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
1901.09681	Network Lens: Node Classification in Topologically Heterogeneous Networks	We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network. We zoom in to the network using lenses of different sizes to capture the local structure of the network. These network signatures are then weighted to provide a set of predicted labels for every node. W...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,815
2210.01292	Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees	This paper proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot controllers, including closed-box ones. A Gaussian Process (GP), trained with randomized short trajectories over the state-space, acts as a surrog...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	321,199
1409.0758	Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-stage Cancer	There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary differential equation models, such as the lack of stochasticity, repres...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	35,757
2502.13668	PeerQA: A Scientific Question Answering Dataset from Peer Reviews	We present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. PeerQA questions have been sourced from peer reviews, which contain questions that reviewers raised while thoroughly examining the scientific article. Answers have been annotated by the original authors of each paper. The datas...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	535,462
2307.13762	Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor	Neuromorphic processors have garnered considerable interest in recent years for their potential in energy-efficient and high-speed computing. The Locally Competitive Algorithm (LCA) has been utilized for power efficient sparse coding on neuromorphic processors, including the first Loihi processor. With the Loihi 2 proc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	381,691
2310.11614	Learning a Hierarchical Planner from Humans in Multiple Generations	A typical way in which a machine acquires knowledge from humans is by programming. Compared to learning from demonstrations or experiences, programmatic learning allows the machine to acquire a novel skill as soon as the program is written, and, by building a library of programs, a machine can quickly learn how to perf...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	400,706
2311.17664	On the Convergence Rate of Linear Datalogo over Stable Semirings	Datalogo is an extension of Datalog, where instead of a program being a collection of union of conjunctive queries over the standard Boolean semiring, a program may now be a collection of sum-sum-product queries over an arbitrary commutative partially ordered pre-semiring. Datalogo is more powerful than Datalog in that...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	411,377
2312.13175	Nonlinear moving horizon estimation for robust state and parameter estimation -- extended version	We propose a moving horizon estimation scheme to estimate the states and the unknown constant parameters of general nonlinear uncertain discrete-time systems. The proposed framework and analysis explicitly do not involve the a priori verification of a particular excitation condition for the parameters. Instead, we use ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	417,230
2006.06480	Adaptation Strategies for Automated Machine Learning on Evolving Data	Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of data stream challenges such as concept drift on the performanc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,446
2211.05189	Deterministic Random Walk Model in NetLogo and the Identification of Asymmetric Saturation Time in Random Graph	Interactive programming environments are powerful tools for promoting innovative network thinking, teaching science of complexity, and exploring emergent phenomena. This paper reports on our recent development of the deterministic random walk model in NetLogo, a leading platform for computational thinking, eco-system t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	329,457
1905.04433	Learning an Unknown Network State in Routing Games	We study learning dynamics induced by myopic travelers who repeatedly play a routing game on a transportation network with an unknown state. The state impacts cost functions of one or more edges of the network. In each stage, travelers choose their routes according to Wardrop equilibrium based on public belief of the s...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	130,466
2203.09831	DTA: Physical Camouflage Attacks using Differentiable Transformation Network	To perform adversarial attacks in the physical world, many studies have proposed adversarial camouflage, a method to hide a target object by applying camouflage patterns on 3D object surfaces. For obtaining optimal physical adversarial camouflage, previous studies have utilized the so-called neural renderer, as it supp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,305
2002.03742	Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications	As camera quality improves and their deployment moves to areas with limited bandwidth, communication bottlenecks can impair real-time constraints of an ITS application, such as video-based real-time pedestrian detection. Video compression reduces the bandwidth requirement to transmit the video but degrades the video qu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,384
2201.11307	Dissecting the impact of different loss functions with gradient surgery	Pair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped closer than images from different classes. The literature reports a large and growing set of variations of the pair-wise loss strategies. Here ...	false	false	false	false	true	true	true	false	false	false	false	true	false	false	false	false	false	false	277,255
2010.14489	Distributed Constraint-Coupled Optimization via Primal Decomposition over Random Time-Varying Graphs	The paper addresses large-scale, convex optimization problems that need to be solved in a distributed way by agents communicating according to a random time-varying graph. Specifically, the goal of the network is to minimize the sum of local costs, while satisfying local and coupling constraints. Agents communicate acc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	203,460
1701.07810	Intelligent Topic Selection for Low-Cost Information Retrieval Evaluation: A New Perspective on Deep vs. Shallow Judging	While test collections provide the cornerstone for Cranfield-based evaluation of information retrieval (IR) systems, it has become practically infeasible to rely on traditional pooling techniques to construct test collections at the scale of today's massive document collections. In this paper, we propose a new intellig...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	67,359
2405.00749	More is Better: Deep Domain Adaptation with Multiple Sources	In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	451,069
2312.03151	Multitask Learning Can Improve Worst-Group Outcomes	In order to create machine learning systems that serve a variety of users well, it is vital to not only achieve high average performance but also ensure equitable outcomes across diverse groups. However, most machine learning methods are designed to improve a model's average performance on a chosen end task without con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,151
2411.11758	The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning	Large Multimodal Models (LMMs) exhibit impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the predominantly Western-centric nature of most data and models. Conversely, multi-agent models have shown significant capability in solving comp...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	509,163
2404.16556	Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models	Few-shot image synthesis entails generating diverse and realistic images of novel categories using only a few example images. While multiple recent efforts in this direction have achieved impressive results, the existing approaches are dependent only upon the few novel samples available at test time in order to generat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,541
2404.03429	Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions	Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in education. With the emergence of generative artificial intelligence, large language models (LLMs) further entitle the systems to complex and co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	444,256
2201.10247	Identification of System Vulnerability under a Smart Sensor Attack via Attack Model Reduction	In this work, we investigate how to make use of model reduction techniques to identify the vulnerability of a closed-loop system, consisting of a plant and a supervisor, that might invite attacks. Here, the system vulnerability refers to the existence of key observation sequences that could be exploited by a specific s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	276,923
2407.14700	Composer's Assistant 2: Interactive Multi-Track MIDI Infilling with Fine-Grained User Control	We introduce Composer's Assistant 2, a system for interactive human-computer composition in the REAPER digital audio workstation. Our work upgrades the Composer's Assistant system (which performs multi-track infilling of symbolic music at the track-measure level) with a wide range of new controls to give users fine-gra...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	474,869
2102.04060	OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications	Many applications of Visual SLAM, such as augmented reality, virtual reality, robotics or autonomous driving, require versatile, robust and precise solutions, most often with real-time capability. In this work, we describe OV$^{2}$SLAM, a fully online algorithm, handling both monocular and stereo camera setups, various...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	218,980
2009.12145	Direct computation of nonlinear mapping via normal form for reduced-order models of finite element nonlinear structures	The direct computation of the third-order normal form for a geometrically nonlinear structure discretised with the finite element (FE) method, is detailed. The procedure allows to define a nonlinear mapping in order to derive accurate reduced-order models (ROM) relying on invariant manifold theory. The proposed reducti...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	197,345
2309.04800	VeRi3D: Generative Vertex-based Radiance Fields for 3D Controllable Human Image Synthesis	Unsupervised learning of 3D-aware generative adversarial networks has lately made much progress. Some recent work demonstrates promising results of learning human generative models using neural articulated radiance fields, yet their generalization ability and controllability lag behind parametric human models, i.e., th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	390,857
2311.13665	A Joint Gradient and Loss Based Clustered Federated Learning Design	In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement FL training within each cluster is proposed. In particular, our designed clustered FL algorithm must overcome two challenges associated with ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	409,824
2411.01683	ROAD-Waymo: Action Awareness at Scale for Autonomous Driving	Autonomous Vehicle (AV) perception systems require more than simply seeing, via e.g., object detection or scene segmentation. They need a holistic understanding of what is happening within the scene for safe interaction with other road users. Few datasets exist for the purpose of developing and training algorithms to c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,180
2410.21258	Quantum computing and persistence in topological data analysis	Topological data analysis (TDA) aims to extract noise-robust features from a data set by examining the number and persistence of holes in its topology. We show that a computational problem closely related to a core task in TDA -- determining whether a given hole persists across different length scales -- is $\mathsf{BQ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	503,150
2005.00675	Opportunistic Decoding with Timely Correction for Simultaneous Translation	Simultaneous translation has many important application scenarios and attracts much attention from both academia and industry recently. Most existing frameworks, however, have difficulties in balancing between the translation quality and latency, i.e., the decoding policy is usually either too aggressive or too conserv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	175,324
2401.13961	TriSAM: Tri-Plane SAM for zero-shot cortical blood vessel segmentation in VEM images	While imaging techniques at macro and mesoscales have garnered substantial attention and resources, microscale Volume Electron Microscopy (vEM) imaging, capable of revealing intricate vascular details, has lacked the necessary benchmarking infrastructure. In this paper, we address a significant gap in this field of neu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,915
2306.11737	Neural ShDF: Reviving an Efficient and Consistent Mesh Segmentation Method	Partitioning a polygonal mesh into meaningful parts can be challenging. Many applications require decomposing such structures for further processing in computer graphics. In the last decade, several methods were proposed to tackle this problem, at the cost of intensive computational times. Recently, machine learning ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	374,695
1906.05061	Probing Multilingual Sentence Representations With X-Probe	This paper extends the task of probing sentence representations for linguistic insight in a multilingual domain. In doing so, we make two contributions: first, we provide datasets for multilingual probing, derived from Wikipedia, in five languages, viz. English, French, German, Spanish and Russian. Second, we evaluate ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	134,917
2207.12280	ArtFID: Quantitative Evaluation of Neural Style Transfer	The field of neural style transfer has experienced a surge of research exploring different avenues ranging from optimization-based approaches and feed-forward models to meta-learning methods. The developed techniques have not just progressed the field of style transfer, but also led to breakthroughs in other areas of c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,963
1412.4067	Monotonicity of quantum relative entropy and recoverability	The relative entropy is a principal measure of distinguishability in quantum information theory, with its most important property being that it is non-increasing with respect to noisy quantum operations. Here, we establish a remainder term for this inequality that quantifies how well one can recover from a loss of info...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,346
2411.13154	DMQR-RAG: Diverse Multi-Query Rewriting for RAG	Large language models often encounter challenges with static knowledge and hallucinations, which undermine their reliability. Retrieval-augmented generation (RAG) mitigates these issues by incorporating external information. However, user queries frequently contain noise and intent deviations, necessitating query rewri...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	509,698
2308.12069	Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning	The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying the driving styles of its nearby AVs can reliably evaluate the risk of collisions and make more reasonable driving decisions. However, there h...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	387,410
2412.11653	Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability	In fact-checking, structure and phrasing of claims critically influence a model's ability to predict verdicts accurately. Social media content in particular rarely serves as optimal input for verification systems, which necessitates pre-processing to extract the claim from noisy context before fact checking. Prior work...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	517,513
1508.01648	Predicting academic major of students using bayesian networks to the case of iran	In this study, which took place current year in the city of Maragheh in IRAN. Number of high school students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and science were studied and compared. The purpose of this research is to predict the academic major of high school st...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	45,807
1811.01314	Modeling Traffic Networks Using Integrated Route and Link Data	Real-time navigation services, such as Google Maps and Waze, are widely used in daily life. These services provide rich data resources in real-time traffic conditions and travel time predictions; however, they have not been fully applied in transportation modeling. This paper aims to use traffic data from Google Maps a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	112,333
2407.05550	MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning	We present the MEEG dataset, a multi-modal collection of music-induced electroencephalogram (EEG) recordings designed to capture emotional responses to various musical stimuli across different valence and arousal levels. This public dataset facilitates an in-depth examination of brainwave patterns within musical contex...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	471,022
1701.06770	Analysis of Breakdown Probability of Wireless Sensor Networks with Unreliable Relay Nodes	In the present paper, we derive an upper bound of the average network breakdown probability of packet networks with unreliable relay nodes. We here assume that relay nodes get independently broken with a given node breakdown probability. A survivor graph is the induced subgraph obtained by removing the broken relay nod...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,194
2412.09150	Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselines	Adversarial attacks on traffic sign classification models were among the first successfully tried in the real world. Since then, the research in this area has been mainly restricted to repeating baseline models, such as LISA-CNN or GTSRB-CNN, and similar experiment settings, including white and black patches on traffic...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	516,379
2408.11822	State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey	With the continuous breakthroughs in core technology, the dawn of large-scale integration of robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The fusion of artificial intelligence technology with robot hardware is seeing broad ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	482,450
2207.13307	Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis	Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on their fixed points and attractors. We consider marker p...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	310,258
1610.09543	FEAST: An Automated Feature Selection Framework for Compilation Tasks	The success of the application of machine-learning techniques to compilation tasks can be largely attributed to the recent development and advancement of program characterization, a process that numerically or structurally quantifies a target program. While great achievements have been made in identifying key features ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	63,079
2407.14102	MSSP : A Versatile Multi-Scenario Adaptable Intelligent Robot Simulation Platform Based on LIDAR-Inertial Fusion	This letter presents a multi-scenario adaptable intelligent robot simulation platform based on LIDAR-inertial fusion, with three main features: (1 The platform includes an versatile robot model that can be freely controlled through manual control or autonomous tracking. This model is equipped with various types of LIDA...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	474,650
2210.00393	(Non)-Coherent MU-MIMO Block Fading Channels with Finite Blocklength and Linear Processing	This paper studies the coherent and non-coherent multiuser multiple-input multiple-output (MU-MIMO) uplink system in the finite blocklength regime. The i.i.d. Gaussian codebook is assumed for each user. To be more specific, the BS first uses two popular linear processing schemes to combine the signals transmitted from ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	320,847
2105.15064	Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning	Algorithmic Bias can be due to bias in the training data or issues with the algorithm itself. These algorithmic issues typically relate to problems with model capacity and regularisation. This underestimation bias may arise because the model has been optimised for good generalisation accuracy without any explicit consi...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	237,902
1901.00536	Visualizing Deep Similarity Networks	For convolutional neural network models that optimize an image embedding, we propose a method to highlight the regions of images that contribute most to pairwise similarity. This work is a corollary to the visualization tools developed for classification networks, but applicable to the problem domains better suited to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	117,798
2411.02275	Breaking the Reclustering Barrier in Centroid-based Deep Clustering	This work investigates an important phenomenon in centroid-based deep clustering (DC) algorithms: Performance quickly saturates after a period of rapid early gains. Practitioners commonly address early saturation with periodic reclustering, which we demonstrate to be insufficient to address performance plateaus. We cal...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	505,421
2104.06703	Deep Permutation Equivariant Structure from Motion	Existing deep methods produce highly accurate 3D reconstructions in stereo and multiview stereo settings, i.e., when cameras are both internally and externally calibrated. Nevertheless, the challenge of simultaneous recovery of camera poses and 3D scene structure in multiview settings with deep networks is still outsta...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	230,168
2107.06129	Bidirectional Regression for Arbitrary-Shaped Text Detection	Arbitrary-shaped text detection has recently attracted increasing interests and witnessed rapid development with the popularity of deep learning algorithms. Nevertheless, existing approaches often obtain inaccurate detection results, mainly due to the relatively weak ability to utilize context information and the inapp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,993
1812.07941	Automatic Detection of Reflective Thinking in Mathematical Problem Solving based on Unconstrained Bodily Exploration	For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinkin...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,908
2003.08494	Progress Extrapolating Algorithmic Learning to Arbitrary Sequence Lengths	Recent neural network models for algorithmic tasks have led to significant improvements in extrapolation to sequences much longer than training, but it remains an outstanding problem that the performance still degrades for very long or adversarial sequences. We present alternative architectures and loss-terms to addres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,751
1607.02748	Adversarial Training For Sketch Retrieval	Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representations learned by GANs have not yet been applied to retrieval. In this paper, we show that the representations learned by GANs can indeed be u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,408
1811.11977	DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama	We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama. To achieve better prediction accuracy, our method leverages two projections of the panorama at once, namely the equirectangular panorama-view and the perspective ceiling-view, that each contains...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,897
2410.16033	TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling	Inference-time alignment enhances the performance of large language models without requiring additional training or fine-tuning but presents challenges due to balancing computational efficiency with high-quality output. Best-of-N (BoN) sampling, as a simple yet powerful approach, generates multiple responses and select...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	500,850
2309.03631	Insights Into the Inner Workings of Transformer Models for Protein Function Prediction	Motivation: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent representations inside of transformer models, which were finetuned to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,447
2010.02814	Anomaly Detection Approach to Identify Early Cases in a Pandemic using Chest X-rays	The current COVID-19 pandemic is now getting contained, albeit at the cost of morethan2.3million human lives. A critical phase in any pandemic is the early detection of cases to develop preventive treatments and strategies. In the case of COVID-19,several studies have indicated that chest radiography images of the infe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	199,176
1905.13568	Quantization Loss Re-Learning Method	In order to quantize the gate parameters of the LSTM (Long Short-Term Memory) neural network model with almost no recognition performance degraded, a new quantization method named Quantization Loss Re-Learn Method is proposed in this paper. The method does lossy quantization on gate parameters during training iteration...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,177
2502.10243	Safety Blind Spot in Remote Driving: Considerations for Risk Assessment of Connection Loss Fallback Strategies	As part of the overall goal of driverless road vehicles, remote driving is a major emerging field of research of its own. Current remote driving concepts for public road traffic often establish a fallback strategy of immediate braking to a standstill in the event of a connection loss. This may seem like the most logica...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	533,793
2501.13462	Generalized graph codes and thier minimum distances	Graph code is a linear code obtained from linear codes $C$ and a certain bipartite graph G. In this paper, I propose an expansion of the definition of graph code to general $l$-partite, and give its lower bound of minimum distance. I also give an example of generalized graph code and calculate its parameters $[n, k, d]...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	526,701
2410.00147	Modeling Turbulence in the Atmospheric Boundary Layer with Spectral Element and Finite Volume Methods	We present large-eddy-simulation (LES) modeling approaches for the simulation of atmospheric boundary layer turbulence that are of direct relevance to wind energy production. In this paper, we study a GABLS benchmark problem using high-order spectral element code Nek5000/RS and a block-structured second-order finite-vo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	493,244
2109.05633	Generating Datasets of 3D Garments with Sewing Patterns	Garments are ubiquitous in both real and many of the virtual worlds. They are highly deformable objects, exhibit an immense variety of designs and shapes, and yet, most garments are created from a set of regularly shaped flat pieces. Exploration of garment structure presents a peculiar case for an object structure esti...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	254,870
2205.00742	FirmTruss Community Search in Multilayer Networks	In applications such as biological, social, and transportation networks, interactions between objects span multiple aspects. For accurately modeling such applications, multilayer networks have been proposed. Community search allows for personalized community discovery and has a wide range of applications in large real-...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	294,358
2501.09372	Image Segmentation with transformers: An Overview, Challenges and Future	Image segmentation, a key task in computer vision, has traditionally relied on convolutional neural networks (CNNs), yet these models struggle with capturing complex spatial dependencies, objects with varying scales, need for manually crafted architecture components and contextual information. This paper explores the s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,122
2011.04749	Longitudinal modeling of MS patient trajectories improves predictions of disability progression	Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,671
2308.05765	Unleashing the Power of Extra-Tree Feature Selection and Random Forest Classifier for Improved Survival Prediction in Heart Failure Patients	Heart failure is a life-threatening condition that affects millions of people worldwide. The ability to accurately predict patient survival can aid in early intervention and improve patient outcomes. In this study, we explore the potential of utilizing data pre-processing techniques and the Extra-Tree (ET) feature sele...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	384,906
2005.06869	Lower bounds for invariant statistical models with applications to principal component analysis	This paper develops nonasymptotic information inequalities for the estimation of the eigenspaces of a covariance operator. These results generalize previous lower bounds for the spiked covariance model, and they show that recent upper bounds for models with decaying eigenvalues are sharp. The proof relies on lower boun...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	177,129
2104.07916	Augmenting Deep Classifiers with Polynomial Neural Networks	Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been achieved by a variety of recently proposed architectures, the majority of which are seemingly disconnected. In this work, we cast the study of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	230,596
2410.17481	AI, Global Governance, and Digital Sovereignty	This essay examines how Artificial Intelligence (AI) systems are becoming more integral to international affairs by affecting how global governors exert power and pursue digital sovereignty. We first introduce a taxonomy of multifaceted AI payoffs for governments and corporations related to instrumental, structural, an...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	501,477
1303.3636	Low-Complexity Adaptive Set-Membership Reduced-rank LCMV Beamforming	This paper proposes a new adaptive algorithm for the implementation of the linearly constrained minimum variance (LCMV) beamformer. The proposed algorithm utilizes the set-membership filtering (SMF) framework and the reduced-rank joint iterative optimization (JIO) scheme. We develop a stochastic gradient (SG) based alg...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,936
2403.08491	Compliant Hierarchical Control for Arbitrary Equality and Inequality Tasks with Strict and Soft Priorities	When a robotic system is redundant with respect to a given task, the remaining degrees of freedom can be used to satisfy additional objectives. With current robotic systems having more and more degrees of freedom, this can lead to an entire hierarchy of tasks that need to be solved according to given priorities. In thi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	437,359
2310.07093	Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning	To advance argumentative stance prediction as a multimodal problem, the First Shared Task in Multimodal Argument Mining hosted stance prediction in crucial social topics of gun control and abortion. Our exploratory study attempts to evaluate the necessity of images for stance prediction in tweets and compare out-of-the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	398,825
2311.01660	Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling	Survival analysis is a widely-used technique for analyzing time-to-event data in the presence of censoring. In recent years, numerous survival analysis methods have emerged which scale to large datasets and relax traditional assumptions such as proportional hazards. These models, while being performant, are very sensit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	405,123
2310.04743	Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models	Chain-of-thought (CoT) prompting, which offers step-by-step problem-solving rationales, has impressively unlocked the reasoning potential of large language models (LLMs). Yet, the standard CoT is less effective in problems demanding multiple reasoning steps. This limitation arises from the complex reasoning process in ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	397,797
1909.09142	Using Quantifier Elimination to Enhance the Safety Assurance of Deep Neural Networks	Advances in the field of Machine Learning and Deep Neural Networks (DNNs) has enabled rapid development of sophisticated and autonomous systems. However, the inherent complexity to rigorously assure the safe operation of such systems hinders their real-world adoption in safety-critical domains such as aerospace and med...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	146,161
1908.01381	On Flying Backwards: Preventing Run-away of Small, Low-speed, Fixed-wing UAVs in Strong Winds	Small, low-speed fixed-wing Unmanned Aerial Vehicles (UAVs) operating autonomously, beyond-visual-line-of-sight (BVLOS) will inevitably encounter winds rising to levels near or exceeding the vehicles' nominal airspeed. In this paper, we develop a nonlinear lateral-directional path following guidance law with explicit c...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	140,749
2405.08577	Intelligent Control in 6G Open RAN: Security Risk or Opportunity?	The Open Radio Access Network (Open RAN) framework, emerging as the cornerstone for Artificial Intelligence (AI)-enabled Sixth-Generation (6G) mobile networks, heralds a transformative shift in radio access network architecture. As the adoption of Open RAN accelerates, ensuring its security becomes critical. The RAN In...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	true	454,142
2309.17341	MixQuant: Mixed Precision Quantization with a Bit-width Optimization Search	Quantization is a technique for creating efficient Deep Neural Networks (DNNs), which involves performing computations and storing tensors at lower bit-widths than f32 floating point precision. Quantization reduces model size and inference latency, and therefore allows for DNNs to be deployed on platforms with constrai...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	395,739
1805.05633	A Deeply-Recursive Convolutional Network for Crowd Counting	The estimation of crowd count in images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. Recently, the convolutional neural network (CNN) based approaches have been shown to be more effective in crowd counting than traditional methods that use handcrafte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	97,469
2007.04192	Agent-Based Modelling: An Overview with Application to Disease Dynamics	Modelling and computational methods have been essential in advancing quantitative science, especially in the past two decades with the availability of vast amount of complex, voluminous, and heterogeneous data. In particular, there has been a surge of interest in agent-based modelling, largely due to its capabilities t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	186,278
1910.10597	Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles	Reinforcement learning (RL) methods have been shown to be capable of learning intelligent behavior in rich domains. However, this has largely been done in simulated domains without adequate focus on the process of building the simulator. In this paper, we consider a setting where we have access to an ensemble of pre-tr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	150,534
2409.09067	SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting	User-defined keyword spotting on a resource-constrained edge device is challenging. However, keywords are often bounded by a maximum keyword length, which has been largely under-leveraged in prior works. Our analysis of keyword-length distribution shows that user-defined keyword spotting can be treated as a length-cons...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	488,161
2105.11834	Improving THz Coverage for 6G URLLC Services via Exploiting Mobile Computing	Terahertz (THz) communication (0.1-10 THz) is regarded as a promising technology, which provides rich available bandwidth and high data rate of terahertz bit per second (Tbps). However, THz signals suffer from high path loss, which profoundly decreases the transmission distance. To improve THz coverage, we consider the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	236,836
2007.02561	Learning from Failure: Training Debiased Classifier from Biased Classifier	Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased. While previous work tackles this issue by using explicit labeling on the spuriously correlated attributes or presuming a particular bias type, we instead utilize a cheap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,784
2111.09131	An efficient two-dimensional heat transfer model for building envelopes	A two-dimensional model is proposed for energy efficiency assessment through the simulation of heat transfer in building envelopes, considering the influence of the surrounding environment. The model is based on the \DF ~approach that provides an explicit scheme with a relaxed stability condition. The model is first va...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	266,926
2204.10817	Reward Reports for Reinforcement Learning	Building systems that are good for society in the face of complex societal effects requires a dynamic approach. Recent approaches to machine learning (ML) documentation have demonstrated the promise of discursive frameworks for deliberation about these complexities. However, these developments have been grounded in a s...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	292,926
2312.02224	Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network	Model Parsing defines the research task of predicting hyperparameters of the generative model (GM), given a generated image as input. Since a diverse set of hyperparameters is jointly employed by the generative model, and dependencies often exist among them, it is crucial to learn these hyperparameter dependencies for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,774
2310.04295	Identifying Representations for Intervention Extrapolation	The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent progress in questions of identifiability, more theoretical results demonstrating concrete advantages of these methods for downstream tasks ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	397,597
2412.11555	TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction	Wildfire monitoring and prediction are essential for understanding wildfire behaviour. With extensive Earth observation data, these tasks can be integrated and enhanced through multi-task deep learning models. We present a comprehensive multi-temporal remote sensing dataset for active fire detection, daily wildfire mon...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	517,473
2402.03353	Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks	This study investigates the relationship between tweet sentiment across diverse categories: news, company opinions, CEO opinions, competitor opinions, and stock market behavior in the biotechnology sector, with a focus on understanding the impact of social media discourse on investor sentiment and decision-making proce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	426,976
1709.00781	Non-Uniform Wavelet Sampling for RF Analog-to-Information Conversion	Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency~(RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost l...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	79,971
2502.06116	Event Vision Sensor: A Review	By monitoring temporal contrast, event-based vision sensors can provide high temporal resolution and low latency while maintaining low power consumption and simplicity in circuit structure. These characteristics have garnered significant attention in both academia and industry. In recent years, the application of back-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,930
2412.08160	DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models	Dynamic graphs exhibit intertwined spatio-temporal evolutionary patterns, widely existing in the real world. Nevertheless, the structure incompleteness, noise, and redundancy result in poor robustness for Dynamic Graph Neural Networks (DGNNs). Dynamic Graph Structure Learning (DGSL) offers a promising way to optimize g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,971
2309.09517	FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks	Federated training of Graph Neural Networks (GNN) has become popular in recent years due to its ability to perform graph-related tasks under data isolation scenarios while preserving data privacy. However, graph heterogeneity issues in federated GNN systems continue to pose challenges. Existing frameworks address the p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	392,647
1901.01536	Exploring applications of deep reinforcement learning for real-world autonomous driving systems	Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthe...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	117,996
2302.08917	Massively Multilingual Shallow Fusion with Large Language Models	While large language models (LLM) have made impressive progress in natural language processing, it remains unclear how to utilize them in improving automatic speech recognition (ASR). In this work, we propose to train a single multilingual language model (LM) for shallow fusion in multiple languages. We push the limits...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	346,229
2502.05547	Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning	Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation methods that withstand poisoning attacks. However, simultaneously addressing both conc...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	531,661

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