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
1911.00584	A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing	This contribution comprises the interplay between a multi-modal variational autoencoder and an environment to a perceived environment, on which an agent can act. Furthermore, we conclude our work with a comparison to curiosity-driven learning.	false	false	false	false	true	false	true	true	false	false	false	false	false	false	true	false	false	false	151,857
2402.06665	The Essential Role of Causality in Foundation World Models for Embodied AI	Recent advances in foundation models, especially in large multi-modal models and conversational agents, have ignited interest in the potential of generally capable embodied agents. Such agents will require the ability to perform new tasks in many different real-world environments. However, current foundation models fai...	false	false	false	false	true	false	true	true	true	false	false	false	false	false	false	false	false	false	428,387
2303.03572	Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning	Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	349,773
1607.06961	Authorship attribution via network motifs identification	Concepts and methods of complex networks can be used to analyse texts at their different complexity levels. Examples of natural language processing (NLP) tasks studied via topological analysis of networks are keyword identification, automatic extractive summarization and authorship attribution. Even though a myriad of ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	58,951
2004.02164	DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation	Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key problem. Traditional methods solve it by discretely searching for the layer-wise pruning ratios, which lacks efficiency. In this paper, we prop...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	171,140
2410.15531	Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage	Evaluating retrieval-augmented generation (RAG) systems remains challenging, particularly for open-ended questions that lack definitive answers and require coverage of multiple sub-topics. In this paper, we introduce a novel evaluation framework based on sub-question coverage, which measures how well a RAG system addre...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	500,590
1806.04635	Circular-shift Linear Network Codes with Arbitrary Odd Block Lengths	Circular-shift linear network coding (LNC) is a class of vector LNC with low encoding and decoding complexities, and with local encoding kernels chosen from cyclic permutation matrices. When $L$ is a prime with primitive root $2$, it was recently shown that a scalar linear solution over GF($2^{L-1}$) induces an $L$-dim...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	100,290
2102.02526	Deep Learning for Short-Term Voltage Stability Assessment of Power Systems	To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	218,438
2101.04262	Clutter Slices Approach for Identification-on-the-fly of Indoor Spaces	Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilit...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	215,101
2411.10546	The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods	This paper introduces a large-scale multi-modal dataset captured in and around well-known landmarks in Oxford using a custom-built multi-sensor perception unit as well as a millimetre-accurate map from a Terrestrial LiDAR Scanner (TLS). The perception unit includes three synchronised global shutter colour cameras, an a...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	508,690
2305.19421	Data and Knowledge for Overtaking Scenarios in Autonomous Driving	Autonomous driving has become one of the most popular research topics within Artificial Intelligence. An autonomous vehicle is understood as a system that combines perception, decision-making, planning, and control. All of those tasks require that the vehicle collects surrounding data in order to make a good decision a...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	369,517
2105.03494	The iWildCam 2021 Competition Dataset	Camera traps enable the automatic collection of large quantities of image data. Ecologists use camera traps to monitor animal populations all over the world. In order to estimate the abundance of a species from camera trap data, ecologists need to know not just which species were seen, but also how many individuals of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,162
2404.18990	Timely Status Updates in Slotted ALOHA Networks With Energy Harvesting	We investigate the age of information (AoI) in a scenario where energy-harvesting devices send status updates to a gateway following the slotted ALOHA protocol and receive no feedback. We let the devices adjust the transmission probabilities based on their current battery level. Using a Markovian analysis, we derive an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	450,470
2312.01677	Multi-task Image Restoration Guided By Robust DINO Features	Multi-task image restoration has gained significant interest due to its inherent versatility and efficiency compared to its single-task counterpart. However, performance decline is observed with an increase in the number of tasks, primarily attributed to the restoration model's challenge in handling different tasks wit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,545
2309.00584	Laminar: A New Serverless Stream-based Framework with Semantic Code Search and Code Completion	This paper introduces Laminar, a novel serverless framework based on dispel4py, a parallel stream-based dataflow library. Laminar efficiently manages streaming workflows and components through a dedicated registry, offering a seamless serverless experience. Leveraging large lenguage models, Laminar enhances the framewo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	389,347
2005.02618	Vehicle Routing and Scheduling for Regular Mobile Healthcare Services	We propose our solution to a particular practical problem in the domain of vehicle routing and scheduling. The generic task is finding the best allocation of the minimum number of \emph{mobile resources} that can provide periodical services in remote locations. These \emph{mobile resources} are based at a single centra...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	175,931
2007.05742	Relation-Guided Representation Learning	Deep auto-encoders (DAEs) have achieved great success in learning data representations via the powerful representability of neural networks. But most DAEs only focus on the most dominant structures which are able to reconstruct the data from a latent space and neglect rich latent structural information. In this work, w...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	186,773
2410.02458	MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation	Large Language Models (LLMs), known for their versatility in textual data, are increasingly being explored for their potential to enhance medical image segmentation, a crucial task for accurate diagnostic imaging. This study explores enhancing Vision Transformers (ViTs) for medical image segmentation by integrating pre...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	494,285
2208.08806	A Generic Information Extraction System for String Constraints	String constraint solving, and the underlying theory of word equations, are highly interesting research topics both for practitioners and theoreticians working in the wide area of satisfiability modulo theories. As string constraint solving algorithms, a.k.a. string solvers, gained a more prominent role in the formal a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	313,485
2205.07575	An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis	The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. In this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	296,649
2211.02982	Event and Entity Extraction from Generated Video Captions	Annotation of multimedia data by humans is time-consuming and costly, while reliable automatic generation of semantic metadata is a major challenge. We propose a framework to extract semantic metadata from automatically generated video captions. As metadata, we consider entities, the entities' properties, relations bet...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	328,778
1909.02195	Automated Let's Play Commentary	Let's Plays of video games represent a relatively unexplored area for experimental AI in games. In this short paper, we discuss an approach to generate automated commentary for Let's Play videos, drawing on convolutional deep neural networks. We focus on Let's Plays of the popular game Minecraft. We compare our approac...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	144,121
2012.08984	Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation	Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models for learning. In this work, we instead focus on learning recommendation policies in t...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	211,929
2312.09249	ZeroRF: Fast Sparse View 360{\deg} Reconstruction with Zero Pretraining	We present ZeroRF, a novel per-scene optimization method addressing the challenge of sparse view 360{\deg} reconstruction in neural field representations. Current breakthroughs like Neural Radiance Fields (NeRF) have demonstrated high-fidelity image synthesis but struggle with sparse input views. Existing methods, such...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	415,651
1510.08568	Feature-Based Diversity Optimization for Problem Instance Classification	Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances that are hard or easy for a given search heurist...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	48,302
1901.06815	A principled methodology for comparing relatedness measures for clustering publications	There are many different relatedness measures, based for instance on citation relations or textual similarity, that can be used to cluster scientific publications. We propose a principled methodology for evaluating the accuracy of clustering solutions obtained using these relatedness measures. We formally show that the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	119,097
2306.07930	Reducing Exposure to Harmful Content via Graph Rewiring	Most media content consumed today is provided by digital platforms that aggregate input from diverse sources, where access to information is mediated by recommendation algorithms. One principal challenge in this context is dealing with content that is considered harmful. Striking a balance between competing stakeholder...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	373,199
2402.11789	Statistical Test on Diffusion Model-based Anomaly Detection by Selective Inference	Advancements in AI image generation, particularly diffusion models, have progressed rapidly. However, the absence of an established framework for quantifying the reliability of AI-generated images hinders their use in critical decision-making tasks, such as medical image diagnosis. In this study, we address the task of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	430,569
2303.04053	Describe me an Aucklet: Generating Grounded Perceptual Category Descriptions	Human speakers can generate descriptions of perceptual concepts, abstracted from the instance-level. Moreover, such descriptions can be used by other speakers to learn provisional representations of those concepts. Learning and using abstract perceptual concepts is under-investigated in the language-and-vision field. T...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	349,948
1504.00976	Convex Denoising using Non-Convex Tight Frame Regularization	This paper considers the problem of signal denoising using a sparse tight-frame analysis prior. The L1 norm has been extensively used as a regularizer to promote sparsity; however, it tends to under-estimate non-zero values of the underlying signal. To more accurately estimate non-zero values, we propose the use of a n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	41,745
2205.14340	Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration	Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project betwee...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	299,329
2109.04684	Enhancing Unsupervised Anomaly Detection with Score-Guided Network	Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems. Owing to the limited number of anomaly labels in these complex systems, unsupervised anomaly detection methods have attracted great attention in recent years. Two major challenges faced by the existing u...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	254,494
2004.07511	Explainable Image Classification with Evidence Counterfactual	The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. Existing explanation methods generally create importance rankings in terms of pixels or pixel groups. However, the resulting explanations lack an optimal size, do not...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	172,805
2308.08410	Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach	The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently. In principle, by matching clinically recorded and eikonal-based electrocardiograms (ECGs), it is possible to build patient-specific models of cardiac electrophysiology in a purely non-invasive ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	385,891
2109.05463	Logic Traps in Evaluating Attribution Scores	Modern deep learning models are notoriously opaque, which has motivated the development of methods for interpreting how deep models predict. This goal is usually approached with attribution method, which assesses the influence of features on model predictions. As an explanation method, the evaluation criteria of attrib...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	254,799
2304.07169	A Comparative Study on Generative Models for High Resolution Solar Observation Imaging	Solar activity is one of the main drivers of variability in our solar system and the key source of space weather phenomena that affect Earth and near Earth space. The extensive record of high resolution extreme ultraviolet (EUV) observations from the Solar Dynamics Observatory (SDO) offers an unprecedented, very large ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	358,255
2406.08723	ECBD: Evidence-Centered Benchmark Design for NLP	Benchmarking is seen as critical to assessing progress in NLP. However, creating a benchmark involves many design decisions (e.g., which datasets to include, which metrics to use) that often rely on tacit, untested assumptions about what the benchmark is intended to measure or is actually measuring. There is currently ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	463,593
2101.02490	Snappability and singularity-distance of pin-jointed body-bar frameworks	It is well-known that there exist rigid frameworks whose physical models can snap between different realizations due to non-destructive elastic deformations of material. We present a method to measure this snapping capability based on the total elastic strain energy density of the framework by using the physical concep...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	214,646
1610.09054	Broadcast Coded Modulation: Multilevel and Bit-interleaved Construction	The capacity of the AWGN broadcast channel is achieved by superposition coding, but superposition of individual coded modulations expands the modulation alphabet and distorts its configuration. Coded modulation over a broadcast channel subject to a specific channel-input modulation constraint remains an important open ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,995
2203.07375	From Big to Small: Adaptive Learning to Partial-Set Domains	Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirement of identical class space shared across domains hinders applications of domain adaptation to partial-set domains. Recent advances show th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,410
2401.01699	WordArt Designer API: User-Driven Artistic Typography Synthesis with Large Language Models on ModelScope	This paper introduces the WordArt Designer API, a novel framework for user-driven artistic typography synthesis utilizing Large Language Models (LLMs) on ModelScope. We address the challenge of simplifying artistic typography for non-professionals by offering a dynamic, adaptive, and computationally efficient alternati...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	419,464
2501.11795	Provably effective detection of effective data poisoning attacks	This paper establishes a mathematically precise definition of dataset poisoning attack and proves that the very act of effectively poisoning a dataset ensures that the attack can be effectively detected. On top of a mathematical guarantee that dataset poisoning is identifiable by a new statistical test that we call the...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	526,041
2412.11169	Design Challenges for Robots in Industrial Applications	Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	517,291
2411.15661	Improving Next Tokens via Second-to-Last Predictions with Generate and Refine	Autoregressive language models like GPT aim to predict next tokens, while autoencoding models such as BERT are trained on tasks such as predicting masked tokens. We train a decoder-only architecture for predicting the second to last token for a sequence of tokens. Our approach yields higher computational training effic...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	510,711
2501.08561	ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins	In this paper, we propose an Adaptive Neuro-Symbolic Learning Framework for digital twin technology called ``ANSR-DT." Our approach combines pattern recognition algorithms with reinforcement learning and symbolic reasoning to enable real-time learning and adaptive intelligence. This integration enhances the understandi...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	524,814
2409.15627	ModCube: Modular, Self-Assembling Cubic Underwater Robot	This paper presents a low-cost, centralized modular underwater robot platform, ModCube, which can be used to study swarm coordination for a wide range of tasks in underwater environments. A ModCube structure consists of multiple ModCube robots. Each robot can move in six DoF with eight thrusters and can be rigidly conn...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	490,987
2310.18841	A randomized algorithm for nonconvex minimization with inexact evaluations and complexity guarantees	We consider minimization of a smooth nonconvex function with inexact oracle access to gradient and Hessian (without assuming access to the function value) to achieve approximate second-order optimality. A novel feature of our method is that if an approximate direction of negative curvature is chosen as the step, we cho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,727
2012.00740	MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent	Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients. However, sharing gradients, instead of centralizing data, may not be as private as one would expect. Reverse engineering attacks on plain...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	209,229
1707.03891	Unsupervised Body Part Regression via Spatially Self-ordering Convolutional Neural Networks	Automatic body part recognition for CT slices can benefit various medical image applications. Recent deep learning methods demonstrate promising performance, with the requirement of large amounts of labeled images for training. The intrinsic structural or superior-inferior slice ordering information in CT volumes is no...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,949
1210.4890	The Complexity of Approximately Solving Influence Diagrams	Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	19,215
2401.04431	Sea wave data reconstruction using micro-seismic measurements and machine learning methods	Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However, being exposed to a harsh environment, they are not reliable, need frequent maintenan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,431
2402.10727	From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation	There are various measures of predictive uncertainty in the literature, but their relationships to each other remain unclear. This paper uses a decomposition of statistical pointwise risk into components, associated with different sources of predictive uncertainty, namely aleatoric uncertainty (inherent data variabilit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	430,086
1910.07779	Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation	Bayesian optimisation is a sample-efficient search methodology that holds great promise for accelerating drug and materials discovery programs. A frequently-overlooked modelling consideration in Bayesian optimisation strategies however, is the representation of heteroscedastic aleatoric uncertainty. In many practical a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,703
1104.4664	Temporal Second Difference Traces	Q-learning is a reliable but inefficient off-policy temporal-difference method, backing up reward only one step at a time. Replacing traces, using a recency heuristic, are more efficient but less reliable. In this work, we introduce model-free, off-policy temporal difference methods that make better use of experience t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	10,101
2402.09388	Entropy-regularized Point-based Value Iteration	Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based planners may be brittle under these types of uncertainty because they rely on an exact model and tend to commit to a single optimal behavio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	429,497
1609.09188	Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis	Academic researchers often need to face with a large collection of research papers in the literature. This problem may be even worse for postgraduate students who are new to a field and may not know where to start. To address this problem, we have developed an online catalog of research papers where the papers have bee...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	61,684
1408.6141	Recursive Total Least-Squares Algorithm Based on Inverse Power Method and Dichotomous Coordinate-Descent Iterations	We develop a recursive total least-squares (RTLS) algorithm for errors-in-variables system identification utilizing the inverse power method and the dichotomous coordinate-descent (DCD) iterations. The proposed algorithm, called DCD-RTLS, outperforms the previously-proposed RTLS algorithms, which are based on the line-...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	35,607
2102.11089	Belief-Propagation Decoding of LDPC Codes with Variable Node-Centric Dynamic Schedules	Belief propagation (BP) decoding of low-density parity-check (LDPC) codes with various dynamic decoding schedules have been proposed to improve the efficiency of the conventional flooding schedule. As the ultimate goal of an ideal LDPC code decoder is to have correct bit decisions, a dynamic decoding schedule should be...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	221,325
1911.07716	The Effectiveness of Variational Autoencoders for Active Learning	The high cost of acquiring labels is one of the main challenges in deploying supervised machine learning algorithms. Active learning is a promising approach to control the learning process and address the difficulties of data labeling by selecting labeled training examples from a large pool of unlabeled instances. In t...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	153,947
1807.05924	Bipedal Walking Robot using Deep Deterministic Policy Gradient	Machine learning algorithms have found several applications in the field of robotics and control systems. The control systems community has started to show interest towards several machine learning algorithms from the sub-domains such as supervised learning, imitation learning and reinforcement learning to achieve auto...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	103,018
2401.06649	Data-Efficient Interactive Multi-Objective Optimization Using ParEGO	Multi-objective optimization is a widely studied problem in diverse fields, such as engineering and finance, that seeks to identify a set of non-dominated solutions that provide optimal trade-offs among competing objectives. However, the computation of the entire Pareto front can become prohibitively expensive, both in...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	421,229
2102.03771	MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square	The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and versatile 3D LiDAR SLAM system. For the front-end, roughly classif...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	218,865
1505.07634	Learning with Symmetric Label Noise: The Importance of Being Unhinged	Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2010] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly show...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	43,558
1802.10153	Slip Detection with Combined Tactile and Visual Information	Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we us...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	91,467
2310.17413	Harnessing GPT-3.5-turbo for Rhetorical Role Prediction in Legal Cases	We propose a comprehensive study of one-stage elicitation techniques for querying a large pre-trained generative transformer (GPT-3.5-turbo) in the rhetorical role prediction task of legal cases. This task is known as requiring textual context to be addressed. Our study explores strategies such as zero-few shots, task ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	403,125
2206.09827	A Distributional Approach for Soft Clustering Comparison and Evaluation	The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a genera...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	303,700
2210.01732	Robust Multi-Agent Coordination from CaTL+ Specifications	We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements. Capability Temporal Logic (CaTL) was recently proposed to formalize such specifications for deploying a team of autonomous agents with different capabilities and cooperation requirements. In this p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	321,367
2209.12895	How does Imaging Impact Patient Flow in Emergency Departments?	Emergency Department (ED) overcrowding continues to be a public health issue as well as a patient safety issue. The underlying factors leading to ED crowding are numerous, varied, and complex. Although lack of in-hospital beds is frequently attributed as the primary reason for crowding, ED's dependencies on other ancil...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	319,702
2310.17477	Secure short-term load forecasting for smart grids with transformer-based federated learning	Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. While advanced deep learning models require large amounts of high-resolution data for accurate short-term load predictions, fine-grained load profiles can expose users' electricity consumption behaviors, which rais...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,150
2501.09298	Physics-informed deep learning for infectious disease forecasting	Accurate forecasting of contagious illnesses has become increasingly important to public health policymaking, and better prediction could prevent the loss of millions of lives. To better prepare for future pandemics, it is essential to improve forecasting methods and capabilities. In this work, we propose a new infecti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	525,087
2408.13818	HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning	The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC). However, hematoxylin and eosin (H\&E) tumor stains are more widely available, and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,304
1009.0896	Memristor Crossbar-based Hardware Implementation of Fuzzy Membership Functions	In May 1, 2008, researchers at Hewlett Packard (HP) announced the first physical realization of a fundamental circuit element called memristor that attracted so much interest worldwide. This newly found element can easily be combined with crossbar interconnect technology which this new structure has opened a new field ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	7,482
2108.09996	MS-DARTS: Mean-Shift Based Differentiable Architecture Search	Differentiable Architecture Search (DARTS) is an effective continuous relaxation-based network architecture search (NAS) method with low search cost. It has attracted significant attentions in Auto-ML research and becomes one of the most useful paradigms in NAS. Although DARTS can produce superior efficiency over tradi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	251,768
0705.1183	Multiple Antenna Secure Broadcast over Wireless Networks	In wireless data networks, communication is particularly susceptible to eavesdropping due to its broadcast nature. Security and privacy systems have become critical for wireless providers and enterprise networks. This paper considers the problem of secret communication over the Gaussian broadcast channel, where a multi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	189
1511.01776	Computational Intractability of Dictionary Learning for Sparse Representation	In this paper we consider the dictionary learning problem for sparse representation. We first show that this problem is NP-hard by polynomial time reduction of the densest cut problem. Then, using successive convex approximation strategies, we propose efficient dictionary learning schemes to solve several practical for...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	48,544
1811.10185	Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring	The image blurring process is generally modelled as the convolution of a blur kernel with a latent image. Therefore, the estimation of the blur kernel is essentially important for blind image deblurring. Unlike existing approaches which focus on approaching the problem by enforcing various priors on the blur kernel and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,433
1611.04209	Asymptotically Optimal Amplifiers for the Moran Process	We study the Moran process as adapted by Lieberman, Hauert and Nowak. This is a model of an evolving population on a graph or digraph where certain individuals, called "mutants" have fitness r and other individuals, called non-mutants have fitness 1. We focus on the situation where the mutation is advantageous, in the ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	63,804
2201.02311	Joint Routing and Charging Problem of Electric Vehicles with Incentive-aware Customers Considering Spatio-temporal Charging Prices	This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	274,507
2410.13616	Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring	This work presents advancements in multi-class vehicle detection using UAV cameras through the development of spatiotemporal object detection models. The study introduces a Spatio-Temporal Vehicle Detection Dataset (STVD) containing 6, 600 annotated sequential frame images captured by UAVs, enabling comprehensive train...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	499,612
cs/0206007	Using the Annotated Bibliography as a Resource for Indicative Summarization	We report on a language resource consisting of 2000 annotated bibliography entries, which is being analyzed as part of our research on indicative document summarization. We show how annotated bibliographies cover certain aspects of summarization that have not been well-covered by other summary corpora, and motivate why...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	537,604
2208.10387	Constants of motion network	The beauty of physics is that there is usually a conserved quantity in an always-changing system, known as the constant of motion. Finding the constant of motion is important in understanding the dynamics of the system, but typically requires mathematical proficiency and manual analytical work. In this paper, we presen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	314,045
1901.07600	Mathematical model of gender bias and homophily in professional hierarchies	Women have become better represented in business, academia, and government over time, yet a dearth of women at the highest levels of leadership remains. Sociologists have attributed the leaky progression of women through professional hierarchies to various cultural and psychological factors, such as self-segregation an...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	119,247
2105.05485	Wireless Covert Communications Aided by Distributed Cooperative Jamming over Slow Fading Channels	In this paper, we study covert communications between {a pair of} legitimate transmitter-receiver against a watchful warden over slow fading channels. There coexist multiple friendly helper nodes who are willing to protect the covert communication from being detected by the warden. We propose an uncoordinated jammer se...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	234,823
1003.5627	Wavelet-Based Mel-Frequency Cepstral Coefficients for Speaker Identification using Hidden Markov Models	To improve the performance of speaker identification systems, an effective and robust method is proposed to extract speech features, capable of operating in noisy environment. Based on the time-frequency multi-resolution property of wavelet transform, the input speech signal is decomposed into various frequency channel...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,028
2007.15140	Computing Optimal Decision Sets with SAT	As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rul...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	189,579
2412.15533	From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption	The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey (BASS), and the Mayall z-band Legacy Survey (MzLS). The citizen science project Galaxy Zoo DECaLS 5 (GZD-5) has provided extensive and detailed morphology labels for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,161
2111.01589	Nonstochastic Bandits and Experts with Arm-Dependent Delays	We study nonstochastic bandits and experts in a delayed setting where delays depend on both time and arms. While the setting in which delays only depend on time has been extensively studied, the arm-dependent delay setting better captures real-world applications at the cost of introducing new technical challenges. In t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,602
2112.13906	Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?	Contrastive Language--Image Pre-training (CLIP) has shown remarkable success in learning with cross-modal supervision from extensive amounts of image--text pairs collected online. Thus far, the effectiveness of CLIP has been investigated primarily in general-domain multimodal problems. This work evaluates the effective...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	273,390
2010.13813	A Path-Dependent Variational Framework for Incremental Information Gathering	Information gathered along a path is inherently submodular; the incremental amount of information gained along a path decreases due to redundant observations. In addition to submodularity, the incremental amount of information gained is a function of not only the current state but also the entire history as well. This ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	203,251
2102.01611	Towards Multi-agent Reinforcement Learning for Wireless Network Protocol Synthesis	This paper proposes a multi-agent reinforcement learning based medium access framework for wireless networks. The access problem is formulated as a Markov Decision Process (MDP), and solved using reinforcement learning with every network node acting as a distributed learning agent. The solution components are developed...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	218,171
1807.09289	Noise Contrastive Priors for Functional Uncertainty	Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of an independent normal prior in weight space imposes relatively weak constr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,691
2412.03531	A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences	The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks s...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	513,994
1909.01709	Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Network	This work builds an automated anomaly detection method for chaotic time series, and more concretely for turbulent, high-dimensional, ocean simulations. We solve this task by extending the Echo State Network by spatially aware input maps, such as convolutions, gradients, cosine transforms, et cetera, as well as a spatia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	143,977
1701.06153	The Evolution of Reputation-Based Cooperation in Regular Networks	Despite recent advances in reputation technologies, it is not clear how reputation systems can affect human cooperation in social networks. Although it is known that two of the major mechanisms in the evolution of cooperation are spatial selection and reputation-based reciprocity, theoretical study of the interplay bet...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	67,078
2101.10279	QFold: Quantum Walks and Deep Learning to Solve Protein Folding	Predicting the 3D structure of proteins is one of the most important problems in current biochemical research. In this article, we explain how to combine recent deep learning advances with the well known technique of quantum walks applied to a Metropolis algorithm. The result, QFold, is a fully scalable hybrid quantum ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,886
2406.17537	SincVAE: a New Approach to Improve Anomaly Detection on EEG Data Using SincNet and Variational Autoencoder	Over the past few decades, electroencephalography (EEG) monitoring has become a pivotal tool for diagnosing neurological disorders, particularly for detecting seizures. Epilepsy, one of the most prevalent neurological diseases worldwide, affects approximately the 1 \% of the population. These patients face significant ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	467,613
2304.14531	High-dimensional Clustering onto Hamiltonian Cycle	Clustering aims to group unlabelled samples based on their similarities. It has become a significant tool for the analysis of high-dimensional data. However, most of the clustering methods merely generate pseudo labels and thus are unable to simultaneously present the similarities between different clusters and outlier...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	361,007
2207.06706	SHREC 2022 Track on Online Detection of Heterogeneous Gestures	This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the detection of gestures belonging to a dictionary of 16 classes characterized by different pose and motion features. The dataset features contin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,967
2410.04188	DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech	Dementia is a sensitive neurocognitive disorder affecting tens of millions of people worldwide and its cases are expected to triple by 2050. Alarmingly, recent advancements in dementia classification make it possible for adversaries to violate affected individuals' privacy and infer their sensitive condition from speec...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	495,162
1505.02476	Identifying influential spreaders in complex networks based on gravity formula	How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as thei...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	42,974

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