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
2502.01972	Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography	Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint inflammation and progressive structural damage. Joint space width (JSW) is a critical indicator in conventional radiography for evaluating disease progression, which has become a prominent research topic in computer-aided diagnostic (CAD) ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	530,116
1412.3009	Brain Tumor Detection Based on Bilateral Symmetry Information	Advances in computing technology have allowed researchers across many fields of endeavor to collect and maintain vast amounts of observational statistical data such as clinical data,biological patient data,data regarding access of web sites,financial data,and the like.Brain Magnetic Resonance Imaging(MRI)segmentation i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	38,250
1905.03037	The Guided Team-Partitioning Problem: Definition, Complexity, and Algorithm	A long line of literature has focused on the problem of selecting a team of individuals from a large pool of candidates, such that certain constraints are respected, and a given objective function is maximized. Even though extant research has successfully considered diverse families of objective functions and constrain...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	130,112
1606.09463	Optimal Locally Repairable Codes with Improved Update Complexity	For a systematic erasure code, update complexity (UC) is defined as the maximum number of parity blocks needed to be changed when some information blocks are updated. Locally repairable codes (LRCs) have been recently proposed and used in real-world distributed storage systems. In this paper, update complexity for opti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,995
1011.2361	Distributed Storage Codes with Repair-by-Transfer and Non-achievability of Interior Points on the Storage-Bandwidth Tradeoff	Regenerating codes are a class of recently developed codes for distributed storage that, like Reed-Solomon codes, permit data recovery from any subset of k nodes within the n-node network. However, regenerating codes possess in addition, the ability to repair a failed node by connecting to an arbitrary subset of d node...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	8,195
2310.09671	FPGA Implementation of OTFS Modulation for 6G Communication Systems	Sixth-generation (6G) communication systems are poised to accommodate high data-rate wireless communication services in highly dynamic channels, with applications including high-speed trains, unmanned aerial vehicles, and intelligent transportation systems. Orthogonal frequency-division multiplexing (OFDM) modulation s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	399,884
1811.02566	Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognition	Recurrent neural networks (RNN) are at the core of modern automatic speech recognition (ASR) systems. In particular, long-short term memory (LSTM) recurrent neural networks have achieved state-of-the-art results in many speech recognition tasks, due to their efficient representation of long and short term dependencies ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	112,620
2007.04883	PIE-NET: Parametric Inference of Point Cloud Edges	We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data. We represent these edges as a collection of parametric curves (i.e.,lines, circles, and B-splines). Accordingly, our deep neural network, coined PIE-NET, is trained for parametric inference of edges. The network re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,502
2208.13682	Data-Driven Distributed Voltage Control for Microgrids: A Koopman-based Approach	This paper presents a distributed data-driven control to regulate the voltage in an alternate current microgrid (MG). Following the hierarchical control frame for MGs, a secondary control for voltage is designed with a data-driven strategy using the Koopman operator. The Koopman operator approach represents the nonline...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	315,111
1905.02905	On recoverability of discrete time signals from sparse observations	The paper investigates recoverability of discrete time signals represented by infinite sequences from incomplte observations. It is shown that there exist wide classes of signals that are everywhere dense in the space of square-summable signals and such that signals from these classes feature robust linear recoverabili...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	130,078
2412.19101	Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning	Cross-Domain Few-Shot Learning (CDFSL) requires the model to transfer knowledge from the data-abundant source domain to data-scarce target domains for fast adaptation, where the large domain gap makes CDFSL a challenging problem. Masked Autoencoder (MAE) excels in effectively using unlabeled data and learning image's g...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	520,709
2202.00798	Hierarchical Entity Alignment for Attribute-Rich Event-Driven Graphs	This paper addresses the problem of entity alignment in attribute-rich event-driven graphs. Unlike many other entity alignment problems, we are interested in aligning entities based on the similarity of their actions, i.e., entities that participate in similar events are more likely to be the same. We model the generat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	278,263
0908.3380	Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-like Transforms	We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions--the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet ...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	4,321
2308.10423	Integrated Sensing and Communications for 3D Object Imaging via Bilinear Inference	We consider an uplink integrated sensing and communications (ISAC) scenario where the detection of data symbols from multiple user equipment (UEs) occurs simultaneously with a three-dimensional (3D) estimation of the environment, extracted from the scattering features present in the channel state information (CSI) and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	386,720
1901.01144	A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques	In this paper, a unified susceptible-exposed-infected-susceptible-aware (SEIS-A) framework is proposed to combine epidemic spreading with individuals' on-line self-consultation behaviors. An epidemic spreading prediction model is established based on the SEIS-A framework. The prediction process contains two phases. In ...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,926
2205.12335	K-12BERT: BERT for K-12 education	Online education platforms are powered by various NLP pipelines, which utilize models like BERT to aid in content curation. Since the inception of the pre-trained language models like BERT, there have also been many efforts toward adapting these pre-trained models to specific domains. However, there has not been a mode...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	298,489
2407.04069	A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations	Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-world applications to ensure they produce reliable performance. Despite...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	470,415
2403.12237	Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT Environments	The hyper-parameter optimization (HPO) process is imperative for finding the best-performing Convolutional Neural Networks (CNNs). The automation process of HPO is characterized by its sizable computational footprint and its lack of transparency; both important factors in a resource-constrained Internet of Things (IoT)...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	439,090
1907.10528	The sameAs Problem: A Survey on Identity Management in the Web of Data	In a decentralised knowledge representation system such as the Web of Data, it is common and indeed desirable for different knowledge graphs to overlap. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Whilst the deductive value of s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	139,648
1302.6819	An Alternative Proof Method for Possibilistic Logic and its Application to Terminological Logics	Possibilistic logic, an extension of first-order logic, deals with uncertainty that can be estimated in terms of possibility and necessity measures. Syntactically, this means that a first-order formula is equipped with a possibility degree or a necessity degree that expresses to what extent the formula is possibly or n...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,449
2011.15124	Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs	Large-scale pretraining and task-specific fine-tuning is now the standard methodology for many tasks in computer vision and natural language processing. Recently, a multitude of methods have been proposed for pretraining vision and language BERTs to tackle challenges at the intersection of these two key areas of AI. Th...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	208,990
2406.01462	The Importance of Online Data: Understanding Preference Fine-tuning via Coverage	Learning from human preference data has emerged as the dominant paradigm for fine-tuning large language models (LLMs). The two most common families of techniques -- online reinforcement learning (RL) such as Proximal Policy Optimization (PPO) and offline contrastive methods such as Direct Preference Optimization (DPO) ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	460,329
2309.11054	Design of Chain-of-Thought in Math Problem Solving	Chain-of-Thought (CoT) plays a crucial role in reasoning for math problem solving. We conduct a comprehensive examination of methods for designing CoT, comparing conventional natural language CoT with various program CoTs, including the self-describing program, the comment-describing program, and the non-describing pro...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	393,258
2207.02110	Centralized Networked Micro Water-Energy Nexus with Proportional Exchange Among Participants	This paper proposes a Networked Micro Water-Energy Nexus (NetMicroWEN) capable of co-optimizing and simultaneously supplying water and energy to local consumers in nearby communities. The system manages different water and energy inputs of different communities in a local network to cooperatively meet their demands. Th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	306,405
2411.17502	Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry	This study develops a deep learning-based approach to automate inbound load plan adjustments for a large transportation and logistics company. It addresses a critical challenge for the efficient and resilient planning of E-commerce operations in presence of increasing uncertainties. The paper introduces an innovative d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	511,474
2412.07192	PrisonBreak: Jailbreaking Large Language Models with Fewer Than Twenty-Five Targeted Bit-flips	We introduce a new class of attacks on commercial-scale (human-aligned) language models that induce jailbreaking through targeted bitwise corruptions in model parameters. Our adversary can jailbreak billion-parameter language models with fewer than 25 bit-flips in all cases$-$and as few as 5 in some$-$using up to 40$\t...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	515,553
2010.01082	Multi-Modal Open-Domain Dialogue	Recent work in open-domain conversational agents has demonstrated that significant improvements in model engagingness and humanness metrics can be achieved via massive scaling in both pre-training data and model size (Adiwardana et al., 2020; Roller et al., 2020). However, if we want to build agents with human-like abi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	198,511
2104.04745	Classical-quantum network coding: a story about tensor	We study here the conditions to perform the distribution of a pure state on a quantum network using quantum operations which can succeed with a non-zero probability, the Stochastic Local Operation and Classical Communication (SLOCC) operations. In their pioneering 2010 work, Kobayashi et al. showed how to convert any...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	229,482
1811.10576	Grammar-based Representation and Identification of Dynamical Systems	In this paper we propose a novel approach to identify dynamical systems. The method estimates the model structure and the parameters of the model simultaneously, automating the critical decisions involved in identification such as model structure and complexity selection. In order to solve the combined model structure ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	114,527
2405.01460	Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders	Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks can be categorized based on whether specific interventions are adopted during training. The first approach is training-time defense, such as ...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	451,355
2311.05878	Central Angle Optimization for 360-degree Holographic 3D Content	In this study, we propose a method to find an optimal central angle in deep learning-based depth map estimation used to produce realistic holographic content. The acquisition of RGB-depth map images as detailed as possible must be performed to generate holograms of high quality, despite the high computational cost. The...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,743
1705.09193	Classification of Quantitative Light-Induced Fluorescence Images Using Convolutional Neural Network	Images are an important data source for diagnosis and treatment of oral diseases. The manual classification of images may lead to misdiagnosis or mistreatment due to subjective errors. In this paper an image classification model based on Convolutional Neural Network is applied to Quantitative Light-induced Fluorescence...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	74,155
1207.7179	Novel Modulation Techniques using Isomers as Messenger Molecules for Nano Communication Networks via Diffusion	In this paper, we propose three novel modulation techniques, i.e., concentration-based, molecular-type-based, and molecular-ratio-based, using isomers as messenger molecules for nano communication networks via diffusion. To evaluate achievable rate performance, we compare the proposed tech- niques with conventional ins...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,833
2304.14238	The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who	Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation. Nevertheless, few papers thoroughly discuss how. We document this by analysing 100 highly-cited papers, and annotating epistemic elements related to intende...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	360,868
2109.04049	BeamTransformer: Microphone Array-based Overlapping Speech Detection	We propose BeamTransformer, an efficient architecture to leverage beamformer's edge in spatial filtering and transformer's capability in context sequence modeling. BeamTransformer seeks to optimize modeling of sequential relationship among signals from different spatial direction. Overlapping speech detection is one of...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	254,268
2305.17087	Communication-Efficient Reinforcement Learning in Swarm Robotic Networks for Maze Exploration	Smooth coordination within a swarm robotic system is essential for the effective execution of collective robot missions. Having efficient communication is key to the successful coordination of swarm robots. This paper proposes a new communication-efficient decentralized cooperative reinforcement learning algorithm for ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	true	368,406
1905.10247	Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation	Neural dialog models often lack robustness to anomalous user input and produce inappropriate responses which leads to frustrating user experience. Although there are a set of prior approaches to out-of-domain (OOD) utterance detection, they share a few restrictions: they rely on OOD data or multiple sub-domains, and th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	132,001
2205.09707	PLAID: An Efficient Engine for Late Interaction Retrieval	Pre-trained language models are increasingly important components across multiple information retrieval (IR) paradigms. Late interaction, introduced with the ColBERT model and recently refined in ColBERTv2, is a popular paradigm that holds state-of-the-art status across many benchmarks. To dramatically speed up the sea...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	297,386
2211.03308	Two-Server Oblivious Transfer for Quantum Messages	Oblivious transfer is considered as a cryptographic primitive task for quantum information processing over quantum network. Although it is possible with two servers, any existing protocol works only with classical messages. We propose two-server oblivious transfer protocols for quantum messages.	false	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	328,897
2307.12656	A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration	Inspired by the fact that the matrix formulated by nonlocal similar patches in a natural image is of low rank, the rank approximation issue have been extensively investigated over the past decades, among which weighted nuclear norm minimization (WNNM) and weighted Schatten $p$-norm minimization (WSNM) are two prevailin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,335
2204.03872	Controllable Missingness from Uncontrollable Missingness: Joint Learning Measurement Policy and Imputation	Due to the cost or interference of measurement, we need to control measurement system. Assuming that each variable can be measured sequentially, there exists optimal policy choosing next measurement for the former observations. Though optimal measurement policy is actually dependent on the goal of measurement, we mainl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	290,461
2008.10365	ATM Cash demand forecasting in an Indian Bank with chaos and deep learning	This paper proposes to model chaos in the ATM cash withdrawal time series of a big Indian bank and forecast the withdrawals using deep learning methods. It also considers the importance of day-of-the-week and includes it as a dummy exogenous variable. We first modelled the chaos present in the withdrawal time series by...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	192,974
2006.15482	Robot Inner Attention Modeling for Task-Adaptive Teaming of Heterogeneous Multi Robots	Attracted by team scale and function diversity, a heterogeneous multi-robot system (HMRS), where multiple robots with different functions and numbers are coordinated to perform tasks, has been widely used for complex and large-scale scenarios, including disaster search and rescue, site surveillance, and social security...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	184,522
2403.07553	The future of document indexing: GPT and Donut revolutionize table of content processing	Industrial projects rely heavily on lengthy, complex specification documents, making tedious manual extraction of structured information a major bottleneck. This paper introduces an innovative approach to automate this process, leveraging the capabilities of two cutting-edge AI models: Donut, a model that extracts info...	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	false	436,931
2211.07035	Elementary Bitcoin economics: from production and transaction demand to values	In this paper we give an elementary analysis of economics of Bitcoin that combines the transaction demand by the consumers and the supply of hashrate by miners. We argue that the decreasing block reward will have no significant effect on the exchange rate (price) of Bitcoin and thus the network will be transitioning to...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	330,101
1511.06728	Hand Pose Estimation through Semi-Supervised and Weakly-Supervised Learning	We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides usefu...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	49,314
2412.10416	SuperMerge: An Approach For Gradient-Based Model Merging	Large language models, such as ChatGPT, Claude, or LLaMA, are gigantic, monolithic, and possess the superpower to simultaneously support thousands of tasks. However, high-throughput applications often prefer smaller task-specific models because of their lower latency and cost. One challenge of using task-specific model...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	516,909
1705.09912	Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising	Most of the existing denoising algorithms are developed for grayscale images, while it is not a trivial work to extend them for color image denoising because the noise statistics in R, G, B channels can be very different for real noisy images. In this paper, we propose a multi-channel (MC) optimization model for real c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,300
2310.16267	Student Classroom Behavior Detection based on Spatio-Temporal Network and Multi-Model Fusion	Using deep learning methods to detect students' classroom behavior automatically is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available spatio-temporal datasets on student behavior, as well as the high cost of manually labeling such da...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	402,648
2103.06678	The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models	In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth la...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	224,376
2204.01839	Coarse-to-Fine Sparse Sequential Recommendation	Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-term preferences. Despite their success, these approaches still struggle to model sparse data, on which they struggle to learn high-quality i...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	289,741
2012.05685	Generative Deep Learning Techniques for Password Generation	Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates. In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: attention-ba...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	210,861
2006.08343	Automated Diagram Generation to Build Understanding and Usability	Causal loop and stock and flow diagrams are broadly used in System Dynamics because they help organize relationships and convey meaning. Using the analytical work of Schoenberg (2019) to select what to include in a compressed model, this paper demonstrates how that information can be clearly presented in an automatical...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	182,159
2103.05668	Core Challenges of Social Robot Navigation: A Survey	Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. D...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	224,045
2303.12068	Machine Learning for Brain Disorders: Transformers and Visual Transformers	Transformers were initially introduced for natural language processing (NLP) tasks, but fast they were adopted by most deep learning fields, including computer vision. They measure the relationships between pairs of input tokens (words in the case of text strings, parts of images for visual Transformers), termed attent...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,123
1601.08132	Interference Management in Heterogeneous Networks with Blind Transmitters	Future multi-tier communication networks will require enhanced network capacity and reduced overhead. In the absence of Channel State Information (CSI) at the transmitters, Blind Interference Alignment (BIA) and Topological Interference Management (TIM) can achieve optimal Degrees of Freedom (DoF), minimising network's...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,500
2112.09456	Compositional Learning-based Planning for Vision POMDPs	The Partially Observable Markov Decision Process (POMDP) is a powerful framework for capturing decision-making problems that involve state and transition uncertainty. However, most current POMDP planners cannot effectively handle high-dimensional image observations prevalent in real world applications, and often requir...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	272,159
1911.12091	Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction	We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemm...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	155,312
2401.05252	PIXART-{\delta}: Fast and Controllable Image Generation with Latent Consistency Models	This technical report introduces PIXART-{\delta}, a text-to-image synthesis framework that integrates the Latent Consistency Model (LCM) and ControlNet into the advanced PIXART-{\alpha} model. PIXART-{\alpha} is recognized for its ability to generate high-quality images of 1024px resolution through a remarkably efficie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,698
2405.10550	LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion	Advances in endoscopy use in surgeries face challenges like inadequate lighting. Deep learning, notably the Denoising Diffusion Probabilistic Model (DDPM), holds promise for low-light image enhancement in the medical field. However, DDPMs are computationally demanding and slow, limiting their practical medical applicat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,801
2406.03431	CattleFace-RGBT: RGB-T Cattle Facial Landmark Benchmark	To address this challenge, we introduce CattleFace-RGBT, a RGB-T Cattle Facial Landmark dataset consisting of 2,300 RGB-T image pairs, a total of 4,600 images. Creating a landmark dataset is time-consuming, but AI-assisted annotation can help. However, applying AI to thermal images is challenging due to suboptimal resu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,235
2405.07838	Adaptive Exploration for Data-Efficient General Value Function Evaluations	General Value Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning. Each GVF computes the expected return for a given policy, based on a unique reward. Existing methods relying on fixed behavior policies or pre-collected data often face data efficiency issues when learning mul...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,882
1306.3584	Recurrent Convolutional Neural Networks for Discourse Compositionality	The compositionality of meaning extends beyond the single sentence. Just as words combine to form the meaning of sentences, so do sentences combine to form the meaning of paragraphs, dialogues and general discourse. We introduce both a sentence model and a discourse model corresponding to the two levels of compositiona...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	25,223
2412.00545	Optimal Particle-based Approximation of Discrete Distributions (OPAD)	Particle-based methods include a variety of techniques, such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC), for approximating a probabilistic target distribution with a set of weighted particles. In this paper, we prove that for any set of particles, there is a unique weighting mechanism that mini...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	512,703
2311.06293	Quantum Neural Networks for Power Flow Analysis	This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum-classical, and classical ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	406,885
2111.00534	Focal Attention Networks: optimising attention for biomedical image segmentation	In recent years, there has been increasing interest to incorporate attention into deep learning architectures for biomedical image segmentation. The modular design of attention mechanisms enables flexible integration into convolutional neural network architectures, such as the U-Net. Whether attention is appropriate to...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	264,250
1506.00508	Isomorphisms in Multilayer Networks	We extend the concept of graph isomorphisms to multilayer networks with any number of "aspects" (i.e., types of layering). In developing this generalization, we identify multiple types of isomorphisms. For example, in multilayer networks with a single aspect, permuting vertex labels, layer labels, and both vertex label...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	43,677
2311.04064	KPI Extraction from Maintenance Work Orders -- A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines	Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	406,088
2308.13460	Learning How to Price Charging in Electric Ride-Hailing Markets	With the electrification of ride-hailing fleets, there will be a need to incentivize where and when the ride-hailing vehicles should charge. In this work, we assume that a central authority wants to control the distribution of the vehicles and can do so by selecting charging prices. Since there will likely be more than...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	387,926
1811.10167	LSICC: A Large Scale Informal Chinese Corpus	Deep learning based natural language processing model is proven powerful, but need large-scale dataset. Due to the significant gap between the real-world tasks and existing Chinese corpus, in this paper, we introduce a large-scale corpus of informal Chinese. This corpus contains around 37 million book reviews and 50 th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	114,430
2411.10251	Morpho-Aware Global Attention for Image Matting	Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) face inherent challenges in image matting, particularly in preserving fine structural details. ViTs, with their global receptive field enabled by the self-attention mechanism, often lose local details such as hair strands. Conversely, CNNs, constrained...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,566
1812.01729	Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning	Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot", vast computational effort is invested for simulating these system in small steps, e.g., using Molecular...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	115,594
1608.05966	KidsTube: Detection, Characterization and Analysis of Child Unsafe Content & Promoters on YouTube	YouTube draws large number of users who contribute actively by uploading videos or commenting on existing videos. However, being a crowd sourced and large content pushed onto it, there is limited control over the content. This makes malicious users push content (videos and comments) which is inappropriate (unsafe), par...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	60,046
0802.2826	Efficient Minimization of DFAs with Partial Transition Functions	Let PT-DFA mean a deterministic finite automaton whose transition relation is a partial function. We present an algorithm for minimizing a PT-DFA in $O(m \lg n)$ time and $O(m+n+\alpha)$ memory, where $n$ is the number of states, $m$ is the number of defined transitions, and $\alpha$ is the size of the alphabet. Time c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	1,316
2402.11329	On a recent extension of a family of biprojective APN functions	APN functions play a big role as primitives in symmetric cryptography as building blocks that yield optimal resistance to differential attacks. In this note, we consider a recent extension of a biprojective APN family by G\"olo\u{g}lu defined on $\mathbb{F}_{2^{2m}}$. We show that this generalization yields functions e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	430,340
2402.02065	Training Implicit Networks for Image Deblurring using Jacobian-Free Backpropagation	Recent efforts in applying implicit networks to solve inverse problems in imaging have achieved competitive or even superior results when compared to feedforward networks. These implicit networks only require constant memory during backpropagation, regardless of the number of layers. However, they are not necessarily e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,365
2308.10496	Using Autoencoders and AutoDiff to Reconstruct Missing Variables in a Set of Time Series	Existing black box modeling approaches in machine learning suffer from a fixed input and output feature combination. In this paper, a new approach to reconstruct missing variables in a set of time series is presented. An autoencoder is trained as usual with every feature on both sides and the neural network parameters ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	386,757
2306.01187	Training neural operators to preserve invariant measures of chaotic attractors	Chaotic systems make long-horizon forecasts difficult because small perturbations in initial conditions cause trajectories to diverge at an exponential rate. In this setting, neural operators trained to minimize squared error losses, while capable of accurate short-term forecasts, often fail to reproduce statistical or...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,334
2408.09634	Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models	It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions to check. However, as we show here, it is possible to efficiently sea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,517
2411.07200	'Explaining RL Decisions with Trajectories': A Reproducibility Study	This work investigates the reproducibility of the paper 'Explaining RL decisions with trajectories'. The original paper introduces a novel approach in explainable reinforcement learning based on the attribution decisions of an agent to specific clusters of trajectories encountered during training. We verify the main cl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	507,438
2302.00819	Introduction to Arithmetic Coding -- Theory and Practice	This introduction to arithmetic coding is divided in two parts. The first explains how and why arithmetic coding works. We start presenting it in very general terms, so that its simplicity is not lost under layers of implementation details. Next, we show some of its basic properties, which are later used in the computa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	343,357
1905.12278	An Inertial Newton Algorithm for Deep Learning	We introduce a new second-order inertial optimization method for machine learning called INNA. It exploits the geometry of the loss function while only requiring stochastic approximations of the function values and the generalized gradients. This makes INNA fully implementable and adapted to large-scale optimization pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,720
1508.05514	Gaussian Mixture Reduction Using Reverse Kullback-Leibler Divergence	We propose a greedy mixture reduction algorithm which is capable of pruning mixture components as well as merging them based on the Kullback-Leibler divergence (KLD). The algorithm is distinct from the well-known Runnalls' KLD based method since it is not restricted to merging operations. The capability of pruning (in ...	false	false	false	false	false	false	true	true	false	false	true	true	false	false	false	false	false	false	46,235
2003.08343	Survey of Privacy-Preserving Collaborative Filtering	Collaborative filtering recommendation systems provide recommendations to users based on their own past preferences, as well as those of other users who share similar interests. The use of recommendation systems has grown widely in recent years, helping people choose which movies to watch, books to read, and items to b...	false	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	168,704
2310.16831	PERF: Panoramic Neural Radiance Field from a Single Panorama	Neural Radiance Field (NeRF) has achieved substantial progress in novel view synthesis given multi-view images. Recently, some works have attempted to train a NeRF from a single image with 3D priors. They mainly focus on a limited field of view with a few occlusions, which greatly limits their scalability to real-world...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	402,894
2406.10653	Justice in Healthcare Artificial Intelligence in Africa	There is an ongoing debate on balancing the benefits and risks of artificial intelligence (AI) as AI is becoming critical to improving healthcare delivery and patient outcomes. Such improvements are essential in resource-constrained settings where millions lack access to adequate healthcare services, such as in Africa....	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	464,495
1404.1511	MTD(f), A Minimax Algorithm Faster Than NegaScout	MTD(f) is a new minimax search algorithm, simpler and more efficient than previous algorithms. In tests with a number of tournament game playing programs for chess, checkers and Othello it performed better, on average, than NegaScout/PVS (the AlphaBeta variant used in practically all good chess, checkers, and Othello p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	32,121
2106.08153	Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology	Deep learning models are routinely employed in computational pathology (CPath) for solving problems of diagnostic and prognostic significance. Typically, the generalization performance of CPath models is analyzed using evaluation protocols such as cross-validation and testing on multi-centric cohorts. However, to ensur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,201
2211.16858	A Major Obstacle for NLP Research: Let's Talk about Time Allocation!	The field of natural language processing (NLP) has grown over the last few years: conferences have become larger, we have published an incredible amount of papers, and state-of-the-art research has been implemented in a large variety of customer-facing products. However, this paper argues that we have been less success...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	333,777
2205.03207	Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms	While the field of Quality-Diversity (QD) has grown into a distinct branch of stochastic optimization, a few problems, in particular locomotion and navigation tasks, have become de facto standards. Are such benchmarks sufficient? Are they representative of the key challenges faced by QD algorithms? Do they provide the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	295,206
2107.08027	Seeing and Believing: Evaluating the Trustworthiness of Twitter Users	Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, there have been many instances where corrupted users found ways to abuse it, as for instance, through raising or lowering user's credibility. As a...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	246,602
2104.00411	Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features	Neural networks have demonstrated remarkable performance in classification and regression tasks on chest X-rays. In order to establish trust in the clinical routine, the networks' prediction mechanism needs to be interpretable. One principal approach to interpretation is feature attribution. Feature attribution methods...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	227,993
2009.00862	Efficient Multi-Robot Exploration with Energy Constraint based on Optimal Transport Theory	This paper addresses an Optimal Transport (OT)-based efficient multi-robot exploration problem, considering the energy constraints of a multi-robot system. The efficiency in this problem implies how a team of robots (agents) covers a given domain, reflecting a priority of areas of interest represented by a density dist...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	194,162
1511.02528	Proceedings Workshop on Models for Formal Analysis of Real Systems	This volume contains the proceedings of MARS 2015, the first workshop on Models for Formal Analysis of Real Systems, held on November 23, 2015 in Suva, Fiji, as an affiliated workshop of LPAR 2015, the 20th International Conference on Logic for Programming, Artificial Intelligence and Reasoning. The workshop emphasis...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	true	48,648
2011.01730	Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks	Generative Adversarial Networks (GANs) have become the most used networks towards solving the problem of image generation. Self-supervised GANs are later proposed to avoid the catastrophic forgetting of the discriminator and to improve the image generation quality without needing the class labels. However, the generali...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	204,685
2008.00181	Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records	E-commerce business is revolutionizing our shopping experiences by providing convenient and straightforward services. One of the most fundamental problems is how to balance the demand and supply in market segments to build an efficient platform. While conventional machine learning models have achieved great success on ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	189,932
1808.01200	Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation	Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,533
2502.10138	Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation	We study the reinforcement learning (RL) problem in a constrained Markov decision process (CMDP), where an agent explores the environment to maximize the expected cumulative reward while satisfying a single constraint on the expected total utility value in every episode. While this problem is well understood in the tab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,747
2406.10349	Online Identification of Time-Varying Systems Using Excitation Sets and Change Point Detection	In this work, we first show that the problem of parameter identification is often ill-conditioned and lacks the persistence of excitation required for the convergence of online learning schemes. To tackle these challenges, we introduce the notion of optimal and greedy excitation sets which contain data points with suff...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	464,369
1912.07112	Joint Beamforming, User Association, and Height Control for Cellular-Enabled UAV Communications	Supporting reliable and seamless mobility for aerial users, such as unmanned aerial vehicles (UAVs), is a key challenge for the next-generation cellular systems. To tackle this challenge, we propose a joint beamforming, user association, and UAV-height control framework for cellular-connected multi-UAV networks with mu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	157,517

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