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
2410.00659	Multimodal Coherent Explanation Generation of Robot Failures	The explainability of a robot's actions is crucial to its acceptance in social spaces. Explaining why a robot fails to complete a given task is particularly important for non-expert users to be aware of the robot's capabilities and limitations. So far, research on explaining robot failures has only considered generatin...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	493,454
2208.00539	Is current research on adversarial robustness addressing the right problem?	Short answer: Yes, Long answer: No! Indeed, research on adversarial robustness has led to invaluable insights helping us understand and explore different aspects of the problem. Many attacks and defenses have been proposed over the last couple of years. The problem, however, remains largely unsolved and poorly understo...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	310,888
2110.15796	Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning	A key goal of unsupervised representation learning is "inverting" a data generating process to recover its latent properties. Existing work that provably achieves this goal relies on strong assumptions on relationships between the latent variables (e.g., independence conditional on auxiliary information). In this paper...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	264,009
2104.02055	Data augmentation for dealing with low sampling rates in NILM	Data have an important role in evaluating the performance of NILM algorithms. The best performance of NILM algorithms is achieved with high-quality evaluation data. However, many existing real-world data sets come with a low sampling quality, and often with gaps, lacking data for some recording periods. As a result, in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	228,574
2403.00245	YOLO-MED : Multi-Task Interaction Network for Biomedical Images	Object detection and semantic segmentation are pivotal components in biomedical image analysis. Current single-task networks exhibit promising outcomes in both detection and segmentation tasks. Multi-task networks have gained prominence due to their capability to simultaneously tackle segmentation and detection tasks, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,909
1302.3051	Some Properties of Generalized Self-reciprocal Polynomials over Finite Fields	Numerous results on self-reciprocal polynomials over finite fields have been studied. In this paper we generalize some of these to a-self reciprocal polynomials defined in [4]. We consider some properties of the divisibility of a-reciprocal polynomials and characterize the parity of the number of irreducible factors fo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	21,980
2408.12879	Frequency-aware Feature Fusion for Dense Image Prediction	Dense image prediction tasks demand features with strong category information and precise spatial boundary details at high resolution. To achieve this, modern hierarchical models often utilize feature fusion, directly adding upsampled coarse features from deep layers and high-resolution features from lower levels. In t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	482,926
2008.12864	Vacuum Driven Auxetic Switching Structure and Its Application on a Gripper and Quadruped	The properties and applications of auxetics have been widely explored in the past years. Through proper utilization of auxetic structures, designs with unprecedented mechanical and structural behaviors can be produced. Taking advantage of this, we present the development of novel and lowcost 3D structures inspired by a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	193,694
2212.01763	Learning Bifunctional Push-grasping Synergistic Strategy for Goal-agnostic and Goal-oriented Tasks	Both goal-agnostic and goal-oriented tasks have practical value for robotic grasping: goal-agnostic tasks target all objects in the workspace, while goal-oriented tasks aim at grasping pre-assigned goal objects. However, most current grasping methods are only better at coping with one task. In this work, we propose a b...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	334,565
2011.06544	Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot	In the future robots will interact more and more with humans and will have to communicate naturally and efficiently. Automatic speech recognition systems (ASR) will play an important role in creating natural interactions and making robots better companions. Humans excel in speech recognition in noisy environments and a...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	206,278
1208.5243	General Managers Role in Balancing Subsidiary Between Internal Competition and Knowledge Sharing	In our work we saw that during the last decades the environment that the MNCs operate in has changed becoming more volatile and less pacedly growing. In this environment the MNCs themselves have become more complex and also flexible. We found that MNCs are essentially three dimensional, that is, they organize around pr...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	18,256
0807.3845	Formal semantics of language and the Richard-Berry paradox	The classical logical antinomy known as Richard-Berry paradox is combined with plausible assumptions about the size i.e. the descriptional complexity of Turing machines formalizing certain sentences, to show that formalization of language leads to contradiction.	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	2,113
1706.06629	Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects	In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-D images as input and segments the scene into different objects (using either motion or semantic cues) while simultaneously tracking and reconstructing their 3D shape in real time. We use a multiple model fitting approach where ea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,707
0901.2367	An Implementable Scheme for Universal Lossy Compression of Discrete Markov Sources	We present a new lossy compressor for discrete sources. For coding a source sequence $x^n$, the encoder starts by assigning a certain cost to each reconstruction sequence. It then finds the reconstruction that minimizes this cost and describes it losslessly to the decoder via a universal lossless compressor. The cost o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,984
1712.07445	Boolean Tensor Decomposition for Conjunctive Queries with Negation	We propose an algorithm for answering conjunctive queries with negation, where the negated relations have bounded degree. Its data complexity matches that of the best known algorithms for the positive subquery of the input query and is expressed in terms of the fractional hypertree width and the submodular width. The q...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	87,044
2110.01758	Quantified Facial Expressiveness for Affective Behavior Analytics	The quantified measurement of facial expressiveness is crucial to analyze human affective behavior at scale. Unfortunately, methods for expressiveness quantification at the video frame-level are largely unexplored, unlike the study of discrete expression. In this work, we propose an algorithm that quantifies facial exp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	258,877
2106.12978	Unsupervised Topic Segmentation of Meetings with BERT Embeddings	Topic segmentation of meetings is the task of dividing multi-person meeting transcripts into topic blocks. Supervised approaches to the problem have proven intractable due to the difficulties in collecting and accurately annotating large datasets. In this paper we show how previous unsupervised topic segmentation metho...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	242,914
2211.12931	Can we Adopt Self-supervised Pretraining for Chest X-Rays?	Chest radiograph (or Chest X-Ray, CXR) is a popular medical imaging modality that is used by radiologists across the world to diagnose heart or lung conditions. Over the last decade, Convolutional Neural Networks (CNN), have seen success in identifying pathologies in CXR images. Typically, these CNNs are pretrained on ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,292
1901.04954	How Many and What Types of SPARQL Queries can be Answered through Zero-Knowledge Link Traversal?	The current de-facto way to query the Web of Data is through the SPARQL protocol, where a client sends queries to a server through a SPARQL endpoint. Contrary to an HTTP server, providing and maintaining a robust and reliable endpoint requires a significant effort that not all publishers are willing or able to make. An...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	118,694
2210.04249	Coresets for Relational Data and The Applications	A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,381
2412.12740	Open-World Panoptic Segmentation	Perception is a key building block of autonomously acting vision systems such as autonomous vehicles. It is crucial that these systems are able to understand their surroundings in order to operate safely and robustly. Additionally, autonomous systems deployed in unconstrained real-world scenarios must be able of dealin...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	518,005
2406.03769	DeepRacer on Physical Track: Parameters Exploration and Performance Evaluation	This paper focuses on the physical racetrack capabilities of AWS DeepRacer. Two separate experiments were conducted. The first experiment (Experiment I) focused on evaluating the impact of hyperparameters on the physical environment. Hyperparameters such as gradient descent batch size and loss type were changed systema...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	461,384
2409.17716	QuForge: A Library for Qudits Simulation	Quantum computing with qudits, an extension of qubits to multiple levels, is a research field less mature than qubit-based quantum computing. However, qudits can offer some advantages over qubits, by representing information with fewer separated components. In this article, we present QuForge, a Python-based library de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,948
2108.12801	Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones	Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situation...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	252,611
2311.16528	Utility Fairness in Contextual Dynamic Pricing with Demand Learning	This paper introduces a novel contextual bandit algorithm for personalized pricing under utility fairness constraints in scenarios with uncertain demand, achieving an optimal regret upper bound. Our approach, which incorporates dynamic pricing and demand learning, addresses the critical challenge of fairness in pricing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,948
2103.12266	Deep Implicit Moving Least-Squares Functions for 3D Reconstruction	Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry, posing a major issue for learning-based shape generation. In this work, we turn the discrete point sets into smooth surfaces by introducin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	226,109
2404.10690	MathWriting: A Dataset For Handwritten Mathematical Expression Recognition	We introduce MathWriting, the largest online handwritten mathematical expression dataset to date. It consists of 230k human-written samples and an additional 400k synthetic ones. MathWriting can also be used for offline HME recognition and is larger than all existing offline HME datasets like IM2LATEX-100K. We introduc...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	447,208
2106.03631	Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets	To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain. We evaluate these models on 6 disentanglement metrics, as well as on downstream classification tasks and ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	239,387
2101.06614	Disentangling Observed Causal Effects from Latent Confounders using Method of Moments	Discovering the complete set of causal relations among a group of variables is a challenging unsupervised learning problem. Often, this challenge is compounded by the fact that there are latent or hidden confounders. When only observational data is available, the problem is ill-posed, i.e. the causal relationships are ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,784
1902.05238	Atomic Norm Denoising for Complex Exponentials with Unknown Waveform Modulations	Non-stationary blind super-resolution is an extension of the traditional super-resolution problem, which deals with the problem of recovering fine details from coarse measurements. The non-stationary blind super-resolution problem appears in many applications including radar imaging, 3D single-molecule microscopy, comp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	121,504
2310.10021	Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance	We propose BOSS, an approach that automatically learns to solve new long-horizon, complex, and meaningful tasks by growing a learned skill library with minimal supervision. Prior work in reinforcement learning require expert supervision, in the form of demonstrations or rich reward functions, to learn long-horizon task...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	400,046
2002.06452	On the Search for Tight Frames of Low Coherence	We introduce a projective Riesz $s$-kernel for the unit sphere $\mathbb{S}^{d-1}$ and investigate properties of $N$-point energy minimizing configurations for such a kernel. We show that these configurations, for $s$ and $N$ sufficiently large, form frames that are well-separated (have low coherence) and are nearly tig...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	164,191
2006.07794	PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks	Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. A recent class of methods to address this problem uses various ways to construct a new training sample by mixing a pair (or more) of training samples. We propose PatchUp, a hidde...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,942
2311.14208	ECRF: Entropy-Constrained Neural Radiance Fields Compression with Frequency Domain Optimization	Explicit feature-grid based NeRF models have shown promising results in terms of rendering quality and significant speed-up in training. However, these methods often require a significant amount of data to represent a single scene or object. In this work, we present a compression model that aims to minimize the entropy...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,031
2311.15502	Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical	Complementary-label learning is a weakly supervised learning problem in which each training example is associated with one or multiple complementary labels indicating the classes to which it does not belong. Existing consistent approaches have relied on the uniform distribution assumption to model the generation of com...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,530
2305.13865	Selective Pre-training for Private Fine-tuning	Text prediction models, when used in applications like email clients or word processors, must protect user data privacy and adhere to model size constraints. These constraints are crucial to meet memory and inference time requirements, as well as to reduce inference costs. Building small, fast, and private domain-speci...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	366,729
1910.04972	On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor	Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019). Gradient-based learning requires iterating several times over a dataset, which is both time-consuming and constrains the training samples to...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	148,932
2304.13337	Nominal Topology for Data Languages	We propose a novel topological perspective on data languages recognizable by orbit-finite nominal monoids. For this purpose, we introduce pro-orbit-finite nominal topological spaces. Assuming globally bounded support sizes, they coincide with nominal Stone spaces and are shown to be dually equivalent to a subcategory o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	360,541
2107.05279	ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment	With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components. The major problem that deters this area of research is the lacking of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,732
1908.08381	ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features	In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require researchers to engineer abstract "features" that encode chemical concepts into a mathematical form compatible with the input to machine-learning m...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	142,536
2405.02857	I$^3$Net: Inter-Intra-slice Interpolation Network for Medical Slice Synthesis	Medical imaging is limited by acquisition time and scanning equipment. CT and MR volumes, reconstructed with thicker slices, are anisotropic with high in-plane resolution and low through-plane resolution. We reveal an intriguing phenomenon that due to the mentioned nature of data, performing slice-wise interpolation fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	451,956
2104.07474	EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition	Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR$\rightarrow$TTS direction is equipped with a language model reward to penalize the ASR hypotheses before forwarding it to TTS. 2) In the TTS$\rightarrow...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	230,432
2101.09301	i-Algebra: Towards Interactive Interpretability of Deep Neural Networks	Providing explanations for deep neural networks (DNNs) is essential for their use in domains wherein the interpretability of decisions is a critical prerequisite. Despite the plethora of work on interpreting DNNs, most existing solutions offer interpretability in an ad hoc, one-shot, and static manner, without accounti...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,551
2004.13748	Learning Polynomials of Few Relevant Dimensions	Polynomial regression is a basic primitive in learning and statistics. In its most basic form the goal is to fit a degree $d$ polynomial to a response variable $y$ in terms of an $n$-dimensional input vector $x$. This is extremely well-studied with many applications and has sample and runtime complexity $\Theta(n^d)$. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	174,646
2307.04498	RCS-based Quasi-Deterministic Ray Tracing for Statistical Channel Modeling	This paper presents a quasi-deterministic ray tracing (QD-RT) method for analyzing the propagation of electromagnetic waves in street canyons. The method uses a statistical bistatic distribution to model the Radar Cross Section (RCS) of various irregular objects such as cars and pedestrians, instead of relying on exact...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	378,424
2401.09375	Self-navigation in crowds: An invariant set-based approach	Self-navigation in non-coordinating crowded environments is formidably challenging within multi-agent systems consisting of non-holonomic robots operating through local sensing. Our primary objective is the development of a novel, rapid, sensor-driven, self-navigation controller that directly computes control commands ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	422,235
2403.14515	Building a Language-Learning Game for Brazilian Indigenous Languages: A Case of Study	In this paper we discuss a first attempt to build a language learning game for brazilian indigenous languages and the challenges around it. We present a design for the tool with gamification aspects. Then we describe a process to automatically generate language exercises and questions from a dependency treebank and a l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	440,113
2401.17161	Hybrid Tendon and Ball Chain Continuum Robots for Enhanced Dexterity in Medical Interventions	A hybrid continuum robot design is introduced that combines a proximal tendon-actuated section with a distal telescoping section comprised of permanent-magnet spheres actuated using an external magnet. While, individually, each section can approach a point in its workspace from one or at most several orientations, the ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	425,124
2406.05580	Adaptive Output Tracking Control with Reference Model System Uncertainties	This paper develops adaptive output tracking control schemes with the reference output signal generated from an unknown reference system whose output derivatives are also unknown. To deal with such reference system uncertainties, an expanded adaptive controller structure is developed to include a parametrized estimator...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	462,195
1310.5034	A Theoretical and Experimental Comparison of the EM and SEM Algorithm	In this paper we provide a new analysis of the SEM algorithm. Unlike previous work, we focus on the analysis of a single run of the algorithm. First, we discuss the algorithm for general mixture distributions. Second, we consider Gaussian mixture models and show that with high probability the update equations of the EM...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	27,863
1403.1458	Phase Transitions in Phase Retrieval	Consider a scenario in which an unknown signal is transformed by a known linear operator, and then the pointwise absolute value of the unknown output function is reported. This scenario appears in several applications, and the goal is to recover the unknown signal -- this is called phase retrieval. Phase retrieval has ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,396
2410.21356	Modeling The Sharing and Diffusion Of Fake News in Social Media	The use of social media platforms has been gradually increasing and fake news spreading is becoming an alarming issue nowadays. The spreading of fake news means disseminating false, confusing, and spurious information which hurts families, communities etc. As a result, this issue has to be resolved sooner so that we ca...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	503,226
2304.04934	Model Sparsity Can Simplify Machine Unlearning	In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Although exact unlearning can be achieved through complete model retraining using the remaining dataset, the associated computational costs have ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,419
2502.13740	Benchmarking of Different YOLO Models for CAPTCHAs Detection and Classification	This paper provides an analysis and comparison of the YOLOv5, YOLOv8 and YOLOv10 models for webpage CAPTCHAs detection using the datasets collected from the web and darknet as well as synthetized data of webpages. The study examines the nano (n), small (s), and medium (m) variants of YOLO architectures and use metrics ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	535,496
2207.00832	Balanced reconstruction codes for single edits	Motivated by the sequence reconstruction problem initiated by Levenshtein, reconstruction codes were introduced by Cai \emph{et al}. to combat errors when a fixed number of noisy channels are available. The central problem on this topic is to design codes with sizes as large as possible, such that every codeword can be...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	305,923
2206.15204	Data-Efficient Learning via Minimizing Hyperspherical Energy	Deep learning on large-scale data is dominant nowadays. The unprecedented scale of data has been arguably one of the most important driving forces for the success of deep learning. However, there still exist scenarios where collecting data or labels could be extremely expensive, e.g., medical imaging and robotics. To f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,521
1707.08722	Algebraic Relations and Triangulation of Unlabeled Image Points	In multiview geometry when correspondences among multiple views are unknown the image points can be understood as being unlabeled. This is a common problem in computer vision. We give a novel approach to handle such a situation by regarding unlabeled point configurations as points on the Chow variety $\text{Sym}_m(\mat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,877
2112.07423	Multi-Modal Perception Attention Network with Self-Supervised Learning for Audio-Visual Speaker Tracking	Multi-modal fusion is proven to be an effective method to improve the accuracy and robustness of speaker tracking, especially in complex scenarios. However, how to combine the heterogeneous information and exploit the complementarity of multi-modal signals remains a challenging issue. In this paper, we propose a novel ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	271,477
2311.03040	Grouping Local Process Models	In recent years, process mining emerged as a proven technology to analyze and improve operational processes. An expanding range of organizations using process mining in their daily operation brings a broader spectrum of processes to be analyzed. Some of these processes are highly unstructured, making it difficult for t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	405,688
2211.03550	Underwater Image Super-Resolution using Generative Adversarial Network-based Model	Single image super-resolution (SISR) models are able to enhance the resolution and visual quality of underwater images and contribute to a better understanding of underwater environments. The integration of these models in Autonomous Underwater Vehicles (AUVs) can improve their performance in vision-based tasks. Real-E...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	328,967
2208.14059	Distributed CPU Scheduling Subject to Nonlinear Constraints	This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	315,212
2208.13441	Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation	Skip connections are fundamental units in encoder-decoder networks, which are able to improve the feature propagtion of the neural networks. However, most methods with skip connections just connected features with the same resolution in the encoder and the decoder, which ignored the information loss in the encoder with...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	315,062
2109.11573	Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data	Depth estimation from a single image is an active research topic in computer vision. The most accurate approaches are based on fully supervised learning models, which rely on a large amount of dense and high-resolution (HR) ground-truth depth maps. However, in practice, color images are usually captured with much highe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	256,982
2403.05690	Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval	Cross-domain retrieval (CDR), as a crucial tool for numerous technologies, is finding increasingly broad applications. However, existing efforts face several major issues, with the most critical being the need for accurate supervision, which often demands costly resources and efforts. Cutting-edge studies focus on achi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,106
2008.03826	Contact-Rich Trajectory Generation in Confined Environments Using Iterative Convex Optimization	Applying intelligent robot arms in dynamic uncertain environments (i.e., flexible production lines) remains challenging, which requires efficient algorithms for real time trajectory generation. The motion planning problem for robot trajectory generation is highly nonlinear and nonconvex, which usually comes with collis...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	191,043
2412.05741	Toxic behavior silences online political conversations	Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	514,954
2409.10403	A Knowledge-Enhanced Disease Diagnosis Method Based on Prompt Learning and BERT Integration	This paper proposes a knowledge-enhanced disease diagnosis method based on a prompt learning framework. The method retrieves structured knowledge from external knowledge graphs related to clinical cases, encodes it, and injects it into the prompt templates to enhance the language model's understanding and reasoning cap...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	488,735
2106.07549	Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph	Discriminating the matched named entity pairs or identifying the entities' canonical forms are critical in text mining tasks. More precise named entity normalization in text mining will benefit other subsequent text analytic applications. We built the named entity normalization model with a novel Edge Weight Updating N...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	240,965
2406.04848	CTBENCH: A Library and Benchmark for Certified Training	Training certifiably robust neural networks is an important but challenging task. While many algorithms for (deterministic) certified training have been proposed, they are often evaluated on different training schedules, certification methods, and systematically under-tuned hyperparameters, making it difficult to compa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,875
1311.4572	3-D position estimation from inertial sensing: minimizing the error from the process of double integration of accelerations	This paper introduces a new approach to 3-D position estimation from acceleration data, i.e., a 3-D motion tracking system having a small size and low-cost magnetic and inertial measurement unit (MIMU) composed by both a digital compass and a gyroscope as interaction technology. A major challenge is to minimize the err...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	28,503
2310.19061	Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V	In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	403,833
2501.08799	Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learning	This study highlights the potential of ChatGPT (specifically GPT-4o) as a competitive alternative for Face Presentation Attack Detection (PAD), outperforming several PAD models, including commercial solutions, in specific scenarios. Our results show that GPT-4o demonstrates high consistency, particularly in few-shot in...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	524,899
2312.16331	Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer	Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management strategies. This paper introduces a transformer-based model called Tom...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	418,350
2009.09932	Supervised Learning with Projected Entangled Pair States	Tensor networks, a model that originated from quantum physics, has been gradually generalized as efficient models in machine learning in recent years. However, in order to achieve exact contraction, only tree-like tensor networks such as the matrix product states and tree tensor networks have been considered, even for ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,742
1805.08948	Scalable Coordinated Exploration in Concurrent Reinforcement Learning	We consider a team of reinforcement learning agents that concurrently operate in a common environment, and we develop an approach to efficient coordinated exploration that is suitable for problems of practical scale. Our approach builds on seed sampling (Dimakopoulou and Van Roy, 2018) and randomized value function lea...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	98,296
2307.09792	A Note on Hardness of Computing Recursive Teaching Dimension	In this short note, we show that the problem of computing the recursive teaching dimension (RTD) for a concept class (given explicitly as input) requires $n^{\Omega(\log n)}$-time, assuming the exponential time hypothesis (ETH). This matches the running time $n^{O(\log n)}$ of the brute-force algorithm for the problem.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	380,295
2101.09809	NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy	Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring possible hierarchy among the covariates. This strategy may not be optimal for complex large-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,720
1803.08884	Inequity aversion improves cooperation in intertemporal social dilemmas	Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas. Models based on behavioral economics are only able to explain this phenomenon for unrealistic stateless matrix games. Recently, multi-agent reinforcement learning has been applied to generalize so...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	true	93,361
1610.00227	Approximate Gram-Matrix Interpolation for Wideband Massive MU-MIMO Systems	Numerous linear and non-linear data-detection and precoding algorithms for wideband massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems that rely on orthogonal frequency-division multiplexing (OFDM) or single-carrier frequency-division multiple access (SC-FDMA) require the computation of the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,805
2011.00186	Self-supervised Representation Learning for Evolutionary Neural Architecture Search	Recently proposed neural architecture search (NAS) algorithms adopt neural predictors to accelerate the architecture search. The capability of neural predictors to accurately predict the performance metrics of neural architecture is critical to NAS, and the acquisition of training datasets for neural predictors is time...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	204,124
1510.00384	Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples	We introduce a method to recover a continuous domain representation of a piecewise constant two-dimensional image from few low-pass Fourier samples. Assuming the edge set of the image is localized to the zero set of a trigonometric polynomial, we show the Fourier coefficients of the partial derivatives of the image sat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	47,510
1903.09673	Compliance Shaping for Control of Strength Amplification Exoskeletons with Elastic Cuffs	Exoskeletons which amplify the strength of their operators can enable heavy-duty manipulation of unknown objects. However, this type of behavior is difficult to accomplish; it requires the exoskeleton to sense and amplify the operator's interaction forces while remaining stable. But, the goals of amplification and robu...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	125,098
1707.09346	Generic second-order macroscopic traffic node model for general multi-input multi-output road junctions via a dynamic system approach	This paper addresses an open problem in traffic modeling: the second-order macroscopic node problem. A second-order macroscopic traffic model, in contrast to a first-order model, allows for variation of driving behavior across subpopulations of vehicles in the flow. The second-order models are thus more descriptive (e....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	77,984
1711.08408	Hybrid Analog and Digital Beamforming for mmWave OFDM Large-Scale Antenna Arrays	Hybrid analog and digital beamforming is a promising candidate for large-scale mmWave MIMO systems because of its ability to significantly reduce the hardware complexity of the conventional fully-digital beamforming schemes while being capable of approaching the performance of fully-digital schemes. Most of the prior w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	85,201
2409.04738	Modeling Drivers' Risk Perception via Attention to Improve Driving Assistance	Advanced Driver Assistance Systems (ADAS) alert drivers during safety-critical scenarios but often provide superfluous alerts due to a lack of consideration for drivers' knowledge or scene awareness. Modeling these aspects together in a data-driven way is challenging due to the scarcity of critical scenario data with i...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	486,486
2306.12377	Geometric Algorithms for $k$-NN Poisoning	We propose a label poisoning attack on geometric data sets against $k$-nearest neighbor classification. We provide an algorithm that can compute an $\varepsilon n$-additive approximation of the optimal poisoning in $n\cdot 2^{2^{O(d+k/\varepsilon)}}$ time for a given data set $X \in \mathbb{R}^d$, where $\|X\| = n$. Our ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	374,922
2206.03853	An Analysis of Selection Bias Issue for Online Advertising	In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space. In this paper, we show a selection bias issue that is present in an auction system. We analyze that the selection bias destroy...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	301,428
1704.02468	Basic Formal Properties of A Relational Model of The Mathematical Theory of Evidence	The paper presents a novel view of the Dempster-Shafer belief function as a measure of diversity in relational data bases. It is demonstrated that under the interpretation The Dempster rule of evidence combination corresponds to the join operator of the relational database theory. This rough-set based interpretation is...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	71,454
2010.09940	Autonomous Scheduling of Agile Spacecraft Constellations with Delay Tolerant Networking for Reactive Imaging	Small spacecraft now have precise attitude control systems available commercially, allowing them to slew in 3 degrees of freedom, and capture images within short notice. When combined with appropriate software, this agility can significantly increase response rate, revisit time and coverage. In prior work, we have demo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	201,717
1506.08690	Portfolio optimization using local linear regression ensembles in RapidMiner	In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfol...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,646
2008.10039	Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs	Annual recruitment data of new graduates are manually analyzed by human resources specialists (HR) in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Every year, different applicants send in job applications to companies. The relationships between applicants' attributes (e.g...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	192,891
2502.04840	Coherent Local Explanations for Mathematical Optimization	The surge of explainable artificial intelligence methods seeks to enhance transparency and explainability in machine learning models. At the same time, there is a growing demand for explaining decisions taken through complex algorithms used in mathematical optimization. However, current explanation methods do not take ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,338
2403.16768	DeepKnowledge: Generalisation-Driven Deep Learning Testing	Despite their unprecedented success, DNNs are notoriously fragile to small shifts in data distribution, demanding effective testing techniques that can assess their dependability. Despite recent advances in DNN testing, there is a lack of systematic testing approaches that assess the DNN's capability to generalise and ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	441,181
1201.6566	Fast and Exact Top-k Search for Random Walk with Restart	Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity score between two nodes in a graph, and it has been successfully used in many applications such as automatic image captioning, recommender syste...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	14,023
2312.05679	Schr\"odinger's control and estimation paradigm with spatio-temporal distributions on graphs	The problem of reconciling a prior probability law on paths with data was introduced by E. Schr\"odinger in 1931/32. It represents an early formulation of a maximum likelihood problem. This specific formulation can also be seen as the control problem to modify the law of a diffusion process so as to match specification...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	414,185
1905.03892	Joint Segmentation and Path Classification of Curvilinear Structures	Detection of curvilinear structures in images has long been of interest. One of the most challenging aspects of this problem is inferring the graph representation of the curvilinear network. Most existing delineation approaches first perform binary segmentation of the image and then refine it using either a set of hand...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,314
2502.05699	Context information can be more important than reasoning for time series forecasting with a large language model	With the evolution of large language models (LLMs), there is growing interest in leveraging LLMs for time series tasks. In this paper, we explore the characteristics of LLMs for time series forecasting by considering various existing and proposed prompting techniques. Forecasting for both short and long time series was...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	531,731
1910.04597	Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects	This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data. We utilize structural T1-weighted brain MRI obtained from two different studies, Cam-CAN and UK Biobank. For the purpose of our investigation, we construct a dataset consisting of brain s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	148,806
2406.18078	Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction	Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, which is the most representative and challenging task in aspect-based sentiment analysis. A key challenge in the ASQP task is the scarcity of labeled data, which limits ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	467,865
1911.02673	Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions	We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	152,417

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