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
2012.08906	Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design	Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	211,905
2403.11251	NeoNeXt: Novel neural network operator and architecture based on the patch-wise matrix multiplications	Most of the computer vision architectures nowadays are built upon the well-known foundation operations: fully-connected layers, convolutions and multi-head self-attention blocks. In this paper we propose a novel foundation operation - NeoCell - which learns matrix patterns and performs patchwise matrix multiplications ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,612
2403.12544	AffineQuant: Affine Transformation Quantization for Large Language Models	The significant resource requirements associated with Large-scale Language Models (LLMs) have generated considerable interest in the development of techniques aimed at compressing and accelerating neural networks. Among these techniques, Post-Training Quantization (PTQ) has emerged as a subject of considerable interest...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	439,238
2211.14853	Safety Envelope for Orthogonal Collocation Methods in Embedded Optimal Control	Orthogonal collocation methods are direct approaches for solving optimal control problems (OCP). A high solution accuracy is achieved with few optimization variables, making it more favorable for embedded and real-time NMPC applications. However, collocation approaches lack a guarantee about the safety of the resulting...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	333,022
1902.06228	Optimizing Online Matching for Ride-Sourcing Services with Multi-Agent Deep Reinforcement Learning	Ride-sourcing services are now reshaping the way people travel by effectively connecting drivers and passengers through mobile internets. Online matching between idle drivers and waiting passengers is one of the most key components in a ride-sourcing system. The average pickup distance or time is an important measureme...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	121,716
1805.10268	Extended Integrated Interleaved Codes over any Field with Applications to Locally Recoverable Codes	Integrated Interleaved (II) and Extended Integrated Interleaved (EII) codes are a versatile alternative for Locally Recoverable (LRC) codes, since they require fields of relatively small size. II and EII codes are generally defined over Reed-Solomon type of codes. A new comprehensive definition of EII codes is presente...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	98,628
2407.09248	Semantic UV mapping to improve texture inpainting for indoor scenes	This work aims to improve texture inpainting after clutter removal in scanned indoor meshes. This is achieved with a new UV mapping pre-processing step which leverages semantic information of indoor scenes to more accurately match the UV islands with the 3D representation of distinct structural elements like walls and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,506
2406.04521	The Gaussian Multiple Access Wiretap Channel with Selfish Transmitters: A Coalitional Game Theory Perspective	This paper considers the Gaussian multiple access wiretap channel (GMAC-WT) with selfish transmitters, i.e., who are each solely interested in maximizing their individual secrecy rate. The question then arises as to whether selfish transmitters can increase their individual secrecy rate by participating in a collective...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	461,715
2501.13111	iServe: An Intent-based Serving System for LLMs	Large Language Models (LLMs) are becoming ubiquitous across industries, where applications demand they fulfill diverse user intents. However, developers currently face the challenge of manually exploring numerous deployment configurations - combinations of parallelism and compression techniques that impact resource usa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	526,563
cs/0404041	NLOMJ--Natural Language Object Model in Java	In this paper we present NLOMJ--a natural language object model in Java with English as the experiment language. This modal describes the grammar elements of any permissible expression in a natural language and their complicated relations with each other with the concept "Object" in OOP(Object Oriented Programming). Di...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	538,160
2106.06951	Effects of Eavesdropper on the Performance of Mixed {\eta}-{\mu} and DGG Cooperative Relaying System	Free-space optical (FSO) channel offers line-of-sight wireless communication with high data rates and high secrecy utilizing unlicensed optical spectrum and also paves the way to the solution of the last-mile access problem. Since atmospheric turbulence is a hindrance to an enhanced secrecy performance, the mixed radio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	240,700
2312.11017	Information Inequalities via Ideas from Additive Combinatorics	Ruzsa's equivalence theorem provided a framework for converting certain families of inequalities in additive combinatorics to entropic inequalities (which sometimes did not possess stand-alone entropic proofs). In this work, we first establish formal equivalences between some families (different from Ruzsa) of inequali...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	416,413
2203.00893	FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry	To achieve accurate and robust pose estimation in Simultaneous Localization and Mapping (SLAM) task, multi-sensor fusion is proven to be an effective solution and thus provides great potential in robotic applications. This paper proposes FAST-LIVO, a fast LiDAR-Inertial-Visual Odometry system, which builds on two tight...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	283,166
1606.07239	Non Local Spatial and Angular Matching : Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising	Diffusion magnetic resonance imaging datasets suffer from low Signal-to-Noise Ratio, especially at high b-values. Acquiring data at high b-values contains relevant information and is now of great interest for microstructural and connectomics studies. High noise levels bias the measurements due to the non-Gaussian natur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	57,678
2406.05654	DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation	Retrieval-Augmented Generation (RAG) offers a promising solution to address various limitations of Large Language Models (LLMs), such as hallucination and difficulties in keeping up with real-time updates. This approach is particularly critical in expert and domain-specific applications where LLMs struggle to cover exp...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	462,233
cs/0211030	Integration of Computational Techniques for the Modelling of Signal Transduction	A cell can be seen as an adaptive autonomous agent or as a society of adaptive autonomous agents, where each can exhibit a particular behaviour depending on its cognitive capabilities. We present an intracellular signalling model obtained by integrating several computational techniques into an agent-based paradigm. Cel...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	537,729
1901.09885	Towards an Extremal Network Theory -- Robust GDoF Gain of Transmitter Cooperation over TIN	Significant progress has been made recently in Generalized Degrees of Freedom (GDoF) characterizations of wireless interference channels (IC) and broadcast channels (BC) under the assumption of finite precision channel state information at the transmitters (CSIT), especially for smaller or highly symmetric network sett...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	119,866
2302.10158	Sparse PCA Beyond Covariance Thresholding	In the Wishart model for sparse PCA we are given $n$ samples $Y_1,\ldots, Y_n$ drawn independently from a $d$-dimensional Gaussian distribution $N({0, Id + \beta vv^\top})$, where $\beta > 0$ and $v\in \mathbb{R}^d$ is a $k$-sparse unit vector, and we wish to recover $v$ (up to sign). We show that if $n \ge \Omega(d)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,690
2002.05925	SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data	Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorates when there exists a large domain shift between training and test data. To address this issue, we propose a new data augmentation approach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	164,042
2405.18497	Capacity Results for Non-Ergodic Multi-Modal Broadcast Channels with Controllable Statistics	Movable antennas and reconfigurable intelligent surfaces enable a new paradigm in which channel statistics can be controlled and altered. Further, the known trajectory and operation protocol of communication satellites results in networks with predictable statistics. The predictability of future changes results in a no...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	458,446
2212.01048	Empirical Asset Pricing via Ensemble Gaussian Process Regression	We introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lends itself to general ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,292
2212.00193	Distilling Reasoning Capabilities into Smaller Language Models	Step-by-step reasoning approaches like chain of thought (CoT) have proved to be very effective in inducing reasoning capabilities in large language models. However, the success of the CoT approach is fundamentally tied to the model size, and billion parameter-scale models are often needed to get CoT to work. In this pa...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	333,968
2412.04505	Achieving Semantic Consistency: Contextualized Word Representations for Political Text Analysis	Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effectively capture long-term semantic changes but often lack stability in short-term contexts due to embeddi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	514,449
2401.15964	Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction	Remaining useful life prediction plays a crucial role in the health management of industrial systems. Given the increasing complexity of systems, data-driven predictive models have attracted significant research interest. Upon reviewing the existing literature, it appears that many studies either do not fully integrate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,666
2304.00249	From Conception to Deployment: Intelligent Stroke Prediction Framework using Machine Learning and Performance Evaluation	Stroke is the second leading cause of death worldwide. Machine learning classification algorithms have been widely adopted for stroke prediction. However, these algorithms were evaluated using different datasets and evaluation metrics. Moreover, there is no comprehensive framework for stroke data analytics. This paper ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	355,616
1812.09746	A Multi-Objective Anytime Rule Mining System to Ease Iterative Feedback from Domain Experts	Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	117,222
2111.02603	On Semantic Cognition, Inductive Generalization, and Language Models	My doctoral research focuses on understanding semantic knowledge in neural network models trained solely to predict natural language (referred to as language models, or LMs), by drawing on insights from the study of concepts and categories grounded in cognitive science. I propose a framework inspired by 'inductive reas...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	264,918
1803.00586	The information and wave-theoretic limits of analog beamforming	The performance of broadband millimeter-wave (mmWave) RF architectures, is generally determined by mathematical concepts such as the Shannon capacity. These systems have also to obey physical laws such as the conservation of energy and the propagation laws. Taking the physical and hardware limitations into account is c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	91,704
0903.0174	Accelerating and Evaluation of Syntactic Parsing in Natural Language Question Answering Systems	With the development of Natural Language Processing (NLP), more and more systems want to adopt NLP in User Interface Module to process user input, in order to communicate with user in a natural way. However, this raises a speed problem. That is, if NLP module can not process sentences in durable time delay, users will ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,254
2307.06129	Channel Estimation for Beyond Diagonal Reconfigurable Intelligent Surfaces with Group-Connected Architectures	We study channel estimation for a beyond diagonal reconfigurable intelligent surface (BD-RIS) aided multiple input single output system. We first describe the channel estimation strategy based on the least square (LS) method, derive the mean square error (MSE) of the LS estimator, and formulate the BD-RIS design proble...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	378,978
2412.09729	Doubly Robust Conformalized Survival Analysis with Right-Censored Data	We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for type-I censoring. This method imputes unobserved censoring times using a suitable model, and then analyzes the imputed data using weighted conformal infe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,616
1908.05612	R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object	Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution and ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	141,762
2101.07240	Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts	Multimodal generative models should be able to learn a meaningful latent representation that enables a coherent joint generation of all modalities (e.g., images and text). Many applications also require the ability to accurately sample modalities conditioned on observations of a subset of the modalities. Often not all ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	215,974
1806.05524	Fast Decoding of Low Density Lattice Codes	Low density lattice codes (LDLC) are a family of lattice codes that can be decoded efficiently using a message-passing algorithm. In the original LDLC decoder, the message exchanged between variable nodes and check nodes are continuous functions, which must be approximated in practice. A promising method is Gaussian ap...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	100,499
2103.07801	Accelerating the timeline for climate action in California	The climate emergency increasingly threatens our communities, ecosystems, food production, health, and economy. It disproportionately impacts lower income communities, communities of color, and the elderly. Assessments since the 2018 IPCC 1.5 Celsius report show that current national and sub-national commitments and ac...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	224,702
2012.06563	Exploring Facial Expressions and Affective Domains for Parkinson Detection	Parkinson's Disease (PD) is a neurological disorder that affects facial movements and non-verbal communication. Patients with PD present a reduction in facial movements called hypomimia which is evaluated in item 3.2 of the MDS-UPDRS-III scale. In this work, we propose to use facial expression analysis from face images...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,156
2201.13323	Constructing coarse-scale bifurcation diagrams from spatio-temporal observations of microscopic simulations: A parsimonious machine learning approach	We address a three-tier data-driven approach to solve the inverse problem in complex systems modelling from spatio-temporal data produced by microscopic simulators using machine learning. In the first step, we exploit manifold learning and in particular parsimonious Diffusion Maps using leave-one-out cross-validation (...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	277,950
1901.08190	Correcting rural building annotations in OpenStreetMap using convolutional neural networks	Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap (OSM), but their quality and quantity are not sufficient for training models that can create accurate rural building maps. The problems wit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,404
1907.08122	MRD-codes arising from the trinomial $x^q+x^{q^3}+cx^{q^5}\in\mathbb{F}_{q^6}[x]$	In [10], the existence of $\mathbb{F}_q$-linear MRD-codes of $\mathbb{F}_q^{6\times 6}$, with dimension $12$, minimum distance $5$ and left idealiser isomorphic to $\mathbb{F}_{q^6}$, defined by a trinomial of $\mathbb{F}_{q^6}[x]$, when $q$ is odd and $q\equiv 0,\pm 1\pmod 5$, has been proved. In this paper we show th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	139,033
1907.07526	Almawave-SLU: A new dataset for SLU in Italian	The widespread use of conversational and question answering systems made it necessary to improve the performances of speaker intent detection and understanding of related semantic slots, i.e., Spoken Language Understanding (SLU). Often, these tasks are approached with supervised learning methods, which needs considerab...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	138,900
2201.09429	End-to-End Neural Speech Coding for Real-Time Communications	Deep-learning based methods have shown their advantages in audio coding over traditional ones but limited attention has been paid on real-time communications (RTC). This paper proposes the TFNet, an end-to-end neural speech codec with low latency for RTC. It takes an encoder-temporal filtering-decoder paradigm that has...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	276,673
2311.09552	Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition	Recognizing fallacies is crucial for ensuring the quality and validity of arguments across various domains. However, computational fallacy recognition faces challenges due to the diverse genres, domains, and types of fallacies found in datasets. This leads to a highly multi-class, and even multi-label, setup with subst...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	408,176
2302.04544	GMConv: Modulating Effective Receptive Fields for Convolutional Kernels	In convolutional neural networks, the convolutions are conventionally performed using a square kernel with a fixed N $\times$ N receptive field (RF). However, what matters most to the network is the effective receptive field (ERF) that indicates the extent with which input pixels contribute to an output pixel. Inspired...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	344,744
2402.06135	Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning	The electronic map plays a crucial role in geographic information systems, serving various urban managerial scenarios and daily life services. Developing effective Map Entity Representation Learning (MERL) methods is crucial to extracting embedding information from electronic maps and converting map entities into repre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,180
1608.08047	Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances	In this paper, nonlinear model reduction for power systems is performed by the balancing of empirical controllability and observability covariances that are calculated around the operating region. Unlike existing model reduction methods, the external system does not need to be linearized but is directly dealt with as a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	60,307
2109.14257	Adaptive-Resolution Field Mapping Using Gaussian Process Fusion with Integral Kernels	Unmanned aerial vehicles are rapidly gaining popularity in a variety of environmental monitoring tasks. A key requirement for their autonomous operation is the ability to perform efficient environmental mapping online, given limited onboard resources constraining operation time, travel distance, and computational capac...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	257,914
1811.00989	CMI: An Online Multi-objective Genetic Autoscaler for Scientific and Engineering Workflows in Cloud Infrastructures with Unreliable Virtual Machines	Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic access to huge amounts of computing resources. Autoscalers are midd...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	112,242
2406.12062	Entropic Regression DMD (ERDMD) Discovers Informative Sparse and Nonuniformly Time Delayed Models	In this work, we present a method which determines optimal multi-step dynamic mode decomposition (DMD) models via entropic regression, which is a nonlinear information flow detection algorithm. Motivated by the higher-order DMD (HODMD) method of \cite{clainche}, and the entropic regression (ER) technique for network de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	465,207
2401.01711	Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs	Conversational question answering systems often rely on semantic parsing to enable interactive information retrieval, which involves the generation of structured database queries from a natural language input. For information-seeking conversations about facts stored within a knowledge graph, dialogue utterances are tra...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	419,467
1902.03376	Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding	Evaluating the clinical similarities between pairwise patients is a fundamental problem in healthcare informatics. A proper patient similarity measure enables various downstream applications, such as cohort study and treatment comparative effectiveness research. One major carrier for conducting patient similarity resea...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	121,088
2311.10863	Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks	Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents, with unknown and stochastic dynamics, tasked with complex missions captured by Linear Temporal Logic (LTL). Due to the sample-inefficiency of the majority of these works, compositional learning methods have been ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	408,696
1905.06327	A data-driven proxy to Stoke's flow in porous media	The objective for this work is to develop a data-driven proxy to high-fidelity numerical flow simulations using digital images. The proposed model can capture the flow field and permeability in a large verity of digital porous media based on solid grain geometry and pore size distribution by detailed analyses of the lo...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,956
2211.16300	Dual adaptive MPC using an exact set-membership reformulation	Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly within the MPC optimization. A predicted worst-case cost is then used to enable performance-oriented...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	333,592
2404.02904	ALOHa: A New Measure for Hallucination in Captioning Models	Despite recent advances in multimodal pre-training for visual description, state-of-the-art models still produce captions containing errors, such as hallucinating objects not present in a scene. The existing prominent metric for object hallucination, CHAIR, is limited to a fixed set of MS COCO objects and synonyms. In ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	444,044
2401.02457	eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning	New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledge about new categories while preserving information about previously learned ones in such dynamic environments. It might also be necessary t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	419,731
1501.03786	Multi-view learning for multivariate performance measures optimization	In this paper, we propose the problem of optimizing multivariate performance measures from multi-view data, and an effective method to solve it. This problem has two features: the data points are presented by multiple views, and the target of learning is to optimize complex multivariate performance measures. We propose...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	39,297
2411.13366	Predicting Wall Thickness Changes in Cold Forging Processes: An Integrated FEM and Neural Network approach	This study presents a novel approach for predicting wall thickness changes in tubes during the nosing process. Specifically, we first provide a thorough analysis of nosing processes and the influencing parameters. We further set-up a Finite Element Method (FEM) simulation to better analyse the effects of varying proces...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,767
2312.16159	Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages	Large language models (LLMs) have shown impressive zero-shot capabilities in various document reranking tasks. Despite their successful implementations, there is still a gap in existing literature on their effectiveness in low-resource languages. To address this gap, we investigate how LLMs function as rerankers in cro...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	418,285
2412.10265	Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication	This paper investigates the adversarial robustness of Deep Neural Networks (DNNs) using Information Bottleneck (IB) objectives for task-oriented communication systems. We empirically demonstrate that while IB-based approaches provide baseline resilience against attacks targeting downstream tasks, the reliance on genera...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	516,847
2204.12802	GTNet: A Tree-Based Deep Graph Learning Architecture	We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the tree representation, messages propagate upward from the leaf nodes to the root node, and each node preserves its initial information prior ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	293,608
1701.05740	On the Performance of X-Duplex Relaying	In this paper, we study a X-duplex relay system with one source, one amplify-and-forward (AF) relay and one destination, where the relay is equipped with a shared antenna and two radio frequency (RF) chains used for transmission or reception. X-duplex relay can adaptively configure the connection between its RF chains ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	67,026
2308.16246	Active Neural Mapping	We address the problem of active mapping with a continually-learned neural scene representation, namely Active Neural Mapping. The key lies in actively finding the target space to be explored with efficient agent movement, thus minimizing the map uncertainty on-the-fly within a previously unseen environment. In this pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,929
2201.07934	Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks	An antithetical concept, adaptive symmetry, to conservative symmetry in physics is proposed to understand the deep neural networks (DNNs). It characterizes the invariance of variance, where a biotic system explores different pathways of evolution with equal probability in absence of feedback signals, and complex functi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	276,177
0903.5426	Testing Goodness-of-Fit via Rate Distortion	A framework is developed using techniques from rate distortion theory in statistical testing. The idea is first to do optimal compression according to a certain distortion function and then use information divergence from the compressed empirical distribution to the compressed null hypothesis as statistic. Only very sp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,453
2311.13565	Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering	We address the task of evidence retrieval for long document question answering, which involves locating relevant paragraphs within a document to answer a question. We aim to assess the applicability of large language models (LLMs) in the task of zero-shot long document evidence retrieval, owing to their unprecedented p...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	409,776
2307.01282	Normalized mutual information is a biased measure for classification and community detection	Normalized mutual information is widely used as a similarity measure for evaluating the performance of clustering and classification algorithms. In this paper, we argue that results returned by the normalized mutual information are biased for two reasons: first, because they ignore the information content of the contin...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	377,298
2006.15791	Probabilistic Classification Vector Machine for Multi-Class Classification	The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems. However, the PCVM is currently only applicable to binary cases. Extending the PCVM to multi-class cases ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,627
2204.04420	Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing	In recent years, deep learning has witnessed its blossom in the field of Electrocardiography (ECG) processing, outperforming traditional signal processing methods in various tasks, for example, classification, QRS detection, wave delineation. Although many neural architectures have been proposed in the literature, ther...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,650
1503.01793	Correct-by-synthesis reinforcement learning with temporal logic constraints	We consider a problem on the synthesis of reactive controllers that optimize some a priori unknown performance criterion while interacting with an uncontrolled environment such that the system satisfies a given temporal logic specification. We decouple the problem into two subproblems. First, we extract a (maximally) p...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	40,863
2406.11815	LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning	In recent years, instruction-tuned Large Multimodal Models (LMMs) have been successful at several tasks, including image captioning and visual question answering; yet leveraging these models remains an open question for robotics. Prior LMMs for robotics applications have been extensively trained on language and action ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	465,073
2002.00670	Learning-based Max-Min Fair Hybrid Precoding for mmWave Multicasting	This paper investigates the joint design of hybrid transmit precoder and analog receive combiners for single-group multicasting in millimeter-wave systems. We propose LB-GDM, a low-complexity learning-based approach that leverages gradient descent with momentum and alternating optimization to design (i) the digital and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	162,424
2502.13069	Interactive Agents to Overcome Ambiguity in Software Engineering	AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions. Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes, safety risks due to tool misuse, and wasted computational resources. In this work, we study ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	535,188
2106.09847	Disinformation, Stochastic Harm, and Costly Effort: A Principal-Agent Analysis of Regulating Social Media Platforms	The spread of disinformation on social platforms is harmful to society. This harm may manifest as a gradual degradation of public discourse; but it can also take the form of sudden dramatic events such as the 2021 insurrection on Capitol Hill. The platforms themselves are in the best position to prevent the spread of d...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	241,807
1505.00566	Estimating the Margin of Victory of an Election using Sampling	The margin of victory of an election is a useful measure to capture the robustness of an election outcome. It also plays a crucial role in determining the sample size of various algorithms in post election audit, polling etc. In this work, we present efficient sampling based algorithms for estimating the margin of vict...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	42,748
2310.17997	Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy	Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit. However, the technology requires coating conductive films on insulator samples and a vacuum...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	403,364
2104.07427	Estimation of atrial fibrillation from lead-I ECGs: Comparison with cardiologists and machine learning model (CurAlive), a clinical validation study	Electrocardiogram recognition of cardiac arrhythmias is critical for cardiac abnormality diagnosis. Because of their strong prediction characteristics, artificial neural networks are the preferred method in medical diagnosis systems. This study presents a method to detect atrial fibrillation with lead-I ECGs using arti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	230,415
1410.8440	Non-Adaptive Group Testing with Inhibitors	Group testing with inhibitors (GTI) introduced by Farach at al. is studied in this paper. There are three types of items, $d$ defectives, $r$ inhibitors and $n-d-r$ normal items in a population of $n$ items. The presence of any inhibitor in a test can prevent the expression of a defective. For this model, we propose a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	37,157
2306.15516	A logic-based framework for database repairs	We introduce a general abstract framework for database repairing in which the repair notions are defined using formal logic. We differentiate between integrity constraints and the so-called query constraints. The former are used to model consistency and desirable properties of the data (such as functional dependencies ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	376,046
2412.19131	Synthetic Discrete Inertia	This letter demonstrates how synthetic inertia can be obtained with the control of flexible discrete devices to keep the power balance of power systems, even if the system does not include any synchronous generator or conventional grid-forming converter. The letter also discusses solutions to cycling issues, which can ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	520,726
2006.14382	Coordination of OLTC and Smart Inverters for Optimal Voltage Regulation of Unbalanced Distribution Networks	Photovoltaic (PV) smart inverters can improve the voltage profile of distribution networks. A multi-objective optimization framework for coordination of reactive power injection of smart inverters and tap operations of on-load tap changers (OLTCs) for multi-phase unbalanced distribution systems is proposed. The optimiz...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	184,206
2311.11652	Web News Timeline Generation with Extended Task Prompting	The creation of news timeline is essential for a comprehensive and contextual understanding of events as they unfold over time. This approach aids in discerning patterns and trends that might be obscured when news is viewed in isolation. By organizing news in a chronological sequence, it becomes easier to track the dev...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	409,031
1701.05989	Bounds and Constructions for Linear Locally Repairable Codes over Binary Fields	For binary $[n,k,d]$ linear locally repairable codes (LRCs), two new upper bounds on $k$ are derived. The first one applies to LRCs with disjoint local repair groups, for general values of $n,d$ and locality $r$, containing some previously known bounds as special cases. The second one is based on solving an optimizatio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,059
2408.07219	Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables	Estimating causal effects from observational data is challenging, especially in the presence of latent confounders. Much work has been done on addressing this challenge, but most of the existing research ignores the bias introduced by the post-treatment variables. In this paper, we propose a novel method of joint Varia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,496
1206.4822	Feature extraction in protein sequences classification : a new stability measure	Feature extraction is an unavoidable task, especially in the critical step of preprocessing biological sequences. This step consists for example in transforming the biological sequences into vectors of motifs where each motif is a subsequence that can be seen as a property (or attribute) characterizing the sequence. He...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,746
2402.15481	Prejudice and Volatility: A Statistical Framework for Measuring Social Discrimination in Large Language Models	This study investigates why and how inconsistency in the generation of Large Language Models (LLMs) might induce or exacerbate societal injustice. For instance, LLMs frequently exhibit contrasting gender stereotypes regarding the same career depending on varied contexts, highlighting the arguably harmful unpredictabili...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	432,158
2110.12981	Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling	In the context of high penetration of renewables, the need to build dynamic models of power system components based on accessible measurement data has become urgent. To address this challenge, firstly, a neural ordinary differential equations (ODE) module and a neural differential-algebraic equations (DAE) module are p...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	263,033
1804.04651	Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode	Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	94,910
2110.14375	Perceptual Score: What Data Modalities Does Your Model Perceive?	Machine learning advances in the last decade have relied significantly on large-scale datasets that continue to grow in size. Increasingly, those datasets also contain different data modalities. However, large multi-modal datasets are hard to annotate, and annotations may contain biases that we are often unaware of. De...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	263,514
2410.20753	Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation	We introduce Plan*RAG, a novel framework that enables structured multi-hop reasoning in retrieval-augmented generation (RAG) through test-time reasoning plan generation. While existing approaches such as ReAct maintain reasoning chains within the language model's context window, we observe that this often leads to plan...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	502,947
1905.01972	A Self-Attentive Emotion Recognition Network	Modern deep learning approaches have achieved groundbreaking performance in modeling and classifying sequential data. Specifically, attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recog...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	129,859
1912.09847	Transfer Learning with Edge Attention for Prostate MRI Segmentation	Prostate cancer is one of the common diseases in men, and it is the most common malignant tumor in developed countries. Studies have shown that the male prostate incidence rate is as high as 2.5% to 16%, Currently, the inci-dence of prostate cancer in Asia is lower than that in the West, but it is increas-ing rapidly. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	158,162
2010.01288	UNISON: Unpaired Cross-lingual Image Captioning	Image captioning has emerged as an interesting research field in recent years due to its broad application scenarios. The traditional paradigm of image captioning relies on paired image-caption datasets to train the model in a supervised manner. However, creating such paired datasets for every target language is prohib...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	198,597
1411.3815	Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction	We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	37,543
2303.12731	Visualizing Semiotics in Generative Adversarial Networks	We perform a set of experiments to demonstrate that images generated using a Generative Adversarial Network can be modified using 'semiotics.' We show that just as physical attributes such as the hue and saturation of an image can be modified, so too can its non-physical, abstract properties using our method. For examp...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	353,365
2105.07233	Characterizing the Interactions Between Classical and Community-aware Centrality Measures in Complex Networks	Identifying vital nodes in networks exhibiting a community structure is a fundamental issue. Indeed, community structure is one of the main properties of real-world networks. Recent works have shown that community-aware centrality measures compare favorably with classical measures agnostic about this ubiquitous propert...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	235,370
0905.1755	Can the Utility of Anonymized Data be used for Privacy Breaches?	Group based anonymization is the most widely studied approach for privacy preserving data publishing. This includes k-anonymity, l-diversity, and t-closeness, to name a few. The goal of this paper is to raise a fundamental issue on the privacy exposure of the current group based approach. This has been overlooked in th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	3,669
2105.01198	Weighted Least Squares Twin Support Vector Machine with Fuzzy Rough Set Theory for Imbalanced Data Classification	Support vector machines (SVMs) are powerful supervised learning tools developed to solve classification problems. However, SVMs are likely to perform poorly in the classification of imbalanced data. The rough set theory presents a mathematical tool for inference in nondeterministic cases that provides methods for remov...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	233,451
2305.11586	PDE-constrained Gaussian process surrogate modeling with uncertain data locations	Gaussian process regression is widely applied in computational science and engineering for surrogate modeling owning to its kernel-based and probabilistic nature. In this work, we propose a Bayesian approach that integrates the variability of input data into the Gaussian process regression for function and partial diff...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,610
2005.07202	Pre-training technique to localize medical BERT and enhance biomedical BERT	Pre-training large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing (NLP). With the introduction of transformer-based language models, such as bidirectional encoder representations from transformers (BERT), the performance of inf...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	177,221
2406.06479	Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data	Data sets with imbalanced class sizes, where one class size is much smaller than that of others, occur exceedingly often in many applications, including those with biological foundations, such as disease diagnosis and drug discovery. Therefore, it is extremely important to be able to identify data elements of classes o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,598

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