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
2411.11135	Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method	We explore the oscillatory behavior observed in inversion methods applied to large-scale text-to-image diffusion models, with a focus on the "Flux" model. By employing a fixed-point-inspired iterative approach to invert real-world images, we observe that the solution does not achieve convergence, instead oscillating be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,928
2301.12473	Large Language Models for Biomedical Knowledge Graph Construction: Information extraction from EMR notes	The automatic construction of knowledge graphs (KGs) is an important research area in medicine, with far-reaching applications spanning drug discovery and clinical trial design. These applications hinge on the accurate identification of interactions among medical and biological entities. In this study, we propose an en...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	342,543
2104.05097	Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks	Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of certifiably robust classifiers. However they remain commonly considered as less accurate, and their properties in learning are still not fully u...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	229,602
2211.09527	Ignore Previous Prompt: Attack Techniques For Language Models	Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malicious user interaction are scarce. By proposing PromptInject, a prosaic alignment framework for mask-...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	331,004
1908.03595	Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification	The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence of the aforementioned technique on local geometry. In this study, focusing on bin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	141,268
1402.4029	Connecting Spiking Neurons to a Spiking Memristor Network Changes the Memristor Dynamics	Memristors have been suggested as neuromorphic computing elements. Spike-time dependent plasticity and the Hodgkin-Huxley model of the neuron have both been modelled effectively by memristor theory. The d.c. response of the memristor is a current spike. Based on these three facts we suggest that memristors are well-pla...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	30,924
0905.3582	Profiling of a network behind an infectious disease outbreak	Stochasticity and spatial heterogeneity are of great interest recently in studying the spread of an infectious disease. The presented method solves an inverse problem to discover the effectively decisive topology of a heterogeneous network and reveal the transmission parameters which govern the stochastic spreads over ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,743
2312.15856	SERF: Fine-Grained Interactive 3D Segmentation and Editing with Radiance Fields	Although significant progress has been made in the field of 2D-based interactive editing, fine-grained 3D-based interactive editing remains relatively unexplored. This limitation can be attributed to two main challenges: the lack of an efficient 3D representation robust to different modifications and the absence of an ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	418,161
0907.2089	Fast In-Memory XPath Search over Compressed Text and Tree Indexes	A large fraction of an XML document typically consists of text data. The XPath query language allows text search via the equal, contains, and starts-with predicates. Such predicates can efficiently be implemented using a compressed self-index of the document's text nodes. Most queries, however, contain some parts of qu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	4,090
2409.10357	2D or not 2D: How Does the Dimensionality of Gesture Representation Affect 3D Co-Speech Gesture Generation?	Co-speech gestures are fundamental for communication. The advent of recent deep learning techniques has facilitated the creation of lifelike, synchronous co-speech gestures for Embodied Conversational Agents. "In-the-wild" datasets, aggregating video content from platforms like YouTube via human pose detection technolo...	false	false	true	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	488,720
2304.02531	Learning to Compare Longitudinal Images	Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing temporal dynamics in biomedical applications. The classical approach for longitudinal comparison involves normalizing for nuisance variatio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	356,459
2008.09032	Sparse phase retrieval via Phaseliftoff	The aim of sparse phase retrieval is to recover a $k$-sparse signal $\mathbf{x}_0\in \mathbb{C}^{d}$ from quadratic measurements $\|\langle \mathbf{a}_i,\mathbf{x}_0\rangle\|^2$ where $\mathbf{a}_i\in \mathbb{C}^d, i=1,\ldots,m$. Noting $\|\langle \mathbf{a}_i,\mathbf{x}_0\rangle\|^2={\text{Tr}}(A_iX_0)$ with $A_i=\mathbf{...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	192,589
2502.08599	SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent	Existing methods for simulating individual identities often oversimplify human complexity, which may lead to incomplete or flattened representations. To address this, we introduce SPeCtrum, a grounded framework for constructing authentic LLM agent personas by incorporating an individual's multidimensional self-concept....	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	533,074
2404.15655	Multi-Modal Proxy Learning Towards Personalized Visual Multiple Clustering	Multiple clustering has gained significant attention in recent years due to its potential to reveal multiple hidden structures of data from different perspectives. The advent of deep multiple clustering techniques has notably advanced the performance by uncovering complex patterns and relationships within large dataset...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,181
2302.12498	Scalable Unbalanced Sobolev Transport for Measures on a Graph	Optimal transport (OT) is a popular and powerful tool for comparing probability measures. However, OT suffers a few drawbacks: (i) input measures required to have the same mass, (ii) a high computational complexity, and (iii) indefiniteness which limits its applications on kernel-dependent algorithmic approaches. To ta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,591
1902.05300	On instabilities of deep learning in image reconstruction - Does AI come at a cost?	Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper we demonstrate a crucial phenomenon: deep learning typically yields unstablemethods for image reconstruction. The instabilities usual...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,509
1901.08761	Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies	Decision making in multi-agent systems (MAS) is a great challenge due to enormous state and joint action spaces as well as uncertainty, making centralized control generally infeasible. Decentralized control offers better scalability and robustness but requires mechanisms to coordinate on joint tasks and to avoid confli...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	119,574
1804.06378	Graph-based Selective Outlier Ensembles	An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable each component to equally participate in the aggregation process. A problem with this approach is that poor components are likely to negativel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	95,287
2107.10950	Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods	In order to apply the recent successes of machine learning and automated plant phenotyping on a large scale using agricultural robotics, efficient and general algorithms must be designed to intelligently split crop fields into small, yet actionable, portions that can then be processed by more complex algorithms. In thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,439
2410.06405	Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects	The Abstraction and Reasoning Corpus (ARC) is a popular benchmark focused on visual reasoning in the evaluation of Artificial Intelligence systems. In its original framing, an ARC task requires solving a program synthesis problem over small 2D images using a few input-output training pairs. In this work, we adopt the r...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	496,184
2405.04370	Diff-IP2D: Diffusion-Based Hand-Object Interaction Prediction on Egocentric Videos	Understanding how humans would behave during hand-object interaction is vital for applications in service robot manipulation and extended reality. To achieve this, some recent works have been proposed to simultaneously forecast hand trajectories and object affordances on human egocentric videos. The joint prediction se...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	452,546
2501.09822	pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data	Traditional Federated Learning (FL) approaches often struggle with data heterogeneity across clients, leading to suboptimal model performance for individual clients. To address this issue, Personalized Federated Learning (PFL) emerges as a solution to the challenges posed by non-independent and identically distributed ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	525,285
1810.07354	Fault Tolerance in Iterative-Convergent Machine Learning	Machine learning (ML) training algorithms often possess an inherent self-correcting behavior due to their iterative-convergent nature. Recent systems exploit this property to achieve adaptability and efficiency in unreliable computing environments by relaxing the consistency of execution and allowing calculation errors...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,623
2408.14378	User-Access Point Association for High Density MIMO Wireless LANs	Wireless local area network (WLAN) access points (APs) are being deployed in high density to improve coverage and throughput. The emerging multiple-input multiple-output (MIMO) implementation for uplink (UL) transmissions promises high per-user throughput and improved aggregate network throughput. However, the high thr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	483,512
1507.04457	Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons	In this paper we consider the collaborative ranking setting: a pool of users each provides a small number of pairwise preferences between $d$ possible items; from these we need to predict preferences of the users for items they have not yet seen. We do so by fitting a rank $r$ score matrix to the pairwise data, and pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	45,177
1910.07972	Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control	We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. Rather than designing shaped reward functions, ACGD adaptively sets the appropriate task difficulty for the learner by controlling where to sample from the demonstration trajectories and wh...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	149,748
2412.02626	Time-Reversal Provides Unsupervised Feedback to LLMs	Large Language Models (LLMs) are typically trained to predict in the forward direction of time. However, recent works have shown that prompting these models to look back and critique their own generations can produce useful feedback. Motivated by this, we explore the question of whether LLMs can be empowered to think (...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	513,611
2405.17633	HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs	Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories. While empathy is influenced by narrative content, intuitively, people respond to the way a story is told as well, through narrative style. Yet the relationship between empathy and narra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	458,034
1008.0425	Quantum Steganography and Quantum Error-Correction	In the current thesis we first talk about the six-qubit quantum error-correcting code and show its connections to entanglement-assisted error-correcting coding theory and then to subsystem codes. This code bridges the gap between the five-qubit (perfect) and Steane codes. We discuss two methods to encode one qubit into...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,176
2105.09297	Extracting Variable-Depth Logical Document Hierarchy from Long Documents: Method, Evaluation, and Application	In this paper, we study the problem of extracting variable-depth "logical document hierarchy" from long documents, namely organizing the recognized "physical document objects" into hierarchical structures. The discovery of logical document hierarchy is the vital step to support many downstream applications. However, lo...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	236,028
2309.16400	Physics-Preserving AI-Accelerated Simulations of Plasma Turbulence	Turbulence in fluids, gases, and plasmas remains an open problem of both practical and fundamental importance. Its irreducible complexity usually cannot be tackled computationally in a brute-force style. Here, we combine Large Eddy Simulation (LES) techniques with Machine Learning (ML) to retain only the largest dynami...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	395,340
2408.07966	Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization	Federated learning is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significant challenge of federated learning is data-level heterogeneity, i.e., skewed or long-tailed distribution of private data. Although various m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	480,795
1903.07789	Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks	Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and spatial correlations between different regions. In addition, it is affe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	124,697
1910.02249	Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics	Bayesian deep learning is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks~(DNNs). Stochastic Gradient Langevin Dynamics~(SGLD) is an effective method to enable Bayesian deep learning on large-scale datasets. Previous theoretical studies have shown various appealing p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,188
2410.04444	G\"odel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement	The rapid advancement of large language models (LLMs) has significantly enhanced the capabilities of AI-driven agents across various tasks. However, existing agentic systems, whether based on fixed pipeline algorithms or pre-defined meta-learning frameworks, cannot search the whole agent design space due to the restric...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	495,283
2409.16968	Bridge to Real Environment with Hardware-in-the-loop for Wireless Artificial Intelligence Paradigms	Nowadays, many machine learning (ML) solutions to improve the wireless standard IEEE802.11p for Vehicular Adhoc Network (VANET) are commonly evaluated in the simulated world. At the same time, this approach could be cost-effective compared to real-world testing due to the high cost of vehicles. There is a risk of unexp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	491,599
2310.20072	Automatic Evaluation of Generative Models with Instruction Tuning	Automatic evaluation of natural language generation has long been an elusive goal in NLP.A recent paradigm fine-tunes pre-trained language models to emulate human judgements for a particular task and evaluation criterion. Inspired by the generalization ability of instruction-tuned models, we propose a learned metric ba...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	404,241
2104.04144	Individual Explanations in Machine Learning Models: A Survey for Practitioners	In recent years, the use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Although these models can often bring substantial improvements in the accuracy and efficiency of organizations, many governments, institutions, and companies are reluctant to their...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	229,303
2006.04093	Multi-view Contrastive Learning for Online Knowledge Distillation	Previous Online Knowledge Distillation (OKD) often carries out mutually exchanging probability distributions, but neglects the useful representational knowledge. We therefore propose Multi-view Contrastive Learning (MCL) for OKD to implicitly capture correlations of feature embeddings encoded by multiple peer networks,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	180,557
1401.2545	Design and Development of a User Specific Dynamic E-Magazine	Internet and electronic media gaining more popularity due to ease and speed, the count of Internet users has increased tremendously. The world is moving faster each day with several events taking place at once and the Internet is flooded with information in every field. There are categories of information ranging from ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	29,756
1901.09997	Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample	We present two sampled quasi-Newton methods (sampled LBFGS and sampled LSR1) for solving empirical risk minimization problems that arise in machine learning. Contrary to the classical variants of these methods that sequentially build Hessian or inverse Hessian approximations as the optimization progresses, our proposed...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,894
1606.01932	Inference of Causal Information Flow in Collective Animal Behavior	Understanding and even defining what constitutes animal interactions remains a challenging problem. Correlational tools may be inappropriate for detecting communication between a set of many agents exhibiting nonlinear behavior. A different approach is to define coordinated motions in terms of an information theoretic ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,879
2206.14452	Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News	A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a multi-instance view. The intuition behind this is based on the news uncertainty of varyi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,292
2102.12593	AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation	In this paper, we propose a novel framework to translate a portrait photo-face into an anime appearance. Our aim is to synthesize anime-faces which are style-consistent with a given reference anime-face. However, unlike typical translation tasks, such anime-face translation is challenging due to complex variations of a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	221,775
1801.07246	Distributed Frequency Offsets Estimation	In this paper, we provide a distributed frequency offset estimation algorithm based on a variant of belief propagation (BP). Each agent in the network pre-compensates its carrier frequency individually so that there is no frequency offset from the desired carrier frequency between each pair of transceiver. The pre-comp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	88,754
2407.21359	ProSpec RL: Plan Ahead, then Execute	Imagining potential outcomes of actions before execution helps agents make more informed decisions, a prospective thinking ability fundamental to human cognition. However, mainstream model-free Reinforcement Learning (RL) methods lack the ability to proactively envision future scenarios, plan, and guide strategies. The...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	477,508
1909.05477	Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning	While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent's policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	145,109
2211.17093	CutFEM forward modeling for EEG source analysis	Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	333,857
1501.00677	Group-based ranking method for online rating systems with spamming attacks	Ranking problem has attracted much attention in real systems. How to design a robust ranking method is especially significant for online rating systems under the threat of spamming attacks. By building reputation systems for users, many well-performed ranking methods have been applied to address this issue. In this Let...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	39,015
1809.02104	Are adversarial examples inevitable?	A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are advers...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	106,960
2408.04441	Causal Inference in Social Platforms Under Approximate Interference Networks	Estimating the total treatment effect (TTE) of a new feature in social platforms is crucial for understanding its impact on user behavior. However, the presence of network interference, which arises from user interactions, often complicates this estimation process. Experimenters typically face challenges in fully captu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	479,389
2411.19297	Enhancing Parameter-Efficient Fine-Tuning of Vision Transformers through Frequency-Based Adaptation	Adapting vision transformer foundation models through parameter-efficient fine-tuning (PEFT) methods has become increasingly popular. These methods optimize a limited subset of parameters, enabling efficient adaptation without the need to fine-tune the entire model while still achieving competitive performance. However...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	512,193
1904.05449	Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices	Human brain functional connectivity (FC) is often measured as the similarity of functional MRI responses across brain regions when a brain is either resting or performing a task. This paper aims to statistically analyze the dynamic nature of FC by representing the collective time-series data, over a set of brain region...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,319
2111.11089	Monocular Road Planar Parallax Estimation	Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using 3D sensors such as LiDAR or directly predicting the depth of points via deep learning. However, the former is expensive, and the latter lacks the u...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	267,544
2111.11638	Network In Graph Neural Network	Graph Neural Networks (GNNs) have shown success in learning from graph structured data containing node/edge feature information, with application to social networks, recommendation, fraud detection and knowledge graph reasoning. In this regard, various strategies have been proposed in the past to improve the expressive...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	267,724
1904.11266	Discrete Optimal Graph Clustering	Graph based clustering is one of the major clustering methods. Most of it work in three separate steps: similarity graph construction, clustering label relaxing and label discretization with k-means. Such common practice has three disadvantages: 1) the predefined similarity graph is often fixed and may not be optimal f...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	128,824
1806.06183	The Neural Painter: Multi-Turn Image Generation	In this work we combine two research threads from Vision/ Graphics and Natural Language Processing to formulate an image generation task conditioned on attributes in a multi-turn setting. By multiturn, we mean the image is generated in a series of steps of user-specified conditioning information. Our proposed approach ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,647
2003.07096	Towards a Collaborative Approach to Decision Making Based on Ontology and Multi-Agent System Application to crisis management	The coordination and cooperation of all the stakeholders involved is a decisive point for the control and the resolution of problems. In the insecurity events, the resolution should refer to a plan that defines a general framework of the procedures to be undertaken and the instructions to be complied with; also, a more...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	168,332
2302.01892	Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics	Distributed aggregative optimization is a recently emerged framework in which the agents of a network want to minimize the sum of local objective functions, each one depending on the agent decision variable (e.g., the local position of a team of robots) and an aggregation of all the agents' variables (e.g., the team ba...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	343,782
2307.16426	High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation	Due to limited camera capacities, digital images usually have a narrower dynamic illumination range than real-world scene radiance. To resolve this problem, High Dynamic Range (HDR) reconstruction is proposed to recover the dynamic range to better represent real-world scenes. However, due to different physical imaging ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	382,605
2409.19370	MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation	Segmenting anatomical structures and lesions from ultrasound images contributes to disease assessment. Weakly supervised learning (WSL) based on sparse annotation has achieved encouraging performance and demonstrated the potential to reduce annotation costs. This study attempts to introduce scribble-based WSL into ultr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,646
2402.16312	Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users	We study the problem of federated contextual combinatorial cascading bandits, where $\|\mathcal{U}\|$ agents collaborate under the coordination of a central server to provide tailored recommendations to the $\|\mathcal{U}\|$ corresponding users. Existing works consider either a synchronous framework, necessitating full age...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	432,509
2104.11401	Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy	In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training dataset augmented from the prior information available in an ART workflow - an approach we term Intentional Deep Overfit Learning (IDOL). Impl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	231,897
2406.11912	AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology	Software agents have emerged as promising tools for addressing complex software engineering tasks. Existing works, on the other hand, frequently oversimplify software development workflows, despite the fact that such workflows are typically more complex in the real world. Thus, we propose AgileCoder, a multi agent syst...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	465,139
1210.7719	Robustness, Canalyzing Functions and Systems Design	We study a notion of robustness of a Markov kernel that describes a system of several input random variables and one output random variable. Robustness requires that the behaviour of the system does not change if one or several of the input variables are knocked out. If the system is required to be robust against too m...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	19,455
2410.02840	Overcoming Representation Bias in Fairness-Aware data Repair using Optimal Transport	Optimal transport (OT) has an important role in transforming data distributions in a manner which engenders fairness. Typically, the OT operators are learnt from the unfair attribute-labelled data, and then used for their repair. Two significant limitations of this approach are as follows: (i) the OT operators for unde...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	494,496
2002.10329	KBSET -- Knowledge-Based Support for Scholarly Editing and Text Processing with Declarative LaTeX Markup and a Core Written in SWI-Prolog	KBSET is an environment that provides support for scholarly editing in two flavors: First, as a practical tool KBSET/Letters that accompanies the development of editions of correspondences (in particular from the 18th and 19th century), completely from source documents to PDF and HTML presentations. Second, as a protot...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	165,368
2006.11223	Unified Representation Learning for Efficient Medical Image Analysis	Medical image analysis typically includes several tasks such as enhancement, segmentation, and classification. Traditionally, these tasks are implemented using separate deep learning models for separate tasks, which is not efficient because it involves unnecessary training repetitions, demands greater computational res...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	183,148
1908.02118	A Public Network Trace of a Control and Automation System	The increasing number of attacks against automation systems such as SCADA and their network infrastructure have demonstrated that there is a need to secure those systems. Unfortunately, directly applying existing ICT security mechanisms to automation systems is hard due to constraints of the latter, such as availabilit...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	true	140,926
2501.07260	Skip Mamba Diffusion for Monocular 3D Semantic Scene Completion	3D semantic scene completion is critical for multiple downstream tasks in autonomous systems. It estimates missing geometric and semantic information in the acquired scene data. Due to the challenging real-world conditions, this task usually demands complex models that process multi-modal data to achieve acceptable per...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	524,333
2111.09056	Improving Person Re-Identification with Temporal Constraints	In this paper we introduce an image-based person re-identification dataset collected across five non-overlapping camera views in the large and busy airport in Dublin, Ireland. Unlike all publicly available image-based datasets, our dataset contains timestamp information in addition to frame number, and camera and perso...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	true	266,891
2207.03667	Identification of Intraday False Data Injection Attack on DER Dispatch Signals	The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communicat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	306,932
2408.01336	Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers	We investigate a problem estimating coefficients of linear regression under sparsity assumption when covariates and noises are sampled from heavy tailed distributions. Additionally, we consider the situation where not only covariates and noises are sampled from heavy tailed distributions but also contaminated by outlie...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	478,193
1905.12717	An adaptive nearest neighbor rule for classification	We introduce a variant of the $k$-nearest neighbor classifier in which $k$ is chosen adaptively for each query, rather than supplied as a parameter. The choice of $k$ depends on properties of each neighborhood, and therefore may significantly vary between different points. (For example, the algorithm will use larger $k...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	132,850
1601.04669	The Image Torque Operator for Contour Processing	Contours are salient features for image description, but the detection and localization of boundary contours is still considered a challenging problem. This paper introduces a new tool for edge processing implementing the Gestaltism idea of edge grouping. This tool is a mid-level image operator, called the Torque opera...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,048
2207.01696	TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts	Inspired by the strong ties between vision and language, the two intimate human sensing and communication modalities, our paper aims to explore the generation of 3D human full-body motions from texts, as well as its reciprocal task, shorthanded for text2motion and motion2text, respectively. To tackle the existing chall...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,256
1802.00714	Incremental Control and Guidance of Hybrid Aircraft Applied to a Tailsitter UAV	Hybrid unmanned aircraft can significantly increase the potential of micro air vehicles, because they combine hovering capability with a wing for fast and efficient forward flight. However, these vehicles are very difficult to control, because their aerodynamics are hard to model and they are susceptible to wind gusts....	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	89,460
2206.13415	Is the Language Familiarity Effect gradual? A computational modelling approach	According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studied in the literature, experiments have only been conducted on a limited number of language pairs and their results only show the presence of ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	304,960
2002.06916	Implementing Dynamic Answer Set Programming	We introduce an implementation of an extension of Answer Set Programming (ASP) with language constructs from dynamic (and temporal) logic that provides an expressive computational framework for modeling dynamic applications. Starting from logical foundations, provided by dynamic and temporal equilibrium logics over fin...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	164,340
2405.03484	Whispy: Adapting STT Whisper Models to Real-Time Environments	Large general-purpose transformer models have recently become the mainstay in the realm of speech analysis. In particular, Whisper achieves state-of-the-art results in relevant tasks such as speech recognition, translation, language identification, and voice activity detection. However, Whisper models are not designed ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,201
2311.02398	CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models	Data sparsity and cold-start problems are persistent challenges in recommendation systems. Cross-domain recommendation (CDR) is a promising solution that utilizes knowledge from the source domain to improve the recommendation performance in the target domain. Previous CDR approaches have mainly followed the Embedding a...	false	false	false	true	true	true	true	false	false	false	false	false	false	false	false	false	false	false	405,427
2012.13240	Robotic Following of Flexible Extended Objects: Relevant Technical Facts on the Kinematics of a Moving Continuum	The paper offers general technical facts on the kinematics of a moving continuum involved in research on robotic following of flexible extended objects.	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	213,165
2402.05952	Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques	The integration of Large Language Models (LLMs) with Graph Representation Learning (GRL) marks a significant evolution in analyzing complex data structures. This collaboration harnesses the sophisticated linguistic capabilities of LLMs to improve the contextual understanding and adaptability of graph models, thereby br...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	428,082
2408.14764	SynthDoc: Bilingual Documents Synthesis for Visual Document Understanding	This paper introduces SynthDoc, a novel synthetic document generation pipeline designed to enhance Visual Document Understanding (VDU) by generating high-quality, diverse datasets that include text, images, tables, and charts. Addressing the challenges of data acquisition and the limitations of existing datasets, Synth...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	483,663
2407.00679	Multi-Task Learning for Affect Analysis	This Project was my Undergraduate Final Year dissertation, supervised by Dimitrios Kollias This research delves into the realm of affective computing for image analysis, aiming to enhance the efficiency and effectiveness of multi-task learning in the context of emotion recognition. This project investigates two primary...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	468,968
2302.06951	Few-shot learning approaches for classifying low resource domain specific software requirements	With the advent of strong pre-trained natural language processing models like BERT, DeBERTa, MiniLM, T5, the data requirement for industries to fine-tune these models to their niche use cases has drastically reduced (typically to a few hundred annotated samples for achieving a reasonable performance). However, the avai...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	345,590
1112.5670	Residual, restarting and Richardson iteration for the matrix exponential, revised	A well-known problem in computing some matrix functions iteratively is the lack of a clear, commonly accepted residual notion. An important matrix function for which this is the case is the matrix exponential. Suppose the matrix exponential of a given matrix times a given vector has to be computed. We develop the appro...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	13,580
2107.07732	Robust Online Control with Model Misspecification	We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification. Our study focuses on robustness, a measure of how much deviation from the assumed linear approximation can be tolerated by a controller while maintaining finite $\ell_2$-g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,509
2410.14827	Making LLMs Vulnerable to Prompt Injection via Poisoning Alignment	In a prompt injection attack, an attacker injects a prompt into the original one, aiming to make the LLM follow the injected prompt and perform a task chosen by the attacker. Existing prompt injection attacks primarily focus on how to blend the injected prompt into the original prompt without altering the LLM itself. O...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	500,228
2108.04890	On the Effect of Pruning on Adversarial Robustness	Pruning is a well-known mechanism for reducing the computational cost of deep convolutional networks. However, studies have shown the potential of pruning as a form of regularization, which reduces overfitting and improves generalization. We demonstrate that this family of strategies provides additional benefits beyond...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,139
2107.05357	Hate versus Politics: Detection of Hate against Policy makers in Italian tweets	Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classificatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	245,752
1102.5448	Continuous Multiclass Labeling Approaches and Algorithms	We study convex relaxations of the image labeling problem on a continuous domain with regularizers based on metric interaction potentials. The generic framework ensures existence of minimizers and covers a wide range of relaxations of the originally combinatorial problem. We focus on two specific relaxations that diffe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	9,387
2411.07138	Nuremberg Letterbooks: A Multi-Transcriptional Dataset of Early 15th Century Manuscripts for Document Analysis	Most datasets in the field of document analysis utilize highly standardized labels, which, while simplifying specific tasks, often produce outputs that are not directly applicable to humanities research. In contrast, the Nuremberg Letterbooks dataset, which comprises historical documents from the early 15th century, ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	507,409
2306.05317	CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models	In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting. For the Problem List Summarization (shared task 1A) at the BioNLP Workshop 2023, we demonstrate that Clinical-T5 fine-tuned to 765 medical clinic notes outperforms other extractive, abstractive and zero-s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	372,146
1304.7095	Proximity Factors of Lattice Reduction-Aided Precoding for Multiantenna Broadcast	Lattice precoding is an effective strategy for multiantenna broadcast. In this paper, we show that approximate lattice precoding in multiantenna broadcast is a variant of the closest vector problem (CVP) known as $\eta$-CVP. The proximity factors of lattice reduction-aided precoding are defined, and their bounds are de...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,220
2309.01063	Semi-supervised 3D Video Information Retrieval with Deep Neural Network and Bi-directional Dynamic-time Warping Algorithm	This paper presents a novel semi-supervised deep learning algorithm for retrieving similar 2D and 3D videos based on visual content. The proposed approach combines the power of deep convolutional and recurrent neural networks with dynamic time warping as a similarity measure. The proposed algorithm is designed to handl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	389,522
2202.07464	Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons	Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequently occurred erroneous behaviors. Therefore, it is necessary to conduct a systematical testing for DNNs before they are deployed to real-wor...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	280,558
2209.13008	USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations	Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	319,733
2101.07077	Yet Another Representation of Binary Decision Trees: A Mathematical Demonstration	A decision tree looks like a simple directed acyclic computational graph, where only the leaf nodes specify the output values and the non-terminals specify their tests or split conditions. From the numerical perspective, we express decision trees in the language of computational graph. We explicitly parameterize the te...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,933
2205.13521	Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality	Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, hierarchy, and robustness. We propose DOMiNO, a method for Diversity Optimization M...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	298,964

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