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
2306.05779	Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration	Deep-learning techniques, particularly the transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. While previous methods have mainly focused on fixed-time risk prediction, time-to-event prediction (also known as survival analysis) is often more appropriate ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	372,332
2109.08027	Robust Stability Analysis of an Uncertain Aircraft Model with Scalar Parametric Uncertainty	A robust controller is specified, and the stability bounds of the uncertain closed-loop system are determined using the small gain, circle, positive real, and Popov criteria. A graphical approach is employed in order to demonstrate the ease with which the above robustness tests can be carried out on a problem of practi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	255,745
1408.5634	An application of topological graph clustering to protein function prediction	We use a semisupervised learning algorithm based on a topological data analysis approach to assign functional categories to yeast proteins using similarity graphs. This new approach to analyzing biological networks yields results that are as good as or better than state of the art existing approaches.	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	35,568
1105.0673	Mark My Words! Linguistic Style Accommodation in Social Media	The psycholinguistic theory of communication accommodation accounts for the general observation that participants in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In its almost f...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	10,236
2410.01821	NFDIcore 2.0: A BFO-Compliant Ontology for Multi-Domain Research Infrastructures	This paper presents NFDIcore 2.0, an ontology compliant with the Basic Formal Ontology (BFO) designed to represent the diverse research communities of the National Research Data Infrastructure (NFDI) in Germany. NFDIcore ensures the interoperability across various research disciplines, thereby facilitating cross-domain...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	493,973
2310.04081	A Deeply Supervised Semantic Segmentation Method Based on GAN	In recent years, the field of intelligent transportation has witnessed rapid advancements, driven by the increasing demand for automation and efficiency in transportation systems. Traffic safety, one of the tasks integral to intelligent transport systems, requires accurately identifying and locating various road elemen...	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	397,530
1909.09011	Optimal Policies of Advanced Sleep Modes for Energy-Efficient 5G networks	We study in this paper optimal control strategy for Advanced Sleep Modes (ASM) in 5G networks. ASM correspond to different levels of sleep modes ranging from deactivation of some components of the base station for several micro-seconds to switching off of almost all of them for one second or more. ASMs are made possibl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	146,123
1907.08646	Fair quantile regression	Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	139,148
2412.09378	From Bench to Bedside: A Review of Clinical Trials in Drug Discovery and Development	Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of the drug but also to explore its dosage, treatment regimens, and pote...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	516,456
2011.09906	Towards Learning Controllable Representations of Physical Systems	Learned representations of dynamical systems reduce dimensionality, potentially supporting downstream reinforcement learning (RL). However, no established methods predict a representation's suitability for control and evaluation is largely done via downstream RL performance, slowing representation design. Towards a pri...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	207,358
2408.08507	More basis reduction for linear codes: backward reduction, BKZ, slide reduction, and more	We expand on recent exciting work of Debris-Alazard, Ducas, and van Woerden [Transactions on Information Theory, 2022], which introduced the notion of basis reduction for codes, in analogy with the extremely successful paradigm of basis reduction for lattices. We generalize DDvW's LLL algorithm and size-reduction algor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	481,018
2402.05535	Batch-Schedule-Execute: On Optimizing Concurrent Deterministic Scheduling for Blockchains (Extended Version)	Executing smart contracts is a compute and storage-intensive task, which currently dominates modern blockchain's performance. Given that computers are becoming increasingly multicore, concurrency is an attractive approach to improve programs' execution runtime. A unique challenge of blockchains is that all replicas (mi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	427,894
2010.05426	Throughput Analysis of Small Cell Networks under D-TDD and FFR	Dynamic time-division duplex (D-TDD) has emerged as an effective solution to accommodate the unaligned downlink and uplink traffic in small cell networks. However, the flexibility of traffic configuration also introduces additional inter-cell interference. In this letter, we study the effectiveness of applying fraction...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	200,123
2204.04088	Stochastic Gradient-based Fast Distributed Multi-Energy Management for an Industrial Park with Temporally-Coupled Constraints	Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	290,534
2005.08230	Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation	Scene graph generation (SGG) aims to predict graph-structured descriptions of input images, in the form of objects and relationships between them. This task is becoming increasingly useful for progress at the interface of vision and language. Here, it is important - yet challenging - to perform well on novel (zero-shot...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	177,561
2405.00518	Graph-Based Multivariate Multiscale Dispersion Entropy: Efficient Implementation and Applications to Real-World Network Data	We introduce Multivariate Multiscale Graph-based Dispersion Entropy (mvDEG), a novel, computationally efficient method for analyzing multivariate time series data in graph and complex network frameworks, and demonstrate its application in real-world data. mvDEG effectively combines temporal dynamics with topological re...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	450,953
2501.07849	Unveiling Provider Bias in Large Language Models for Code Generation	Large Language Models (LLMs) have emerged as the new recommendation engines, outperforming traditional methods in both capability and scope, particularly in code generation applications. Our research reveals a novel provider bias in LLMs, namely without explicit input prompts, these models show systematic preferences f...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	524,532
2501.02441	A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Models	Large Language Models (LLMs) are rapidly gaining enormous popularity in recent years. However, the training of LLMs has raised significant privacy and legal concerns, particularly regarding the inclusion of copyrighted materials in their training data without proper attribution or licensing, which falls under the broad...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	522,485
2306.04640	ModuleFormer: Modularity Emerges from Mixture-of-Experts	Large Language Models (LLMs) have achieved remarkable results. However, existing models are expensive to train and deploy, and it is also difficult to expand their knowledge beyond pre-training data without forgetting previous knowledge. This paper proposes a new neural network architecture, ModuleFormer, that leverage...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	371,831
2305.15007	Quaternion-based non-singular terminal sliding mode control for a satellite-mounted space manipulator	In this paper, a robust control solution for a satellite equipped with a robotic manipulator is presented. First, the dynamic model of the system is derived based on quaternions to describe the evolution of the attitude of the base satellite. Then, a non-singular terminal sliding mode controller that employs quaternion...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	367,390
1911.03955	Distributed Recursive Filtering for Spatially Interconnected Systems with Randomly Occurred Missing Measurements	This paper proposed a distributed filter for spatially interconnected systems (SISs), which considers missing measurements in the sensors of sub-systems. An SIS is established by many similar sub-systems that directly interact or communicate with connective neighbors. Despite that the interactions are simple and tracta...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	152,842
2210.10781	Generalization Properties of Decision Trees on Real-valued and Categorical Features	We revisit binary decision trees from the perspective of partitions of the data. We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension. We consider three types of features: real-valued, categorical ordinal and categorical nominal, with different split rules fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,063
1507.07984	A constrained optimization perspective on actor critic algorithms and application to network routing	We propose a novel actor-critic algorithm with guaranteed convergence to an optimal policy for a discounted reward Markov decision process. The actor incorporates a descent direction that is motivated by the solution of a certain non-linear optimization problem. We also discuss an extension to incorporate function appr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	45,520
1904.08626	Ontology-based Design of Experiments on Big Data Solutions	Big data solutions are designed to cope with data of huge Volume and wide Variety, that need to be ingested at high Velocity and have potential Veracity issues, challenging characteristics that are usually referred to as the "4Vs of Big Data". In order to evaluate possibly complex big data solutions, stress tests requi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	128,131
2306.16551	Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance	This paper investigates the impact of LiDAR configuration shifts on the performance of 3D LiDAR point cloud semantic segmentation models, a topic not extensively studied before. We explore the effect of using different LiDAR channels when training and testing a 3D LiDAR point cloud semantic segmentation model, utilizin...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	376,394
1904.10158	Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles	In this paper, we investigate the decision making of autonomous vehicles in an unsignalized intersection in presence of malicious vehicles, which are vehicles that do not respect the law by not using the proper rules of the right of way. Each vehicle computes its control input as a Nash equilibrium of a game determined...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	128,568
1610.09345	Fault Detection in IEEE 14-Bus Power System with DG Penetration Using Wavelet Transform	Wavelet transform is proposed in this paper for detection of islanding and fault disturbances distributed generation (DG) based power system. An IEEE 14-bus system with DG penetration is considered for the detection of disturbances under different operating conditions. The power system is a hybrid combination of photov...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	63,043
1502.06434	ANN Model to Predict Stock Prices at Stock Exchange Markets	Stock exchanges are considered major players in financial sectors of many countries. Most Stockbrokers, who execute stock trade, use technical, fundamental or time series analysis in trying to predict stock prices, so as to advise clients. However, these strategies do not usually guarantee good returns because they gui...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	40,491
2407.03277	Evaluating Automatic Metrics with Incremental Machine Translation Systems	We introduce a dataset comprising commercial machine translations, gathered weekly over six years across 12 translation directions. Since human A/B testing is commonly used, we assume commercial systems improve over time, which enables us to evaluate machine translation (MT) metrics based on their preference for more r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	470,098
2205.07999	An Exponentially Increasing Step-size for Parameter Estimation in Statistical Models	Using gradient descent (GD) with fixed or decaying step-size is a standard practice in unconstrained optimization problems. However, when the loss function is only locally convex, such a step-size schedule artificially slows GD down as it cannot explore the flat curvature of the loss function. To overcome that issue, w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,784
1810.10065	Statistical mechanics of low-rank tensor decomposition	Often, large, high dimensional datasets collected across multiple modalities can be organized as a higher order tensor. Low-rank tensor decomposition then arises as a powerful and widely used tool to discover simple low dimensional structures underlying such data. However, we currently lack a theoretical understanding ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,186
2111.02298	STC speaker recognition systems for the NIST SRE 2021	This paper presents a description of STC Ltd. systems submitted to the NIST 2021 Speaker Recognition Evaluation for both fixed and open training conditions. These systems consists of a number of diverse subsystems based on using deep neural networks as feature extractors. During the NIST 2021 SRE challenge we focused o...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,826
2307.14539	Jailbreak in pieces: Compositional Adversarial Attacks on Multi-Modal Language Models	We introduce new jailbreak attacks on vision language models (VLMs), which use aligned LLMs and are resilient to text-only jailbreak attacks. Specifically, we develop cross-modality attacks on alignment where we pair adversarial images going through the vision encoder with textual prompts to break the alignment of the ...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	381,965
2405.07673	An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation	Massively multilingual neural machine translation (MMNMT) has been proven to enhance the translation quality of low-resource languages. In this paper, we empirically investigate the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. To assess this, we create a rob...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	453,814
2402.03110	Non-Stationary Latent Auto-Regressive Bandits	We consider the stochastic multi-armed bandit problem with non-stationary rewards. We present a novel formulation of non-stationarity in the environment where changes in the mean reward of the arms over time are due to some unknown, latent, auto-regressive (AR) state of order $k$. We call this new environment the laten...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	426,855
1102.5597	Fast and Faster: A Comparison of Two Streamed Matrix Decomposition Algorithms	With the explosion of the size of digital dataset, the limiting factor for decomposition algorithms is the \emph{number of passes} over the input, as the input is often stored out-of-core or even off-site. Moreover, we're only interested in algorithms that operate in \emph{constant memory} w.r.t. to the input size, so ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	9,404
1812.04353	Proximal Mean-field for Neural Network Quantization	Compressing large Neural Networks (NN) by quantizing the parameters, while maintaining the performance is highly desirable due to reduced memory and time complexity. In this work, we cast NN quantization as a discrete labelling problem, and by examining relaxations, we design an efficient iterative optimization procedu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	116,196
2011.04853	Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast	This paper considers the problem of multi-modal future trajectory forecast with ranking. Here, multi-modality and ranking refer to the multiple plausible path predictions and the confidence in those predictions, respectively. We propose Social-STAGE, Social interaction-aware Spatio-Temporal multi-Attention Graph convol...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	205,719
2112.00260	Ranking Distance Calibration for Cross-Domain Few-Shot Learning	Recent progress in few-shot learning promotes a more realistic cross-domain setting, where the source and target datasets are from different domains. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. This encourages us to explore more inform...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	269,072
2403.14664	ClickTree: A Tree-based Method for Predicting Math Students' Performance Based on Clickstream Data	The prediction of student performance and the analysis of students' learning behavior play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behavior, educators can gain valuable insights into the factors that influence academic outcomes and identify ...	true	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	440,187
2404.01243	A Unified and Interpretable Emotion Representation and Expression Generation	Canonical emotions, such as happy, sad, and fearful, are easy to understand and annotate. However, emotions are often compound, e.g. happily surprised, and can be mapped to the action units (AUs) used for expressing emotions, and trivially to the canonical ones. Intuitively, emotions are continuous as represented by th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,333
2410.13056	Channel-Wise Mixed-Precision Quantization for Large Language Models	Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes. Weight-only quantization presents a promising solution to reduce the memory...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	499,344
2103.00508	Citizen Participation and Machine Learning for a Better Democracy	The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct c...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	222,310
2408.02796	Gaussian Mixture based Evidential Learning for Stereo Matching	In this paper, we introduce a novel Gaussian mixture based evidential learning solution for robust stereo matching. Diverging from previous evidential deep learning approaches that rely on a single Gaussian distribution, our framework posits that individual image data adheres to a mixture-of-Gaussian distribution in st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	478,766
2309.14552	Tactile Estimation of Extrinsic Contact Patch for Stable Placement	Precise perception of contact interactions is essential for fine-grained manipulation skills for robots. In this paper, we present the design of feedback skills for robots that must learn to stack complex-shaped objects on top of each other (see Fig.1). To design such a system, a robot should be able to reason about th...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	394,655
1903.02706	Twitter Speaks: A Case of National Disaster Situational Awareness	In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	123,554
2109.00965	Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge	We present a summary of the domain adaptive cascade R-CNN method for mitosis detection of digital histopathology images. By comprehensive data augmentation and adapting existing popular detection architecture, our proposed method has achieved an F1 score of 0.7500 on the preliminary test set in MItosis DOmain Generaliz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	253,301
2003.06311	Predictive Analysis for Detection of Human Neck Postures using a robust integration of kinetics and kinematics	Human neck postures and movements need to be monitored, measured, quantified and analyzed, as a preventive measure in healthcare applications. Improper neck postures are an increasing source of neck musculoskeletal disorders, requiring therapy and rehabilitation. The motivation for the research presented in this paper ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,082
2006.05612	Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis	Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,136
2310.16742	Interferometric Neural Networks	On the one hand, artificial neural networks have many successful applications in the field of machine learning and optimization. On the other hand, interferometers are integral parts of any field that deals with waves such as optics, astronomy, and quantum physics. Here, we introduce neural networks composed of interfe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	402,854
2104.12627	Analyzing Green View Index and Green View Index best path using Google Street View and deep learning	As an important part of urban landscape research, analyzing and studying street-level greenery can increase the understanding of a city's greenery, contributing to better urban living environment planning and design. Planning the best path of urban greenery is a means to effectively maximize the use of urban greenery, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	232,275
2207.07072	A Query-Optimal Algorithm for Finding Counterfactuals	We design an algorithm for finding counterfactuals with strong theoretical guarantees on its performance. For any monotone model $f : X^d \to \{0,1\}$ and instance $x^\star$, our algorithm makes \[ {S(f)^{O(\Delta_f(x^\star))}\cdot \log d}\] queries to $f$ and returns {an {\sl optimal}} counterfactual for $x^\star$: a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	308,094
2402.00772	Neural Risk Limiting Dispatch in Power Networks: Formulation and Generalization Guarantees	Risk limiting dispatch (RLD) has been proposed as an approach that effectively trades off economic costs with operational risks for power dispatch under uncertainty. However, how to solve the RLD problem with provably near-optimal performance still remains an open problem. This paper presents a learning-based solution ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	425,716
2310.02564	Performance Analysis and Optimization of Reconfigurable Multi-Functional Surface Assisted Wireless Communications	Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two key challenges, i.e., double-fading attenuation and dependence on grid/battery. To address these challenges, this paper proposes a new RIS a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	396,904
2411.13904	Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planning	How are LLM-based agents used in the future? While many of the existing work on agents has focused on improving the performance of a specific family of objective and challenging tasks, in this work, we take a different perspective by thinking about full delegation: agents take over humans' routine decision-making proce...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	509,966
2105.14322	RPG: Learning Recursive Point Cloud Generation	In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the target 3D model, the generation starts from a single point and gets expanded recursively to produce the high-resolution point cloud via a seque...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,626
1304.1113	On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief Networks	We introduce a new heuristic algorithm for the problem of finding minimum size loop cutsets in multiply connected belief networks. We compare this algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower bounds on the performance of these algorithms with respect to one another and with respect to op...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,466
2501.14053	The Redundancy of Non-Singular Channel Simulation	Channel simulation is an alternative to quantization and entropy coding for performing lossy source coding. Recently, channel simulation has gained significant traction in both the machine learning and information theory communities, as it integrates better with machine learning-based data compression algorithms and ha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	526,961
1804.05814	Multidimensional Constellations for Uplink SCMA Systems --- A Comparative Study	Sparse code multiple access (SCMA) is a class of non-orthogonal multiple access (NOMA) that is proposed to support uplink machine-type communication services. In an SCMA system, designing multidimensional constellation plays an important role in the performance of the system. Since the behaviour of multidimensional con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	95,149
2409.12960	LVCD: Reference-based Lineart Video Colorization with Diffusion Models	We propose the first video diffusion framework for reference-based lineart video colorization. Unlike previous works that rely solely on image generative models to colorize lineart frame by frame, our approach leverages a large-scale pretrained video diffusion model to generate colorized animation videos. This approach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	489,788
2303.07205	The Science of Detecting LLM-Generated Texts	The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans. However, this has also sparked concerns about the potential misuse of such texts, such as spreading misinformation and causing disru...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	351,169
1412.8079	Persian Sentiment Analyzer: A Framework based on a Novel Feature Selection Method	In the recent decade, with the enormous growth of digital content in internet and databases, sentiment analysis has received more and more attention between information retrieval and natural language processing researchers. Sentiment analysis aims to use automated tools to detect subjective information from reviews. On...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	38,886
2407.05262	FastSpiker: Enabling Fast Training for Spiking Neural Networks on Event-based Data through Learning Rate Enhancements for Autonomous Embedded Systems	Autonomous embedded systems (e.g., robots) typically necessitate intelligent computation with low power/energy processing for completing their tasks. Such requirements can be fulfilled by embodied neuromorphic intelligence with spiking neural networks (SNNs) because of their high learning quality (e.g., accuracy) and s...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	true	false	false	470,897
2105.00071	Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark	Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress towards models that do not exhibit this issue requires evaluation metrics that can quantify its prevalence. To this end, we introduce the B...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	233,076
1811.10789	Flexible Attributed Network Embedding	Network embedding aims to find a way to encode network by learning an embedding vector for each node in the network. The network often has property information which is highly informative with respect to the node's position and role in the network. Most network embedding methods fail to utilize this information during ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,587
2309.09450	Are You Worthy of My Trust?: A Socioethical Perspective on the Impacts of Trustworthy AI Systems on the Environment and Human Society	With ubiquitous exposure of AI systems today, we believe AI development requires crucial considerations to be deemed trustworthy. While the potential of AI systems is bountiful, though, is still unknown-as are their risks. In this work, we offer a brief, high-level overview of societal impacts of AI systems. To do so, ...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	392,613
2112.09624	Reciprocity, community detection, and link prediction in dynamic networks	Many complex systems change their structure over time, in these cases dynamic networks can provide a richer representation of such phenomena. As a consequence, many inference methods have been generalized to the dynamic case with the aim to model dynamic interactions. Particular interest has been devoted to extend the ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	272,201
2009.10638	Sense-Deliberate-Act Cognitive Agents for Sense-Compute-Control Applications in the Internet of Things & Services	In this paper, we advocate Agent-Oriented Software Engi-neering (AOSE) through employing Belief-Desire-Intention (BDI) intel-ligent agents for developing Sense-Compute-Control (SCC) applications in the Internet of Things and Services (IoTS). We argue that not only the agent paradigm, in general, but also cognitive BDI ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	196,945
2309.15940	Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs	We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for grounding a variety of entities, such as object instances, agents, and regions, with free-form text-based queries. Unlike conventional semantic-based object localization approaches, our system facilitates context-aware entity localization, allo...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	395,158
2409.02730	Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties	When modeling physical properties of molecules with machine learning, it is desirable to incorporate $SO(3)$-covariance. While such models based on low body order features are not complete, we formulate and prove general completeness properties for higher order methods, and show that $6k-5$ of these features are enough...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	485,820
2305.09703	Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting	Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explain...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	364,745
2406.02204	The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification	In Data Assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining data with a model, however, while accurately estimating uncertainty, is computationally expensive and infeasible to run in real-time for complex systems. Here, ...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	460,665
2407.09571	ImPORTance -- Machine Learning-Driven Analysis of Global Port Significance and Network Dynamics for Improved Operational Efficiency	Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them. To accomplish this...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	472,650
2406.17659	DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning	Vision-language models (VLMs) have been applied to robot task planning problems, where the robot receives a task in natural language and generates plans based on visual inputs. While current VLMs have demonstrated strong vision-language understanding capabilities, their performance is still far from being satisfactory ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	467,667
1911.09304	Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings	Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two m...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	154,473
2306.08889	Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion	While VideoQA Transformer models demonstrate competitive performance on standard benchmarks, the reasons behind their success are not fully understood. Do these models capture the rich multimodal structures and dynamics from video and text jointly? Or are they achieving high scores by exploiting biases and spurious fea...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	373,587
2210.08990	Improving Object-centric Learning with Query Optimization	The ability to decompose complex natural scenes into meaningful object-centric abstractions lies at the core of human perception and reasoning. In the recent culmination of unsupervised object-centric learning, the Slot-Attention module has played an important role with its simple yet effective design and fostered many...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	324,377
2405.13803	"I Like Sunnie More Than I Expected!": Exploring User Expectation and Perception of an Anthropomorphic LLM-based Conversational Agent for Well-Being Support	The human-computer interaction (HCI) research community has a longstanding interest in exploring the mismatch between users' actual experiences and expectation toward new technologies, for instance, large language models (LLMs). In this study, we compared users' (N = 38) initial expectations against their post-interact...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	456,089
2109.03880	Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning	We apply a reinforcement meta-learning framework to optimize an integrated and adaptive guidance and flight control system for an air-to-air missile. The system is implemented as a policy that maps navigation system outputs directly to commanded rates of change for the missile's control surface deflections. The system ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	254,200
2205.07877	A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification	Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constraine...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,753
2311.13134	Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos	The demand for compact cameras capable of recording high-speed scenes with high resolution is steadily increasing. However, achieving such capabilities often entails high bandwidth requirements, resulting in bulky, heavy systems unsuitable for low-capacity platforms. To address this challenge, leveraging a coded exposu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,633
1801.07292	Convergence of Value Aggregation for Imitation Learning	Value aggregation is a general framework for solving imitation learning problems. Based on the idea of data aggregation, it generates a policy sequence by iteratively interleaving policy optimization and evaluation in an online learning setting. While the existence of a good policy in the policy sequence can be guarant...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	88,757
1907.05267	Perturbation theory approach to study the latent space degeneracy of Variational Autoencoders	The use of Variational Autoencoders in different Machine Learning tasks has drastically increased in the last years. They have been developed as denoising, clustering and generative tools, highlighting a large potential in a wide range of fields. Their embeddings are able to extract relevant information from highly dim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,307
2211.02930	1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems	This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme in...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	328,756
2304.03147	Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions	Deep neural networks have been critical in the task of Visual Question Answering (VQA), with research traditionally focused on improving model accuracy. Recently, however, there has been a trend towards evaluating the robustness of these models against adversarial attacks. This involves assessing the accuracy of VQA mo...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	356,686
2009.02035	What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS	In incremental text to speech synthesis (iTTS), the synthesizer produces an audio output before it has access to the entire input sentence. In this paper, we study the behavior of a neural sequence-to-sequence TTS system when used in an incremental mode, i.e. when generating speech output for token n, the system has ac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	194,451
2009.09496	Learning Soft Labels via Meta Learning	One-hot labels do not represent soft decision boundaries among concepts, and hence, models trained on them are prone to overfitting. Using soft labels as targets provide regularization, but different soft labels might be optimal at different stages of optimization. Also, training with fixed labels in the presence of no...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,599
2008.02863	A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition	This paper presents a transfer learning method in speech emotion recognition based on a Time-Delay Neural Network (TDNN) architecture. A major challenge in the current speech-based emotion detection research is data scarcity. The proposed method resolves this problem by applying transfer learning techniques in order to...	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,732
2406.09936	ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision Transformers	This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain Vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer, it merges similar tokens within a small local window and (2) halfway through the network, it mer...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,151
2406.19107	FDLite: A Single Stage Lightweight Face Detector Network	Face detection is frequently attempted by using heavy pre-trained backbone networks like ResNet-50/101/152 and VGG16/19. Few recent works have also proposed lightweight detectors with customized backbones, novel loss functions and efficient training strategies. The novelty of this work lies in the design of a lightweig...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	468,304
2402.02457	A Risk-aware Planning Framework of UGVs in Off-Road Environment	Planning module is an essential component of intelligent vehicle study. In this paper, we address the risk-aware planning problem of UGVs through a global-local planning framework which seamlessly integrates risk assessment methods. In particular, a global planning algorithm named Coarse2fine A* is proposed, which inco...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	426,573
1305.6126	Problems on q-Analogs in Coding Theory	The interest in $q$-analogs of codes and designs has been increased in the last few years as a consequence of their new application in error-correction for random network coding. There are many interesting theoretical, algebraic, and combinatorial coding problems concerning these q-analogs which remained unsolved. The ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,815
1809.02129	Structural Consistency and Controllability for Diverse Colorization	Colorizing a given gray-level image is an important task in the media and advertising industry. Due to the ambiguity inherent to colorization (many shades are often plausible), recent approaches started to explicitly model diversity. However, one of the most obvious artifacts, structural inconsistency, is rarely consid...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	106,968
2112.02999	Dynamic Mirror Descent based Model Predictive Control for Accelerating Robot Learning	Recent works in Reinforcement Learning (RL) combine model-free (Mf)-RL algorithms with model-based (Mb)-RL approaches to get the best from both: asymptotic performance of Mf-RL and high sample-efficiency of Mb-RL. Inspired by these works, we propose a hierarchical framework that integrates online learning for the Mb-tr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	270,041
2007.03615	Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data	There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	186,110
1903.01707	The Complexity of Morality: Checking Markov Blanket Consistency with DAGs via Morality	A family of Markov blankets in a faithful Bayesian network satisfies the symmetry and consistency properties. In this paper, we draw a bijection between families of consistent Markov blankets and moral graphs. We define the new concepts of weak recursive simpliciality and perfect elimination kits. We prove that they ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	123,323
1609.08445	AP16-OL7: A Multilingual Database for Oriental Languages and A Language Recognition Baseline	We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	61,600
2109.12871	Strong entanglement distribution of quantum networks	Large-scale quantum networks have been employed to overcome practical constraints of transmissions and storage for single entangled systems. Our goal in this article is to explore the strong entanglement distribution of quantum networks. We firstly show any connected network consisting of generalized EPR states and GHZ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	257,443
1911.11632	Minimal Linear Codes Constructed from Functions	In this paper, we consider minimal linear codes in a general construction of linear codes from q-ary functions. First, we give the sufficient and necessary condition for codewords to be minimal. Second, as an application, we present four constructions of minimal linear codes which contained some recent results as speci...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	155,183
1807.10584	Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps	Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they ne...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	103,976

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