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
2412.19517	Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model	Differential equations (DEs) are crucial for modeling the evolution of natural or engineered systems. Traditionally, the parameters in DEs are adjusted to fit data from system observations. However, in fields such as politics, economics, and biology, available data are often independently collected at distinct time poi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	520,880
2302.05334	The Role of Codeword-to-Class Assignments in Error-Correcting Codes: An Empirical Study	Error-correcting codes (ECC) are used to reduce multiclass classification tasks to multiple binary classification subproblems. In ECC, classes are represented by the rows of a binary matrix, corresponding to codewords in a codebook. Codebooks are commonly either predefined or problem dependent. Given predefined codeboo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,009
2306.10508	QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction	Estimating the joint distribution of on-road agents' future trajectories is essential for autonomous driving. In this technical report, we propose a next-generation framework for joint multi-agent trajectory prediction called QCNeXt. First, we adopt the query-centric encoding paradigm for the task of joint multi-agent ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	374,261
2401.15578	ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping	In a real-world infrared imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct images due to cross-level semantic gaps and insufficient characterization of the global column features. To tackle this problem, we propose a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	424,504
1503.08596	Fast Optimal Transport Averaging of Neuroimaging Data	Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	41,607
2406.00133	Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach	Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but using simplifying assumptions which can lead to poor accu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	459,722
1911.00068	Confident Learning: Estimating Uncertainty in Dataset Labels	Learning exists in the context of data, yet notions of confidence typically focus on model predictions, not label quality. Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,714
2010.02005	Gender prediction using limited Twitter Data	Transformer models have shown impressive performance on a variety of NLP tasks. Off-the-shelf, pre-trained models can be fine-tuned for specific NLP classification tasks, reducing the need for large amounts of additional training data. However, little research has addressed how much data is required to accurately fine-...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	198,875
1902.02880	Mean Field Limit of the Learning Dynamics of Multilayer Neural Networks	Can multilayer neural networks -- typically constructed as highly complex structures with many nonlinearly activated neurons across layers -- behave in a non-trivial way that yet simplifies away a major part of their complexities? In this work, we uncover a phenomenon in which the behavior of these complex networks -- ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,965
1911.11094	Convex Optimization over Fixed Value Point Set of Quasi-Nonexpansive Random Operators on Hilbert Spaces	In this paper, a new optimization framework is defined that includes the optimization framework recently proposed in [1]-[2] as a special case. The convex optimization in [1]-[2] includes centralized optimization and distributed optimization over random networks, so does the optimization defined here. It is shown that ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	155,022
2307.09570	SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology	Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have been recently proposed for universal use in segmentation tasks. SAM shows remarkable promise in instance segmentation on natural images. How...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,213
1703.07579	An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning	We propose an end-to-end approach to the natural language object retrieval task, which localizes an object within an image according to a natural language description, i.e., referring expression. Previous works divide this problem into two independent stages: first, compute region proposals from the image without the e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,422
2404.00898	CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series	Data Augmentation is a common technique used to enhance the performance of deep learning models by expanding the training dataset. Automatic Data Augmentation (ADA) methods are getting popular because of their capacity to generate policies for various datasets. However, existing ADA methods primarily focused on overall...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	443,155
2206.09774	Improving Triplet-Based Channel Charting on Distributed Massive MIMO Measurements	The objective of channel charting is to learn a virtual map of the radio environment from high-dimensional CSI that is acquired by a multi-antenna wireless system. Since, in static environments, CSI is a function of the transmitter location, a mapping from CSI to channel chart coordinates can be learned in a self-super...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	303,685
2009.09972	Line Flow based SLAM	We propose a visual SLAM method by predicting and updating line flows that represent sequential 2D projections of 3D line segments. While feature-based SLAM methods have achieved excellent results, they still face problems in challenging scenes containing occlusions, blurred images, and repetitive textures. To address ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	196,761
1607.02791	Syntactic Phylogenetic Trees	In this paper we identify several serious problems that arise in the use of syntactic data from the SSWL database for the purpose of computational phylogenetic reconstruction. We show that the most naive approach fails to produce reliable linguistic phylogenetic trees. We identify some of the sources of the observed pr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	58,418
2301.13105	Generalization on the Unseen, Logic Reasoning and Degree Curriculum	This paper considers the learning of logical (Boolean) functions with a focus on the generalization on the unseen (GOTU) setting, a strong case of out-of-distribution generalization. This is motivated by the fact that the rich combinatorial nature of data in certain reasoning tasks (e.g., arithmetic/logic) makes repres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,785
1211.5914	A survey of uncertainty principles and some signal processing applications	The goal of this paper is to review the main trends in the domain of uncertainty principles and localization, emphasize their mutual connections and investigate practical consequences. The discussion is strongly oriented towards, and motivated by signal processing problems, from which significant advances have been mad...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	19,941
2308.02421	Differentiable short-time Fourier transform with respect to the hop length	In this paper, we propose a differentiable version of the short-time Fourier transform (STFT) that allows for gradient-based optimization of the hop length or the frame temporal position by making these parameters continuous. Our approach provides improved control over the temporal positioning of frames, as the continu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	383,615
2502.08882	2D Integrated Bayesian Tomography of Plasma Electron Density Profile for HL-3 Based on Gaussian Process	This paper introduces an integrated Bayesian model that combines line integral measurements and point values using Gaussian Process (GP). The proposed method leverages Gaussian Process Regression (GPR) to incorporate point values into 2D profiles and employs coordinate mapping to integrate magnetic flux information for...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,204
1102.3044	The Multiplexing Gain of a Two-cell MIMO Channel with Unequal CSI	In this work, the joint precoding across two distant transmitters (TXs), sharing the knowledge of the data symbols to be transmitted, to two receivers (RXs), each equipped with one antenna, is discussed. We consider a distributed channel state information (CSI) configuration where each TX has its own local estimate of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,201
1010.1514	Quarantine generated phase transition in epidemic spreading	We study the critical effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered (SIR) model in the presence of quarantine, where susceptible individuals protect themselves by disconnecting their links to infected neigh...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	7,826
2210.00676	Some pointwise and decidable properties of non-uniform cellular automata	For non-uniform cellular automata (NUCA) with finite memory over an arbitrary universe with multiple local transition rules, we show that pointwise nilpotency, pointwise periodicity, and pointwise eventual periodicity properties are respectively equivalent to nilpotency, periodicity, and eventual periodicity. Moreover,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	320,956
2410.13896	From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images	Domain adaptation, which bridges the distributions across different modalities, plays a crucial role in multimodal medical image analysis. In endoscopic imaging, combining pre-operative data with intra-operative imaging is important for surgical planning and navigation. However, existing domain adaptation methods are h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	499,762
2411.19352	OMuleT: Orchestrating Multiple Tools for Practicable Conversational Recommendation	In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users to input free-form text to request recommendations, and then receive a list of relevant and diverse items. While previous work on synthetic ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	512,210
1905.03852	Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis	High-level synthesis (HLS) shortens the development time of hardware designs and enables faster design space exploration at a higher abstraction level. Optimization of complex applications in HLS is challenging due to the effects of implementation issues such as routing congestion. Routing congestion estimation is abse...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	130,305
2408.07445	Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach	Multimodal networks have demonstrated remarkable performance improvements over their unimodal counterparts. Existing multimodal networks are designed in a multi-branch fashion that, due to the reliance on fusion strategies, exhibit deteriorated performance if one or more modalities are missing. In this work, we propose...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,588
0907.0329	Evidence of coevolution in multi-objective evolutionary algorithms	This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	4,015
1907.06266	Hybrid Model-Based and Data-Driven Wind Velocity Estimator for an Autonomous Robotic Airship	In the context of autonomous airships, several works in control and guidance use wind velocity to design a control law. However, in general, this information is not directly measured in robotic airships. This paper presents three alternative versions for estimation of wind velocity. Firstly, an Extended Kalman Filter i...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	138,572
2401.08453	Co-existence of Terrestrial and Non-Terrestrial Networks in S-band	Co-existence of terrestrial and non-terrestrial networks (NTN) is foreseen as an important component to fulfill the global coverage promised for sixth-generation (6G) of cellular networks. Due to ever rising spectrum demand, using dedicated frequency bands for terrestrial network (TN) and NTN may not be feasible. As a ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	421,893
2110.04020	Pathologies in priors and inference for Bayesian transformers	In recent years, the transformer has established itself as a workhorse in many applications ranging from natural language processing to reinforcement learning. Similarly, Bayesian deep learning has become the gold-standard for uncertainty estimation in safety-critical applications, where robustness and calibration are ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	259,717
2404.09997	An Efficient Evolutionary Algorithm for Diversified Top-k (Weight) Clique Search Problems	In many real-world problems and applications, finding only a single element, even though the best, among all possible candidates, cannot fully meet the requirements. We may wish to have a collection where each individual is not only outstanding but also distinctive. Diversified Top-k (DTk) problems are a kind of combin...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	446,918
2010.07608	Fully Unsupervised Person Re-identification viaSelective Contrastive Learning	Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras. Unsupervised person ReID attracts a lot of attention recently, due to it works without intensive manual annotation and thus shows great potential of adapting to new conditions. Representation learning pl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,886
2308.05878	Composable Core-sets for Diversity Approximation on Multi-Dataset Streams	Core-sets refer to subsets of data that maximize some function that is commonly a diversity or group requirement. These subsets are used in place of the original data to accomplish a given task with comparable or even enhanced performance if biases are removed. Composable core-sets are core-sets with the property that ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	384,938
2407.21438	A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap	Human-object interactions (HOI) detection aims at capturing human-object pairs in images and corresponding actions. It is an important step toward high-level visual reasoning and scene understanding. However, due to the natural bias from the real world, existing methods mostly struggle with rare human-object pairs and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,536
2405.15047	Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detection	This paper presents an innovative approach, called credal wrapper, to formulating a credal set representation of model averaging for Bayesian neural networks (BNNs) and deep ensembles, capable of improving uncertainty estimation in classification tasks. Given a finite collection of single distributions derived from BNN...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,713
2204.02453	A Comprehensive Framework based on Dynamic and Steady State Analysis to Evaluate Power System Resiliency to Extreme Weather Conditions	Power system robustness against high impact low probability events is becoming a major concern. To depict distinct phases of a system response during these disturbances, an irregular polygon model is derived from the conventional trapezoid model and the model is analytically investigated for transmission system perform...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	289,946
cs/0508019	On the Minimal Pseudo-Codewords of Codes from Finite Geometries	In order to understand the performance of a code under maximum-likelihood (ML) decoding, it is crucial to know the minimal codewords. In the context of linear programming (LP) decoding, it turns out to be necessary to know the minimal pseudo-codewords. This paper studies the minimal codewords and minimal pseudo-codewor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	538,856
2302.03567	From Utilitarian to Rawlsian Designs for Algorithmic Fairness	There is a lack of consensus within the literature as to how `fairness' of algorithmic systems can be measured, and different metrics can often be at odds. In this paper, we approach this task by drawing on the ethical frameworks of utilitarianism and John Rawls. Informally, these two theories of distributive justice m...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	344,390
2312.09691	Quilt: Robust Data Segment Selection against Concept Drifts	Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data streams where the joint distribution of the data X and label y, P(X, y), changes over time and possibly degrade model accuracy. Existing concept ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	415,844
2302.08021	Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus	We propose a new method based on discrete Fourier analysis to analyze the time evolutionary algorithms spend on plateaus. This immediately gives a concise proof of the classic estimate of the expected runtime of the $(1+1)$ evolutionary algorithm on the Needle problem due to Garnier, Kallel, and Schoenauer (1999). We...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	345,906
2412.11155	Partial Identifiability in Inverse Reinforcement Learning For Agents With Non-Exponential Discounting	The aim of inverse reinforcement learning (IRL) is to infer an agent's preferences from observing their behaviour. Usually, preferences are modelled as a reward function, $R$, and behaviour is modelled as a policy, $\pi$. One of the central difficulties in IRL is that multiple preferences may lead to the same observed ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	517,282
2408.12254	A Language-agnostic Model of Child Language Acquisition	This work reimplements a recent semantic bootstrapping child-language acquisition model, which was originally designed for English, and trains it to learn a new language: Hebrew. The model learns from pairs of utterances and logical forms as meaning representations, and acquires both syntax and word meanings simultaneo...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	482,655
1208.5537	Planning Random path distributions for ambush games in unstructured environments	Operating vehicles in adversarial environments require non-conventional planning techniques. A two-player, zero-sum non-cooperative game is introduced, which is solved via a linear program. An extension is proposed to construct networks displaying good representations of the environment characteristics, while offering ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	18,277
2205.13471	Characterising Research Areas in the field of AI	Interest in Artificial Intelligence (AI) continues to grow rapidly, hence it is crucial to support researchers and organisations in understanding where AI research is heading. In this study, we conducted a bibliometric analysis on 257K articles in AI, retrieved from OpenAlex. We identified the main conceptual themes by...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	298,945
2309.02338	Sustainability assessment of Low Earth Orbit (LEO) satellite broadband megaconstellations	The growth of megaconstellations is rapidly increasing the number of rocket launches. While Low Earth Orbit (LEO) broadband satellites help to connect unconnected communities and achieve the Sustainable Development Goals (SDGs), there are also significant environmental emissions impacts from burning rocket fuels. We pr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	390,014
1912.09232	Improving Clique Decompositions of Semidefinite Relaxations for Optimal Power Flow Problems	Semidefinite Programming (SDP) provides tight lower bounds for Optimal Power Flow problems. However, solving large-scale SDP problems requires exploiting sparsity. In this paper, we experiment several clique decomposition algorithms that lead to different reformulations and we show that the resolution is highly sensiti...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	158,025
2406.17935	Sequential Editing for Lifelong Training of Speech Recognition Models	Automatic Speech Recognition (ASR) traditionally assumes known domains, but adding data from a new domain raises concerns about computational inefficiencies linked to retraining models on both existing and new domains. Fine-tuning solely on new domain risks Catastrophic Forgetting (CF). To address this, Lifelong Learni...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	467,799
2301.06388	Closed-Loop Magnetic Manipulation for Robotic Transesophageal Echocardiography	This paper presents a closed-loop magnetic manipulation framework for robotic transesophageal echocardiography (TEE) acquisitions. Different from previous work on intracorporeal robotic ultrasound acquisitions that focus on continuum robot control, we first investigate the use of magnetic control methods for more direc...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	340,632
2311.16764	Radiology-Aware Model-Based Evaluation Metric for Report Generation	We propose a new automated evaluation metric for machine-generated radiology reports using the successful COMET architecture adapted for the radiology domain. We train and publish four medically-oriented model checkpoints, including one trained on RadGraph, a radiology knowledge graph. Our results show that our metric ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	411,037
1907.11769	Automatically Learning Construction Injury Precursors from Text	In light of the increasing availability of digitally recorded safety reports in the construction industry, it is important to develop methods to exploit these data to improve our understanding of safety incidents and ability to learn from them. In this study, we compare several approaches to automatically learn injury ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	139,933
1907.05765	Learning to Handle Parameter Perturbations in Combinatorial Optimization: an Application to Facility Location	We present an approach to couple the resolution of Combinatorial Optimization problems with methods from Machine Learning, applied to the single source, capacitated, facility location problem. Our study is framed in the context where a reference facility location optimization problem is given. Assuming there exist data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,449
2405.04985	An Artificial Intelligence Approach for Interpreting Creative Combinational Designs	Combinational creativity, a form of creativity involving the blending of familiar ideas, is pivotal in design innovation. While most research focuses on how combinational creativity in design is achieved through blending elements, this study focuses on the computational interpretation, specifically identifying the 'bas...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	452,754
1712.04853	A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits	The advantages of neural machine translation (NMT) have been extensively validated for offline translation of several language pairs for different domains of spoken and written language. However, research on interactive learning of NMT by adaptation to human post-edits has so far been confined to simulation experiments...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	86,665
1205.6210	Learning Dictionaries with Bounded Self-Coherence	Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary learning method adapts an initial dictionary to a particular signal class by iteratively computing an approximate factorization of a training ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,211
2209.14399	FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations	In edge computing, users' service profiles are migrated due to user mobility. Reinforcement learning (RL) frameworks have been proposed to do so, often trained on simulated data. However, existing RL frameworks overlook occasional server failures, which although rare, impact latency-sensitive applications like autonomo...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	320,228
2105.01966	Joint Communication and Radar Sensing with Reconfigurable Intelligent Surfaces	In this paper, we use a reconfigurable intelligent surface (RIS) to enhance the radar sensing and communication capabilities of a mmWave dual function radar communication system. To simultaneously localize the target and to serve the user, we propose to adaptively partition the RIS by reserving separate RIS elements fo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	233,689
2204.10688	Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds	Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,880
2411.19309	GRAPE: Generalizing Robot Policy via Preference Alignment	Despite the recent advancements of vision-language-action (VLA) models on a variety of robotics tasks, they suffer from critical issues such as poor generalizability to unseen tasks, due to their reliance on behavior cloning exclusively from successful rollouts. Furthermore, they are typically fine-tuned to replicate d...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	512,198
1805.11303	Trust-based dynamic linear threshold models for non-competitive and competitive influence propagation	What are the key-features that enable an information diffusion model to explain the inherent dynamic, and often competitive, nature of real-world propagation phenomena? In this paper we aim to answer this question by proposing a novel class of diffusion models, inspired by the classic Linear Threshold model, and built ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	98,893
2310.07707	MatFormer: Nested Transformer for Elastic Inference	Foundation models are applied in a broad spectrum of settings with different inference constraints, from massive multi-accelerator clusters to resource-constrained standalone mobile devices. However, the substantial costs associated with training these models often limit the number of unique model sizes that can be off...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	399,086
2102.06543	Computing Betweenness Centrality in Link Streams	Betweeness centrality is one of the most important concepts in graph analysis. It was recently extended to link streams, a graph generalization where links arrive over time. However, its computation raises non-trivial issues, due in particular to the fact that time is considered as continuous. We provide here the first...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	219,787
2311.06242	Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks	We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform a diversity of tasks with simple instructions, a capability that implie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,862
1905.09889	forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction	A unique challenge in predictive model building for omics data has been the small number of samples $(n)$ versus the large amount of features $(p)$. This "$n\ll p$" property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating external gene network info...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,866
1104.5534	QoS Provisioning for Multimedia Transmission in Cognitive Radio Networks	In cognitive radio (CR) networks, the perceived reduction of application layer quality of service (QoS), such as multimedia distortion, by secondary users may impede the success of CR technologies. Most previous work in CR networks ignores application layer QoS. In this paper we take an integrated design approach to jo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	10,165
2402.02037	EffiBench: Benchmarking the Efficiency of Automatically Generated Code	Code generation models have increasingly become integral to aiding software development. Although current research has thoroughly examined the correctness of the code produced by code generation models, a vital aspect that plays a pivotal role in green computing and sustainability efforts has often been neglected. This...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	426,348
1905.03046	PiNet: A Permutation Invariant Graph Neural Network for Graph Classification	We propose an end-to-end deep learning learning model for graph classification and representation learning that is invariant to permutation of the nodes of the input graphs. We address the challenge of learning a fixed size graph representation for graphs of varying dimensions through a differentiable node attention po...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	130,119
2111.08885	Jump Interval-Learning for Individualized Decision Making	An individualized decision rule (IDR) is a decision function that assigns each individual a given treatment based on his/her observed characteristics. Most of the existing works in the literature consider settings with binary or finitely many treatment options. In this paper, we focus on the continuous treatment settin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,842
2204.06919	Proof of Federated Training: Accountable Cross-Network Model Training and Inference	Blockchain has widely been adopted to design accountable federated learning frameworks; however, the existing frameworks do not scale for distributed model training over multiple independent blockchain networks. For storing the pre-trained models over blockchain, current approaches primarily embed a model using its str...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	291,494
2412.19163	Master Stability Functions in Complex Networks	Synchronization is an emergent phenomenon in coupled dynamical networks. The Master Stability Function (MSF) is a highly elegant and powerful tool for characterizing the stability of synchronization states. However, a significant challenge lies in determining the MSF for complex dynamical networks driven by nonlinear i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	520,743
1107.0193	On the origin of ambiguity in efficient communication	This article studies the emergence of ambiguity in communication through the concept of logical irreversibility and within the framework of Shannon's information theory. This leads us to a precise and general expression of the intuition behind Zipf's vocabulary balance in terms of a symmetry equation between the comple...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	11,137
1603.09364	Partial Face Detection for Continuous Authentication	In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped and occluded faces captured using a smartphone's front-facing camera for continuous authentication. The key idea is to detect facial segments ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,906
2310.03472	Ammonia-Net: A Multi-task Joint Learning Model for Multi-class Segmentation and Classification in Tooth-marked Tongue Diagnosis	In Traditional Chinese Medicine, the tooth marks on the tongue, stemming from prolonged dental pressure, serve as a crucial indicator for assessing qi (yang) deficiency, which is intrinsically linked to visceral health. Manual diagnosis of tooth-marked tongue solely relies on experience. Nonetheless, the diversity in s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,294
1405.3033	Phonetic based SoundEx & ShapeEx algorithm for Sindhi Spell Checker System	This paper presents a novel combinational phonetic algorithm for Sindhi Language, to be used in developing Sindhi Spell Checker which has yet not been developed prior to this work. The compound textual forms and glyphs of Sindhi language presents a substantial challenge for developing Sindhi spell checker system and ge...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	33,043
2502.01669	Addressing Delayed Feedback in Conversion Rate Prediction via Influence Functions	In the realm of online digital advertising, conversion rate (CVR) prediction plays a pivotal role in maximizing revenue under cost-per-conversion (CPA) models, where advertisers are charged only when users complete specific actions, such as making a purchase. A major challenge in CVR prediction lies in the delayed feed...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	529,972
2011.02092	Soft Robot Optimal Control Via Reduced Order Finite Element Models	Finite element methods have been successfully used to develop physics-based models of soft robots that capture the nonlinear dynamic behavior induced by continuous deformation. These high-fidelity models are therefore ideal for designing controllers for complex dynamic tasks such as trajectory optimization and trajecto...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	204,818
1902.02342	Deep Morphological Simplification Network (MS-Net) for Guided Registration of Brain Magnetic Resonance Images	Objective: Deformable brain MR image registration is challenging due to large inter-subject anatomical variation. For example, the highly complex cortical folding pattern makes it hard to accurately align corresponding cortical structures of individual images. In this paper, we propose a novel deep learning way to simp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,853
1909.09266	Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation	The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	146,205
2305.05344	Trustworthy Multi-phase Liver Tumor Segmentation via Evidence-based Uncertainty	Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the complementary multi-phase information for liver tumor segmentation (LiTS), which are crucial to assist the diagnosis of liver cancer clinically. However, the performances of existing multi-phase liver tumor segmentation (MPLiTS)-based meth...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,109
1708.03915	Joint Beamforming Design and Power Allocation for Full-Duplex NOMA Cognitive Relay Systems	In this paper, we consider a non-orthogonal multiple access cognitive radio network, where a full-duplex multi-antenna relay assists transmission from a base station (BS) to a cognitive far user, whereas, at the same time, the BS transmits to a cognitive near user. Our objective is to enlarge the far-near user rate reg...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,845
2102.05612	Personalization for Web-based Services using Offline Reinforcement Learning	Large-scale Web-based services present opportunities for improving UI policies based on observed user interactions. We address challenges of learning such policies through model-free offline Reinforcement Learning (RL) with off-policy training. Deployed in a production system for user authentication in a major social n...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	219,493
1903.07462	Online Observability of Boolean Control Networks	Observabililty is an important topic of Boolean control networks (BCNs). In this paper, we propose a new type of observability named online observability to present the sufficient and necessary condition of determining the initial states of BCNs, when their initial states cannot be reset. And we design an algorithm to ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	124,628
2002.00819	Knowledge Graph Embedding for Link Prediction: A Comparative Analysis	Knowledge Graphs (KGs) have found many applications in industry and academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even state-of-the-art KGs suffer from incompleteness. Link ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	162,480
2210.08073	Eliciting Compatible Demonstrations for Multi-Human Imitation Learning	Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. While the ideal dataset for imitation learning is homogenous and low-variance -- reflecting a single, optimal method for performing a task -- natural human behavior has a great deal of heterogeneity,...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	323,970
1805.00732	Alternative passive maps in the Brayton-Moser framework: Implications on control and optimization	In the recent years, passivity theory has gained renewed attention because of its advantages and practicality in modeling of multi-domain systems and constructive control techniques. Unlike Lyapunov theory, passivity theory takes a behavioral approach in its control design methodologies. Hence, it provides solutions, w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	96,500
1909.00968	Image Inpainting with Learnable Bidirectional Attention Maps	Most convolutional network (CNN)-based inpainting methods adopt standard convolution to indistinguishably treat valid pixels and holes, making them limited in handling irregular holes and more likely to generate inpainting results with color discrepancy and blurriness. Partial convolution has been suggested to address ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,770
2309.05528	On the detection of Out-Of-Distribution samples in Multiple Instance Learning	The deployment of machine learning solutions in real-world scenarios often involves addressing the challenge of out-of-distribution (OOD) detection. While significant efforts have been devoted to OOD detection in classical supervised settings, the context of weakly supervised learning, particularly the Multiple Instanc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	391,119
2206.02765	Communication-constrained hypothesis testing: Optimality, robustness, and reverse data processing inequalities	We study hypothesis testing under communication constraints, where each sample is quantized before being revealed to a statistician. Without communication constraints, it is well known that the sample complexity of simple binary hypothesis testing is characterized by the Hellinger distance between the distributions. We...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	301,019
1810.03875	Towards Verifying Semantic Roles Co-occurrence	Semantic role theory considers roles as a small universal set of unanalyzed entities. It means that formally there are no restrictions on role combinations. We argue that the semantic roles co-occur in verb representations. It means that there are hidden restrictions on role combinations. To demonstrate that a practica...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	109,903
2412.12552	SAModified: A Foundation Model-Based Zero-Shot Approach for Refining Noisy Land-Use Land-Cover Maps	Land-use and land cover (LULC) analysis is critical in remote sensing, with wide-ranging applications across diverse fields such as agriculture, utilities, and urban planning. However, automating LULC map generation using machine learning is rendered challenging due to noisy labels. Typically, the ground truths (e.g. E...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	517,919
2207.12673	A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States	Ship roll motion in high sea states has large amplitudes and nonlinear dynamics, and its prediction is significant for operability, safety, and survivability. This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states. A hybrid neural network is propos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,085
2404.14809	A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications	A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, financial networks, and biomedical systems. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various N...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	true	false	448,827
1801.04520	Non-Parametric Transformation Networks	ConvNets, through their architecture, only enforce invariance to translation. In this paper, we introduce a new class of deep convolutional architectures called Non-Parametric Transformation Networks (NPTNs) which can learn \textit{general} invariances and symmetries directly from data. NPTNs are a natural generalizati...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	88,295
2312.11392	SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing	Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models, yielding promising results in specific adaptations of foundational generative diffusion models...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,549
2108.00508	Correlation of biological and computer viruses through evolutionary game theory	Computer viruses have many similarities to biological viruses, and their association may offer new perspectives and new opportunities in the effort to tackle and even eradicate them. Evolutionary game theory has been established as a useful tool for modeling viral behaviors. This work attempts to correlate a well-known...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	248,738
2010.03094	Correlated Differential Privacy: Feature Selection in Machine Learning	Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	199,269
2501.02523	Face-MakeUp: Multimodal Facial Prompts for Text-to-Image Generation	Facial images have extensive practical applications. Although the current large-scale text-image diffusion models exhibit strong generation capabilities, it is challenging to generate the desired facial images using only text prompt. Image prompts are a logical choice. However, current methods of this type generally fo...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	522,522
2110.05661	BotNet Detection on Social Media	As our reliance on social media platforms and web services increase day by day, exploiters view these platforms as an opportunity to manipulate our thoughts ad actions. These platforms have become an open playground for social bot accounts. Social bots not only learn human conversations, manners, and presence but also ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,341
2102.06245	Knowledge Infused Policy Gradients for Adaptive Pandemic Control	COVID-19 has impacted nations differently based on their policy implementations. The effective policy requires taking into account public information and adaptability to new knowledge. Epidemiological models built to understand COVID-19 seldom provide the policymaker with the capability for adaptive pandemic control (A...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	219,684
2008.12363	Analyzing Worldwide Social Distancing through Large-Scale Computer Vision	In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale (nationwide or worldwide) is difficult. To make matters worse, trad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,556

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