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
2501.03160	Statistical Reconstruction For Anisotropic X-ray Dark-Field Tomography	Anisotropic X-ray Dark-Field Tomography (AXDT) is a novel imaging technology that enables the extraction of fiber structures on the micrometer scale, far smaller than standard X-ray Computed Tomography (CT) setups. Directional and structural information is relevant in medical diagnostics and material testing. Compared ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	522,779
2403.16818	Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization	Due to the significance of its various applications, source localization has garnered considerable attention as one of the most important means to confront diffusion hazards. Multi-source localization from a single-snapshot observation is especially relevant due to its prevalence. However, the inherent complexities of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	441,200
1508.07123	Proposal of ROS-compliant FPGA Component for Low-Power Robotic Systems	In recent years, robots are required to be autonomous and their robotic software are sophisticated. Robots have a problem of insufficient performance, since it cannot equip with a high-performance microprocessor due to battery-power operation. On the other hand, FPGA devices can accelerate specific functions in a robot...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	46,381
2012.05360	MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videos	Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric mapping is a task to build an object-level reconstruction where objects are separate and meaningful en...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,755
1404.7530	Design and analysis of experiments in networks: Reducing bias from interference	Estimating the effects of interventions in networks is complicated when the units are interacting, such that the outcomes for one unit may depend on the treatment assignment and behavior of many or all other units (i.e., there is interference). When most or all units are in a single connected component, it is impossibl...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	32,696
1809.10818	Learning Confidence Sets using Support Vector Machines	The goal of confidence-set learning in the binary classification setting is to construct two sets, each with a specific probability guarantee to cover a class. An observation outside the overlap of the two sets is deemed to be from one of the two classes, while the overlap is an ambiguity region which could belong to e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,991
2202.01096	Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributions	Transfer learning approaches have shown to significantly improve performance on downstream tasks. However, it is common for prior works to only report where transfer learning was beneficial, ignoring the significant trial-and-error required to find effective settings for transfer. Indeed, not all task combinations lead...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	278,364
2205.14395	Characterizing Tourist Daily Trip Chains Using Mobile Phone Big Data	Tourists tend to visit multiple destinations out of their variety-seeking motivations in their trips. Thus, it is critical to discover travel patterns involving multi-destinations in tourism research. Existing relevant research most relied on survey data or focused on citizens due to the lack of large-scale, fine-grain...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	299,347
0901.2850	On finitely recursive programs	Disjunctive finitary programs are a class of logic programs admitting function symbols and hence infinite domains. They have very good computational properties, for example ground queries are decidable while in the general case the stable model semantics is highly undecidable. In this paper we prove that a larger class...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	3,005
1908.04968	Faster Unsupervised Semantic Inpainting: A GAN Based Approach	In this paper, we propose to improve the inference speed and visual quality of contemporary baseline of Generative Adversarial Networks (GAN) based unsupervised semantic inpainting. This is made possible with better initialization of the core iterative optimization involved in the framework. To our best knowledge, this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,615
2008.08601	Neural Networks and Quantum Field Theory	We propose a theoretical understanding of neural networks in terms of Wilsonian effective field theory. The correspondence relies on the fact that many asymptotic neural networks are drawn from Gaussian processes, the analog of non-interacting field theories. Moving away from the asymptotic limit yields a non-Gaussian ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,460
2401.04004	Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data	Fault detection and isolation in complex systems are critical to ensure reliable and efficient operation. However, traditional fault detection methods often struggle with issues such as nonlinearity and multivariate characteristics of the time series variables. This article proposes a generative adversarial wavelet neu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,297
1504.03547	SDP-based State Estimation of Multi-phase Active Distribution Networks using micro-PMUs	Distribution system state estimation (DSSE) is an essential tool for operation of distribution networks, the results of which enables the operator to have a thorough observation of the system. Thus, most distribution management systems (DMS) include a single-phase state estimator. Due to non-convexity of the SE problem...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	42,044
2305.04501	SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning	In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contrastive learning methods generally produce views via random corruption or learning, which could lead to the loss of essential information and ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	362,793
1004.0892	Secure Broadcasting over Fading Channels with Statistical QoS Constraints	In this paper, the fading broadcast channel with confidential messages is studied in the presence of statistical quality of service (QoS) constraints in the form of limitations on the buffer length. We employ the effective capacity formulation to measure the throughput of the confidential and common messages. We assume...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,088
2310.18336	AITA Generating Moral Judgements of the Crowd with Reasoning	Morality is a fundamental aspect of human behavior and ethics, influencing how we interact with each other and the world around us. When faced with a moral dilemma, a person's ability to make clear moral judgments can be clouded. Due to many factors such as personal biases, emotions and situational factors people can f...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	403,485
2208.01510	s-LIME: Reconciling Locality and Fidelity in Linear Explanations	The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models. This emphasis relies on the postulate that the more locally we look at the vicinity of an instance, the simpler the black-box model becomes, and the more accurately we can mimic ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	311,183
1802.02558	Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification	This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large type I error (relevant textual messages are classified as irrelevant)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	89,792
2012.10685	Unsupervised Scale-Invariant Multispectral Shape Matching	Alignment between non-rigid stretchable structures is one of the most challenging tasks in computer vision, as the invariant properties are hard to define, and there is no labeled data for real datasets. We present unsupervised neural network architecture based upon the spectral domain of scale-invariant geometry. We b...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	212,411
2407.19872	OpenUAS: Embeddings of Cities in Japan with Anchor Data for Cross-city Analysis of Area Usage Patterns	We publicly release OpenUAS, a dataset of area embeddings based on urban usage patterns, including embeddings for over 1.3 million 50-meter square meshes covering a total area of 3,300 square kilometers. This dataset is valuable for analyzing area functions in fields such as market analysis, urban planning, transportat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,964
1906.11415	Few-Shot Video Classification via Temporal Alignment	There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video. While most previous works neglect long-term temporal or...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	136,659
2403.06872	Exploring Large Language Models and Hierarchical Frameworks for Classification of Large Unstructured Legal Documents	Legal judgment prediction suffers from the problem of long case documents exceeding tens of thousands of words, in general, and having a non-uniform structure. Predicting judgments from such documents becomes a challenging task, more so on documents with no structural annotation. We explore the classification of these ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	436,642
cs/0608103	Logic programs with monotone abstract constraint atoms	We introduce and study logic programs whose clauses are built out of monotone constraint atoms. We show that the operational concept of the one-step provability operator generalizes to programs with monotone constraint atoms, but the generalization involves nondeterminism. Our main results demonstrate that our formalis...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	539,659
2306.14292	RecBaselines2023: a new dataset for choosing baselines for recommender models	The number of proposed recommender algorithms continues to grow. The authors propose new approaches and compare them with existing models, called baselines. Due to the large number of recommender models, it is difficult to estimate which algorithms to choose in the article. To solve this problem, we have collected and ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	375,627
2202.04829	Target-aware Molecular Graph Generation	Generating molecules with desired biological activities has attracted growing attention in drug discovery. Previous molecular generation models are designed as chemocentric methods that hardly consider the drug-target interaction, limiting their practical applications. In this paper, we aim to generate molecular drugs ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	279,684
2304.12568	Performance Optimization using Multimodal Modeling and Heterogeneous GNN	Growing heterogeneity and configurability in HPC architectures has made auto-tuning applications and runtime parameters on these systems very complex. Users are presented with a multitude of options to configure parameters. In addition to application specific solutions, a common approach is to use general purpose searc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	360,267
2304.03892	Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning	The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living,...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	356,979
1701.03135	Guaranteed recovery of quantum processes from few measurements	Quantum process tomography is the task of reconstructing unknown quantum channels from measured data. In this work, we introduce compressed sensing-based methods that facilitate the reconstruction of quantum channels of low Kraus rank. Our main contribution is the analysis of a natural measurement model for this task: ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,652
2107.12095	Robotic Occlusion Reasoning for Efficient Object Existence Prediction	Reasoning about potential occlusions is essential for robots to efficiently predict whether an object exists in an environment. Though existing work shows that a robot with active perception can achieve various tasks, it is still unclear if occlusion reasoning can be achieved. To answer this question, we introduce the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	247,804
2204.04078	General Incremental Learning with Domain-aware Categorical Representations	Continual learning is an important problem for achieving human-level intelligence in real-world applications as an agent must continuously accumulate knowledge in response to streaming data/tasks. In this work, we consider a general and yet under-explored incremental learning problem in which both the class distributio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,530
2502.12292	Independence Tests for Language Models	We consider the following problem: given the weights of two models, can we test whether they were trained independently -- i.e., from independent random initializations? We consider two settings: constrained and unconstrained. In the constrained setting, we make assumptions about model architecture and training and pro...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	534,785
1808.03877	Several classes of minimal linear codes with few weights from weakly regular plateaued functions	Minimal linear codes have significant applications in secret sharing schemes and secure two-party computation. There are several methods to construct linear codes, one of which is based on functions over finite fields. Recently, many construction methods of linear codes based on functions have been proposed in the lite...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	105,021
2012.07335	LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding	The pre-training models such as BERT have achieved great results in various natural language processing problems. However, a large number of parameters need significant amounts of memory and the consumption of inference time, which makes it difficult to deploy them on edge devices. In this work, we propose a knowledge ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	211,424
2307.10458	Complying with the EU AI Act	The EU AI Act is the proposed EU legislation concerning AI systems. This paper identifies several categories of the AI Act. Based on this categorization, a questionnaire is developed that serves as a tool to offer insights by creating quantitative data. Analysis of the data shows various challenges for organizations in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	380,550
2006.12590	C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning	The James-Stein (JS) shrinkage estimator is a biased estimator that captures the mean of Gaussian random vectors.While it has a desirable statistical property of dominance over the maximum likelihood estimator (MLE) in terms of mean squared error (MSE), not much progress has been made on extending the estimator onto ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,635
2203.13718	Digital Fingerprinting of Microstructures	Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. A statistical framework is systematically developed for compressed characterisation of a population of images, which includes some classical computer vision methods as sp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,738
2409.09797	Domain and Content Adaptive Convolutions for Cross-Domain Adenocarcinoma Segmentation	Recent advances in computer-aided diagnosis for histopathology have been largely driven by the use of deep learning models for automated image analysis. While these networks can perform on par with medical experts, their performance can be impeded by out-of-distribution data. The Cross-Organ and Cross-Scanner Adenocarc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	488,481
2107.01351	EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation	The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. Existing works often fail in predicting the abnormal areas, e.g, sudden brighter and darker areas and are inclined to predict a pixel to back...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,460
1802.08530	Training wide residual networks for deployment using a single bit for each weight	For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase when this requirement is imposed. Here, we report large improvements in error rates ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	91,123
1903.01078	Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize	Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any supervision in the form of ground truth disparity or depth. The estimated depth provides additional information complementary to individual semantic features, which can be helpful for other vision tasks...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	123,189
2305.19274	Memory as a Mass-based Graph: Towards a Conceptual Framework for the Simulation Model of Human Memory in AI	There are two approaches for simulating memory as well as learning in artificial intelligence; the functionalistic approach and the cognitive approach. The necessary condition to put the second approach into account is to provide a model of brain activity that contains a quite good congruence with observational facts s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	369,453
2106.02954	Denoising Word Embeddings by Averaging in a Shared Space	We introduce a new approach for smoothing and improving the quality of word embeddings. We consider a method of fusing word embeddings that were trained on the same corpus but with different initializations. We project all the models to a shared vector space using an efficient implementation of the Generalized Procrust...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	239,103
2012.11547	Offline Reinforcement Learning from Images with Latent Space Models	Offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. Offline RL enables extensive use and re-use of historical datasets, while also alleviating safety concerns associated with online exploration, thereby expanding the real-world applicability ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	212,668
2412.15079	A Traffic Adapative Physics-informed Learning Control for Energy Savings of Connected and Automated Vehicles	Model predictive control has emerged as an effective approach for real-time optimal control of connected and automated vehicles. However, nonlinear dynamics of vehicle and traffic systems make accurate modeling and real-time optimization challenging. Learning-based control offer a promising alternative, as they adapt t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	518,936
2206.11056	Generational Differences in Automobility: Comparing America's Millennials and Gen Xers Using Gradient Boosting Decision Trees	Whether the Millennials are less auto-centric than the previous generations has been widely discussed in the literature. Most existing studies use regression models and assume that all factors are linear-additive in contributing to the young adults' driving behaviors. This study relaxes this assumption by applying a no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,138
2409.00257	Improving the Region of Attraction of a Multi-rotor UAV by Estimating Unknown Disturbances	This study presents a machine learning-aided approach to accurately estimate the region of attraction (ROA) of a multi-rotor unmanned aerial vehicle (UAV) controlled using a linear quadratic regulator (LQR) controller. Conventional ROA estimation approaches rely on a nominal dynamic model for ROA calculation, leading t...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	484,843
2010.02011	A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications	A physics-informed neural network is developed to solve conductive heat transfer partial differential equation (PDE), along with convective heat transfer PDEs as boundary conditions (BCs), in manufacturing and engineering applications where parts are heated in ovens. Since convective coefficients are typically unknown,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,878
2205.04329	SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization	There are considerable interests in automatic stroke lesion segmentation on magnetic resonance (MR) images in the medical imaging field, as stroke is an important cerebrovascular disease. Although deep learning-based models have been proposed for this task, generalizing these models to unseen sites is difficult due to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,608
2208.11435	Bidirectional Contrastive Split Learning for Visual Question Answering	Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications such as home robots and medical diagnoses. One significant challenge is to devise a robust decentralized learning framework for various client models where centralized data collection is refrained due to confidentiality concern...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	314,426
2408.11925	An Open Knowledge Graph-Based Approach for Mapping Concepts and Requirements between the EU AI Act and International Standards	The many initiatives on trustworthy AI result in a confusing and multipolar landscape that organizations operating within the fluid and complex international value chains must navigate in pursuing trustworthy AI. The EU's AI Act will now shift the focus of such organizations toward conformance with the technical requir...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	482,505
2303.06268	Trust your neighbours: Penalty-based constraints for model calibration	Ensuring reliable confidence scores from deep networks is of pivotal importance in critical decision-making systems, notably in the medical domain. While recent literature on calibrating deep segmentation networks has led to significant progress, their uncertainty is usually modeled by leveraging the information of ind...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,767
2408.07673	Deep Learning: a Heuristic Three-stage Mechanism for Grid Searches to Optimize the Future Risk Prediction of Breast Cancer Metastasis Using EHR-based Clinical Data	A grid search, at the cost of training and testing a large number of models, is an effective way to optimize the prediction performance of deep learning models. A challenging task concerning grid search is the time management. Without a good time management scheme, a grid search can easily be set off as a mission that ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	480,683
2011.13528	The NEOLIX Open Dataset for Autonomous Driving	With the gradual maturity of 5G technology,autonomous driving technology has attracted moreand more attention among the research commu-nity. Autonomous driving vehicles rely on the co-operation of artificial intelligence, visual comput-ing, radar, monitoring equipment and GPS, whichenables computers to operate motor ve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,504
2203.14242	"This is Fake News": Characterizing the Spontaneous Debunking from Twitter Users to COVID-19 False Information	False information spreads on social media, and fact-checking is a potential countermeasure. However, there is a severe shortage of fact-checkers; an efficient way to scale fact-checking is desperately needed, especially in pandemics like COVID-19. In this study, we focus on spontaneous debunking by social media users, ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	287,933
2212.08808	Convergence, Consensus and Dissensus in the Weighted-Median Opinion Dynamics	Mechanistic and tractable mathematical models play a key role in understanding how social influence shapes public opinions. Recently, a weighted-median mechanism has been proposed as a new micro-foundation of opinion dynamics and validated via experimental data. Numerical studies also indicate that this new mechanism r...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	336,880
1601.00088	Understanding Symmetric Smoothing Filters: A Gaussian Mixture Model Perspective	Many patch-based image denoising algorithms can be formulated as applying a smoothing filter to the noisy image. Expressed as matrices, the smoothing filters must be row normalized so that each row sums to unity. Surprisingly, if we apply a column normalization before the row normalization, the performance of the smoot...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	50,603
2307.11823	HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness	Convolutional Neural Networks (CNN) are known to exhibit poor generalization performance under distribution shifts. Their generalization have been studied extensively, and one line of work approaches the problem from a frequency-centric perspective. These studies highlight the fact that humans and CNNs might focus on d...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	381,047
2003.00847	Learning to Deblur and Generate High Frame Rate Video with an Event Camera	Event cameras are bio-inspired cameras which can measure the change of intensity asynchronously with high temporal resolution. One of the event cameras' advantages is that they do not suffer from motion blur when recording high-speed scenes. In this paper, we formulate the deblurring task on traditional cameras directe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	166,460
1710.01693	Model-free prediction of noisy chaotic time series by deep learning	We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations. The proposed deep learning model aims to predict the conditional probability distribution of a state variable. The Long Short-Term Memory network (LSTM) is employed to model the nonlinear dynamics and a s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	82,045
1903.12258	Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market	Stock market prediction is still a challenging problem because there are many factors effect to the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment and economic factors. This work explores the predictability in the stock market using Deep Convolu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,670
1910.07641	RGB-D Individual Segmentation	Fine-grained recognition task deals with sub-category classification problem, which is important for real-world applications. In this work, we are particularly interested in the segmentation task on the \emph{finest-grained} level, which is specifically named "individual segmentation". In other words, the individual-le...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,671
2312.02522	MASP: Scalable GNN-based Planning for Multi-Agent Navigation	We investigate multi-agent navigation tasks, where multiple agents need to reach initially unassigned goals in a limited time. Classical planning-based methods suffer from expensive computation overhead at each step and offer limited expressiveness for complex cooperation strategies. In contrast, reinforcement learning...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	412,905
2304.10691	SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model	Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. Firstly, there is a shortage of dermatologists accessible to diagnose pa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	359,512
2406.10196	TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Planners	Travel planning is a complex task that involves generating a sequence of actions related to visiting places subject to constraints and maximizing some user satisfaction criteria. Traditional approaches rely on problem formulation in a given formal language, extracting relevant travel information from web sources, and u...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	464,268
1909.09072	Learning Optimal and Near-Optimal Lexicographic Preference Lists	We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists). Given a set of examples that are pairwise ordinal preferences over a universe of objects built of attributes of discrete values, we want to learn (1) an optimal LP-list that decides the maximum ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	146,144
2208.09322	Entropy Augmented Reinforcement Learning	Deep reinforcement learning was instigated with the presence of trust region methods, being scalable and efficient. However, the pessimism of such algorithms, among which it forces to constrain in a trust region by all means, has been proven to suppress the exploration and harm the performance. Exploratory algorithm su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,660
2109.13058	Channel Customization for Joint Tx-RISs-Rx Design in Hybrid mmWave Systems	In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are introduced in this study to customize the wireless channel. Utilizing the RIS to reshape elect...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	257,505
1102.2825	Algorithmic Aspects of Energy-Delay Tradeoff in Multihop Cooperative Wireless Networks	We consider the problem of energy-efficient transmission in delay constrained cooperative multihop wireless networks. The combinatorial nature of cooperative multihop schemes makes it difficult to design efficient polynomial-time algorithms for deciding which nodes should take part in cooperation, and when and with wha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	9,176
2407.09019	Heterogeneous Subgraph Network with Prompt Learning for Interpretable Depression Detection on Social Media	Massive social media data can reflect people's authentic thoughts, emotions, communication, etc., and therefore can be analyzed for early detection of mental health problems such as depression. Existing works about early depression detection on social media lacked interpretability and neglected the heterogeneity of soc...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	472,415
2306.09116	Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning	Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer. Unlike other organs with simpler shapes or topology, the airway's complex tree structure imposes an unbearable burden to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	373,683
2403.16794	CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation	Curb detection is a crucial function in intelligent driving, essential for determining drivable areas on the road. However, the complexity of road environments makes curb detection challenging. This paper introduces CurbNet, a novel framework for curb detection utilizing point cloud segmentation. To address the lack of...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	441,192
2004.10063	Cyber-Physical Mobility Lab: An Open-Source Platform for Networked and Autonomous Vehicles	This paper introduces our Cyber-Physical Mobility Lab (CPM Lab). It is an open-source development environment for networked and autonomous vehicles with focus on networked decision-making, trajectory planning, and control. The CPM Lab hosts 20 physical model-scale vehicles ({\mu}Cars) which we can seamlessly extend by ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	173,531
2108.08224	Transformers predicting the future. Applying attention in next-frame and time series forecasting	Recurrent Neural Networks were, until recently, one of the best ways to capture the timely dependencies in sequences. However, with the introduction of the Transformer, it has been proven that an architecture with only attention-mechanisms without any RNN can improve on the results in various sequence processing tasks ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,181
1412.8669	Fast and accurate determination of modularity and its effect size	We present a fast spectral algorithm for community detection in complex networks. Our method searches for the partition with the maximum value of the modularity via the interplay of several refinement steps that include both agglomeration and division. We validate the accuracy of the algorithm by applying it to several...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	38,941
1710.00196	New binary and ternary LCD codes	LCD codes are linear codes with important cryptographic applications. Recently, a method has been presented to transform any linear code into an LCD code with the same parameters when it is supported on a finite field with cardinality larger than 3. Hence, the study of LCD codes is mainly open for binary and ternary fi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	81,823
2410.17540	The Dispersion of Broadcast Channels With Degraded Message Sets Using Gaussian Codebooks	We study the two-user broadcast channel with degraded message sets and derive second-order achievability rate regions. Specifically, the channel noises are not necessarily Gaussian and we use spherical codebooks for both users. The weak user with worse channel quality applies nearest neighbor decoding by treating the s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	501,511
2501.00054	AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors	Security concerns surrounding text-to-image diffusion models have driven researchers to unlearn inappropriate concepts through fine-tuning. Recent fine-tuning methods typically align the prediction distributions of unsafe prompts with those of predefined text anchors. However, these techniques exhibit a considerable pe...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	521,512
2409.16510	Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity	Six-dimensional movable antenna (6DMA) is an innovative technology to improve wireless network capacity by adjusting 3D positions and 3D rotations of antenna surfaces based on channel spatial distribution. However, the existing works on 6DMA have assumed a central processing unit (CPU) to jointly process the signals of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	491,377
2402.07309	HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs	Hypergraphs are characterized by complex topological structure, representing higher-order interactions among multiple entities through hyperedges. Lately, hypergraph-based deep learning methods to learn informative data representations for the problem of node classification on text-attributed hypergraphs have garnered ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	428,651
1706.08629	Dense Non-rigid Structure-from-Motion Made Easy - A Spatial-Temporal Smoothness based Solution	This paper proposes a simple spatial-temporal smoothness based method for solving dense non-rigid structure-from-motion (NRSfM). First, we revisit the temporal smoothness and demonstrate that it can be extended to dense case directly. Second, we propose to exploit the spatial smoothness by resorting to the Laplacian of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,023
2109.10596	Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances	This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state-space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,680
1006.3573	Nested Polar Codes for Wiretap and Relay Channels	We show that polar codes asymptotically achieve the whole capacity-equivocation region for the wiretap channel when the wiretapper's channel is degraded with respect to the main channel, and the weak secrecy notion is used. Our coding scheme also achieves the capacity of the physically degraded receiver-orthogonal rela...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,829
1206.3460	Constrained Distributed Algebraic Connectivity Maximization in Robotic Networks	We consider the problem of maximizing the algebraic connectivity of the communication graph in a network of mobile robots by moving them into appropriate positions. We define the Laplacian of the graph as dependent on the pairwise distance between the robots and we approximate the problem as a sequence of Semi-Definite...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	16,565
2502.02936	Every Angle Is Worth A Second Glance: Mining Kinematic Skeletal Structures from Multi-view Joint Cloud	Multi-person motion capture over sparse angular observations is a challenging problem under interference from both self- and mutual-occlusions. Existing works produce accurate 2D joint detection, however, when these are triangulated and lifted into 3D, available solutions all struggle in selecting the most accurate can...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	530,536
2308.10852	Uncertainty benchmarks for time-dependent transport problems	Verification solutions for uncertainty quantification are presented for time dependent transport problems where $c$, the scattering ratio, is uncertain. The method of polynomial chaos expansions is employed for quick and accurate calculation of the quantities of interest and uncollided solutions are used to treat part ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	386,912
2109.04870	MultiAzterTest: a Multilingual Analyzer on Multiple Levels of Language for Readability Assessment	Readability assessment is the task of determining how difficult or easy a text is or which level/grade it has. Traditionally, language dependent readability formula have been used, but these formulae take few text characteristics into account. However, Natural Language Processing (NLP) tools that assess the complexity ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	254,572
2303.04630	Mining the contribution of intensive care clinical course to outcome after traumatic brain injury	Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1,166 pre-ICU and ICU variables) to model the individu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,162
2411.05969	Toward Transdisciplinary Approaches to Audio Deepfake Discernment	This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Intelligence methods and linguistics. With an avalanche of tools for the generation of realistic-sounding fake speech on one side, the detection...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	506,911
2201.00982	Reliable Transactions in Serverless-Edge Architecture	Modern edge applications demand novel solutions where edge applications do not have to rely on a single cloud provider (which cannot be in the vicinity of every edge device) or dedicated edge servers (which cannot scale as clouds) for processing compute-intensive tasks. A recent computing philosophy, Sky computing, pro...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	274,115
2410.15299	Does ChatGPT Have a Poetic Style?	Generating poetry has become a popular application of LLMs, perhaps especially of OpenAI's widely-used chatbot ChatGPT. What kind of poet is ChatGPT? Does ChatGPT have its own poetic style? Can it successfully produce poems in different styles? To answer these questions, we prompt the GPT-3.5 and GPT-4 models to genera...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	500,469
2210.14373	Shared Autonomous Vehicle Mobility for a Transportation Underserved City	This paper proposes the use of an on-demand, ride hailed and ride-Shared Autonomous Vehicle (SAV) service as a feasible solution to serve the mobility needs of a small city where fixed route, circulator type public transportation may be too expensive to operate. The presented work builds upon our earlier work that mode...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	326,516
1811.00233	Survey on Vision-based Path Prediction	Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to b...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	112,046
2108.12765	Reservoir Computers with Random and Optimized Time-Shifts	We investigate the effects of application of random time-shifts to the readouts of a reservoir computer in terms of both accuracy (training error) and performance (testing error.) For different choices of the reservoir parameters and different `tasks', we observe a substantial improvement in both accuracy and performan...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	252,602
2107.01106	Screening for a Reweighted Penalized Conditional Gradient Method	The conditional gradient method (CGM) is widely used in large-scale sparse convex optimization, having a low per iteration computational cost for structured sparse regularizers and a greedy approach to collecting nonzeros. We explore the sparsity acquiring properties of a general penalized CGM (P-CGM) for convex regula...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,373
2305.07969	GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content	This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models. To this end, we first collected and released a pre-processed dataset named OpenGPTText, which consists of rephrased content generated using ChatGPT. We then designed, implemented, and trained two different...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	364,102
2311.10771	Automatic Restoration of Diacritics for Speech Data Sets	Automatic text-based diacritic restoration models generally have high diacritic error rates when applied to speech transcripts as a result of domain and style shifts in spoken language. In this work, we explore the possibility of improving the performance of automatic diacritic restoration when applied to speech data b...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	408,644
1207.2597	Automated Training and Maintenance through Kinect	In this paper, we have worked on reducing burden on mechanic involving complex automobile maintenance activities that are performed in centralised workshops. We have presented a system prototype that combines Augmented Reality with Kinect. With the use of Kinect, very high quality sensors are available at considerably ...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	17,401
2102.05628	On the Regularity of Attention	Attention is a powerful component of modern neural networks across a wide variety of domains. In this paper, we seek to quantify the regularity (i.e. the amount of smoothness) of the attention operation. To accomplish this goal, we propose a new mathematical framework that uses measure theory and integral operators to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,496
2001.05215	Direct Visual-Inertial Ego-Motion Estimation via Iterated Extended Kalman Filter	This letter proposes a reactive navigation strategy for recovering the altitude, translational velocity and orientation of Micro Aerial Vehicles. The main contribution lies in the direct and tight fusion of Inertial Measurement Unit (IMU) measurements with monocular feedback under an assumption of a single planar scene...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	160,476
1809.10853	Adaptive Input Representations for Neural Language Modeling	We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	109,002

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