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
2310.19283	rTsfNet: a DNN model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition	Although many deep learning (DL) algorithms have been proposed for the IMU-based HAR domain, traditional machine learning that utilizes handcrafted time series features (TSFs) still often performs well. It is not rare that combinations among DL and TSFs show better accuracy than DL-only approaches. However, there is a ...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,932
1309.1204	Achieving High Performance with Unified Residual Evaluation	We examine residual evaluation, perhaps the most basic operation in numerical simulation. By raising the level of abstraction in this operation, we can eliminate specialized code, enable optimization, and greatly increase the extensibility of existing code.	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	26,842
2112.06652	DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals	The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal patterns such as evoked responses, transient bursts of neural oscillations but also blinks or heartbeats for data cleaning. Several works ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,253
2110.05270	Investigating Transfer Learning Capabilities of Vision Transformers and CNNs by Fine-Tuning a Single Trainable Block	In recent developments in the field of Computer Vision, a rise is seen in the use of transformer-based architectures. They are surpassing the state-of-the-art set by CNN architectures in accuracy but on the other hand, they are computationally very expensive to train from scratch. As these models are quite recent in th...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	260,224
2101.04378	Rethinking Interactive Image Segmentation: Feature Space Annotation	Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious - a bottleneck for several deep learning applications. We take a step back to propose interactive and simultaneous segment annotation from multiple images guided by feature space pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,139
1706.04892	Second-Order Kernel Online Convex Optimization with Adaptive Sketching	Kernel online convex optimization (KOCO) is a framework combining the expressiveness of non-parametric kernel models with the regret guarantees of online learning. First-order KOCO methods such as functional gradient descent require only $\mathcal{O}(t)$ time and space per iteration, and, when the only information on t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,406
2302.00564	Automatically Marginalized MCMC in Probabilistic Programming	Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models. The advent of probabilistic programming languages (PPLs) frees users from writing inference algorithms and lets users focus on modeling. However, many models are difficult for HMC to solve directly, and often require ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,259
2002.00526	DANCE: Enhancing saliency maps using decoys	Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier. Unfortunately, recent evidence suggests that many saliency methods poorly perform, especial...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	162,377
2406.09884	Enhancing Fake News Detection in Social Media via Label Propagation on Cross-modal Tweet Graph	Fake news detection in social media has become increasingly important due to the rapid proliferation of personal media channels and the consequential dissemination of misleading information. Existing methods, which primarily rely on multimodal features and graph-based techniques, have shown promising performance in det...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	true	464,127
1910.12261	Typical Snapshots Selection for Shortest Path Query in Dynamic Road Networks	Finding the shortest paths in road network is an important query in our life nowadays, and various index structures are constructed to speed up the query answering. However, these indexes can hardly work in real-life scenario because the traffic condition changes dynamically, which makes the pathfinding slower than in ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	151,023
2406.02158	Radar Spectra-Language Model for Automotive Scene Parsing	Radar sensors are low cost, long-range, and weather-resilient. Therefore, they are widely used for driver assistance functions, and are expected to be crucial for the success of autonomous driving in the future. In many perception tasks only pre-processed radar point clouds are considered. In contrast, radar spectra ar...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	460,642
2109.00031	Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning	DNA-based storage is an emerging technology that enables digital information to be archived in DNA molecules. This method enjoys major advantages over magnetic and optical storage solutions such as exceptional information density, enhanced data durability, and negligible power consumption to maintain data integrity. To...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	252,980
2407.21488	Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval	Generative retrieval (GR) has emerged as a transformative paradigm in search and recommender systems, leveraging numeric-based identifier representations to enhance efficiency and generalization. Notably, methods like TIGER employing Residual Quantization-based Semantic Identifiers (RQ-SID), have shown significant prom...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	477,559
1805.11797	Grow and Prune Compact, Fast, and Accurate LSTMs	Long short-term memory (LSTM) has been widely used for sequential data modeling. Researchers have increased LSTM depth by stacking LSTM cells to improve performance. This incurs model redundancy, increases run-time delay, and makes the LSTMs more prone to overfitting. To address these problems, we propose a hidden-laye...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	99,017
2303.17448	NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images	Change detection (CD) in heterogeneous remote sensing images has been widely used for disaster monitoring and land-use management. In the past decade, the heterogeneous CD problem has significantly benefited from the development of deep neural networks (DNNs). However, the purely data-driven DNNs perform like a black b...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	355,203
2409.07128	Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review	Biometric authentication has garnered significant attention as a secure and efficient method of identity verification. Among the various modalities, hand vein biometrics, including finger vein, palm vein, and dorsal hand vein recognition, offer unique advantages due to their high accuracy, low susceptibility to forgery...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	487,396
2101.11846	A Spatial-Temporal Graph Neural Network Framework for Automated Software Bug Triaging	The bug triaging process, an essential process of assigning bug reports to the most appropriate developers, is related closely to the quality and costs of software development. As manual bug assignment is a labor-intensive task, especially for large-scale software projects, many machine-learning-based approaches have b...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	217,409
2411.12286	GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping	Inferring affordable (i.e., graspable) parts of arbitrary objects based on human specifications is essential for robots advancing toward open-vocabulary manipulation. Current grasp planners, however, are hindered by limited vision-language comprehension and time-consuming 3D radiance modeling, restricting real-time, op...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	509,365
2004.11147	Binarized Graph Neural Network	Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,831
2402.08859	Large Language Model with Graph Convolution for Recommendation	In recent years, efforts have been made to use text information for better user profiling and item characterization in recommendations. However, text information can sometimes be of low quality, hindering its effectiveness for real-world applications. With knowledge and reasoning capabilities capsuled in Large Language...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	429,267
2110.05213	It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data	Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on real interpretation data. To illustrate this argument, we propose an interpretation test set and conduct a realistic evaluation of SiMT train...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	260,205
2202.09195	A Review on Methods and Applications in Multimodal Deep Learning	Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning (MMDL) is to create models that can process and link information using various modalities. Despite the extensive development made for unimodal learning, it still cannot co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	281,117
1702.03310	Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity and Linear Models	This paper considers unbalanced multiphase distribution systems with generic topology and different load models, and extends the Z-bus iterative load-flow algorithm based on a fixed-point interpretation of the AC load-flow equations. Explicit conditions for existence and uniqueness of load-flow solutions are presented....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	68,109
2109.04760	ReconfigISP: Reconfigurable Camera Image Processing Pipeline	Image Signal Processor (ISP) is a crucial component in digital cameras that transforms sensor signals into images for us to perceive and understand. Existing ISP designs always adopt a fixed architecture, e.g., several sequential modules connected in a rigid order. Such a fixed ISP architecture may be suboptimal for re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,531
2112.06841	Probably approximately correct quantum source coding	Information-theoretic lower bounds are often encountered in several branches of computer science, including learning theory and cryptography. In the quantum setting, Holevo's and Nayak's bounds give an estimate of the amount of classical information that can be stored in a quantum state. Previous works have shown how t...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	271,312
2302.07911	From Reality Keys to Oraclize. A Deep Dive into the History of Bitcoin Oracles	Before the advent of alternative blockchains such as Ethereum, the future of decentralization was all in the hands of Bitcoin. Together with Nakamoto itself, early developers were trying to leverage Bitcoin potential to decentralize traditionally centralized applications. However, being Bitcoin a decentralized machine,...	false	false	false	false	false	false	false	false	false	true	false	false	true	true	false	false	false	true	345,861
2006.09134	AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks	Generative Adversarial Networks (GANs) are formulated as minimax game problems, whereby generators attempt to approach real data distributions by virtue of adversarial learning against discriminators. The intrinsic problem complexity poses the challenge to enhance the performance of generative networks. In this work, w...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	182,442
1210.5693	Hierarchical clustering for graph visualization	This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,301
1910.13444	Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs	Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,392
2402.07166	Social Evolution of Published Text and The Emergence of Artificial Intelligence Through Large Language Models and The Problem of Toxicity and Bias	We provide a birds eye view of the rapid developments in AI and Deep Learning that has led to the path-breaking emergence of AI in Large Language Models. The aim of this study is to place all these developments in a pragmatic broader historical social perspective without any exaggerations while at the same time without...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	428,591
2108.04224	Contrast R-CNN for Continual Learning in Object Detection	The continual learning problem has been widely studied in image classification, while rare work has been explored in object detection. Some recent works apply knowledge distillation to constrain the model to retain old knowledge, but this rigid constraint is detrimental for learning new knowledge. In our paper, we prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,937
1602.03571	High Dimensional Inference with Random Maximum A-Posteriori Perturbations	This paper presents a new approach, called perturb-max, for high-dimensional statistical inference that is based on applying random perturbations followed by optimization. This framework injects randomness to maximum a-posteriori (MAP) predictors by randomly perturbing the potential function for the input. A classic re...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	52,016
2405.00256	ASAM: Boosting Segment Anything Model with Adversarial Tuning	In the evolving landscape of computer vision, foundation models have emerged as pivotal tools, exhibiting exceptional adaptability to a myriad of tasks. Among these, the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation. However, SAM, like its counterparts, encounters limitations in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,851
1904.05106	An Application-Specific VLIW Processor with Vector Instruction Set for CNN Acceleration	In recent years, neural networks have surpassed classical algorithms in areas such as object recognition, e.g. in the well-known ImageNet challenge. As a result, great effort is being put into developing fast and efficient accelerators, especially for Convolutional Neural Networks (CNNs). In this work we present ConvAi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	127,215
2110.07603	Sub-word Level Lip Reading With Visual Attention	The goal of this paper is to learn strong lip reading models that can recognise speech in silent videos. Most prior works deal with the open-set visual speech recognition problem by adapting existing automatic speech recognition techniques on top of trivially pooled visual features. Instead, in this paper we focus on t...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	261,056
2206.08666	The Sensorium competition on predicting large-scale mouse primary visual cortex activity	The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety of goals for improving our understanding of this complex system, not o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	303,244
2306.03195	Lumos in the Night Sky: AI-enabled Visual Tool for Exploring Night-Time Light Patterns	We introduce NightPulse, an interactive tool for Night-time light (NTL) data visualization and analytics, which enables researchers and stakeholders to explore and analyze NTL data with a user-friendly platform. Powered by efficient system architecture, NightPulse supports image segmentation, clustering, and change pat...	true	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	371,223
2205.09651	Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT	This paper presents Wojood, a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. Wojood consists of about 550K Modern Standard Arabic (MSA) and dialect tokens that are manually annotated with 21 entity types including person,...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	297,356
2107.04930	TeliNet: Classifying CT scan images for COVID-19 diagnosis	COVID-19 has led to hundreds of millions of cases and millions of deaths worldwide since its onset. The fight against this pandemic is on-going on multiple fronts. While vaccinations are picking up speed, there are still billions of unvaccinated people. In this fight against the virus, diagnosis of the disease and isol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,605
1402.0246	Distributed Kalman Filtering over Massive Data Sets: Analysis Through Large Deviations of Random Riccati Equations	This paper studies the convergence of the estimation error process and the characterization of the corresponding invariant measure in distributed Kalman filtering for potentially unstable and large linear dynamic systems. A gossip network protocol termed Modified Gossip Interactive Kalman Filtering (M-GIKF) is proposed...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,545
2410.06698	Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras	Event cameras are novel bio-inspired vision sensors that measure pixel-wise brightness changes asynchronously instead of images at a given frame rate. They offer promising advantages, namely a high dynamic range, low latency, and minimal motion blur. Modern computer vision algorithms often rely on artificial neural net...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	496,314
2311.01153	ACES: Translation Accuracy Challenge Sets at WMT 2023	We benchmark the performance of segmentlevel metrics submitted to WMT 2023 using the ACES Challenge Set (Amrhein et al., 2022). The challenge set consists of 36K examples representing challenges from 68 phenomena and covering 146 language pairs. The phenomena range from simple perturbations at the word/character level ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	404,933
1911.04382	GRASS: Graph Spectral Sparsification Leveraging Scalable Spectral Perturbation Analysis	Spectral graph sparsification aims to find ultra-sparse subgraphs whose Laplacian matrix can well approximate the original Laplacian eigenvalues and eigenvectors. In recent years, spectral sparsification techniques have been extensively studied for accelerating various numerical and graph-related applications. Prior ne...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	152,975
2208.09399	Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models	The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines. Here, we focus on time series data and put forward SSSD, an imputation model that relies on two emerging technologies, (conditional) diffusion models as state-of-the-art generative models and structured state...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,686
2109.08438	TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models	Time series forecasting is a demanding task ranging from weather to failure forecasting with black-box models achieving state-of-the-art performances. However, understanding and debugging are not guaranteed. We propose TS-MULE, a local surrogate model explanation method specialized for time series extending the LIME ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	255,905
2311.04496	PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders	Pre-training is playing an increasingly important role in learning generic feature representation for Person Re-identification (ReID). We argue that a high-quality ReID representation should have three properties, namely, multi-level awareness, occlusion robustness, and cross-region invariance. To this end, we propose ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,244
2211.11225	TimbreCLIP: Connecting Timbre to Text and Images	We present work in progress on TimbreCLIP, an audio-text cross modal embedding trained on single instrument notes. We evaluate the models with a cross-modal retrieval task on synth patches. Finally, we demonstrate the application of TimbreCLIP on two tasks: text-driven audio equalization and timbre to image generation.	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,642
2407.05975	LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages	Large Language Models (LLMs) demonstrate remarkable translation capabilities in high-resource language tasks, yet their performance in low-resource languages is hindered by insufficient multilingual data during pre-training. To address this, we conduct extensive multilingual continual pre-training on the LLaMA series m...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	471,198
2404.05689	Automated discovery of symbolic laws governing skill acquisition from naturally occurring data	Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws of skill learning from large-scale training log data. A two-stage algo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	445,175
1610.05182	Learning and Transfer of Modulated Locomotor Controllers	We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor primitives by training on simple tasks. This pre-trained module is fixed and connected to a low-frequency, high-level "cortical" network, with...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	62,485
2401.16185	LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning	Large language models (LLMs) have demonstrated significant potential in various tasks, including those requiring human-level intelligence, such as vulnerability detection. However, recent efforts to use LLMs for vulnerability detection remain preliminary, as they lack a deep understanding of whether a subject LLM's vul...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	424,728
2310.05224	Generative Spoken Language Model based on continuous word-sized audio tokens	In NLP, text language models based on words or subwords are known to outperform their character-based counterparts. Yet, in the speech community, the standard input of spoken LMs are 20ms or 40ms-long discrete units (shorter than a phoneme). Taking inspiration from word-based LM, we introduce a Generative Spoken Langua...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	398,050
2110.11075	Enabling a Social Robot to Process Social Cues to Detect when to Help a User	It is important for socially assistive robots to be able to recognize when a user needs and wants help. Such robots need to be able to recognize human needs in a real-time manner so that they can provide timely assistance. We propose an architecture that uses social cues to determine when a robot should provide assista...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	262,353
2408.06816	MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty	Although large language models (LLMs) are capable of performing various tasks, they still suffer from producing plausible but incorrect responses. To improve the reliability of LLMs, recent research has focused on uncertainty quantification to predict whether a response is correct or not. However, most uncertainty quan...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	480,357
1705.06891	Energy-Efficient Resource Allocation for Elastic Optical Networks using Convex Optimization	We propose a two-stage algorithm for energy-efficient resource allocation constrained to QoS and physical requirements in OFDM-based EONs. The first stage deals with routing, grooming and traffic ordering and aims at minimizing amplifier power consumption and number of active transponders. We provide a heuristic proced...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,699
1608.07352	Connectivity-Aware Traffic Phase Scheduling for Heterogeneously Connected Vehicles	We consider a transportation system of heterogeneously connected vehicles, where not all vehicles are able to communicate. Heterogeneous connectivity in transportation systems is coupled to practical constraints such that (i) not all vehicles may be equipped with devices having communication interfaces, (ii) some vehic...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	60,216
2406.03061	Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing	Collecting time series data spatially distributed in many locations is often important for analyzing climate change and its impacts on ecosystems. However, comprehensive spatial data collection is not always feasible, requiring us to predict climate variables at some locations. This study focuses on a prediction of cli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,071
2404.13564	Masked Latent Transformer with the Random Masking Ratio to Advance the Diagnosis of Dental Fluorosis	Dental fluorosis is a chronic disease caused by long-term overconsumption of fluoride, which leads to changes in the appearance of tooth enamel. It is an important basis for early non-invasive diagnosis of endemic fluorosis. However, even dental professionals may not be able to accurately distinguish dental fluorosis a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	448,346
1608.00361	Fast and robust pushbroom hyperspectral imaging via DMD-based scanning	We describe a new pushbroom hyperspectral imaging device that has no macro moving part. The main components of the proposed hyperspectral imager are a digital micromirror device (DMD), a CMOS image sensor with no filter as the spectral sensor, a CMOS color (RGB) image sensor as the auxiliary image sensor, and a diffrac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	59,278
1805.05225	RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition	We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1% BLEU improvement absolute and it allows to train deeper...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	97,391
2312.03709	UID as a Guiding Metric for Automated Authorship Obfuscation	Protecting the anonymity of authors has become a difficult task given the rise of automated authorship attributors. These attributors are capable of attributing the author of a text amongst a pool of authors with great accuracy. In order to counter the rise of these automated attributors, there has also been a rise of ...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	413,368
2207.02595	FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling	Current deep video quality assessment (VQA) methods are usually with high computational costs when evaluating high-resolution videos. This cost hinders them from learning better video-quality-related representations via end-to-end training. Existing approaches typically consider naive sampling to reduce the computation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	306,564
2308.16735	Post-Deployment Adaptation with Access to Source Data via Federated Learning and Source-Target Remote Gradient Alignment	Deployment of Deep Neural Networks in medical imaging is hindered by distribution shift between training data and data processed after deployment, causing performance degradation. Post-Deployment Adaptation (PDA) addresses this by tailoring a pre-trained, deployed model to the target data distribution using limited lab...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	389,091
2409.02685	RouterRetriever: Exploring the Benefits of Routing over Multiple Expert Embedding Models	Information retrieval methods often rely on a single embedding model trained on large, general-domain datasets like MSMARCO. While this approach can produce a retriever with reasonable overall performance, models trained on domain-specific data often yield better results within their respective domains. While prior wor...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	485,798
2410.04536	Multi-LED Classification as Pretext For Robot Heading Estimation	We propose a self-supervised approach for visual robot detection and heading estimation by learning to estimate the states (OFF or ON) of four independent robot-mounted LEDs. Experimental results show a median image-space position error of 14 px and relative heading MAE of 17 degrees, versus a supervised upperbound sco...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	495,332
2307.09971	Solving scalability issues in calculating PV hosting capacity in low voltage distribution networks	The share of end-users with installed rooftop photovoltaic (PV) systems is continuously growing. Since most end-users are located at the low voltage (LV) level and due to technical limitations of LV networks, it is necessary to calculate PV hosting capacity. Most approaches in calculating a network's hosting capacity a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	380,366
2207.08044	DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking	The adversarial attack can force a CNN-based model to produce an incorrect output by craftily manipulating human-imperceptible input. Exploring such perturbations can help us gain a deeper understanding of the vulnerability of neural networks, and provide robustness to deep learning against miscellaneous adversaries. D...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,437
2208.09305	Real and simulated CBM data interacting with an ESCAPE datalake	Integration of the ESCAPE and CBM software environment. The ESCAPE datalake are utilized by the CBM experiment for the storage, distribution and retrieval of real SIS18 and simulated SIS100 particle physics data.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	313,654
1905.09598	CUDA-Self-Organizing feature map based visual sentiment analysis of bank customer complaints for Analytical CRM	With the widespread use of social media, companies now have access to a wealth of customer feedback data which has valuable applications to Customer Relationship Management (CRM). Analyzing customer grievances data, is paramount as their speedy non-redressal would lead to customer churn resulting in lower profitability...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	131,777
2407.20770	Non-Bayesian Social Learning with Multiview Observations	Non-Bayesian social learning enables multiple agents to conduct networked signal and information processing through observing environmental signals and information aggregating. Traditional non-Bayesian social learning models only consider single signals, limiting their applications in scenarios where multiple viewpoint...	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	477,282
2103.15534	An Adversarial Human Pose Estimation Network Injected with Graph Structure	Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods. In this paper, we design a novel generative adversarial network (GAN) to improve the localization accuracy o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,243
1812.02407	Elastic Gossip: Distributing Neural Network Training Using Gossip-like Protocols	Distributing Neural Network training is of particular interest for several reasons including scaling using computing clusters, training at data sources such as IOT devices and edge servers, utilizing underutilized resources across heterogeneous environments, and so on. Most contemporary approaches primarily address sca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	115,754
2408.02657	Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining	We present Lumina-mGPT, a family of multimodal autoregressive models capable of various vision and language tasks, particularly excelling in generating flexible photorealistic images from text descriptions. Unlike existing autoregressive image generation approaches, Lumina-mGPT employs a pretrained decoder-only transfo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	478,706
1507.04452	Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems with One-Bit ADCs	In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a high-resolution analog-to-digital converter (ADC) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,176
2004.02746	A new approach for generation of generalized basic probability assignment in the evidence theory	The process of information fusion needs to deal with a large number of uncertain information with multi-source, heterogeneity, inaccuracy, unreliability, and incompleteness. In practical engineering applications, Dempster-Shafer evidence theory is widely used in multi-source information fusion owing to its effectivenes...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	171,324
2103.00535	A multi-objective time series analysis of community mobility reduction comparing first and second COVID-19 waves	With the logistic challenges faced by most countries for the production, distribution, and application of vaccines for the novel coronavirus disease~(COVID-19), social distancing~(SD) remains the most tangible approach to mitigate the spread of the virus. To assist SD monitoring, several tech companies have made public...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	222,316
2101.07922	LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition	Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These systems are typically built by scraping social media profiles for user images. Adversarial perturbations have been proposed for bypassing f...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	216,168
2109.07955	Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data	Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-space profiles to ensure good image quality. Several undersampling-based reconstruction techniques have been proposed during the last decades t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	255,716
2302.08715	EEP-3DQA: Efficient and Effective Projection-based 3D Model Quality Assessment	Currently, great numbers of efforts have been put into improving the effectiveness of 3D model quality assessment (3DQA) methods. However, little attention has been paid to the computational costs and inference time, which is also important for practical applications. Unlike 2D media, 3D models are represented by more ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,157
1810.07990	Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection	Among underwater perceptual sensors, imaging sonar has been highlighted for its perceptual robustness underwater. The major challenge of imaging sonar, however, arises from the difficulty in defining visual features despite limited resolution and high noise levels. Recent developments in deep learning provide a powerfu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	110,737
1210.2283	Unfolding accessibility provides a macroscopic approach to temporal networks	An accessibility graph of a network contains a link, wherever there is a path of arbitrary length between two nodes. We generalize the concept of accessibility to temporal networks. Building an accessibility graph by consecutively adding paths of growing length (unfolding), we obtain information about the distribution ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,005
2006.01208	Automatic Discovery of Novel Intents & Domains from Text Utterances	One of the primary tasks in Natural Language Understanding (NLU) is to recognize the intents as well as domains of users' spoken and written language utterances. Most existing research formulates this as a supervised classification problem with a closed-world assumption, i.e. the domains or intents to be identified are...	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	179,702
2312.08624	Mixed Reality Communication for Medical Procedures: Teaching the Placement of a Central Venous Catheter	Medical procedures are an essential part of healthcare delivery, and the acquisition of procedural skills is a critical component of medical education. Unfortunately, procedural skill is not evenly distributed among medical providers. Skills may vary within departments or institutions, and across geographic regions, de...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,374
2008.09018	Balanced Order Batching with Task-Oriented Graph Clustering	Balanced order batching problem (BOBP) arises from the process of warehouse picking in Cainiao, the largest logistics platform in China. Batching orders together in the picking process to form a single picking route, reduces travel distance. The reason for its importance is that order picking is a labor intensive proce...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	192,585
2102.01647	A Novel Use of Discrete Wavelet Transform Features in the Prediction of Epileptic Seizures from EEG Data	This paper demonstrates the predictive superiority of discrete wavelet transform (DWT) over previously used methods of feature extraction in the diagnosis of epileptic seizures from EEG data. Classification accuracy, specificity, and sensitivity are used as evaluation metrics. We specifically show the immense potential...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,183
2101.12173	Entanglement-assisted capacity regions and protocol designs for quantum multiple-access channels	We solve the entanglement-assisted (EA) classical capacity region of quantum multiple-access channels with an arbitrary number of senders. As an example, we consider the bosonic thermal-loss multiple-access channel and solve the one-shot capacity region enabled by an entanglement source composed of sender-receiver pair...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	217,507
1710.04806	Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions	Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	82,536
1909.01300	The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset	In this paper we present The Oxford Radar RobotCar Dataset, a new dataset for researching scene understanding using Millimetre-Wave FMCW scanning radar data. The target application is autonomous vehicles where this modality is robust to environmental conditions such as fog, rain, snow, or lens flare, which typically ch...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	143,867
2412.00117	Proceedings of the 2024 XCSP3 Competition	This document represents the proceedings of the 2024 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'24 (30th International Conference on Principles and Practice of Constraint Programming).	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	512,508
2304.05506	Frontier Semantic Exploration for Visual Target Navigation	This work focuses on the problem of visual target navigation, which is very important for autonomous robots as it is closely related to high-level tasks. To find a special object in unknown environments, classical and learning-based approaches are fundamental components of navigation that have been investigated thoroug...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	357,646
2106.13064	Advancing biological super-resolution microscopy through deep learning: a brief review	Super-resolution microscopy overcomes the diffraction limit of conventional light microscopy in spatial resolution. By providing novel spatial or spatio-temporal information on biological processes at nanometer resolution with molecular specificity, it plays an increasingly important role in life sciences. However, its...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,950
2401.12108	On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data	In parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private ci...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	423,259
1612.02179	Model-based Adversarial Imitation Learning	Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle $D$ that discriminates between the expert's data distribution and that of the generative model $G$. The generative model ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	65,200
2410.23903	Neural Network Verification with PyRAT	As AI systems are becoming more and more popular and used in various critical domains (health, transport, energy, ...), the need to provide guarantees and trust of their safety is undeniable. To this end, we present PyRAT, a tool based on abstract interpretation to verify the safety and the robustness of neural network...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	504,240
2008.12537	The UU-test for Statistical Modeling of Unimodal Data	Deciding on the unimodality of a dataset is an important problem in data analysis and statistical modeling. It allows to obtain knowledge about the structure of the dataset, ie. whether data points have been generated by a probability distribution with a single or more than one peaks. Such knowledge is very useful for ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,607
2311.18147	DisCGen: A Framework for Discourse-Informed Counterspeech Generation	Counterspeech can be an effective method for battling hateful content on social media. Automated counterspeech generation can aid in this process. Generated counterspeech, however, can be viable only when grounded in the context of topic, audience and sensitivity as these factors influence both the efficacy and appropr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	411,572
2401.04649	From axial C-hedra to general P-nets	We give a full classification of continuous flexible discrete axial cone-nets, which are called axial C-hedra. The obtained result can also be used to construct their semi-discrete analogs. Moreover, we identify a novel subclass within the determined class of (semi-)discrete axial cone-nets, whose members are named axi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	420,495
2309.02979	Come Closer: The Effects of Robot Personality on Human Proxemics Behaviours	Social Robots in human environments need to be able to reason about their physical surroundings while interacting with people. Furthermore, human proxemics behaviours around robots can indicate how people perceive the robots and can inform robot personality and interaction design. Here, we introduce Charlie, a situated...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	390,233
2305.02264	Multi-dimensional Signal Recovery using Low-rank Deconvolution	In this work we present Low-rank Deconvolution, a powerful framework for low-level feature-map learning for efficient signal representation with application to signal recovery. Its formulation in multi-linear algebra inherits properties from convolutional sparse coding and low-rank approximation methods as in this sett...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,971
1711.10678	AttGAN: Facial Attribute Editing by Only Changing What You Want	Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,643

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