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
1912.03126	Knowledge extraction from the learning of sequences in a long short term memory (LSTM) architecture	We introduce a general method to extract knowledge from a recurrent neural network (Long Short Term Memory) that has learnt to detect if a given input sequence is valid or not, according to an unknown generative automaton. Based on the clustering of the hidden states, we explain how to build and validate an automaton t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	156,515
2311.14482	Sliding Window FastEdit: A Framework for Lesion Annotation in Whole-body PET Images	Deep learning has revolutionized the accurate segmentation of diseases in medical imaging. However, achieving such results requires training with numerous manual voxel annotations. This requirement presents a challenge for whole-body Positron Emission Tomography (PET) imaging, where lesions are scattered throughout the...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	410,126
2409.15634	NavRL: Learning Safe Flight in Dynamic Environments	Safe flight in dynamic environments requires autonomous unmanned aerial vehicles (UAVs) to make effective decisions when navigating cluttered spaces with moving obstacles. Traditional approaches often decompose decision-making into hierarchical modules for prediction and planning. Although these handcrafted systems can...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	490,991
2109.13724	Translating from Morphologically Complex Languages: A Paraphrase-Based Approach	We propose a novel approach to translating from a morphologically complex language. Unlike previous research, which has targeted word inflections and concatenations, we focus on the pairwise relationship between morphologically related words, which we treat as potential paraphrases and handle using paraphrasing techniq...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	257,723
1911.03315	Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees	Model predictive control allows to provide high performance and safety guarantees in the form of constraint satisfaction. These properties, however, can be satisfied only if the underlying model, used for prediction, of the controlled process is sufficiently accurate. One way to address this challenge is by data-driven...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	152,614
2409.00447	The MERIT Dataset: Modelling and Efficiently Rendering Interpretable Transcripts	This paper introduces the MERIT Dataset, a multimodal (text + image + layout) fully labeled dataset within the context of school reports. Comprising over 400 labels and 33k samples, the MERIT Dataset is a valuable resource for training models in demanding Visually-rich Document Understanding (VrDU) tasks. By its nature...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	484,929
2411.03834	Reachability analysis for piecewise affine systems with neural network-based controllers	Neural networks (NN) have been successfully applied to approximate various types of complex control laws, resulting in low-complexity NN-based controllers that are fast to evaluate. However, when approximating control laws using NN, performance and stability guarantees of the original controller may not be preserved. R...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	506,055
2103.03759	Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC)	Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to assist the pathologists by marking critical regions that have a high probability of exhibiting pathological features in Whole Slide Images (W...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	223,404
2201.12803	Generalizing similarity in noisy setups: the DIBS phenomenon	This work uncovers an interplay among data density, noise, and the generalization ability in similarity learning. We consider Siamese Neural Networks (SNNs), which are the basic form of contrastive learning, and explore two types of noise that can impact SNNs, Pair Label Noise (PLN) and Single Label Noise (SLN). Our in...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	277,786
2109.02533	Neural Ensemble Search via Bayesian Sampling	Recently, neural architecture search (NAS) has been applied to automate the design of neural networks in real-world applications. A large number of algorithms have been developed to improve the search cost or the performance of the final selected architectures in NAS. Unfortunately, these NAS algorithms aim to select o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	253,781
1506.02431	The Effects of Twitter Sentiment on Stock Price Returns	Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock comp...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	43,920
2006.07487	Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization	Control variates are a well-established tool to reduce the variance of Monte Carlo estimators. However, for large-scale problems including high-dimensional and large-sample settings, their advantages can be outweighed by a substantial computational cost. This paper considers control variates based on Stein operators, p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,809
2305.11480	CCGen: Explainable Complementary Concept Generation in E-Commerce	We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Lenses 2) Batteries 3) Camera Cases 4) Memory Cards 5) Battery Chargers. CCGen is beneficial for various applications like query suggestion an...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	365,554
2407.07235	Speech After Gender: A Trans-Feminine Perspective on Next Steps for Speech Science and Technology	As experts in voice modification, trans-feminine gender-affirming voice teachers have unique perspectives on voice that confound current understandings of speaker identity. To demonstrate this, we present the Versatile Voice Dataset (VVD), a collection of three speakers modifying their voices along gendered axes. The V...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	471,679
2004.03151	Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation	Self-supervised neural machine translation (SSNMT) jointly learns to identify and select suitable training data from comparable (rather than parallel) corpora and to translate, in a way that the two tasks support each other in a virtuous circle. In this study, we provide an in-depth analysis of the sampling choices the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,467
2411.01790	Thinking Forward and Backward: Effective Backward Planning with Large Language Models	Large language models (LLMs) have exhibited remarkable reasoning and planning capabilities. Most prior work in this area has used LLMs to reason through steps from an initial to a goal state or criterion, thereby effectively reasoning in a forward direction. Nonetheless, many planning problems exhibit an inherent asymm...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	505,224
1608.08984	Towards Competitive Classifiers for Unbalanced Classification Problems: A Study on the Performance Scores	Although a great methodological effort has been invested in proposing competitive solutions to the class-imbalance problem, little effort has been made in pursuing a theoretical understanding of this matter. In order to shed some light on this topic, we perform, through a novel framework, an exhaustive analysis of th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	60,417
2308.04011	Generalization bound for estimating causal effects from observational network data	Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	384,251
1603.01360	Neural Architectures for Named Entity Recognition	State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures---one based on bidirectional LSTMs and conditional ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	52,881
1809.00216	Evaluation of Neural Networks for Image Recognition Applications: Designing a 0-1 MILP Model of a CNN to create adversarials	Image Recognition is a central task in computer vision with applications ranging across search, robotics, self-driving cars and many others. There are three purposes of this document: 1. We follow up on (Fischetti & Jo, December, 2017) and show how standard convolutional neural network can be optimized to a more sophis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,524
2102.02407	An efficient optimization based microstructure reconstruction approach with multiple loss functions	Stochastic microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s). This process enables computational analyses on ensembles of microstructures without having to perform exhaustive and costly experimental character...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,397
2110.15277	A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick	With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,830
2208.06811	Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering	Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges. In this paper, we propose a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,833
2410.15625	Improving Parallel Program Performance with LLM Optimizers via Agent-System Interface	Modern scientific discovery increasingly relies on high-performance computing for complex modeling and simulation. A key challenge in improving parallel program performance is efficiently mapping tasks to processors and data to memory, a process dictated by intricate, low-level system code known as mappers. Developing ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	500,644
2204.05413	A Switching Thrust Tracking Controller for Load Constrained Wind Turbines	Wind turbines are prone to structural degradation, particularly in offshore locations. Based on the structural health condition of the tower, power de-rating strategies can be used to reduce structural loads at the cost of power losses.This paper introduces a novel closed-loop switching control architecture to constrai...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	291,008
1308.4643	Topological security assessment of technological networks	The spreading of dangerous malware or faults in inter-dependent networks of electronics devices has raised deep concern, because from the ICT networks infections may propagate to other Critical Infrastructures producing the well-known domino or cascading effect. Researchers are attempting to develop a high level analys...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	26,555
2401.17859	Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment	In Multi-Modal Knowledge Graphs (MMKGs), Multi-Modal Entity Alignment (MMEA) is crucial for identifying identical entities across diverse modal attributes. However, semantic inconsistency, mainly due to missing modal attributes, poses a significant challenge. Traditional approaches rely on attribute interpolation, but ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	425,354
2207.09086	MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views	We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Dete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,791
1804.03789	Geometric Consistency for Self-Supervised End-to-End Visual Odometry	With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-t...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	94,693
1910.01860	Online repeated posted price auctions with a demand side platform	We consider an online ad network problem in which an ad exchange auctions ad slots and intermediaries called demand side platforms (DSPs) buy these ad slots for their clients (advertisers). An intermediary represents multiple advertisers. Different types of ad slots are auctioned by the ad exchange, e.g., video ad, ban...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	148,062
2209.12332	On the Optimal Linear Contraction Order of Tree Tensor Networks, and Beyond	The contraction cost of a tensor network depends on the contraction order. However, the optimal contraction ordering problem is known to be NP-hard. We show that the linear contraction ordering problem for tree tensor networks admits a polynomial-time algorithm, by drawing connections to database join ordering. The res...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	319,494
2304.08310	TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm	Energy management systems (EMS) have traditionally been implemented using rule-based control (RBC) and model predictive control (MPC) methods. However, recent research has explored the use of reinforcement learning (RL) as a promising alternative. This paper introduces TreeC, a machine learning method that utilizes the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	358,669
2109.05222	Fundamental limits of over-the-air optimization: Are analog schemes optimal?	We consider over-the-air convex optimization on a $d-$dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance $\sigma^2$. The codewords satisfy an average power constraint $P$, resulting in the signal-to-noise ratio (SNR) of $P/\sigma^2$. We derive bounds for the convergen...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	254,712
2208.08382	Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups	Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. Specifically, unequal accuracy rates were obtained for women and dark-skinned people. To mitigate the bias of gender classifiers, the vision community has developed several strategies. However, ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	313,356
1906.02732	A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis	This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models. Specifically, we propose a new spectral analysis of the generalization error, expressed in terms of the power spectra of the sampling pattern and t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,157
1907.01713	End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning	In this paper, a novel deep reinforcement learning (DRL)-based method is proposed to navigate the robot team through unknown complex environments, where the geometric centroid of the robot team aims to reach the goal position while avoiding collisions and maintaining connectivity. Decentralized robot-level policies are...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	137,420
2502.05843	Training-free Anomaly Event Detection via LLM-guided Symbolic Pattern Discovery	Anomaly event detection plays a crucial role in various real-world applications. However, current approaches predominantly rely on supervised learning, which faces significant challenges: the requirement for extensive labeled training data and lack of interpretability in decision-making processes. To address these limi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,802
2306.12333	Autonomous Navigation with Convergence Guarantees in Complex Dynamic Environments	This article addresses the obstacle avoidance problem for setpoint stabilization and path-following tasks in complex dynamic 2D environments that go beyond conventional scenes with isolated convex obstacles. A combined motion planner and controller is proposed for setpoint stabilization that integrates the favorable co...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	374,907
2307.02770	Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback	Diffusion models have recently shown remarkable success in high-quality image generation. Sometimes, however, a pre-trained diffusion model exhibits partial misalignment in the sense that the model can generate good images, but it sometimes outputs undesirable images. If so, we simply need to prevent the generation of ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	377,808
2205.11434	SiSPRNet: End-to-End Learning for Single-Shot Phase Retrieval	With the success of deep learning methods in many image processing tasks, deep learning approaches have also been introduced to the phase retrieval problem recently. These approaches are different from the traditional iterative optimization methods in that they usually require only one intensity measurement and can rec...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	298,141
2307.08209	Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection	Voxel-based methods have achieved state-of-the-art performance for 3D object detection in autonomous driving. However, their significant computational and memory costs pose a challenge for their application to resource-constrained vehicles. One reason for this high resource consumption is the presence of a large number...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	379,713
2106.04982	Cooperative Online Learning with Feedback Graphs	We study the interplay between communication and feedback in a cooperative online learning setting, where a network of communicating agents learn a common sequential decision-making task through a feedback graph. We bound the network regret in terms of the independence number of the strong product between the communica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,927
1907.07080	Data-driven strategies for optimal bicycle network growth	Urban transportation networks, from sidewalks and bicycle paths to streets and rail lines, provide the backbone for movement and socioeconomic life in cities. These networks can be understood as layers of a larger multiplex transport network. Because most cities are car-centric, the most developed layer is typically th...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	138,780
1912.07703	Hamiltonian Point of View on Parallel Interconnection of Buck Converters Extended version	In this paper, parallel interconnection of DC/DC converters is considered. For this topology of converters feeding a common load, it has been recently shown that dynamics related to voltage regulation can be completely separated from the current distribution without considering frequency separation arguments, which ine...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	157,658
2104.08026	NoisyCUR: An algorithm for two-cost budgeted matrix completion	Matrix completion is a ubiquitous tool in machine learning and data analysis. Most work in this area has focused on the number of observations necessary to obtain an accurate low-rank approximation. In practice, however, the cost of observations is an important limiting factor, and experimentalists may have on hand mul...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	230,635
2304.05804	A Palm-Shape Variable-Stiffness Gripper based on 3D-Printed Fabric Jamming	Soft grippers have excellent adaptability for a variety of objects and tasks. Jamming-based variable stiffness materials can further increase soft grippers' gripping force and capacity. Previous universal grippers enabled by granular jamming have shown great capability of handling objects with various shapes and weight...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	357,748
2501.08763	Few-Shot Learner Generalizes Across AI-Generated Image Detection	Current fake image detectors trained on large synthetic image datasets perform satisfactorily on limited studied generative models. However, they suffer a notable performance decline over unseen models. Besides, collecting adequate training data from online generative models is often expensive or infeasible. To overcom...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,889
2204.07122	MIMO Channel Estimation using Score-Based Generative Models	Channel estimation is a critical task in multiple-input multiple-output (MIMO) digital communications that substantially effects end-to-end system performance. In this work, we introduce a novel approach for channel estimation using deep score-based generative models. A model is trained to estimate the gradient of the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	291,569
2202.03574	Structured Prediction Problem Archive	Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one place a large number of datasets in easy to read formats for a diverse set of problem classes. We provide archival links to datasets, descript...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	279,251
1805.02036	Compositional Representation of Morphologically-Rich Input for Neural Machine Translation	Neural machine translation (NMT) models are typically trained with fixed-size input and output vocabularies, which creates an important bottleneck on their accuracy and generalization capability. As a solution, various studies proposed segmenting words into sub-word units and performing translation at the sub-lexical l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	96,761
2307.10519	Probabilistic Multimodal Depth Estimation Based on Camera-LiDAR Sensor Fusion	Multi-modal depth estimation is one of the key challenges for endowing autonomous machines with robust robotic perception capabilities. There have been outstanding advances in the development of uni-modal depth estimation techniques based on either monocular cameras, because of their rich resolution, or LiDAR sensors, ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	380,579
2005.05325	A Relational Gradient Descent Algorithm For Support Vector Machine Training	We consider gradient descent like algorithms for Support Vector Machine (SVM) training when the data is in relational form. The gradient of the SVM objective can not be efficiently computed by known techniques as it suffers from the ``subtraction problem''. We first show that the subtraction problem can not be surmount...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	176,702
2401.07854	$M^{2}$Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction	Colorectal cancer (CRC) micro-satellite instability (MSI) prediction on histopathology images is a challenging weakly supervised learning task that involves multi-instance learning on gigapixel images. To date, radiology images have proven to have CRC MSI information and efficient patient imaging techniques. Different ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	421,677
2412.16248	Optimizing Low-Speed Autonomous Driving: A Reinforcement Learning Approach to Route Stability and Maximum Speed	Autonomous driving has garnered significant attention in recent years, especially in optimizing vehicle performance under varying conditions. This paper addresses the challenge of maintaining maximum speed stability in low-speed autonomous driving while following a predefined route. Leveraging reinforcement learning (R...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	519,438
2412.04301	SwiftEdit: Lightning Fast Text-Guided Image Editing via One-Step Diffusion	Recent advances in text-guided image editing enable users to perform image edits through simple text inputs, leveraging the extensive priors of multi-step diffusion-based text-to-image models. However, these methods often fall short of the speed demands required for real-world and on-device applications due to the cost...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,350
1205.1923	Using data mining techniques for diagnosis and prognosis of cancer disease	Breast cancer is one of the leading cancers for women in developed countries including India. It is the second most common cause of cancer death in women. The high incidence of breast cancer in women has increased significantly in the last years. In this paper we have discussed various data mining approaches that have ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	15,867
2006.05657	Methodology for Realizing VMM with Binary RRAM Arrays: Experimental Demonstration of Binarized-ADALINE Using OxRAM Crossbar	In this paper, we present an efficient hardware mapping methodology for realizing vector matrix multiplication (VMM) on resistive memory (RRAM) arrays. Using the proposed VMM computation technique, we experimentally demonstrate a binarized-ADALINE (Adaptive Linear) classifier on an OxRAM crossbar. An 8x8 OxRAM crossbar...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	181,156
2207.14467	GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation	Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation. However, vanilla Transformer mainly exploits the top-layer representation, assuming the lower layers provide trivial or redundant information and thus ignoring the bottom-l...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	310,596
2303.15316	XVoxel-Based Parametric Design Optimization of Feature Models	Parametric optimization is an important product design technique, especially in the context of the modern parametric feature-based CAD paradigm. Realizing its full potential, however, requires a closed loop between CAD and CAE (i.e., CAD/CAE integration) with automatic design modifications and simulation updates. Conve...	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	354,447
2403.09727	Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems	The development of generative large language models (G-LLM) opened up new opportunities for the development of new types of knowledge-based systems similar to ChatGPT, Bing, or Gemini. Fine-tuning (FN) and Retrieval-Augmented Generation (RAG) are the techniques that can be used to implement domain adaptation for the de...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	437,890
2205.05391	Query-Based Keyphrase Extraction from Long Documents	Transformer-based architectures in natural language processing force input size limits that can be problematic when long documents need to be processed. This paper overcomes this issue for keyphrase extraction by chunking the long documents while keeping a global context as a query defining the topic for which relevant...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	295,917
2010.08895	Fourier Neural Operator for Parametric Partial Differential Equations	The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	201,345
2209.00227	Improved Sparse Vector Code Based on Optimized Spreading Matrix for Short-Packet URLLC in mMTC	Recently, the sparse vector code (SVC) is emerging as a promising solution for short-packet transmission in massive machine type communication (mMTC) as well as ultra-reliable and low-latency communication (URLLC). In the SVC process, the encoding and decoding stages are jointly modeled as a standard compressed sensing...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	315,521
2310.00243	Age-Optimal Multi-Flow Status Updating with Errors: A Sample-Path Approach	In this paper, we study an age of information minimization problem in continuous-time and discrete-time status updating systems that involve multiple packet flows, multiple servers, and transmission errors. Four scheduling policies are proposed. We develop a unifying sample-path approach and use it to show that, when t...	false	false	false	true	false	false	false	false	false	true	true	false	false	false	false	false	false	true	395,889
2412.00245	Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare	Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addresses this gap by constructing an SDoH-enriched knowledge graph using the MIMIC-III dataset and PrimeKG. We introduce a novel fairness formula...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	512,572
1601.05994	Depth and Reflection Total Variation for Single Image Dehazing	Haze removal has been a very challenging problem due to its ill-posedness, which is more ill-posed if the input data is only a single hazy image. In this paper, we present a new approach for removing haze from a single input image. The proposed method combines the model widely used to describe the formation of a haze i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,192
2003.13821	NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain	Significant advances have been made in recent years on Natural Language Processing with machines surpassing human performance in many tasks, including but not limited to Question Answering. The majority of deep learning methods for Question Answering targets domains with large datasets and highly matured literature. Th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,314
2502.04314	BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation	This paper presents BOUQuET, a multicentric and multi-register/domain dataset and benchmark, and its broader collaborative extension initiative. This dataset is handcrafted in non-English languages first, each of these source languages being represented among the 23 languages commonly used by half of the world's popula...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	531,072
2203.16251	Robust Generation Dispatch with Strategic Renewable Power Curtailment and Decision-Dependent Uncertainty	As renewable energy sources replace traditional power sources (such as thermal generators), uncertainty grows while there are fewer controllable units. To reduce operational risks and avoid frequent real-time emergency controls, a preparatory schedule of renewable generation curtailment is required. This paper proposes...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	288,713
1701.03186	Feedback Capacity over Networks	In this paper, we investigate the fundamental limitations of feedback mechanism in dealing with uncertainties for network systems. The study of maximum capability of feedback control was pioneered in Xie and Guo (2000) for scalar systems with nonparametric nonlinear uncertainty. In a network setting, nodes with unknown...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	66,661
2310.04440	Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction	Electrifying heavy-duty trucks offers a substantial opportunity to curtail carbon emissions, advancing toward a carbon-neutral future. However, the inherent challenges of limited battery energy and the sheer weight of heavy-duty trucks lead to reduced mileage and prolonged charging durations. Consequently, battery-swap...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	397,653
2403.09903	Wildfire Resilient Unit Commitment under Uncertain Demand	Public safety power shutoffs (PSPS) are a common pre-emptive measure to reduce wildfire risk due to power system equipment. System operators use PSPS to de-energize electric grid elements that are either prone to failure or located in regions at a high risk of experiencing a wildfire. Successful power system operation ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	437,955
2203.08914	Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance	The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	285,948
2408.00348	Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks	Machine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. ML's proponents laud its capacity to handle vast, complicated, and erratic medical data. It's common knowledge that attackers might cause misclassification by delibe...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	477,805
2403.14838	An Analysis of the Preferences of Distribution Indicators in Evolutionary Multi-Objective Optimization	The distribution of objective vectors in a Pareto Front Approximation (PFA) is crucial for representing the associated manifold accurately. Distribution Indicators (DIs) assess the distribution of a PFA numerically, utilizing concepts like distance calculation, Biodiversity, Entropy, Potential Energy, or Clustering. De...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	440,275
1705.06609	On the weak order ideal associated to linear codes	In this work we study a weak order ideal associated with the coset leaders of a non-binary linear code. This set allows the incrementally computation of the coset leaders and the definitions of the set of leader codewords. This set of codewords has some nice properties related to the monotonicity of the weight compatib...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,659
1909.06276	Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification	We introduce a novel parameterized convolutional neural network for aspect level sentiment classification. Using parameterized filters and parameterized gates, we incorporate aspect information into convolutional neural networks (CNN). Experiments demonstrate that our parameterized filters and parameterized gates effec...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	145,334
1906.08443	Physical Layer Security for Ultra-Reliable and Low-Latency Communications	Ultra-reliable and low-latency communication (URLLC) is one category of service to be provided by next-generation wireless networks. Motivated by increasing security concerns in such networks, this article focuses on physical layer security (PLS) in the context of URLLC. The PLS technique mainly uses transmission desig...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	135,865
2205.11252	Exploring the stimulative effect on following drivers in a consecutive lane-change using microscopic vehicle trajectory data	Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effect on following drivers in a consecutive lane-changing scenario. The microscopic trajectory data from t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,072
1411.4109	Resolution of Difficult Pronouns Using the ROSS Method	A new natural language understanding method for disambiguation of difficult pronouns is described. Difficult pronouns are those pronouns for which a level of world or domain knowledge is needed in order to perform anaphoral or other types of resolution. Resolution of difficult pronouns may in some cases require a prior...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	37,588
2202.11457	Duality of generalized twisted Reed-Solomon codes and Hermitian self-dual MDS or NMDS codes	Self-dual MDS and NMDS codes over finite fields are linear codes with significant combinatorial and cryptographic applications. In this paper, firstly, we investigate the duality properties of generalized twisted Reed-Solomon (abbreviated GTRS) codes in some special cases. In what follows, a new systematic approach is ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	281,897
2502.06619	Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification	Domain-generalizable re-identification (DG Re-ID) aims to train a model on one or more source domains and evaluate its performance on unseen target domains, a task that has attracted growing attention due to its practical relevance. While numerous methods have been proposed, most rely on discriminative or contrastive l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	532,156
2211.03553	Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling	Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the interpretability of latent variables as biological processes that define a cell's identity. Outside of biological appl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,968
2110.01889	Deep Neural Networks and Tabular Data: A Survey	Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted. However, their adaptation to tabular d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	258,931
0911.3357	Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks and Computation	Sensor networks potentially feature large numbers of nodes that can sense their environment over time, communicate with each other over a wireless network, and process information. They differ from data networks in that the network as a whole may be designed for a specific application. We study the theoretical foundati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	4,964
1908.00090	An Optimal Linear Dynamic Detection Method for Replay Attack in Cyber-Physical Systems	The problem of detecting replay attack to the linear stochastic system with Kalman filer state estimator and LQG controller is addressed. To this end, a dynamic attack detector method is proposed which is coupled with the dynamics of the system. While preserving stability of the main system, conditions on parameters of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	140,424
2102.03885	Few-shot time series segmentation using prototype-defined infinite hidden Markov models	We propose a robust framework for interpretable, few-shot analysis of non-stationary sequential data based on flexible graphical models to express the structured distribution of sequential events, using prototype radial basis function (RBF) neural network emissions. A motivational link is demonstrated between prototypi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,905
2303.11770	Adaptive Super-Twisting Controller Design for Accurate Trajectory Tracking Performance of Unmanned Aerial Vehicles	In this paper, an adaptive super-twisting controller is designed for an agile maneuvering quadrotor unmanned aerial vehicle to achieve accurate trajectory tracking in the presence of external disturbances. A cascaded control architecture is designed to determine the desired accelerations using the proposed controller a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	353,002
1804.04380	Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification	This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish valence tasks. Our system consists of three parts: training task-specific word embeddings, training a model consisting of gated-recurrent-units (GRU) with ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	94,838
2109.09567	A proactive malicious software identification approach for digital forensic examiners	Digital investigators often get involved with cases, which seemingly point the responsibility to the person to which the computer belongs, but after a thorough examination malware is proven to be the cause, causing loss of precious time. Whilst Anti-Virus (AV) software can assist the investigator in identifying the pre...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	256,326
2410.00513	Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing	Recent efforts have aimed to utilize multilingual pretrained language models (mPLMs) to extend semantic parsing (SP) across multiple languages without requiring extensive annotations. However, achieving zero-shot cross-lingual transfer for SP remains challenging, leading to a performance gap between source and target l...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	493,414
2409.03451	Automatic occlusion removal from 3D maps for maritime situational awareness	We introduce a novel method for updating 3D geospatial models, specifically targeting occlusion removal in large-scale maritime environments. Traditional 3D reconstruction techniques often face problems with dynamic objects, like cars or vessels, that obscure the true environment, leading to inaccurate models or requir...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,050
2307.00336	On the Impact of Sample Size in Reconstructing Graph Signals	Reconstructing a signal on a graph from observations on a subset of the vertices is a fundamental problem in the field of graph signal processing. It is often assumed that adding additional observations to an observation set will reduce the expected reconstruction error. We show that under the setting of noisy observat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	376,969
2308.13563	Large Language Models in Analyzing Crash Narratives -- A Comparative Study of ChatGPT, BARD and GPT-4	In traffic safety research, extracting information from crash narratives using text analysis is a common practice. With recent advancements of large language models (LLM), it would be useful to know how the popular LLM interfaces perform in classifying or extracting information from crash narratives. To explore this, o...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	387,970
1410.0474	A travelling wave approach to a multi-agent system with a path-graph topology	The paper presents a novel approach for the analysis and control of a multi-agent system with non-identical agents and a path-graph topology. With the help of irrational wave transfer functions, the approach describes the interaction among the agents from the `local' perspective and identifies travelling waves in the s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	36,473
2411.18025	Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision	Integrating RGB and NIR stereo imaging provides complementary spectral information, potentially enhancing robotic 3D vision in challenging lighting conditions. However, existing datasets and imaging systems lack pixel-level alignment between RGB and NIR images, posing challenges for downstream vision tasks. In this pap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,703
2405.10474	Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled by Auto-regressive LLMs' Prompting	Over the last decade, a wide range of training and deployment strategies for Large Language Models (LLMs) have emerged. Among these, the prompting paradigms of Auto-regressive LLMs (AR-LLMs) have catalyzed a significant surge in Artificial Intelligence (AI). This paper aims to emphasize the significance of utilizing fr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	454,772
2306.14487	Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image	Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation. However, the requirement of an extensive collection of high-resolution images prese...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	375,700
1912.13163	Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks	Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. Rather than sharing, and disclosing, the training dataset with the server, the model parameters (e.g. neural networks weights and biases) are optimized collectively by large populations of interconnected devic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	159,021
2409.14307	DilateQuant: Accurate and Efficient Diffusion Quantization via Weight Dilation	Diffusion models have shown excellent performance on various image generation tasks, but the substantial computational costs and huge memory footprint hinder their low-latency applications in real-world scenarios. Quantization is a promising way to compress and accelerate models. Nevertheless, due to the wide range and...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	490,416

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