoskeletons power only hip ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	69,039
1711.04001	Automated Migration of Hierarchical Data to Relational Tables using Programming-by-Example	While many applications export data in hierarchical formats like XML and JSON, it is often necessary to convert such hierarchical documents to a relational representation. This paper presents a novel programming-by-example approach, and its implementation in a tool called Mitra, for automatically migrating tree-structu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	84,308
2406.14983	Hierarchical thematic classification of major conference proceedings	In this paper, we develop a decision support system for the hierarchical text classification. We consider text collections with a fixed hierarchical structure of topics given by experts in the form of a tree. The system sorts the topics by relevance to a given document. The experts choose one of the most relevant topic...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	466,569
2312.02682	H-GAP: Humanoid Control with a Generalist Planner	Humanoid control is an important research challenge offering avenues for integration into human-centric infrastructures and enabling physics-driven humanoid animations. The daunting challenges in this field stem from the difficulty of optimizing in high-dimensional action spaces and the instability introduced by the bi...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	412,971
1709.10203	On the Approximation of Toeplitz Operators for Nonparametric $\mathcal{H}_\infty$-norm Estimation	Given a stable SISO LTI system $G$, we investigate the problem of estimating the $\mathcal{H}_\infty$-norm of $G$, denoted $\|\|G\|\|_\infty$, when $G$ is only accessible via noisy observations. Wahlberg et al. recently proposed a nonparametric algorithm based on the power method for estimating the top eigenvalue of a matr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	81,742
2304.09870	Heterogeneous-Agent Reinforcement Learning	The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research. However, many research endeavours heavily rely on parameter sharing among agents, which confines them to only homogeneous-agent setting and leads to training instability and lac...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	359,209
1810.06665	Stop Illegal Comments: A Multi-Task Deep Learning Approach	Deep learning methods are often difficult to apply in the legal domain due to the large amount of labeled data required by deep learning methods. A recent new trend in the deep learning community is the application of multi-task models that enable single deep neural networks to perform more than one task at the same ti...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	110,472
cmp-lg/9806012	Bayesian Stratified Sampling to Assess Corpus Utility	This paper describes a method for asking statistical questions about a large text corpus. We exemplify the method by addressing the question, "What percentage of Federal Register documents are real documents, of possible interest to a text researcher or analyst?" We estimate an answer to this question by evaluating 200...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,886
2203.08650	Complexity Reduction of Learned In-Loop Filtering in Video Coding	In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output. Conventional in-loop filters are obtained by hand-crafted methods. Recently, learned filters based on convolutional neural networks that utilize attention mechanisms have...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,867
1305.6864	Resolution-aware network coded storage	In this paper, we show that coding can be used in storage area networks (SANs) to improve various quality of service metrics under normal SAN operating conditions, without requiring additional storage space. For our analysis, we develop a model which captures modern characteristics such as constrained I/O access bandwi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,857
2410.06030	Data Quality Issues in Vulnerability Detection Datasets	Vulnerability detection is a crucial yet challenging task to identify potential weaknesses in software for cyber security. Recently, deep learning (DL) has made great progress in automating the detection process. Due to the complex multi-layer structure and a large number of parameters, a DL model requires massive labe...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	496,022
0809.3140	Monadic Datalog over Finite Structures with Bounded Treewidth	Bounded treewidth and Monadic Second Order (MSO) logic have proved to be key concepts in establishing fixed-parameter tractability results. Indeed, by Courcelle's Theorem we know: Any property of finite structures, which is expressible by an MSO sentence, can be decided in linear time (data complexity) if the structure...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	2,369
1805.02730	Building Disease Detection Algorithms with Very Small Numbers of Positive Samples	Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive samples also create unbalanced datasets which limit the true positive rates of trained models. As unbalanced datasets are mostly unavoidabl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	96,904
2309.13914	Matrix Factorization in Tropical and Mixed Tropical-Linear Algebras	Matrix Factorization (MF) has found numerous applications in Machine Learning and Data Mining, including collaborative filtering recommendation systems, dimensionality reduction, data visualization, and community detection. Motivated by the recent successes of tropical algebra and geometry in machine learning, we inves...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,402
2202.12138	How reparametrization trick broke differentially-private text representation learning	As privacy gains traction in the NLP community, researchers have started adopting various approaches to privacy-preserving methods. One of the favorite privacy frameworks, differential privacy (DP), is perhaps the most compelling thanks to its fundamental theoretical guarantees. Despite the apparent simplicity of the g...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	282,120
2410.16710	Influential Language Data Selection via Gradient Trajectory Pursuit	Curating a desirable dataset for training has been the core of building highly capable large language models (Touvron et al., 2023; Achiam et al., 2023; Team et al.,2024). Gradient influence scores (Pruthi et al., 2020; Xia et al., 2024) are shown to be correlated with model performance and are commonly used as the cri...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	501,155
2105.02867	Age of Gossip in Networks with Community Structure	We consider a network consisting of a single source and $n$ receiver nodes that are grouped into $m$ equal size communities, i.e., clusters, where each cluster includes $k$ nodes and is served by a dedicated cluster head. The source node keeps versions of an observed process and updates each cluster through the associa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	233,957
2211.16001	A two-scale solver for linear elasticity problems in the context of parallel message passing	This paper pushes further the intrinsic capabilities of the GFEM$^{gl}$ global-local approach introduced initially in [1]. We develop a distributed computing approach using MPI (Message Passing Interface) both for the global and local problems. Regarding local problems, a specific scheduling strategy is introduced. The...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	333,481
2010.11126	Study of star clusters in the M83 galaxy with a convolutional neural network	We present a study of evolutionary and structural parameters of star cluster candidates in the spiral galaxy M83. For this we use a convolutional neural network trained on mock clusters and capable of fast identification and localization of star clusters, as well as inference of their parameters from multi-band images....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	202,131
1707.04352	Advances in Artificial Intelligence Require Progress Across all of Computer Science	Advances in Artificial Intelligence require progress across all of computer science.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	77,030
2301.11010	On the Optimal Beamwidth of UAV-Assisted Networks Operating at Millimeter Waves	The millimeter-wave (mm-wave) bands enable very large antenna arrays that can generate narrow beams for beamforming and spatial multiplexing. However, directionality introduces beam misalignment and leads to reduced energy efficiency. Thus, employing the narrowest possible beam in a cell may not necessarily imply maxim...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	341,993
2406.08697	Orthogonalized Estimation of Difference of $Q$-functions	Offline reinforcement learning is important in many settings with available observational data but the inability to deploy new policies online due to safety, cost, and other concerns. Many recent advances in causal inference and machine learning target estimation of causal contrast functions such as CATE, which is suff...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,586

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