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
2403.18085	ANOCA: AC Network-aware Optimal Curtailment Approach for Dynamic Hosting Capacity	With exponential growth in distributed energy resources (DERs) coupled with at-capacity distribution grid infrastructure, prosumers cannot always export all extra power to the grid without violating technical limits. Consequently, a slew of dynamic hosting capacity (DHC) algorithms have emerged for optimal utilization ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	441,752
1902.10297	Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks	We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positive and negative examples from a regular language, and ask if there is a simple decoding function that maps states of this RNN to states of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	122,641
1806.02964	BSN: Boundary Sensitive Network for Temporal Action Proposal Generation	Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content. This problem requires methods not only generating proposals with precise temporal bo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,895
2210.14085	Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19	This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work \cite{spira2021} constructed a dataset of respiratory insufficiency COVID-19 patient utterances and analyzed it by means of a convolutional neural network achieving an acc...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	326,421
1306.2003	Comparing Edge Detection Methods based on Stochastic Entropies and Distances for PolSAR Imagery	Polarimetric synthetic aperture radar (PolSAR) has achieved a prominent position as a remote imaging method. However, PolSAR images are contaminated by speckle noise due to the coherent illumination employed during the data acquisition. This noise provides a granular aspect to the image, making its processing and analy...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	25,090
2111.04105	DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering	Machine learning models based on sensitive data in the real-world promise advances in areas ranging from medical screening to disease outbreaks, agriculture, industry, defense science, and more. In many applications, learning participant communication rounds benefit from collecting their own private data sets, teaching...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,388
2406.04481	Optimizing Autonomous Driving for Safety: A Human-Centric Approach with LLM-Enhanced RLHF	Reinforcement Learning from Human Feedback (RLHF) is popular in large language models (LLMs), whereas traditional Reinforcement Learning (RL) often falls short. Current autonomous driving methods typically utilize either human feedback in machine learning, including RL, or LLMs. Most feedback guides the car agent's lea...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	461,696
2107.11678	Deep-learning-driven Reliable Single-pixel Imaging with Uncertainty Approximation	Single-pixel imaging (SPI) has the advantages of high-speed acquisition over a broad wavelength range and system compactness, which are difficult to achieve by conventional imaging sensors. However, a common challenge is low image quality arising from undersampling. Deep learning (DL) is an emerging and powerful tool i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,659
1407.0166	Simultaneous Wireless Information and Power Transfer for Two-hop OFDM Relay System	This paper investigates the simultaneous wireless information and power transfer (SWIPT) for two-hop orthogonal frequency division multiplexing (OFDM) decode-and-forward (DF) relay communication system, where a relay harvests energy from radio frequency signals transmitted by the source and then uses the harvested ener...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,307
1903.03659	A highly parallel multilevel Newton-Krylov-Schwarz method with subspace-based coarsening and partition-based balancing for the multigroup neutron transport equations on 3D unstructured meshes	The multigroup neutron transport equations have been widely used to study the motion of neutrons and their interactions with the background materials. Numerical simulation of the multigroup neutron transport equations is computationally challenging because the equations is defined on a high dimensional phase space (1D ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	123,780
2008.00097	Backpropagation through Signal Temporal Logic Specifications: Infusing Logical Structure into Gradient-Based Methods	This paper presents a technique, named STLCG, to compute the quantitative semantics of Signal Temporal Logic (STL) formulas using computation graphs. STLCG provides a platform which enables the incorporation of logical specifications into robotics problems that benefit from gradient-based solutions. Specifically, STL i...	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	189,896
1901.10550	Personalized Treatment Selection using Causal Heterogeneity	Randomized experimentation (also known as A/B testing or bucket testing) is widely used in the internet industry to measure the metric impact obtained by different treatment variants. A/B tests identify the treatment variant showing the best performance, which then becomes the chosen or selected treatment for the entir...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,045
2402.03038	Automatic Combination of Sample Selection Strategies for Few-Shot Learning	In few-shot learning, such as meta-learning, few-shot fine-tuning or in-context learning, the limited number of samples used to train a model have a significant impact on the overall success. Although a large number of sample selection strategies exist, their impact on the performance of few-shot learning is not extens...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	426,833
1707.06122	Twitter Activity Timeline as a Signature of Urban Neighborhood	Modern cities are complex systems, evolving at a fast pace. Thus, many urban planning, political, and economic decisions require a deep and up-to-date understanding of the local context of urban neighborhoods. This study shows that the structure of openly available social media records, such as Twitter, offers a possib...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	77,354
2011.08683	Fisher Information of a Family of Generalized Normal Distributions	In this brief note we compute the Fisher information of a family of generalized normal distributions. Fisher information is usually defined for regular distributions, i.e. continuously differentiable (log) density functions whose support does not depend on the family parameter $\theta$. Although the uniform distributio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	206,958
1801.04686	Hierarchical Coding for Distributed Computing	Coding for distributed computing supports low-latency computation by relieving the burden of straggling workers. While most existing works assume a simple master-worker model, we consider a hierarchical computational structure consisting of groups of workers, motivated by the need to reflect the architectures of real-w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	88,327
0802.2451	Capacity of General Discrete Noiseless Channels	This paper concerns the capacity of the discrete noiseless channel introduced by Shannon. A sufficient condition is given for the capacity to be well-defined. For a general discrete noiseless channel allowing non-integer valued symbol weights, it is shown that the capacity--if well-defined--can be determined from the r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,304
2004.06772	Channel Hardening in Massive MIMO: Model Parameters and Experimental Assessment	Reliability is becoming increasingly important for many applications envisioned for future wireless systems. A technology that could improve reliability in these systems is massive MIMO (Multiple-Input Multiple-Output). One reason for this is a phenomenon called channel hardening, which means that as the number of ante...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	172,596
2205.06127	Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks	A fundamental problem in adversarial machine learning is to quantify how much training data is needed in the presence of evasion attacks. In this paper we address this issue within the framework of PAC learning, focusing on the class of decision lists. Given that distributional assumptions are essential in the adversar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,145
2308.04792	NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain	Intelligent autonomous path planning is essential for enhancing the exploration efficiency of mobile robots operating in uneven terrains like planetary surfaces and off-road environments.In this paper, we propose the NNPP model for computing the heuristic region, enabling foundation algorithms like Astar to find the op...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	384,560
2501.08312	Everybody Likes to Sleep: A Computer-Assisted Comparison of Object Naming Data from 30 Languages	Object naming - the act of identifying an object with a word or a phrase - is a fundamental skill in interpersonal communication, relevant to many disciplines, such as psycholinguistics, cognitive linguistics, or language and vision research. Object naming datasets, which consist of concept lists with picture pairings,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	524,712
2407.07890	Training on the Test Task Confounds Evaluation and Emergence	We study a fundamental problem in the evaluation of large language models that we call training on the test task. Unlike wrongful practices like training on the test data, leakage, or data contamination, training on the test task is not a malpractice. Rather, the term describes a growing set of practices that utilize k...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	471,939
1511.09209	Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks	We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN). The fine-grained image classification problem is characterised by large intra-class variations and small inter-class variations. To overcome these problems our proposed MixDCNN ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,647
2012.02224	Personality-Driven Gaze Animation with Conditional Generative Adversarial Networks	We present a generative adversarial learning approach to synthesize gaze behavior of a given personality. We train the model using an existing data set that comprises eye-tracking data and personality traits of 42 participants performing an everyday task. Given the values of Big-Five personality traits (openness, consc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	209,691
2502.06599	Joint parameter and state estimation for regularized time-discrete multibody dynamics	We develop a method for offline parameter estimation of discrete multibody dynamics with regularized and frictional kinematic constraints. This setting leads to unobserved degrees of freedom, which we handle using joint state and parameter estimation. Our method finds the states and parameters as the solution to a nonl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	532,146
2211.11593	Investigating methods to improve photovoltaic thermal models at second-to-minute timescales	This paper presents a range of methods to improve the accuracy of equation-based thermal models of PV modules at second-to-minute timescales. We present an RC-equivalent conceptual model for PV modules, where wind effects are captured. We show how the thermal time constant $\tau$ of PV modules can be determined from me...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	331,798
1911.12249	Literature Review of Action Recognition in the Wild	The literature review presented below on Action Recognition in the wild is the in-depth study of Research Papers. Action Recognition problem in the untrimmed videos is a challenging task and most of the papers have tackled this problem using hand-crafted features with shallow learning techniques and sophisticated end-t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	155,346
2102.09109	Understanding and Creating Art with AI: Review and Outlook	Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art, motivates us to examine and discuss the creative and explorative...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	220,667
1711.08752	A Survey on Network Embedding	Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	85,261
1908.07613	Implications of Quantum Computing for Artificial Intelligence alignment research	We explain some key features of quantum computing via three heuristics and apply them to argue that a deep understanding of quantum computing is unlikely to be helpful to address current bottlenecks in Artificial Intelligence Alignment. Our argument relies on the claims that Quantum Computing leads to compute overhang ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	142,324
1711.00609	Security Against Impersonation Attacks in Distributed Systems	In a multi-agent system, transitioning from a centralized to a distributed decision-making strategy can introduce vulnerability to adversarial manipulation. We study the potential for adversarial manipulation in a class of graphical coordination games where the adversary can pose as a friendly agent in the game, thereb...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	true	false	false	true	83,748
2405.18507	Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction	In the complex landscape of hematologic samples such as peripheral blood or bone marrow derived from flow cytometry (FC) data, cell-level prediction presents profound challenges. This work explores injecting hierarchical prior knowledge into graph neural networks (GNNs) for single-cell multi-class classification of tab...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	458,450
2210.17505	Space-Fluid Adaptive Sampling by Self-Organisation	A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	false	true	327,716
2402.08472	Large Language Models for the Automated Analysis of Optimization Algorithms	The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajector...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	429,111
2302.06556	VA-DepthNet: A Variational Approach to Single Image Depth Prediction	We introduce VA-DepthNet, a simple, effective, and accurate deep neural network approach for the single-image depth prediction (SIDP) problem. The proposed approach advocates using classical first-order variational constraints for this problem. While state-of-the-art deep neural network methods for SIDP learn the scene...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	345,445
2102.04209	Guilty Artificial Minds	The concepts of blameworthiness and wrongness are of fundamental importance in human moral life. But to what extent are humans disposed to blame artificially intelligent agents, and to what extent will they judge their actions to be morally wrong? To make progress on these questions, we adopted two novel strategies. Fi...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	219,026
1107.0045	Graduality in Argumentation	Argumentation is based on the exchange and valuation of interacting arguments, followed by the selection of the most acceptable of them (for example, in order to take a decision, to make a choice). Starting from the framework proposed by Dung in 1995, our purpose is to introduce 'graduality' in the selection of the bes...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	11,116
2307.02002	Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication	Unmanned aerial vehicles (UAVs) have gained popularity due to their flexible mobility, on-demand deployment, and the ability to establish high probability line-of-sight wireless communication. As a result, UAVs have been extensively used as aerial base stations (ABSs) to supplement ground-based cellular networks for va...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	377,546
1902.10280	A New Algorithm for Improved Blind Detection of Polar Coded PDCCH in 5G New Radio	In recent release of the new cellular standard known as 5G New Radio (5G-NR), the physical downlink control channel (PDCCH) has adopted polar codes for error protection. Similar to 4G-LTE, each active user equipment (UE) must blindly detect its own PDCCH in the downlink search space. This work investigates new ways to ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	122,636
2311.07766	Vision-Language Integration in Multimodal Video Transformers (Partially) Aligns with the Brain	Integrating information from multiple modalities is arguably one of the essential prerequisites for grounding artificial intelligence systems with an understanding of the real world. Recent advances in video transformers that jointly learn from vision, text, and sound over time have made some progress toward this goal,...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	407,460
2411.15130	Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots	Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	510,452
2304.04640	NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems	Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with co...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	357,297
1905.13132	Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs	Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information. Many news articles describe the occurrence of specific events and named entities including people, places or objects. In this paper, we propose a graph traversal...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	133,001
2109.02385	Printed Texts Tracking and Following for a Finger-Wearable Electro-Braille System Through Opto-electrotactile Feedback	This paper presents our recent development on a portable and refreshable text reading and sensory substitution system for the blind or visually impaired (BVI), called Finger-eye. The system mainly consists of an opto-text processing unit and a compact electro-tactile based display that can deliver text-related electric...	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	253,731
2409.16073	Open-World Object Detection with Instance Representation Learning	While humans naturally identify novel objects and understand their relationships, deep learning-based object detectors struggle to detect and relate objects that are not observed during training. To overcome this issue, Open World Object Detection(OWOD) has been introduced to enable models to detect unknown objects in ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	491,193
2412.04078	Mind the Gap: Towards Generalizable Autonomous Penetration Testing via Domain Randomization and Meta-Reinforcement Learning	With increasing numbers of vulnerabilities exposed on the internet, autonomous penetration testing (pentesting) has emerged as a promising research area. Reinforcement learning (RL) is a natural fit for studying this topic. However, two key challenges limit the applicability of RL-based autonomous pentesting in real-wo...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	514,249
1108.4548	Ant Colony Optimization of Rough Set for HV Bushings Fault Detection	Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	11,780
2405.02809	Does Optimal Control Always Benefit from Better Prediction? An Analysis Framework for Predictive Optimal Control	The ``prediction + optimal control'' scheme has shown good performance in many applications of automotive, traffic, robot, and building control. In practice, the prediction results are simply considered correct in the optimal control design process. However, in reality, these predictions may never be perfect. Under a c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	451,930
cs/0306006	Experience with the Open Source based implementation for ATLAS Conditions Data Management System	Conditions Data in high energy physics experiments is frequently seen as every data needed for reconstruction besides the event data itself. This includes all sorts of slowly evolving data like detector alignment, calibration and robustness, and data from detector control system. Also, every Conditions Data Object is a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	537,860
1812.10119	Sequence to Sequence Learning for Query Expansion	Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering's. We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets. Starting from open datasets, we built a Query Expansi...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	117,296
2105.06284	Ergodic Capacity of High Throughput Satellite Systems With Mixed FSO-RF Transmission	We study a high throughput satellite system, where the feeder link uses free-space optical (FSO) and the user link uses radio frequency (RF) communication. In particular, we first propose a transmit diversity using Alamouti space time block coding to mitigate the atmospheric turbulence in the feeder link. Then, based o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	235,077
2103.00167	Inferring Unobserved Events in Systems With Shared Resources and Queues	To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	222,172
1107.3199	Performance Guarantee under Longest-Queue-First Schedule in Wireless Networks	Efficient link scheduling in a wireless network is challenging. Typical optimal algorithms require solving an NP-hard sub-problem. To meet the challenge, one stream of research focuses on finding simpler sub-optimal algorithms that have low complexity but high efficiency in practice. In this paper, we study the perform...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	11,320
2403.08222	Robust Decision Aggregation with Adversarial Experts	We consider a robust aggregation problem in the presence of both truthful and adversarial experts. The truthful experts will report their private signals truthfully, while the adversarial experts can report arbitrarily. We assume experts are marginally symmetric in the sense that they share the same common prior and ma...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	437,239
2403.08013	Supervised Time Series Classification for Anomaly Detection in Subsea Engineering	Time series classification is of significant importance in monitoring structural systems. In this work, we investigate the use of supervised machine learning classification algorithms on simulated data based on a physical system with two states: Intact and Broken. We provide a comprehensive discussion of the preprocess...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	437,140
2004.01770	Software Engineering For Automated Game Design	As we develop more assistive and automated game design systems, the question of how these systems should be integrated into game development workflows, and how much adaptation may be required, becomes increasingly important. In this paper we explore the impact of software engineering decisions on the ability of an auto...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	171,003
2205.14764	6N-DoF Pose Tracking for Tensegrity Robots	Tensegrity robots, which are composed of compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, low weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	299,480
1904.04875	Non-Lambertian Surface Shape and Reflectance Reconstruction Using Concentric Multi-Spectral Light Field	Recovering the shape and reflectance of non-Lambertian surfaces remains a challenging problem in computer vision since the view-dependent appearance invalidates traditional photo-consistency constraint. In this paper, we introduce a novel concentric multi-spectral light field (CMSLF) design that is able to recover the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,148
2303.03614	A Fast Insertion Operator for Ridesharing over Time-Dependent Road Networks	Ridesharing has become a promising travel mode recently due to the economic and social benefits. As an essential operator, "insertion operator" has been extensively studied over static road networks. When a new request appears, the insertion operator is used to find the optimal positions of a worker's current route to ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	349,790
2309.15668	A New Centralized Multi-Node Repair Scheme of MSR codes with Error-Correcting Capability	Minimum storage regenerating (MSR) codes, with the MDS property and the optimal repair bandwidth, are widely used in distributed storage systems (DSS) for data recovery. In this paper, we consider the construction of $(n,k,l)$ MSR codes in the centralized model that can repair $h$ failed nodes simultaneously with $e$ o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	395,063
2105.07464	Few-NERD: A Few-Shot Named Entity Recognition Dataset	Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empiric...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	235,439
2208.09708	DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization	Efficiently deploying deep neural networks on low-resource edge devices is challenging due to their ever-increasing resource requirements. To address this issue, researchers have proposed multiplication-free neural networks, such as Power-of-Two quantization, or also known as Shift networks, which aim to reduce memory ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	313,807
2111.10653	Real-time Human Detection Model for Edge Devices	Building a small-sized fast surveillance system model to fit on limited resource devices is a challenging, yet an important task. Convolutional Neural Networks (CNNs) have replaced traditional feature extraction and machine learning models in detection and classification tasks. Various complex large CNN models are prop...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	267,401
1207.1765	Object Recognition with Multi-Scale Pyramidal Pooling Networks	We present a Multi-Scale Pyramidal Pooling Network, featuring a novel pyramidal pooling layer at multiple scales and a novel encoding layer. Thanks to the former the network does not require all images of a given classification task to be of equal size. The encoding layer improves generalisation performance in comparis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	17,330
0812.2301	Cooperative Hybrid ARQ Protocols: Unified Frameworks for Protocol Analysis	Cooperative hybrid-ARQ (HARQ) protocols, which can exploit the spatial and temporal diversities, have been widely studied. The efficiency of cooperative HARQ protocols is higher than that of cooperative protocols, because retransmissions are only performed when necessary. We classify cooperative HARQ protocols as three...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,781
2407.19174	Reducing Spurious Correlation for Federated Domain Generalization	The rapid development of multimedia has provided a large amount of data with different distributions for visual tasks, forming different domains. Federated Learning (FL) can efficiently use this diverse data distributed on different client media in a decentralized manner through model sharing. However, in open-world sc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,673
2211.14311	Sub-1ms Instinctual Interference Adaptive GaN LNA Front-End with Power and Linearity Tuning	One of the major challenges in communication, radar, and electronic warfare receivers arises from nearby device interference. The paper presents a 2-6 GHz GaN LNA front-end with onboard sensing, processing, and feedback utilizing microcontroller-based controls to achieve adaptation to a variety of interference scenario...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	332,783
2302.08947	Learning from Label Proportion with Online Pseudo-Label Decision by Regret Minimization	This paper proposes a novel and efficient method for Learning from Label Proportions (LLP), whose goal is to train a classifier only by using the class label proportions of instance sets, called bags. We propose a novel LLP method based on an online pseudo-labeling method with regret minimization. As opposed to the pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,241
2204.04109	Fast metric embedding into the Hamming cube	We consider the problem of embedding a subset of $\mathbb{R}^n$ into a low-dimensional Hamming cube in an almost isometric way. We construct a simple, data-oblivious, and computationally efficient map that achieves this task with high probability: we first apply a specific structured random matrix, which we call the do...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	290,538
2112.08099	Encoding Individual Source Sequences for the Wiretap Channel	We consider the problem of encoding a deterministic source sequence (a.k.a.\ individual sequence) for the degraded wiretap channel by means of an encoder and decoder that can both be implemented as finite--state machines. Our first main result is a necessary condition for both reliable and secure transmission in terms ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	271,695
2501.08566	Towards Lightweight and Stable Zero-shot TTS with Self-distilled Representation Disentanglement	Zero-shot Text-To-Speech (TTS) synthesis shows great promise for personalized voice customization through voice cloning. However, current methods for achieving zero-shot TTS heavily rely on large model scales and extensive training datasets to ensure satisfactory performance and generalizability across various speakers...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	524,818
1912.10508	Direct and Indirect Effects -- An Information Theoretic Perspective	Information theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g. neuroscience and climate science) domains. While these causal measures are desirable in that they are model agnostic and can capture non-linear interactions, t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	158,337
2405.20772	Reinforcement Learning for Sociohydrology	In this study, we discuss how reinforcement learning (RL) provides an effective and efficient framework for solving sociohydrology problems. The efficacy of RL for these types of problems is evident because of its ability to update policies in an iterative manner - something that is also foundational to sociohydrology,...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	459,512
1406.1827	Recursive Neural Networks Can Learn Logical Semantics	Tree-structured recursive neural networks (TreeRNNs) for sentence meaning have been successful for many applications, but it remains an open question whether the fixed-length representations that they learn can support tasks as demanding as logical deduction. We pursue this question by evaluating whether two such model...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	33,677
1910.04810	Variational Path Optimization of Linear Pentapods with a Simple Singularity Variety	The class of linear pentapods with a simple singularity variety is obtained by imposing architectural restrictions on the design in such a way that the manipulators singularity variety is linear in orientation position variables. It turns out that such simplification leads to crucial computational advantages while main...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	148,865
2106.03530	CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System	Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained lan...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	239,354
2404.03704	Improvement of Performance in Freezing of Gait detection in Parkinsons Disease using Transformer networks and a single waist worn triaxial accelerometer	Freezing of gait (FOG) is one of the most incapacitating symptoms in Parkinsons disease, affecting more than 50 percent of patients in advanced stages of the disease. The presence of FOG may lead to falls and a loss of independence with a consequent reduction in the quality of life. Wearable technology and artificial i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	444,374
2007.07675	3D Polarized Modulation: System Analysis and Performance	In this paper we present a novel modulation technique for dual polarization communication systems, which reduces the error rate compared with the existent schemes. This modulation places the symbols in a 3D constellation, rather than the classic approach of 2D. Adjusting the phase of these symbols depending on the info...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	187,401
2106.07030	The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware	The capabilities of natural neural systems have inspired new generations of machine learning algorithms as well as neuromorphic very large-scale integrated (VLSI) circuits capable of fast, low-power information processing. However, it has been argued that most modern machine learning algorithms are not neurophysiologic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	240,739
2301.06957	FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks	Recent Anomaly Detection techniques have progressed the field considerably but at the cost of increasingly complex training pipelines. Such techniques require large amounts of training data, resulting in computationally expensive algorithms that are unsuitable for settings where only a small amount of normal samples ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,791
1908.01241	Robust Max Entrywise Error Bounds for Tensor Estimation from Sparse Observations via Similarity Based Collaborative Filtering	Consider the task of estimating a 3-order $n \times n \times n$ tensor from noisy observations of randomly chosen entries in the sparse regime. We introduce a similarity based collaborative filtering algorithm for estimating a tensor from sparse observations and argue that it achieves sample complexity that nearly matc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	140,707
2012.08131	Deep Layout of Custom-size Furniture through Multiple-domain Learning	In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene. This model is aimed to support professional interior designers to produce interior decoration solutions with custom-size furniture more quickly. The proposed model combines a deep layout module, a domai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,670
2304.06833	Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective	In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature considers integrating the estimation and optimization processes by selecting model parameters that lead to the best empirical objective performan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	358,121
2307.02055	Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impact	This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. However, very powerful and pre-trained CNN models working very accurately on image datasets for...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	377,572
2105.09880	DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single Camera	Existing multi-camera solutions for automatic scorekeeping in steel-tip darts are very expensive and thus inaccessible to most players. Motivated to develop a more accessible low-cost solution, we present a new approach to keypoint detection and apply it to predict dart scores from a single image taken from any camera ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	236,192
2112.05267	The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)	The portrayal of negative emotions such as anger can vary widely between cultures and contexts, depending on the acceptability of expressing full-blown emotions rather than suppression to maintain harmony. The majority of emotional datasets collect data under the broad label ``anger", but social signals can range from ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	270,789
2111.09074	Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection	Feature selection is an intractable problem, therefore practical algorithms often trade off the solution accuracy against the computation time. In this paper, we propose a novel multi-stage feature selection framework utilizing multiple levels of approximations, or surrogates. Such a framework allows for using wrapper ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	266,897
2306.04848	Interpreting and Improving Diffusion Models from an Optimization Perspective	Denoising is intuitively related to projection. Indeed, under the manifold hypothesis, adding random noise is approximately equivalent to orthogonal perturbation. Hence, learning to denoise is approximately learning to project. In this paper, we use this observation to interpret denoising diffusion models as approximat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	371,930
1909.04305	Inverse Ising inference from high-temperature re-weighting of observations	Maximum Likelihood Estimation (MLE) is the bread and butter of system inference for stochastic systems. In some generality, MLE will converge to the correct model in the infinite data limit. In the context of physical approaches to system inference, such as Boltzmann machines, MLE requires the arduous computation of pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,763
2103.05985	Multi-Pretext Attention Network for Few-shot Learning with Self-supervision	Few-shot learning is an interesting and challenging study, which enables machines to learn from few samples like humans. Existing studies rarely exploit auxiliary information from large amount of unlabeled data. Self-supervised learning is emerged as an efficient method to utilize unlabeled data. Existing self-supervis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	224,155
1703.07500	Can Attackers with Limited Information Exploit Historical Data to Mount Successful False Data Injection Attacks on Power Systems?	This paper studies physical consequences of unobservable false data injection (FDI) attacks designed only with information inside a sub-network of the power system. The goal of this attack is to overload a chosen target line without being detected via measurements. To overcome the limited information, a multiple linear...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	70,408
2502.01191	Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model	Concept Bottleneck Models (CBMs) aim to enhance interpretability by predicting human-understandable concepts as intermediates for decision-making. However, these models often face challenges in ensuring reliable concept representations, which can propagate to downstream tasks and undermine robustness, especially under ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	529,743
2012.09159	DECOR-GAN: 3D Shape Detailization by Conditional Refinement	We introduce a deep generative network for 3D shape detailization, akin to stylization with the style being geometric details. We address the challenge of creating large varieties of high-resolution and detailed 3D geometry from a small set of exemplars by treating the problem as that of geometric detail transfer. Give...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	211,980
2207.02923	A Local Optimization Framework for Multi-Objective Ergodic Search	Robots have the potential to perform search for a variety of applications under different scenarios. Our work is motivated by humanitarian assistant and disaster relief (HADR) where often it is critical to find signs of life in the presence of conflicting criteria, objectives, and information. We believe ergodic search...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	306,658
1910.12243	Solving Optimization Problems through Fully Convolutional Networks: an Application to the Travelling Salesman Problem	In the new wave of artificial intelligence, deep learning is impacting various industries. As a closely related area, optimization algorithms greatly contribute to the development of deep learning. But the reverse applications are still insufficient. Is there any efficient way to solve certain optimization problem thro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	151,015
1904.12658	MSDC-Net: Multi-Scale Dense and Contextual Networks for Automated Disparity Map for Stereo Matching	Disparity prediction from stereo images is essential to computer vision applications including autonomous driving, 3D model reconstruction, and object detection. To predict accurate disparity map, we propose a novel deep learning architecture for detectingthe disparity map from a rectified pair of stereo images, called...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,189
2011.11761	A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network	This work addresses the inverse identification of apparent elastic properties of random heterogeneous materials using machine learning based on artificial neural networks. The proposed neural network-based identification method requires the construction of a database from which an artificial neural network can be train...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	207,932
2404.02148	Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models	Recent advancements in 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models. These models are pretrained on Internet-scale image data and fine-tuned on massive 3D data, offering the capability of producing highly consistent multi-view images. However, due to the scarcity of synch...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,738
1503.07026	A new model-free design for vehicle control and its validation through an advanced simulation platform	A new model-free setting and the corresponding "intelligent" P and PD controllers are employed for the longitudinal and lateral motions of a vehicle. This new approach has been developed and used in order to ensure simultaneously a best profile tracking for the longitudinal and lateral behaviors. The longitudinal speed...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	41,430
2101.05500	Joint Dimensionality Reduction for Separable Embedding Estimation	Low-dimensional embeddings for data from disparate sources play critical roles in multi-modal machine learning, multimedia information retrieval, and bioinformatics. In this paper, we propose a supervised dimensionality reduction method that learns linear embeddings jointly for two feature vectors representing data of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,448

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