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
2301.03596	Membership Inference Attacks Against Latent Factor Model	The advent of the information age has led to the problems of information overload and unclear demands. As an information filtering system, personalized recommendation systems predict users' behavior and preference for items and improves users' information acquisition efficiency. However, recommendation systems usually ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	339,846
2208.04011	Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features	While storing invoice content as metadata to avoid paper document processing may be the future trend, almost all of daily issued invoices are still printed on paper or generated in digital formats such as PDFs. In this paper, we introduce the OCRMiner system for information extraction from scanned document images which...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	311,968
2108.00058	Interruption flows for reliability evaluation of power distribution networks	Energy networks should strive for reliability. How can it be assessed, measured, and improved? What are the best trade-offs between investments and their worth? The flow-based framework for the reliability assessment of energy networks proposed in this paper addresses these questions with a focus on power distribution ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	248,583
1309.3214	Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers	In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on the Elman ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	27,005
2308.15730	Fully Embedded Time-Series Generative Adversarial Networks	Generative Adversarial Networks (GANs) should produce synthetic data that fits the underlying distribution of the data being modeled. For real valued time-series data, this implies the need to simultaneously capture the static distribution of the data, but also the full temporal distribution of the data for any potenti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,772
1406.7589	Learning from Others, Together: Brokerage, Closure and Team Performance	Scholarship on teams has focused on the relationship between a team's performance, however defined, and the network structure among team members. For example, Uzzi and Spiro (2005) find that the creative performance of Broadway musical teams depends heavily on the internal cohesion of team members and their past collab...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	34,253
2011.04475	Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs	Melanoma is not the most common form of skin cancer, but it is the most deadly. Currently, the disease is diagnosed by expert dermatologists, which is costly and requires timely access to medical treatment. Recent advances in deep learning have the potential to improve diagnostic performance, expedite urgent referrals ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	205,600
2112.01288	How to quantify fields or textures? A guide to the scattering transform	Extracting information from stochastic fields or textures is a ubiquitous task in science, from exploratory data analysis to classification and parameter estimation. From physics to biology, it tends to be done either through a power spectrum analysis, which is often too limited, or the use of convolutional neural netw...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,432
2103.04753	Applicability and Surrogacy of Uncorrelated Airspace Encounter Models at Low Altitudes	The National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	223,753
2009.08586	A Contraction Approach to Model-based Reinforcement Learning	Despite its experimental success, Model-based Reinforcement Learning still lacks a complete theoretical understanding. To this end, we analyze the error in the cumulative reward using a contraction approach. We consider both stochastic and deterministic state transitions for continuous (non-discrete) state and action s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	196,282
0811.0764	A Bayesian Framework for Collaborative Multi-Source Signal Detection	This paper introduces a Bayesian framework to detect multiple signals embedded in noisy observations from a sensor array. For various states of knowledge on the communication channel and the noise at the receiving sensors, a marginalization procedure based on recent tools of finite random matrix theory, in conjunction ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	2,634
1503.03952	A Switched Dynamical System Framework for Analysis of Massively Parallel Asynchronous Numerical Algorithms	In the near future, massively parallel computing systems will be necessary to solve computation intensive applications. The key bottleneck in massively parallel implementation of numerical algorithms is the synchronization of data across processing elements (PEs) after each iteration, which results in significant idle ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	41,104
2010.06871	A Heteroscedastic Likelihood Model for Two-frame Optical Flow	Machine vision is an important sensing technology used in mobile robotic systems. Advancing the autonomy of such systems requires accurate characterisation of sensor uncertainty. Vision includes intrinsic uncertainty due to the camera sensor and extrinsic uncertainty due to environmental lighting and texture, which pro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	200,630
1709.07536	A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions	The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In this paper, we apply MP to the automation of software performance regression testing. A performanc...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	81,292
1509.05520	An Experimental Survey on Correlation Filter-based Tracking	Over these years, Correlation Filter-based Trackers (CFTs) have aroused increasing interests in the field of visual object tracking, and have achieved extremely compelling results in different competitions and benchmarks. In this paper, our goal is to review the developments of CFTs with extensive experimental results....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	47,060
2203.02193	Time-to-Label: Temporal Consistency for Self-Supervised Monocular 3D Object Detection	Monocular 3D object detection continues to attract attention due to the cost benefits and wider availability of RGB cameras. Despite the recent advances and the ability to acquire data at scale, annotation cost and complexity still limit the size of 3D object detection datasets in the supervised settings. Self-supervis...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	283,674
2408.03867	Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition	Existing state-of-the-art methods for surgical phase recognition either rely on the extraction of spatial-temporal features at a short-range temporal resolution or adopt the sequential extraction of the spatial and temporal features across the entire temporal resolution. However, these methods have limitations in model...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,174
2406.00332	A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning	The adaptation and use of Machine Learning (ML) in our daily lives has led to concerns in lack of transparency, privacy, reliability, among others. As a result, we are seeing research in niche areas such as interpretability, causality, bias and fairness, and reliability. In this survey paper, we focus on a critical con...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	459,802
2101.00376	RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge	Question: I have five fingers but I am not alive. What am I? Answer: a glove. Answering such a riddle-style question is a challenging cognitive process, in that it requires complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning skills, which are all important abil...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	214,062
1912.00832	Epistemic Uncertainty Quantification in Deep Learning Classification by the Delta Method	The Delta method is a classical procedure for quantifying epistemic uncertainty in statistical models, but its direct application to deep neural networks is prevented by the large number of parameters $P$. We propose a low cost variant of the Delta method applicable to $L_2$-regularized deep neural networks based on th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,905
1705.10450	RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data	In recent years, deep convolutional neural network (DCNN) has seen a breakthrough progress in natural image recognition because of three points: universal approximation ability via DCNN, large-scale database (such as ImageNet), and supercomputing ability powered by GPU. The remote sensing field is still lacking a large...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,397
2312.09295	Networking for the Metaverse: The Standardization Landscape	New applications are being supported by current and future networks. In particular, it is expected that Metaverse applications will be deployed in the near future, as 5G and 6G network provide sufficient bandwidth and sufficiently low latency to provide a satisfying end-user experience. However, networks still need to ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	415,663
2409.00250	Medical Report Generation Is A Multi-label Classification Problem	Medical report generation is a critical task in healthcare that involves the automatic creation of detailed and accurate descriptions from medical images. Traditionally, this task has been approached as a sequence generation problem, relying on vision-and-language techniques to generate coherent and contextually releva...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,840
2012.06970	Completely regular codes in Johnson and Grassmann graphs with small covering radii	Let L be a Desarguesian 2-spread in the Grassmann graph $J_q(n,2)$. We prove that the collection of the 4-subspaces, which do not contain subspaces from L is a completely regular code in $J_q(n,4)$. Similarly, we construct a completely regular code in the Johnson graph $J(n,6)$ from the Steiner quadruple system of the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	211,292
2202.03133	Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?	Recent Spiking Neural Networks (SNNs) works focus on an image classification task, therefore various coding techniques have been proposed to convert an image into temporal binary spikes. Among them, rate coding and direct coding are regarded as prospective candidates for building a practical SNN system as they show sta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	279,086
2201.05751	Optimal Single-User Interactive Beam Alignment with Feedback Delay	Communication in Millimeter wave (mmWave) band relies on narrow beams due to directionality, high path loss, and shadowing. One can use beam alignment (BA) techniques to find and adjust the direction of these narrow beams. In this paper, BA at the base station (BS) is considered, where the BS sends a set of BA packets ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	275,477
1811.04543	Variational Community Partition with Novel Network Structure Centrality Prior	In this paper, we proposed a novel two-stage optimization method for network community partition, which is based on inherent network structure information. The introduced optimization approach utilizes the new network centrality measure of both links and vertices to construct the key affinity description of the given n...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	113,117
1805.00900	Images & Recipes: Retrieval in the cooking context	Recent advances in the machine learning community allowed different use cases to emerge, as its association to domains like cooking which created the computational cuisine. In this paper, we tackle the picture-recipe alignment problem, having as target application the large-scale retrieval task (finding a recipe given ...	false	false	false	false	true	true	false	false	true	false	false	true	false	false	false	false	false	false	96,538
2203.09192	Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists	Natural Language Processing (NLP) models risk overfitting to specific terms in the training data, thereby reducing their performance, fairness, and generalizability. E.g., neural hate speech detection models are strongly influenced by identity terms like gay, or women, resulting in false positives, severe unintended bi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	286,070
1512.07043	Sign properties of Metzler matrices with applications	Several results about sign properties of Metzler matrices are obtained. It is first established that checking the sign-stability of a Metzler sign-matrix can be either characterized in terms of the Hurwitz stability of the unit sign-matrix in the corresponding qualitative class, or in terms the negativity of the diagon...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	50,382
1902.00741	Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer Underlying Classical and Quantum Entropy, Observational Semantics and Cognitive Computation	A new conceptual foundation for the notion of "information" is proposed, based on the concept of a "distinction graph": a graph in which two nodes are connected iff they cannot be distinguished by a particular observer. The "graphtropy" of a distinction graph is defined as the average connection probability of two node...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	120,491
2005.07809	Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions	Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for mental health concerns implemented in a conversational setting with broad empirical support for its effectiveness across a range of presenting problems and client populations. The quality of a CBT session is typically assessed by trained human rate...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	177,395
1801.04959	Dynamic compensation and homeostasis: a feedback control perspective	"Dynamic compensation" is a robustness property where a perturbed biological circuit maintains a suitable output [Karin O., Swisa A., Glaser B., Dor Y., Alon U. (2016). Mol. Syst. Biol., 12: 886]. In spite of several attempts, no fully convincing analysis seems now to be on hand. This communication suggests an explanat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	88,366
1711.07404	Non-Contextual Modeling of Sarcasm using a Neural Network Benchmark	One of the most crucial components of natural human-robot interaction is artificial intuition and its influence on dialog systems. The intuitive capability that humans have is undeniably extraordinary, and so remains one of the greatest challenges for natural communicative dialogue between humans and robots. In this pa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	84,984
1905.08945	Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation	Neural Machine Translation (NMT) has been proven to achieve impressive results. The NMT system translation results depend strongly on the size and quality of parallel corpora. Nevertheless, for many language pairs, no rich-resource parallel corpora exist. As described in this paper, we propose a corpus augmentation met...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	131,616
2411.08642	Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models	The remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3D Gaussian splatting, offers a powerful alternative to GANs and diffusion models. These methods can produce high-fidelity images and lifelike avatars, highlighting the ne...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	507,963
2201.00995	Fundamental Limitations of Control and Filtering in Continuous-Time Systems: An Information-Theoretic Analysis	While information theory has been introduced to investigate and characterize the control and filtering limitations for a few decades, the existing information-theoretic methods are indirect and cumbersome for analyzing the fundamental limitations of continuous-time systems. To answer this challenge, we lift the informa...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	274,119
1905.12980	Interactive-predictive neural multimodal systems	Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revision process. The interactive-predictive framework aims to minimize the human effort spent on this process...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	132,959
1907.03128	Multi-level Wavelet Convolutional Neural Networks	In computer vision, convolutional networks (CNNs) often adopts pooling to enlarge receptive field which has the advantage of low computational complexity. However, pooling can cause information loss and thus is detrimental to further operations such as features extraction and analysis. Recently, dilated filter has been...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	137,776
2210.14031	A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images	Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	326,406
2306.01438	Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection	LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocity hints. Though seemingly natural, how to efficiently combine them for improved feature representation is still unclear. The main challenge ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	370,458
1912.10074	Trellis-Coded Non-Orthogonal Multiple Access	In this letter, we propose a trellis-coded nonorthogonal multiple access (NOMA) scheme. The signals for different users are produced by trellis coded modulation (TCM) and then superimposed on different power levels. By interpreting the encoding process via the tensor product of trellises, we introduce a joint detection...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	158,213
1007.5030	Analysis of a Splitting Estimator for Rare Event Probabilities in Jackson Networks	We consider a standard splitting algorithm for the rare-event simulation of overflow probabilities in any subset of stations in a Jackson network at level n, starting at a fixed initial position. It was shown in DeanDup09 that a subsolution to the Isaacs equation guarantees that a subexponential number of function eval...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	7,139
2406.14455	MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction	Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data. However, the effectiveness of GDL based methods heavily depends on the quality of modeling the multi-modal population graphs and tends to d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	466,314
2305.05548	CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition	Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction. However, effectively combining global and local features of EEG signals to improve performance in emotion recognition is still a difficult task. In this study,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,195
1810.13084	Provably Accelerated Randomized Gossip Algorithms	In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iteration all nodes of the ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	true	111,902
1812.11314	Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies	In this paper, we propose a novel meta-learning method in a reinforcement learning setting, based on evolution strategies (ES), exploration in parameter space and deterministic policy gradients. ES methods are easy to parallelize, which is desirable for modern training architectures; however, such methods typically req...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	117,537
2203.14372	Algorithmic support of a personal virtual assistant for automating the processing of client requests	This article describes creating algorithmic support for the functioning of a personal virtual assistant, which allows automating the processing of customer requests. The study aims to reduce errors and processing time for a client request in business systems - text chats or voice channels using a text transcription sys...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	287,983
1905.08297	Better Technical Debt Detection via SURVEYing	Software analytics can be improved by surveying; i.e. rechecking and (possibly) revising the labels offered by prior analysis. Surveying is a time-consuming task and effective surveyors must carefully manage their time. Specifically, they must balance the cost of further surveying against the additional benefits of tha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	131,440
2204.06550	Improving generalization to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents	Adapting a Reinforcement Learning (RL) agent to an unseen environment is a difficult task due to typical over-fitting on the training environment. RL agents are often capable of solving environments very close to the trained environment, but when environments become substantially different, their performance quickly dr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	291,380
2406.18966	UniGen: A Unified Framework for Textual Dataset Generation Using Large Language Models	Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expensive human-generated datasets. Despite this, challenges remain in the areas of generalization, controllability, diversity, and truthfulness ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	468,254
2403.17111	Vision-Based Dexterous Motion Planning by Dynamic Movement Primitives with Human Hand Demonstration	This paper proposes a vision-based framework for a 7-degree-of-freedom robotic manipulator, with the primary objective of facilitating its capacity to acquire information from human hand demonstrations for the execution of dexterous pick-and-place tasks. Most existing works only focus on the position demonstration with...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	441,316
2212.12731	Forecasting through deep learning and modal decomposition in two-phase concentric jets	This work aims to improve fuel chamber injectors' performance in turbofan engines, thus implying improved performance and reduction of pollutants. This requires the development of models that allow real-time prediction and improvement of the fuel/air mixture. However, the work carried out to date involves using experim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,117
2206.12360	Cooperative Control in Eco-Driving of Electric Connected and Autonomous Vehicles in an Un-Signalized Urban Intersection	This paper addresses the problem of finding the optimal Eco-Driving (ED) speed profile of an electric Connected and Automated Vehicle (CAV) in an isolated urban un-signalized intersection. The problem is formulated as a single-level optimization and solved using Pontryagin's Minimum Principle (PMP). Analytical solution...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	304,567
2012.02364	Deep Learning for Medical Anomaly Detection -- A Survey	Machine learning-based medical anomaly detection is an important problem that has been extensively studied. Numerous approaches have been proposed across various medical application domains and we observe several similarities across these distinct applications. Despite this comparability, we observe a lack of structure...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	209,743
2111.01657	LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision	With increasing scale and complexity of cloud operations, automated detection of anomalies in monitoring data such as logs will be an essential part of managing future IT infrastructures. However, many methods based on artificial intelligence, such as supervised deep learning models, require large amounts of labeled tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,621
1911.04801	MSDF: A Deep Reinforcement Learning Framework for Service Function Chain Migration	Under dynamic traffic, service function chain (SFC) migration is considered as an effective way to improve resource utilization. However, the lack of future network information leads to non-optimal solutions, which motivates us to study reinforcement learning based SFC migration from a long-term perspective. In this pa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	153,079
2411.12972	A Foundation Model for Unified Urban Spatio-Temporal Flow Prediction	Urban spatio-temporal flow prediction, encompassing traffic flows and crowd flows, is crucial for optimizing city infrastructure and managing traffic and emergency responses. Traditional approaches have relied on separate models tailored to either grid-based data, representing cities as uniform cells, or graph-based da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,620
1011.5599	HyperANF: Approximating the Neighbourhood Function of Very Large Graphs on a Budget	The neighbourhood function N(t) of a graph G gives, for each t, the number of pairs of nodes <x, y> such that y is reachable from x in less that t hops. The neighbourhood function provides a wealth of information about the graph (e.g., it easily allows one to compute its diameter), but it is very expensive to compute i...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	8,338
2501.02392	Syntactic Evolution in Language Usage	This research aims to investigate the dynamic nature of linguistic style throughout various stages of life, from post teenage to old age. By employing linguistic analysis tools and methodologies, the study will delve into the intricacies of how individuals adapt and modify their language use over time. The research use...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	522,464
2010.09162	Hybrid Beamforming and Adaptive RF Chain Activation for Uplink Cell-Free Millimeter-Wave Massive MIMO Systems	In this work, we investigate hybrid analog-digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. {We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the dig...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	201,429
2010.01040	Attention-Based Clustering: Learning a Kernel from Context	In machine learning, no data point stands alone. We believe that context is an underappreciated concept in many machine learning methods. We propose Attention-Based Clustering (ABC), a neural architecture based on the attention mechanism, which is designed to learn latent representations that adapt to context within an...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	198,491
2406.18012	View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis	The inspection and monitoring of infrastructure assets typically requires identifying visual anomalies in scenes periodically photographed over time. Images collected manually or with robots such as unmanned aerial vehicles from the same scene at different instances in time are typically not perfectly aligned. Supervis...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	467,832
2411.02336	MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D	Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due to local discontinuities, inconsistencies across multiple views, and t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,450
1811.06497	Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer	For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	113,538
2004.13131	Effects of Assortativity on Consensus Formation with Heterogeneous Agents	Despite the widespread use of Barabasi's scale-free networks and Erdos-Renyi networks of which degree correlation (assortativity) is neutral, numerous studies demonstrated that online social networks tend to show assortative mixing (positive degree correlation), while non-social networks show a disassortative mixing (n...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	174,447
2304.12155	The African Stopwords project: curating stopwords for African languages	Stopwords are fundamental in Natural Language Processing (NLP) techniques for information retrieval. One of the common tasks in preprocessing of text data is the removal of stopwords. Currently, while high-resource languages like English benefit from the availability of several stopwords, low-resource languages, such a...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	360,100
2206.04053	Unsupervised Knowledge Adaptation for Passenger Demand Forecasting	Considering the multimodal nature of transport systems and potential cross-modal correlations, there is a growing trend of enhancing demand forecasting accuracy by learning from multimodal data. These multimodal forecasting models can improve accuracy but be less practical when different parts of multimodal datasets ar...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	301,499
2304.02232	Fairness-Aware Optimization of Vehicle-to-Vehicle Interaction for Smart EV Charging Coordination	As the number of electric vehicles (EVs) continues to grow, there is an increasing need for smart charging strategies. This paper exploits the vehicle-to-vehicle (V2V) concept to leverage EVs' diverse charging patterns and unlock the value of flexibility by enabling energy transfer among EVs. We formulate a cost minimi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	356,370
2411.17136	Autoencoder Enhanced Realised GARCH on Volatility Forecasting	Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aimin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	511,321
1005.0167	A digital interface for Gaussian relay and interference networks: Lifting codes from the discrete superposition model	For every Gaussian network, there exists a corresponding deterministic network called the discrete superposition network. We show that this discrete superposition network provides a near-optimal digital interface for operating a class consisting of many Gaussian networks in the sense that any code for the discrete supe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,371
1608.00668	Global Vertices and the Noising Paradox	A theoretical and experimental analysis related to the identification of vertices of unknown shapes is presented. Shapes are seen as real functions of their closed boundary. Unlike traditional approaches, which see curvature as the rate of change of the tangent to the curve, an alternative global perspective of curvatu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,317
2408.00290	Multi-Modal Parameter-Efficient Fine-tuning via Graph Neural Network	With the advent of the era of foundation models, pre-training and fine-tuning have become common paradigms. Recently, parameter-efficient fine-tuning has garnered widespread attention due to its better balance between the number of learnable parameters and performance. However, some current parameter-efficient fine-tun...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	477,776
2310.03577	Liquid Cooling System for a High Power, Medium Frequency, and Medium Voltage Isolated Power Converter	Power electronics systems, widely used in various applications such as industrial automation, electric cars, and renewable energy, have the primary function of converting and controlling electrical power to the desired type of load. Despite their reliability and efficiency, power losses in these systems generate signif...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	397,331
2409.14101	PoseAugment: Generative Human Pose Data Augmentation with Physical Plausibility for IMU-based Motion Capture	The data scarcity problem is a crucial factor that hampers the model performance of IMU-based human motion capture. However, effective data augmentation for IMU-based motion capture is challenging, since it has to capture the physical relations and constraints of the human body, while maintaining the data distribution ...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	490,328
1704.03192	On the modeling of neural cognition for social network applications	In this paper, we study neural cognition in social network. A stochastic model is introduced and shown to incorporate two well-known models in Pavlovian conditioning and social networks as special case, namely Rescorla-Wagner model and Friedkin-Johnsen model. The interpretation and comparison of these model are discuss...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	71,587
1306.2843	On Some Recent Insights in Integral Biomathics	This paper summarizes the results in Integral Biomathics obtained to this moment and provides an outlook for future research in the field.	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	25,158
2312.10569	Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data	Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these data into scalar statistics (e.g., the mean), but these summaries can be misleading. For example, disparate distributions can have the same ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,223
2208.04976	Machine Learning 1- and 2-electron reduced density matrices of polymeric molecules	Encoding the electronic structure of molecules using 2-electron reduced density matrices (2RDMs) as opposed to many-body wave functions has been a decades-long quest as the 2RDM contains sufficient information to compute the exact molecular energy but requires only polynomial storage. We focus on linear polymers with v...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	312,279
1707.07673	Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms	Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main E...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	77,678
2405.01673	ShadowNav: Autonomous Global Localization for Lunar Navigation in Darkness	The ability to determine the pose of a rover in an inertial frame autonomously is a crucial capability necessary for the next generation of surface rover missions on other planetary bodies. Currently, most on-going rover missions utilize ground-in-the-loop interventions to manually correct for drift in the pose estimat...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	451,452
2002.12463	Certified Defense to Image Transformations via Randomized Smoothing	We extend randomized smoothing to cover parameterized transformations (e.g., rotations, translations) and certify robustness in the parameter space (e.g., rotation angle). This is particularly challenging as interpolation and rounding effects mean that image transformations do not compose, in turn preventing direct cer...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	166,040
1108.4475	Coordinated Beamforming for Multiuser MISO Interference Channel under Rate Outage Constraints	This paper studies the coordinated beamforming design problem for the multiple-input single-output (MISO) interference channel, assuming only channel distribution information (CDI) at the transmitters. Under a given requirement on the rate outage probability for receivers, we aim to maximize the system utility (e.g., t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	11,774
1910.11672	Rare Event Simulation for non-Markovian repairable Fault Trees	Dynamic Fault Trees (DFT) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	150,856
cmp-lg/9408012	Approximate N-Gram Markov Model for Natural Language Generation	This paper proposes an Approximate n-gram Markov Model for bag generation. Directed word association pairs with distances are used to approximate (n-1)-gram and n-gram training tables. This model has parameters of word association model, and merits of both word association model and Markov Model. The training knowledge...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,161
2311.17350	Implicit-explicit Integrated Representations for Multi-view Video Compression	With the increasing consumption of 3D displays and virtual reality, multi-view video has become a promising format. However, its high resolution and multi-camera shooting result in a substantial increase in data volume, making storage and transmission a challenging task. To tackle these difficulties, we propose an impl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	411,266
2207.14722	Automatic Reward Design via Learning Motivation-Consistent Intrinsic Rewards	Reward design is a critical part of the application of reinforcement learning, the performance of which strongly depends on how well the reward signal frames the goal of the designer and how well the signal assesses progress in reaching that goal. In many cases, the extrinsic rewards provided by the environment (e.g., ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	310,688
2406.15731	Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning	Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privacy, FL incorporates Secure Aggregation (SA) to prevent the server from obtaining individual gradients, thus effectively resisting GIAs. In th...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	466,838
2012.07477	Aggregative Self-Supervised Feature Learning from a Limited Sample	Self-supervised learning (SSL) is an efficient approach that addresses the issue of limited training data and annotation shortage. The key part in SSL is its proxy task that defines the supervisory signals and drives the learning toward effective feature representations. However, most SSL approaches usually focus on a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,468
2209.01513	An interpretative and adaptive MPC for nonlinear systems	Model predictive control (MPC) for nonlinear systems suffers a trade-off between the model accuracy and real-time computational burden. One widely used approximation method is the successive linearization MPC (SL-MPC) with EKF method, in which the EKF algorithm is to handle unmeasured disturbances and unavailable full ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	315,912
2005.08209	Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis	Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise. In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. Acknowledging the importance of con...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	177,550
1507.02351	Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions	The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximization in social networks: One seeks to select among certain accessible nodes in a network, and then select, adaptively, among neighbors of those nodes as they become accessible in order to maximize a global objective function. More g...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	44,974
1711.07677	Corporate payments networks and credit risk rating	Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risks of the elements and the topology of the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two propert...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	85,049
1807.04634	On the Decomposition of Forces	We show that any continuously differentiable force is decomposed into the sum of a Rayleigh force and a gyroscopic force. We also extend this result to piecewise continuously differentiable forces. Our result improves the result on the decomposition of forces in a book by David Merkin and further extends it to piecewis...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	102,757
2209.04049	Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics	The symbolic AI community is increasingly trying to embrace machine learning in neuro-symbolic architectures, yet is still struggling due to cultural barriers. To break the barrier, this rather opinionated personal memo attempts to explain and rectify the conventions in Statistics, Machine Learning, and Deep Learning f...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	316,668
2407.02509	Variables are a Curse in Software Vulnerability Prediction	Deep learning-based approaches for software vulnerability prediction currently mainly rely on the original text of software code as the feature of nodes in the graph of code and thus could learn a representation that is only specific to the code text, rather than the representation that depicts the 'intrinsic' function...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	469,769
2104.03797	Design and assessment of an eco--driving PMP algorithm for optimal deceleration and gear shifting in trucks	In this paper, an eco--driving Pontryagin maximum principle (PMP) algorithm is designed for optimal deceleration and gear shifting in trucks based on switching among a finite set of driving modes. The PMP algorithm is implemented and assessed in the IPG TruckMaker traffic simulator as an eco--driving assistance system ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	229,179
2308.03205	Autonomous Ground Navigation in Highly Constrained Spaces: Lessons learned from The 2nd BARN Challenge at ICRA 2023	The 2nd BARN (Benchmark Autonomous Robot Navigation) Challenge took place at the 2023 IEEE International Conference on Robotics and Automation (ICRA 2023) in London, UK and continued to evaluate the performance of state-of-the-art autonomous ground navigation systems in highly constrained environments. Compared to The ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	383,936
2403.16206	Rumor Detection with a novel graph neural network approach	The wide spread of rumors on social media has caused a negative impact on people's daily life, leading to potential panic, fear, and mental health problems for the public. How to debunk rumors as early as possible remains a challenging problem. Existing studies mainly leverage information propagation structure to detec...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	440,925
2307.07675	On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms	Efficient learning in multi-armed bandit mechanisms such as pay-per-click (PPC) auctions typically involves three challenges: 1) inducing truthful bidding behavior (incentives), 2) using personalization in the users (context), and 3) circumventing manipulations in click patterns (corruptions). Each of these challenges ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	379,494

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