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
2006.09942	Pitch Control by LQR for Fixed Wing Aircraft During Microburst Encounter	In this study, a linear mathematical model representing longitudinal flight dynamics of an airplane is developed and responses of the aircraft during a microburst encounter are investigated. The effects of microburst that are acting on the aircraft are attempted to be suppressed with the elevator control surface of the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	182,710
1811.10004	Visual Attention on the Sun: What Do Existing Models Actually Predict?	Visual attention prediction is a classic problem that seems to be well addressed in the deep learning era. One compelling concern, however, gradually arise along with the rapidly growing performance scores over existing visual attention datasets: do existing deep models really capture the inherent mechanism of human vi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,388
2310.07748	Implementation of Fuzzy Control Algorithm in Two-Wheeled Differential Drive Platform	Designing and developing Artificial Intelligence controllers on separately dedicated chips have many advantages. This report reviews the development of a real-time fuzzy logic controller for optimizing locomotion control of a two-wheeled differential drive platform using an Arduino Uno board. Based on the Raspberry Pi ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	399,110
1903.01891	Language and Dialect Identification of Cuneiform Texts	This article introduces a corpus of cuneiform texts from which the dataset for the use of the Cuneiform Language Identification (CLI) 2019 shared task was derived as well as some preliminary language identification experiments conducted using that corpus. We also describe the CLI dataset and how it was derived from the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	123,364
2005.14327	On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition	Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E methods: recurrent neural network transducer (RNN-T), RNN attention-based encoder-decoder (AED), and Transformer-AED. In this study, we conduct an...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	179,237
2310.11676	PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection	Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e-commerce. However, challenges have arisen due to the diversity of anomalies and the dearth of labeled data. Existing methodologies - recons...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	400,734
2010.13179	Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection	Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising. Previous graph learning algorithms either i) make some assumptions on connectivity (e.g., graph sparsity), or ii) make simple graph edge assumptions such as posit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,035
2306.01741	GPT Models Meet Robotic Applications: Co-Speech Gesturing Chat System	This technical paper introduces a chatting robot system that utilizes recent advancements in large-scale language models (LLMs) such as GPT-3 and ChatGPT. The system is integrated with a co-speech gesture generation system, which selects appropriate gestures based on the conceptual meaning of speech. Our motivation is ...	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	370,574
2308.12175	Unsupervised anomalies detection in IIoT edge devices networks using federated learning	In a connection of many IoT devices that each collect data, normally training a machine learning model would involve transmitting the data to a central server which requires strict privacy rules. However, some owners are reluctant of availing their data out of the company due to data security concerns. Federated learni...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	387,444
2206.10216	A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems	Hazard and Operability Analysis (HAZOP) is a powerful safety analysis technique with a long history in industrial process control domain. With the increasing use of Machine Learning (ML) components in cyber physical systems--so called Learning-Enabled Systems (LESs), there is a recent trend of applying HAZOP-like analy...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	303,837
2209.03736	Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives	We introduce Knowledge-Driven Program Synthesis (KDPS) as a variant of the program synthesis task that requires the agent to solve a sequence of program synthesis problems. In KDPS, the agent should use knowledge from the earlier problems to solve the later ones. We propose a novel method based on PushGP to solve the K...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	316,581
2005.09635	InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs	Although Generative Adversarial Networks (GANs) have made significant progress in face synthesis, there lacks enough understanding of what GANs have learned in the latent representation to map a random code to a photo-realistic image. In this work, we propose a framework called InterFaceGAN to interpret the disentangle...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	177,971
2207.06504	A Coupling Approach to Analyzing Games with Dynamic Environments	The theory of learning in games has extensively studied situations where agents respond dynamically to each other by optimizing a fixed utility function. However, in real situations, the strategic environment varies as a result of past agent choices. Unfortunately, the analysis techniques that enabled a rich characteri...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	307,902
2010.00522	Understanding the Role of Adversarial Regularization in Supervised Learning	Despite numerous attempts sought to provide empirical evidence of adversarial regularization outperforming sole supervision, the theoretical understanding of such phenomena remains elusive. In this study, we aim to resolve whether adversarial regularization indeed performs better than sole supervision at a fundamental ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	198,315
2307.10573	Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting	Language models can be prompted to reason through problems in a manner that significantly improves performance. However, \textit{why} such prompting improves performance is unclear. Recent work showed that using logically \textit{invalid} Chain-of-Thought (CoT) prompting improves performance almost as much as logically...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	380,599
2203.00995	Learning Efficiently Function Approximation for Contextual MDP	We study learning contextual MDPs using a function approximation for both the rewards and the dynamics. We consider both the case that the dynamics dependent or independent of the context. For both models we derive polynomial sample and time complexity (assuming an efficient ERM oracle). Our methodology gives a general...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,216
cs/0507002	The Three Node Wireless Network: Achievable Rates and Cooperation Strategies	We consider a wireless network composed of three nodes and limited by the half-duplex and total power constraints. This formulation encompasses many of the special cases studied in the literature and allows for capturing the common features shared by them. Here, we focus on three special cases, namely 1) Relay Channel,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,809
2109.09689	The Case for Claim Difficulty Assessment in Automatic Fact Checking	Fact-checking is the process of evaluating the veracity of claims (i.e., purported facts). In this opinion piece, we raise an issue that has received little attention in prior work -- that some claims are far more difficult to fact-check than others. We discuss the implications this has for both practical fact-checking...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	256,361
2412.12890	Suppressing Uncertainty in Gaze Estimation	Uncertainty in gaze estimation manifests in two aspects: 1) low-quality images caused by occlusion, blurriness, inconsistent eye movements, or even non-face images; 2) incorrect labels resulting from the misalignment between the labeled and actual gaze points during the annotation process. Allowing these uncertainties ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	518,077
1506.03942	Optimal $\gamma$ and $C$ for $\epsilon$-Support Vector Regression with RBF Kernels	The objective of this study is to investigate the efficient determination of $C$ and $\gamma$ for Support Vector Regression with RBF or mahalanobis kernel based on numerical and statistician considerations, which indicates the connection between $C$ and kernels and demonstrates that the deviation of geometric distance ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,112
2402.03814	Masked Graph Autoencoder with Non-discrete Bandwidths	Masked graph autoencoders have emerged as a powerful graph self-supervised learning method that has yet to be fully explored. In this paper, we unveil that the existing discrete edge masking and binary link reconstruction strategies are insufficient to learn topologically informative representations, from the perspecti...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,205
2411.08161	Shaping Frequency Dynamics in Modern Power Systems with Grid-forming Converters	In this paper, frequency dynamics in modern power systems with a high penetration of converter-based generation is analysed. A fundamental analysis of the frequency dynamics is performed to identify the limitations and challenges when the converter penetration is increased. The voltage-source behaviour is found as an e...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	507,786
2311.07460	KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems	Significant progress has been made in anomaly detection and run-time monitoring to improve the safety and security of cyber-physical systems (CPS). However, less attention has been paid to hazard mitigation. This paper proposes a combined knowledge and data driven approach, KnowSafe, for the design of safety engines th...	false	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false	false	false	407,330
2312.05429	Mitigating Nonlinear Algorithmic Bias in Binary Classification	This paper proposes the use of causal modeling to detect and mitigate algorithmic bias that is nonlinear in the protected attribute. We provide a general overview of our approach. We use the German Credit data set, which is available for download from the UC Irvine Machine Learning Repository, to develop (1) a predicti...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	414,078
1605.09533	Robust Deep-Learning-Based Road-Prediction for Augmented Reality Navigation Systems	This paper proposes an approach that predicts the road course from camera sensors leveraging deep learning techniques. Road pixels are identified by training a multi-scale convolutional neural network on a large number of full-scene-labeled night-time road images including adverse weather conditions. A framework is pre...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	56,580
2405.20657	DORY: Deliberative Prompt Recovery for LLM	Prompt recovery in large language models (LLMs) is crucial for understanding how LLMs work and addressing concerns regarding privacy, copyright, etc. The trend towards inference-only APIs complicates this task by restricting access to essential outputs for recovery. To tackle this challenge, we extract prompt-related i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	459,457
2108.03257	(Just) A Spoonful of Refinements Helps the Registration Error Go Down	We tackle data-driven 3D point cloud registration. Given point correspondences, the standard Kabsch algorithm provides an optimal rotation estimate. This allows to train registration models in an end-to-end manner by differentiating the SVD operation. However, given the initial rotation estimate supplied by Kabsch, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,600
2008.08432	Deep Neural Networks for automatic extraction of features in time series satellite images	Many earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized in time series. In this work, we exploit both temporal and spatial information provided by these images to generate land cover maps. For th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	192,420
1507.04507	Asymmetry in in-degree and out-degree distributions of large-scale industrial networks	Many natural, physical and social networks commonly exhibit power-law degree distributions. In this paper, we discover previously unreported asymmetrical patterns in the degree distributions of incoming and outgoing links in the investigation of large-scale industrial networks, and provide interpretations. In industria...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	45,183
2009.06679	Data Augmentation and Clustering for Vehicle Make/Model Classification	Vehicle shape information is very important in Intelligent Traffic Systems (ITS). In this paper we present a way to exploit a training data set of vehicles released in different years and captured under different perspectives. Also the efficacy of clustering to enhance the make/model classification is presented. Both s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,710
2203.15129	A Study of Reinforcement Learning Algorithms for Aggregates of Minimalistic Robots	The aim of this paper is to study how to apply deep reinforcement learning for the control of aggregates of minimalistic robots. We define aggregates as groups of robots with a physical connection that compels them to form a specified shape. In our case, the robots are pre-attached to an object that must be collectivel...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	288,250
1912.06810	Proppy: A System to Unmask Propaganda in Online News	We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation. The system constantly monitors a number of news sources, deduplicates and clusters the ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	157,435
1507.02454	Optimized Compressed Sensing via Incoherent Frames Designed by Convex Optimization	The construction of highly incoherent frames, sequences of vectors placed on the unit hyper sphere of a finite dimensional Hilbert space with low correlation between them, has proven very difficult. Algorithms proposed in the past have focused in minimizing the absolute value off-diagonal entries of the Gram matrix of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,990
2308.14216	Machine Learning for Administrative Health Records: A Systematic Review of Techniques and Applications	Machine learning provides many powerful and effective techniques for analysing heterogeneous electronic health records (EHR). Administrative Health Records (AHR) are a subset of EHR collected for administrative purposes, and the use of machine learning on AHRs is a growing subfield of EHR analytics. Existing reviews of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,236
1910.12037	Region Mutual Information Loss for Semantic Segmentation	Semantic segmentation is a fundamental problem in computer vision. It is considered as a pixel-wise classification problem in practice, and most segmentation models use a pixel-wise loss as their optimization riterion. However, the pixel-wise loss ignores the dependencies between pixels in an image. Several ways to exp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,951
1707.08289	Fast Deep Matting for Portrait Animation on Mobile Phone	Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,796
2205.09934	Towards Explanation for Unsupervised Graph-Level Representation Learning	Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}" Existing explanation methods focus on the supervised settings, \eg, node class...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	297,480
2104.11633	Estimating the Number of HIV+ Latino MSM Using RDS, SS-PSE, and the Census	This paper presents a method for estimating the overall size of a hidden population using results from a respondent driven sampling (RDS) survey. We use data from the Latino MSM Community Involvement survey (LMSM-CI), an RDS dataset that contains information collected regarding the Latino MSM communities in Chicago and...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	231,974
1703.07815	Cross-View Image Matching for Geo-localization in Urban Environments	In this paper, we address the problem of cross-view image geo-localization. Specifically, we aim to estimate the GPS location of a query street view image by finding the matching images in a reference database of geo-tagged bird's eye view images, or vice versa. To this end, we present a new framework for cross-view im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,458
1601.04619	Comparison-based Image Quality Assessment for Parameter Selection	Image quality assessment (IQA) is traditionally classified into full-reference (FR) IQA and no-reference (NR) IQA according to whether the original image is required. Although NR-IQA is widely used in practical applications, room for improvement still remains because of the lack of the reference image. Inspired by the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,043
1209.1236	Coordination of autonomic functionalities in communications networks	Future communication networks are expected to feature autonomic (or self-organizing) mechanisms to ease deployment (self-configuration), tune parameters automatically (self-optimization) and repair the network (self-healing). Self-organizing mechanisms have been designed as stand-alone entities, even though multiple me...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	18,427
1906.02606	Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis	It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	134,122
2409.19945	One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric	Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where the number of samples varies signific...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	492,923
2502.09813	Suture Thread Modeling Using Control Barrier Functions for Autonomous Surgery	Automating surgical systems enhances precision and safety while reducing human involvement in high-risk environments. A major challenge in automating surgical procedures like suturing is accurately modeling the suture thread, a highly flexible and compliant component. Existing models either lack the accuracy needed for...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	533,611
2108.02347	FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention	We propose FMMformers, a class of efficient and flexible transformers inspired by the celebrated fast multipole method (FMM) for accelerating interacting particle simulation. FMM decomposes particle-particle interaction into near-field and far-field components and then performs direct and coarse-grained computation, re...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	249,288
2011.04558	Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel	Spectral clustering is a popular method for community detection in network graphs: starting from a matrix representation of the graph, the nodes are clustered on a low dimensional projection obtained from a truncated spectral decomposition of the matrix. Estimating correctly the number of communities and the dimension ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,622
2107.14588	On the Configurations of Closed Kinematic Chains in three-dimensional Space	A kinematic chain in three-dimensional Euclidean space consists of $n$ links that are connected by spherical joints. Such a chain is said to be within a closed configuration when its link lengths form a closed polygonal chain in three dimensions. We investigate the space of configurations, described in terms of joint a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	248,512
1212.5315	A hybrid FD-FV method for first-order hyperbolic conservation laws on Cartesian grids: The smooth problem case	We present a class of hybrid FD-FV (finite difference and finite volume) methods for solving general hyperbolic conservation laws written in first-order form. The presentation focuses on one- and two-dimensional Cartesian grids; however, the generalization to higher dimensions is straightforward. These methods use both...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	20,540
2409.15196	HOTVCOM: Generating Buzzworthy Comments for Videos	In the era of social media video platforms, popular ``hot-comments'' play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or ``danmaku'' in English, offering imme...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	490,799
1103.2469	Blind Compressed Sensing Over a Structured Union of Subspaces	This paper addresses the problem of simultaneous signal recovery and dictionary learning based on compressive measurements. Multiple signals are analyzed jointly, with multiple sensing matrices, under the assumption that the unknown signals come from a union of a small number of disjoint subspaces. This problem is impo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,585
2112.08796	Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing	The Mixup scheme suggests mixing a pair of samples to create an augmented training sample and has gained considerable attention recently for improving the generalizability of neural networks. A straightforward and widely used extension of Mixup is to combine with regional dropout-like methods: removing random patches f...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	271,940
1608.00250	On Regularization Parameter Estimation under Covariate Shift	This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting. In such a setting, there are differences between the distributions generating the training data (source domain) and the test data (target domain). The usual cross-validation procedure requ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	59,250
1805.02220	Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification	Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC on a single passage, multi-passage MRC is more challenging, since we are likely to get multiple confusing answer candidates from different p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	96,809
1704.02455	A New Pseudo-color Technique Based on Intensity Information Protection for Passive Sensor Imagery	Remote sensing image processing is so important in geo-sciences. Images which are obtained by different types of sensors might initially be unrecognizable. To make an acceptable visual perception in the images, some pre-processing steps (for removing noises and etc) are preformed which they affect the analysis of image...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	71,452
1704.01244	Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells	With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,219
2006.07868	Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression	Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because labeled training data is sparse and expensive, while high prediction accuracy is required from models of these dynamical systems. Due to the high...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	181,983
1805.07494	Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks	Inspired by number series tests to measure human intelligence, we suggest number sequence prediction tasks to assess neural network models' computational powers for solving algorithmic problems. We define the complexity and difficulty of a number sequence prediction task with the structure of the smallest automaton tha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	97,844
2211.14676	Maximizing the Probability of Fixation in the Positional Voter Model	The Voter model is a well-studied stochastic process that models the invasion of a novel trait $A$ (e.g., a new opinion, social meme, genetic mutation, magnetic spin) in a network of individuals (agents, people, genes, particles) carrying an existing resident trait $B$. Individuals change traits by occasionally samplin...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	332,944
2107.04515	Extremum-Seeking Adaptive-Droop for Model-free and Localized Volt-VAR Optimization	In an active power distribution system, Volt-VAR optimization (VVO) methods are employed to achieve network-level objectives such as minimization of network power losses. The commonly used model-based centralized and distributed VVO algorithms perform poorly in the absence of a communication system and with model and m...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	245,482
2411.19950	AlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos	We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation. By representing 3D planes as rectangles with alpha channels, AlphaTablets combine the advantages of current 2D and 3D plane representations, enabling accurate, consistent and ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	512,440
2105.10603	Automatic calibration of time of flight based non-line-of-sight reconstruction	Time of flight based Non-line-of-sight (NLOS) imaging approaches require precise calibration of illumination and detector positions on the visible scene to produce reasonable results. If this calibration error is sufficiently high, reconstruction can fail entirely without any indication to the user. In this work, we hi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	236,445
2410.01708	Examining the Role of Relationship Alignment in Large Language Models	The rapid development and deployment of Generative AI in social settings raise important questions about how to optimally personalize them for users while maintaining accuracy and realism. Based on a Facebook public post-comment dataset, this study evaluates the ability of Llama 3.0 (70B) to predict the semantic tones ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	493,905
2207.12035	What makes you change your mind? An empirical investigation in online group decision-making conversations	People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision. In this work, we investigate methods...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	309,866
2410.00388	Find Everything: A General Vision Language Model Approach to Multi-Object Search	The Multi-Object Search (MOS) problem involves navigating to a sequence of locations to maximize the likelihood of finding target objects while minimizing travel costs. In this paper, we introduce a novel approach to the MOS problem, called Finder, which leverages vision language models (VLMs) to locate multiple object...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	493,355
2309.16490	Active SLAM Utility Function Exploiting Path Entropy	In this article we present a utility function for Active SLAM (A-SLAM) which utilizes map entropy along with D-Optimality criterion metrices for weighting goal frontier candidates. We propose a utility function for frontier goal selection that exploits the occupancy grid map by utilizing the path entropy and favors unk...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	395,369
1102.4926	New Worst-Case Upper Bound for X3SAT	The rigorous theoretical analyses of algorithms for exact 3-satisfiability (X3SAT) have been proposed in the literature. As we know, previous algorithms for solving X3SAT have been analyzed only regarding the number of variables as the parameter. However, the time complexity for solving X3SAT instances depends not only...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	9,346
1807.05983	Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection	The low resolution of objects of interest in aerial images makes pedestrian detection and action detection extremely challenging tasks. Furthermore, using deep convolutional neural networks to process large images can be demanding in terms of computational requirements. In order to alleviate these challenges, we propos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,032
2203.10960	AI based Log Analyser: A Practical Approach	The analysis of logs is a vital activity undertaken for fault or cyber incident detection, investigation and technical forensics analysis for system and cyber resilience. The potential application of AI algorithms for Log analysis could augment such complex and laborious tasks. However, such solution has its constraint...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	286,747
2107.02398	From General to Specific: Online Updating for Blind Super-Resolution	Most deep learning-based super-resolution (SR) methods are not image-specific: 1) They are trained on samples synthesized by predefined degradations (e.g. bicubic downsampling), regardless of the domain gap between training and testing data. 2) During testing, they super-resolve all images by the same set of model weig...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,803
2312.08334	LD-SDM: Language-Driven Hierarchical Species Distribution Modeling	We focus on the problem of species distribution modeling using global-scale presence-only data. Most previous studies have mapped the range of a given species using geographical and environmental features alone. To capture a stronger implicit relationship between species, we encode the taxonomic hierarchy of species us...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,265
1207.4089	A Two-Stage Combined Classifier in Scale Space Texture Classification	Textures often show multiscale properties and hence multiscale techniques are considered useful for texture analysis. Scale-space theory as a biologically motivated approach may be used to construct multiscale textures. In this paper various ways are studied to combine features on different scales for texture classific...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	17,531
2109.10444	Fairness-aware Class Imbalanced Learning	Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	256,607
2107.04101	Inertia Pricing in Stochastic Electricity Markets	Maintaining the stability of renewable-dominant power systems requires the procurement of virtual inertia services from non-synchronous resources (e.g., batteries, wind turbines) in addition to inertia traditionally provided by synchronous resources (e.g., thermal generators). However, the pricing of inertia provision ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	245,348
2406.02550	Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks	Large language models can solve tasks that were not present in the training set. This capability is believed to be due to in-context learning and skill composition. In this work, we study the emergence of in-context learning and skill composition in a collection of modular arithmetic tasks. Specifically, we consider a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	460,823
2004.01628	Weighted Random Search for Hyperparameter Optimization	We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,968
1706.06409	Revisiting L21-norm Robustness with Vector Outlier Regularization	In many real-world applications, data usually contain outliers. One popular approach is to use L2,1 norm function as a robust error/loss function. However, the robustness of L2,1 norm function is not well understood so far. In this paper, we propose a new Vector Outlier Regularization (VOR) framework to understand and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,671
2311.12668	From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design	Engineering design is undergoing a transformative shift with the advent of AI, marking a new era in how we approach product, system, and service planning. Large language models have demonstrated impressive capabilities in enabling this shift. Yet, with text as their only input modality, they cannot leverage the large b...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	409,427
2205.03766	Scheduled Multi-task Learning for Neural Chat Translation	Neural Chat Translation (NCT) aims to translate conversational text into different languages. Existing methods mainly focus on modeling the bilingual dialogue characteristics (e.g., coherence) to improve chat translation via multi-task learning on small-scale chat translation data. Although the NCT models have achieved...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	295,407
0712.3807	Improved Collaborative Filtering Algorithm via Information Transformation	In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering (CF) using Pearson correlation. Furthermore, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	1,071
2205.12901	Fairness of Exposure in Light of Incomplete Exposure Estimation	Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based on the idea that all items or item groups should get exposure proportional to the merit of the item or the collective merit of the items in the group. Often, stochastic ranking policies are used to ensure fairness of exposure. P...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	298,739
1605.04360	Variational Inference with Agent-Based Models	In this paper, we develop a variational method to track and make predictions about a real-world system from continuous imperfect observations about this system, using an agent-based model that describes the system dynamics. By combining the power of big data with the power of model-thinking in the stochastic process fr...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	55,857
1908.01878	How Does Learning Rate Decay Help Modern Neural Networks?	Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empirically observed to help both optimization and generalization. Common beliefs in how lrDecay works come from the optimization analysis of (S...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	140,878
2305.09676	Integrating Node Importance and Network Topological Properties for Link Prediction in Complex Network	Link prediction is one of the most important and challenging tasks in complex network analysis, which aims to predict the likelihood of the existence of missing links based on the known information in the network. As critical topological properties in the network, node degree and clustering coefficient are well-suited ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	364,734
2406.12104	End-to-end Text-to-SQL Generation within an Analytics Insight Engine	Recent advancements in Text-to-SQL have pushed database management systems towards greater democratization of data access. Today's language models are at the core of these advancements. They enable impressive Text-to-SQL generation as experienced in the development of Distyl AI's Analytics Insight Engine. Its early dep...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	true	false	465,224
2305.08167	Random Generator of Orthogonal Matrices in Finite Fields	We propose a superfast method for constructing orthogonal matrices $M\in\mathcal{O}(n,q)$ in finite fields $GF(q)$. It can be used to construct $n\times n$ orthogonal matrices in $Z_p$ with very high values of $n$ and $p$, and also orthogonal matrices with a certain circulant structure. Equally well one can construct p...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	364,187
2302.13170	Partial Label Learning for Emotion Recognition from EEG	Fully supervised learning has recently achieved promising performance in various electroencephalography (EEG) learning tasks by training on large datasets with ground truth labels. However, labeling EEG data for affective experiments is challenging, as it can be difficult for participants to accurately distinguish betw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	347,843
2405.06713	Unveiling the Competitive Dynamics: A Comparative Evaluation of American and Chinese LLMs	The strategic significance of Large Language Models (LLMs) in economic expansion, innovation, societal development, and national security has been increasingly recognized since the advent of ChatGPT. This study provides a comprehensive comparative evaluation of American and Chinese LLMs in both English and Chinese cont...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	453,423
2005.07410	Performance Analysis for Multi-Antenna Small Cell Networks with Clustered Dynamic TDD	Small cell networks with dynamic time-division duplex (D-TDD) have emerged as a potential solution to address the asymmetric traffic demands in 5G wireless networks. By allowing the dynamic adjustment of cell-specific UL/DL configuration, D-TDD flexibly allocates percentage of subframes to UL and DL transmissions to ac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	177,273
2305.13362	On quantum backpropagation, information reuse, and cheating measurement collapse	The success of modern deep learning hinges on the ability to train neural networks at scale. Through clever reuse of intermediate information, backpropagation facilitates training through gradient computation at a total cost roughly proportional to running the function, rather than incurring an additional factor propor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	366,475
2011.09986	Estimation of Shortest Path Covariance Matrices	We study the sample complexity of estimating the covariance matrix $\mathbf{\Sigma} \in \mathbb{R}^{d\times d}$ of a distribution $\mathcal D$ over $\mathbb{R}^d$ given independent samples, under the assumption that $\mathbf{\Sigma}$ is graph-structured. In particular, we focus on shortest path covariance matrices, whe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	207,384
2208.06348	Can Brain Signals Reveal Inner Alignment with Human Languages?	Brain Signals, such as Electroencephalography (EEG), and human languages have been widely explored independently for many downstream tasks, however, the connection between them has not been well explored. In this study, we explore the relationship and dependency between EEG and language. To study at the representation ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	312,686
2403.03412	Advancing Out-of-Distribution Detection through Data Purification and Dynamic Activation Function Design	In the dynamic realms of machine learning and deep learning, the robustness and reliability of models are paramount, especially in critical real-world applications. A fundamental challenge in this sphere is managing Out-of-Distribution (OOD) samples, significantly increasing the risks of model misclassification and unc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	435,181
2304.13950	Fairness Uncertainty Quantification: How certain are you that the model is fair?	Fairness-aware machine learning has garnered significant attention in recent years because of extensive use of machine learning in sensitive applications like judiciary systems. Various heuristics, and optimization frameworks have been proposed to enforce fairness in classification \cite{del2020review} where the later ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	360,769
2009.10753	Entropic Compressibility of L\'evy Processes	In contrast to their seemingly simple and shared structure of independence and stationarity, L\'evy processes exhibit a wide variety of behaviors, from the self-similar Wiener process to piecewise-constant compound Poisson processes. Inspired by the recent paper of Ghourchian, Amini, and Gohari (2018), we characterize ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	196,971
1701.03849	Deep Neural Networks for Czech Multi-label Document Classification	This paper is focused on automatic multi-label document classification of Czech text documents. The current approaches usually use some pre-processing which can have negative impact (loss of information, additional implementation work, etc). Therefore, we would like to omit it and use deep neural networks that learn fr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	66,761
2411.00017	Applying Data Driven Decision Making to rank Vocational and Educational Training Programs with TOPSIS	In this paper we present a multi-criteria classification of Vocational and Educational Programs in Extremadura (Spain) during the period 2009-2016. This ranking has been carried out through the integration into a complete database of the detailed information of individuals finishing such studies together with their lab...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	504,398
2309.16140	CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting	Contrastive Language-Image Pre-training (CLIP) starts to emerge in many computer vision tasks and has achieved promising performance. However, it remains underexplored whether CLIP can be generalized to 3D hand pose estimation, as bridging text prompts with pose-aware features presents significant challenges due to the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	395,232
2403.19943	TDANet: A Novel Temporal Denoise Convolutional Neural Network With Attention for Fault Diagnosis	Fault diagnosis plays a crucial role in maintaining the operational integrity of mechanical systems, preventing significant losses due to unexpected failures. As intelligent manufacturing and data-driven approaches evolve, Deep Learning (DL) has emerged as a pivotal technique in fault diagnosis research, recognized for...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	442,548
cs/0612103	The Boundary Between Privacy and Utility in Data Anonymization	We consider the privacy problem in data publishing: given a relation I containing sensitive information 'anonymize' it to obtain a view V such that, on one hand attackers cannot learn any sensitive information from V, and on the other hand legitimate users can use V to compute useful statistics on I. These are conflict...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	539,981
2002.08742	Disentangled Speech Embeddings using Cross-modal Self-supervision	The objective of this paper is to learn representations of speaker identity without access to manually annotated data. To do so, we develop a self-supervised learning objective that exploits the natural cross-modal synchrony between faces and audio in video. The key idea behind our approach is to tease apart--without a...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	164,852

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