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
2009.10331	Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey	The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed R...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	196,868
1402.4742	IVOA Recommendation: TAPRegExt: a VOResource Schema Extension for Describing TAP Services	This document describes an XML encoding standard for metadata about services implementing the table access protocol TAP [TAP], referred to as TAPRegExt. Instance documents are part of the service's registry record or can be obtained from the service itself. They deliver information to both humans and software on the la...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	30,993
1807.02710	Improving DNN-based Music Source Separation using Phase Features	Music source separation with deep neural networks typically relies only on amplitude features. In this paper we show that additional phase features can improve the separation performance. Using the theoretical relationship between STFT phase and amplitude, we conjecture that derivatives of the phase are a good feature ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,331
2103.13799	Bertinho: Galician BERT Representations	This paper presents a monolingual BERT model for Galician. We follow the recent trend that shows that it is feasible to build robust monolingual BERT models even for relatively low-resource languages, while performing better than the well-known official multilingual BERT (mBERT). More particularly, we release two monol...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	226,610
2312.07969	ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation	Liver tumor segmentation is essential for computer-aided diagnosis, surgical planning, and prognosis evaluation. However, obtaining and maintaining a large-scale dataset with dense annotations is challenging. Semi-Supervised Learning (SSL) is a common technique to address these challenges. Recently, Segment Anything Mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,141
1807.06572	Explicating feature contribution using Random Forest proximity distances	In Random Forests, proximity distances are a metric representation of data into decision space. By observing how changes in input map to the movement of instances in this space we are able to determine the independent contribution of each feature to the decision-making process. For binary feature vectors, this process ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,146
2502.01113	GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation	Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relationships between pieces of knowledge, limiting their performance in intricate reasoning that requires integrating knowledge from multiple sour...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	529,703
2307.04634	Toward optimal placement of spatial sensors	This paper addresses the challenges of optimally placing a finite number of sensors to detect Poisson-distributed targets in a bounded domain. We seek to rigorously account for uncertainty in the target arrival model throughout the problem. Sensor locations are selected to maximize the probability that no targets are m...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	378,471
2302.13927	Real-time Reconstruction of Markov Sources and Remote Actuation over Wireless Channels	In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely an $N$-state Markov cha...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	348,097
2410.13995	Adversarial Inception for Bounded Backdoor Poisoning in Deep Reinforcement Learning	Recent works have demonstrated the vulnerability of Deep Reinforcement Learning (DRL) algorithms against training-time, backdoor poisoning attacks. These attacks induce pre-determined, adversarial behavior in the agent upon observing a fixed trigger during deployment while allowing the agent to solve its intended task ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	499,809
1708.08180	An Optimized Union-Find Algorithm for Connected Components Labeling Using GPUs	In this paper, we report an optimized union-find (UF) algorithm that can label the connected components on a 2D image efficiently by employing the GPU architecture. The proposed method contains three phases: UF-based local merge, boundary analysis, and link. The coarse labeling in local merge reduces the number atomic ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,608
2210.09385	Adaptive Oracle-Efficient Online Learning	The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale. More recent sophisticated techniques, which we refer to as oracle-efficient methods, address this problem by ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	324,509
1906.09567	On the Local Input-Output Stability of Event-Triggered Control Systems	This paper studies performance preserving event design in nonlinear event-based control systems based on a local L2-type performance criterion. Considering a finite gain local L2-stable disturbance driven continuous-time system, we propose a triggering mechanism so that the resulting sampled-data system preserves simil...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	136,207
2004.03561	Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering	We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token- and utterance-level language modeling and utterance order prediction, that learn both token and utterance embeddings for bett...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	171,608
2306.13918	Multi-task multi-station earthquake monitoring: An all-in-one seismic Phase picking, Location, and Association Network (PLAN)	Earthquake monitoring is vital for understanding the physics of earthquakes and assessing seismic hazards. A standard monitoring workflow includes the interrelated and interdependent tasks of phase picking, association, and location. Although deep learning methods have been successfully applied to earthquake monitoring...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,456
2201.07708	Debiased Graph Neural Networks with Agnostic Label Selection Bias	Most existing Graph Neural Networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with test set. In reality, the test data is not even available during the training process, making selection bias agnostic. Training GNNs with biased selec...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	276,111
2011.09704	Causal Contextual Prediction for Learned Image Compression	Over the past several years, we have witnessed impressive progress in the field of learned image compression. Recent learned image codecs are commonly based on autoencoders, that first encode an image into low-dimensional latent representations and then decode them for reconstruction purposes. To capture spatial depend...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,271
2210.05187	Broad-persistent Advice for Interactive Reinforcement Learning Scenarios	The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provide...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	322,743
2208.03685	A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement	Bayesian global optimization (BGO) is an efficient surrogate-assisted technique for problems involving expensive evaluations. A parallel technique can be used to parallelly evaluate the true-expensive objective functions in one iteration to boost the execution time. An effective and straightforward approach is to desig...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	311,867
2302.11720	Irregular Repetition Slotted ALOHA Over the Binary Adder Channel	We propose an irregular repetition slotted ALOHA (IRSA) based random-access protocol for the binary adder channel (BAC). The BAC captures important physical-layer concepts, such as packet generation, per-slot decoding, and information rate, which are neglected in the commonly considered collision channel model. We divi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	347,292
2110.08817	A deep learning pipeline for localization, differentiation, and uncertainty estimation of liver lesions using multi-phasic and multi-sequence MRI	Objectives: to propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization, with uncertainty estimation. Methods: we enrolled 400 patients who had either liver resection or a biopsy and was diagnosed with either hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma, o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,566
2008.13270	Shannon Meets Turing: Non-Computability and Non-Approximability of the Finite State Channel Capacity	The capacity of finite state channels (FSCs) has been established as the limit of a sequence of multi-letter expressions only and, despite tremendous effort, a corresponding finite-letter characterization remains unknown to date. This paper analyzes the capacity of FSCs from a fundamental, algorithmic point of view by ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	193,806
2109.06746	Why Existing Machine Learning Methods Fails At Extracting the Information of Future Returns Out of Historical Sctock Prices : the Curve-Shape-Feature and Non-Curve-Shape-Feature Modes	The financial time series analysis is important access to touch the complex laws of financial markets. Among many goals of the financial time series analysis, one is to construct a model that can extract the information of the future return out of the known historical stock data, such as stock price, financial news, an...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	255,267
2411.16618	StructFormer: Document Structure-based Masked Attention and its Impact on Language Model Pre-Training	Most state-of-the-art techniques for Language Models (LMs) today rely on transformer-based architectures and their ubiquitous attention mechanism. However, the exponential growth in computational requirements with longer input sequences confines Transformers to handling short passages. Recent efforts have aimed to addr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	511,092
2412.12590	Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback	In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	517,936
2303.08105	Image Guidance for Robot-Assisted Ankle Fracture Repair	This project concerns developing and validating an image guidance framework for application to a robotic-assisted fibular reduction in ankle fracture surgery. The aim is to produce and demonstrate proper functioning of software for automatic determination of directions for fibular repositioning with the ultimate goal o...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	351,506
2305.00521	StyleLipSync: Style-based Personalized Lip-sync Video Generation	In this paper, we present StyleLipSync, a style-based personalized lip-sync video generative model that can generate identity-agnostic lip-synchronizing video from arbitrary audio. To generate a video of arbitrary identities, we leverage expressive lip prior from the semantically rich latent space of a pre-trained Styl...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	361,360
1906.04934	Transferrable Operative Difficulty Assessment in Robot-assisted Teleoperation: A Domain Adaptation Approach	Providing an accurate and efficient assessment of operative difficulty is important for designing robot-assisted teleoperation interfaces that are easy and natural for human operators to use. In this paper, we aim to develop a data-driven approach to numerically characterize the operative difficulty demand of complex t...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	134,882
1305.1052	Hybridization of Otsu Method and Median Filter for Color Image Segmentation	In this article a novel algorithm for color image segmentation has been developed. The proposed algorithm based on combining two existing methods in such a novel way to obtain a significant method to partition the color image into significant regions. On the first phase, the traditional Otsu method for gray channel ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,401
1706.05917	Transmission line parameter identification using PMU measurements	Accurate knowledge of transmission line (TL) impedance parameters helps to improve accuracy in relay settings and power flow modeling. To improve TL parameter estimates, various algorithms have been proposed in the past to identify TL parameters based on measurements from Phasor Measurement Units (PMUs). These methods ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	75,597
1604.07209	Unbiased Comparative Evaluation of Ranking Functions	Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge. Unlike traditional approaches that make this selection deterministically, probabilistic sampling has shown intriguing promise since it enables the design of estimators that are p...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	55,061
2009.05778	Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm	Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this work, three maj...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	195,422
2408.04884	Enhancing Relevance of Embedding-based Retrieval at Walmart	Embedding-based neural retrieval (EBR) is an effective search retrieval method in product search for tackling the vocabulary gap between customer search queries and products. The initial launch of our EBR system at Walmart yielded significant gains in relevance and add-to-cart rates [1]. However, despite EBR generally ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	479,569
2502.08326	Model-Free Counterfactual Subset Selection at Scale	Ensuring transparency in AI decision-making requires interpretable explanations, particularly at the instance level. Counterfactual explanations are a powerful tool for this purpose, but existing techniques frequently depend on synthetic examples, introducing biases from unrealistic assumptions, flawed models, or skewe...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	true	true	532,978
1809.02274	Optimal Relaying Beamforming in Multiple Access Broadcast Channel (MABC) Bidirectional Cognitive Radio Networks in Presence of Interferers	In this paper, a general cognitive radio system consisting of a set of users with different level of spectrum access including two primary transceivers and several types of secondary users is considered. It is assumed that two secondary users operate based on an underlay model at the same frequency bandwidth and at the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	107,014
1712.09315	Who is Smarter? Intelligence Measure of Learning-based Cognitive Radios	Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing cognition, the research on measuring CR cognitive capabilities and intelligence is largely open. Deriving the intelligence meas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	87,332
2108.01229	Taking Cognition Seriously: A generalised physics of cognition	The study of complex systems through the lens of category theory consistently proves to be a powerful approach. We propose that cognition deserves the same category-theoretic treatment. We show that by considering a highly-compact cognitive system, there are fundamental physical trade-offs resulting in a utility proble...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	248,958
2101.11483	Investigating Dissemination of Scientific Information on Twitter: A Study of Topic Networks in Opioid Publications	One way to assess a certain aspect of the value of scientific research is to measure the attention it receives on social media. While previous research has mostly focused on the "number of mentions" of scientific research on social media, the current study applies "topic networks" to measure public attention to scienti...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	217,296
2410.02297	Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis	In the domain of Aspect-Based Sentiment Analysis (ABSA), generative methods have shown promising results and achieved substantial advancements. However, despite these advancements, the tasks of extracting sentiment quadruplets, which capture the nuanced sentiment expressions within a sentence, remain significant challe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	494,214
2304.09835	An XAI framework for robust and transparent data-driven wind turbine power curve models	Wind turbine power curve models translate ambient conditions into turbine power output. They are essential for energy yield prediction and turbine performance monitoring. In recent years, increasingly complex machine learning methods have become state-of-the-art for this task. Nevertheless, they frequently encounter cr...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	359,190
2109.12880	Wasserstein Patch Prior for Image Superresolution	In this paper, we introduce a Wasserstein patch prior for superresolution of two- and three-dimensional images. Here, we assume that we have given (additionally to the low resolution observation) a reference image which has a similar patch distribution as the ground truth of the reconstruction. This assumption is e.g. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	257,447
1308.4809	Block Markov Superposition Transmission: Construction of Big Convolutional Codes from Short Codes	A construction of big convolutional codes from short codes called block Markov superposition transmission (BMST) is proposed. The BMST is very similar to superposition blockMarkov encoding (SBME), which has been widely used to prove multiuser coding theorems. The encoding process of BMST can be as fast as that of the i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,568
1212.3228	Language Without Words: A Pointillist Model for Natural Language Processing	This paper explores two separate questions: Can we perform natural language processing tasks without a lexicon?; and, Should we? Existing natural language processing techniques are either based on words as units or use units such as grams only for basic classification tasks. How close can a machine come to reasoning ab...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	20,378
1901.10089	A maximum principle argument for the uniform convergence of graph Laplacian regressors	This paper investigates the use of methods from partial differential equations and the Calculus of variations to study learning problems that are regularized using graph Laplacians. Graph Laplacians are a powerful, flexible method for capturing local and global geometry in many classes of learning problems, and the tec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,925
2207.01144	Efficient Interactive Coding Achieving Optimal Error Resilience Over the Binary Channel	Given a noiseless protocol $\pi_0$ computing a function $f(x, y)$ of Alice and Bob's private inputs $x, y$, the goal of interactive coding is to construct an error-resilient protocol $\pi$ computing $f$ such that even if some fraction of the communication is adversarially corrupted, both parties still learn $f(x, y)$. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	306,050
2303.11602	Convergence of variational Monte Carlo simulation and scale-invariant pre-training	We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem. We study both the energy minimization phase and the supervised pre-training phase that is commonly used prior to energy minimization. For the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,919
2407.08903	TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing	Heterogeneous collaborative computing with NPU and CPU has received widespread attention due to its substantial performance benefits. To ensure data confidentiality and integrity during computing, Trusted Execution Environments (TEE) is considered a promising solution because of its comparatively lower overhead. Howeve...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	472,351
2502.04873	Training-free Task-oriented Grasp Generation	This paper presents a training-free pipeline for task-oriented grasp generation that combines pre-trained grasp generation models with vision-language models (VLMs). Unlike traditional approaches that focus solely on stable grasps, our method incorporates task-specific requirements by leveraging the semantic reasoning ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	531,348
2403.17128	Benchmarking Video Frame Interpolation	Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target. However, the current evaluation of frame interpolation techniques is not ideal. Due to the plethora of test datasets available and inconsistent computation of error metr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,320
1907.03842	Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE	This paper analyses the application of no-reference metric NIQE to the task of video-codec comparison. A number of issues in the metric behaviour on videos was detected and described. The metric has outlying scores on black and solid-coloured frames. The proposed averaging technique for metric quality scores helped to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	137,948
1812.02466	Deep Embedding using Bayesian Risk Minimization with Application to Sketch Recognition	In this paper, we address the problem of hand-drawn sketch recognition. Inspired by the Bayesian decision theory, we present a deep metric learning loss with the objective to minimize the Bayesian risk of misclassification. We estimate this risk for every mini-batch during training, and learn robust deep embeddings by ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,763
1707.09958	(k,q)-Compressed Sensing for dMRI with Joint Spatial-Angular Sparsity Prior	Advanced diffusion magnetic resonance imaging (dMRI) techniques, like diffusion spectrum imaging (DSI) and high angular resolution diffusion imaging (HARDI), remain underutilized compared to diffusion tensor imaging because the scan times needed to produce accurate estimations of fiber orientation are significantly lon...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,121
2212.14556	Multisensor Multiobject Tracking with Improved Sampling Efficiency	Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	338,655
2502.01568	Visual Theory of Mind Enables the Invention of Writing Systems	Abstract symbolic writing systems are semiotic codes that are ubiquitous in modern society but are otherwise absent in the animal kingdom. Anthropological evidence suggests that the earliest forms of some writing systems originally consisted of iconic pictographs, which signify their referent via visual resemblance. Wh...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	529,921
2402.02533	Identifying and Extracting Pedestrian Behavior in Critical Traffic Situations	A better understanding of interactive pedestrian behavior in critical traffic situations is essential for the development of enhanced pedestrian safety systems. Real-world traffic observations play a decisive role in this, since they represent behavior in an unbiased way. In this work, we present an approach of how a s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	426,606
2304.07215	Towards Learning and Verifying Maximal Neural Lyapunov Functions	The search for Lyapunov functions is a crucial task in the analysis of nonlinear systems. In this paper, we present a physics-informed neural network (PINN) approach to learning a Lyapunov function that is nearly maximal for a given stable set. A Lyapunov function is considered nearly maximal if its sub-level sets can ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	358,267
2108.08537	Multi-task Federated Learning for Heterogeneous Pancreas Segmentation	Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data. For example, one client might have patient data with "healthy'' pancreases only while datasets from other clients may contain cases wi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,290
1908.06657	Quantum Expectation-Maximization for Gaussian Mixture Models	The Expectation-Maximization (EM) algorithm is a fundamental tool in unsupervised machine learning. It is often used as an efficient way to solve Maximum Likelihood (ML) estimation problems, especially for models with latent variables. It is also the algorithm of choice to fit mixture models: generative models that rep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	142,076
2305.05057	Crack Detection of Asphalt Concrete Using Combined Fracture Mechanics and Digital Image Correlation	Cracking is a common failure mode in asphalt concrete (AC) pavements. Many tests have been developed to characterize the fracture behavior of AC. Accurate crack detection during testing is crucial to describe AC fracture behavior. This paper proposed a framework to detect surface cracks in AC specimens using two-dimens...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,982
2109.05791	Unified Kinematic and Dynamical Modeling of a Soft Robotic Arm by a Piecewise Universal Joint Model	The compliance of soft robotic arms renders the development of accurate kinematic & dynamical models especially challenging. The most widely used model in soft robotic kinematics assumes Piecewise Constant Curvature (PCC). However, PCC fails to effectively handle external forces, or even the influence of gravity, since...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	254,948
2305.10899	Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark	With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently needed to facilitate the field. To this end, the URUR dataset is introduced, in the meaning of Ultra-Hi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	365,283
1804.00443	A Note on the Hardness of the Critical Tuple Problem	The notion of critical tuple was introduced by Miklau and Suciu (Gerome Miklau and Dan Suciu. A formal analysis of information disclosure in data exchange. J. Comput. Syst. Sci., 73(3):507-534, 2007), who also claimed that the problem of checking whether a tuple is non-critical is complete for the second level of the p...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	94,035
1607.05906	Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining	Purpose: To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction signals obtained via longitudinal observational data. Methods: We considered six drug families that are commonly associated with myocardial...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	58,812
1105.0286	Dynamic Interference Mitigation for Generalized Partially Connected Quasi-static MIMO Interference Channel	Recent works on MIMO interference channels have shown that interference alignment can significantly increase the achievable degrees of freedom (DoF) of the network. However, most of these works have assumed a fully connected interference graph. In this paper, we investigate how the partial connectivity can be exploited...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	10,209
2310.14948	Physics-Informed Graph Convolutional Networks: Towards a generalized framework for complex geometries	Since the seminal work of [9] and their Physics-Informed neural networks (PINNs), many efforts have been conducted towards solving partial differential equations (PDEs) with Deep Learning models. However, some challenges remain, for instance the extension of such models to complex three-dimensional geometries, and a st...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,094
1408.0062	Energy Efficiency Scheme with Cellular Partition Zooming for Massive MIMO Systems	Massive multiple-input multiple-output (Massive MIMO) has been realized as a promising technology for next generation wireless mobile communications, in which Spectral efficiency (SE) and energy efficiency (EE) are two critical issues. Prior estimates have indicated that 57% energy of the cellular system need to be sup...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,055
1709.10426	Training an adaptive dialogue policy for interactive learning of visually grounded word meanings	We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic parsing/generation framework - Dynamic Syntax and Type Theory with Records (DS-TTR) - with a set of visual classifiers that are learned throughout t...	false	false	false	false	true	false	true	true	true	false	false	false	false	false	false	false	false	false	81,777
2203.03150	On the Construction of Distribution-Free Prediction Intervals for an Image Regression Problem in Semiconductor Manufacturing	The high-volume manufacturing of the next generation of semiconductor devices requires advances in measurement signal analysis. Many in the semiconductor manufacturing community have reservations about the adoption of deep learning; they instead prefer other model-based approaches for some image regression problems, an...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	283,989
1605.08427	Performance Enhancement of the Golden Code by Utilizing ORIOL Antenna	In this paper, a novel method is exposed to improve the performance of the Golden code, by using octagonal reconfigurable isolated orthogonal element (ORIOL) antennas, instead of a conventional microstrip patch antenna. The aforementioned antenna, should be employed in both the transmitter and the receiver sides, to ap...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	56,436
2402.19002	GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction	Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian's intentions and decision-making, which is a multi-modal problem. Most recent studies use past trajectories to predict a variety of poten...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	433,647
2303.08209	Learning to Adapt the Parameters of Behavior Trees and Motion Generators (BTMGs) to Task Variations	The ability to learn new tasks and quickly adapt to different variations or dimensions is an important attribute in agile robotics. In our previous work, we have explored Behavior Trees and Motion Generators (BTMGs) as a robot arm policy representation to facilitate the learning and execution of assembly tasks. The cur...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	351,547
2102.06820	Characterizing English Variation across Social Media Communities with BERT	Much previous work characterizing language variation across Internet social groups has focused on the types of words used by these groups. We extend this type of study by employing BERT to characterize variation in the senses of words as well, analyzing two months of English comments in 474 Reddit communities. The spec...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	219,888
2009.07970	Skeletonization and Reconstruction based on Graph Morphological Transformations	Multiscale shape skeletonization on pixel adjacency graphs is an advanced intriguing research subject in the field of image processing, computer vision and data mining. The previous works in this area almost focused on the graph vertices. We proposed novel structured based graph morphological transformations based on e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	196,086
2409.08493	Intelligent LiDAR Navigation: Leveraging External Information and Semantic Maps with LLM as Copilot	Traditional robot navigation systems primarily utilize occupancy grid maps and laser-based sensing technologies, as demonstrated by the popular move_base package in ROS. Unlike robots, humans navigate not only through spatial awareness and physical distances but also by integrating external information, such as elevato...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	487,928
2101.04176	Learning with Comparison Feedback: Online Estimation of Sample Statistics	We study an online version of the noisy binary search problem where feedback is generated by a non-stochastic adversary rather than perturbed by random noise. We reframe this as maintaining an accurate estimate for the median of an adversarial sequence of integers, $x_1, x_2, \dots$, in a model where each number $x_t$ ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	215,078
2409.15791	Development of Bidirectional Series Elastic Actuator with Torsion Coil Spring and Implementation to the Legged Robot	Many studies have been conducted on Series Elastic Actuators (SEA) for robot joints because they are effective in terms of flexibility, safety, and energy efficiency. The ability of SEA to robustly handle unexpected disturbances has raised expectations for practical applications in environments where robots interact wi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	491,065
1103.4169	Difference-Huffman Coding of Multidimensional Databases	A new compression method called difference-Huffman coding (DHC) is introduced in this paper. It is verified empirically that DHC results in a smaller multidimensional physical representation than those for other previously published techniques (single count header compression, logical position compression, base-offset ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	9,703
1704.02787	Deep Affordance-grounded Sensorimotor Object Recognition	It is well-established by cognitive neuroscience that human perception of objects constitutes a complex process, where object appearance information is combined with evidence about the so-called object "affordances", namely the types of actions that humans typically perform when interacting with them. This fact has rec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	71,509
2012.11937	Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems	Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access. This challenge can be separated into three subtasks, (1) knowledge-seeking turn det...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	212,783
1809.01495	A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems	Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to que...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	106,818
1810.08701	An Approach to Energy Efficiency in a Multi-Hop Network Control System through a Trade-Off between H-inf Norm and Global Number of Transmissions	The present work proposes a new approach to energy effiency for filtering of a system interconnected by a Multi-Hop network. The minimization of the energy cost per time unit is obtained through limiting the number of packets retransmission in the Hop-by-Hop mechanism. We explore a trade-Off between system performance,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	110,888
q-bio/0511045	The use of the GARP genetic algorithm and internet grid computing in the Lifemapper world atlas of species biodiversity	Lifemapper (http://www.lifemapper.org) is a predictive electronic atlas of the Earth's biological biodiversity. Using a screensaver version of the GARP genetic algorithm for modeling species distributions, Lifemapper harnesses vast computing resources through 'volunteers' PCs similar to SETI@home, to develop models of ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	540,851
2301.10056	Side Eye: Characterizing the Limits of POV Acoustic Eavesdropping from Smartphone Cameras with Rolling Shutters and Movable Lenses	Our research discovers how the rolling shutter and movable lens structures widely found in smartphone cameras modulate structure-borne sounds onto camera images, creating a point-of-view (POV) optical-acoustic side channel for acoustic eavesdropping. The movement of smartphone camera hardware leaks acoustic information...	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	true	341,687
1903.06017	Dynamics Concentration of Large-Scale Tightly-Connected Networks	The ability to achieve coordinated behavior --engineered or emergent-- on networked systems has attracted widespread interest over several fields. This has led to remarkable advances on the development of a theoretical understanding of the conditions under which agents within a network can reach agreement (consensus) o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	124,291
2404.08184	Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity	Domain shift differences between training data for deep learning models and the deployment context can result in severe performance issues for models which fail to generalize. We study the domain shift problem under the context of remote photoplethysmography (rPPG), a technique for video-based heart rate inference. We ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	446,142
cs/0407061	A measure of similarity between graph vertices	We introduce a concept of similarity between vertices of directed graphs. Let G_A and G_B be two directed graphs. We define a similarity matrix whose (i, j)-th real entry expresses how similar vertex j (in G_A) is to vertex i (in G_B. The similarity matrix can be obtained as the limit of the normalized even iterates of...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	538,282
2005.11768	KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension And Generation	This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different large-scale pre-trained models as the backbone for three subtasks. The results show that our evidence-searching approach improves model performance on...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	178,568
2201.10957	Social Learning under Randomized Collaborations	We study a social learning scheme where at every time instant, each agent chooses to receive information from one of its neighbors at random. We show that under this sparser communication scheme, the agents learn the truth eventually and the asymptotic convergence rate remains the same as the standard algorithms which ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	277,147
2402.01767	HiQA: A Hierarchical Contextual Augmentation RAG for Multi-Documents QA	Retrieval-augmented generation (RAG) has rapidly advanced the language model field, particularly in question-answering (QA) systems. By integrating external documents during the response generation phase, RAG significantly enhances the accuracy and reliability of language models. This method elevates the quality of res...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	426,207
1605.00335	Gaussian Process Autonomous Mapping and Exploration for Range Sensing Mobile Robots	Most of the existing robotic exploration schemes use occupancy grid representations and geometric targets known as frontiers. The occupancy grid representation relies on the assumption of independence between grid cells and ignores structural correlations present in the environment. We develop a Gaussian Processes (GPs...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	55,330
2202.09270	When Kinematics Dominates Mechanics: Locally Volume-Preserving Primitives for Model Reduction in Finite Elasticity	A new, and extremely fast, computational modeling paradigm is introduced here for specific finite elasticity problems that arise in the context of soft robotics. Whereas continuum mechanics is a very classical area of study, and significant effort has been devoted to the development of intricate constitutive models for...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	281,137
1310.5815	Selective linking from social platforms to university websites: a case study of the Spanish academic system	Mention indicators have frequently been used in Webometric studies because they provide a powerful tool for determining the degree of visibility and impact of web resources. Among mention indicators, hypertextual links were a central part of many studies until Yahoo discontinued the linkdomain command in 2011. Selectiv...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	27,931
2202.12396	Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications	This paper studies stochastic optimization for a sum of compositional functions, where the inner-level function of each summand is coupled with the corresponding summation index. We refer to this family of problems as finite-sum coupled compositional optimization (FCCO). It has broad applications in machine learning fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,218
2203.10258	TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations	Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	286,464
2312.09793	PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs	In this paper, we derive a PAC-Bayes bound on the generalisation gap, in a supervised time-series setting for a special class of discrete-time non-linear dynamical systems. This class includes stable recurrent neural networks (RNN), and the motivation for this work was its application to RNNs. In order to achieve the r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	415,886
1802.07810	Manipulating and Measuring Model Interpretability	With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed, there have been relatively few experimental studies investigating whether these...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	90,962
2410.08497	Towards Sharper Risk Bounds for Minimax Problems	Minimax problems have achieved success in machine learning such as adversarial training, robust optimization, reinforcement learning. For theoretical analysis, current optimal excess risk bounds, which are composed by generalization error and optimization error, present 1/n-rates in strongly-convex-strongly-concave (SC...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	497,142
1201.2719	Ultrametric Model of Mind, II: Application to Text Content Analysis	In a companion paper, Murtagh (2012), we discussed how Matte Blanco's work linked the unrepressed unconscious (in the human) to symmetric logic and thought processes. We showed how ultrametric topology provides a most useful representational and computational framework for this. Now we look at the extent to which we ca...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	13,796
2105.12192	NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains	Natural language processing (NLP) tasks (text classification, named entity recognition, etc.) have seen revolutionary improvements over the last few years. This is due to language models such as BERT that achieve deep knowledge transfer by using a large pre-trained model, then fine-tuning the model on specific tasks. T...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	236,929
2309.16631	Robust Offline Reinforcement Learning -- Certify the Confidence Interval	Currently, reinforcement learning (RL), especially deep RL, has received more and more attention in the research area. However, the security of RL has been an obvious problem due to the attack manners becoming mature. In order to defend against such adversarial attacks, several practical approaches are developed, such ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	395,424

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