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
2112.07513	CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning	Localizing text instances in natural scenes is regarded as a fundamental challenge in computer vision. Nevertheless, owing to the extremely varied aspect ratios and scales of text instances in real scenes, most conventional text detectors suffer from the sub-text problem that only localizes the fragments of text instan...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	271,503
2404.17183	Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety using Zero Shot Classification: An Observational Study	Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptom...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	449,768
2103.09656	Set-to-Sequence Methods in Machine Learning: a Review	Machine learning on sets towards sequential output is an important and ubiquitous task, with applications ranging from language modeling and meta-learning to multi-agent strategy games and power grid optimization. Combining elements of representation learning and structured prediction, its two primary challenges includ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	225,223
2410.02234	GORAM: Graph-oriented ORAM for Efficient Ego-centric Queries on Federated Graphs	Ego-centric queries, focusing on a target vertex and its direct neighbors, are essential for various applications. Enabling such queries on graphs owned by mutually distrustful data providers, without breaching privacy, holds promise for more comprehensive results. In this paper, we propose GORAM, a graph-oriented da...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	494,183
2007.03274	Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network	Inspired by the mammal's auditory localization pathway, in this paper we propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with a microphone array. The key of this model rel...	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	186,011
1609.02450	HashTag Erasure Codes: From Theory to Practice	Minimum-Storage Regenerating (MSR) codes have emerged as a viable alternative to Reed-Solomon (RS) codes as they minimize the repair bandwidth while they are still optimal in terms of reliability and storage overhead. Although several MSR constructions exist, so far they have not been practically implemented mainly due...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	60,732
2206.11061	An Ontological Approach to Analysing Social Service Provisioning	This paper introduces ontological concepts required to evaluate and manage the coverage of social services in a Smart City context. Here, we focus on the perspective of key stakeholders, namely social purpose organizations and the clients they serve. The Compass ontology presented here extends the Common Impact Data St...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	304,140
2003.13532	Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation	Generative Adversarial Networks (GANs) are popular tools for generative modeling. The dynamics of their adversarial learning give rise to convergence pathologies during training such as mode and discriminator collapse. In machine learning, ensembles of predictors demonstrate better results than a single predictor for m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	170,234
2008.09985	Detecting signal from science:The structure of research communities and prior knowledge improves prediction of genetic regulatory experiments	The explosive growth of scientists, scientific journals, articles and findings in recent years exponentially increases the difficulty scientists face in navigating prior knowledge. This challenge is exacerbated by uncertainty about the reproducibility of published findings. The availability of massive digital archives,...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	192,873
1208.0077	Keyword-aware Optimal Route Search	Identifying a preferable route is an important problem that finds applications in map services. When a user plans a trip within a city, the user may want to find "a most popular route such that it passes by shopping mall, restaurant, and pub, and the travel time to and from his hotel is within 4 hours." However, none o...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	17,854
2405.17039	BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation	Large language models (LLMs) have catalyzed a paradigm shift in natural language processing, yet their limited controllability poses a significant challenge for downstream applications. We aim to address this by drawing inspiration from the neural mechanisms of the human brain, specifically Broca's and Wernicke's areas...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	457,731
2409.06754	Scaling Law Hypothesis for Multimodal Model	We propose a scaling law hypothesis for multimodal models processing text, audio, images, and video within a shared token and embedding space. Our framework predicts model performance based on modality-specific compression and tokenization efficiency, extending established scaling laws from text-based decoder models to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	487,267
1112.1831	Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach	A "community" in a social network is usually understood to be a group of nodes more densely connected with each other than with the rest of the network. This is an important concept in most domains where networks arise: social, technological, biological, etc. For many years algorithms for finding communities implicitly...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	13,370
1612.03864	Effector Detection in Social Networks	In a social network, influence diffusion is the process of spreading innovations from user to user. An activation state identifies who are the active users who have adopted the target innovation. Given an activation state of a certain diffusion, effector detection aims to reveal the active users who are able to best ex...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	65,432
2209.13217	Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach	In this paper, we propose a Bi-layer Predictionbased Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for Mixed Integer Programming (MIP) problems. A graph convolutional network (GCN) is employed to predict binary variables' values. After that, a subset of binary va...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	319,813
2404.11136	On the Performance of RIS-assisted Networks with HQAM	In this paper, we investigate the application of hexagonal quadrature amplitude modulation (HQAM) in reconfigurable intelligent surface (RIS)-assisted networks, specifically focusing on its efficiency in reducing the number of required reflecting elements. Specifically, we present analytical expressions for the average...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	447,396
1109.1093	Multi Agent Communication System for Online Auction with Decision Support System by JADE and TRACE	The success of online auctions has given buyers access to greater product diversity with potentially lower prices. It has provided sellers with access to large numbers of potential buyers and reduced transaction costs by enabling auctions to take place without regard to time or place. However it is difficult to spend m...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	11,993
2202.03590	Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Media	While false rumors pose a threat to the successful overcoming of the COVID-19 pandemic, an understanding of how rumors diffuse in online social networks is - even for non-crisis situations - still in its infancy. Here we analyze a large sample consisting of COVID-19 rumor cascades from Twitter that have been fact-check...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	279,262
2101.09162	A Robust Blockchain Readiness Index Model	As the blockchain ecosystem gets more mature many businesses, investors, and entrepreneurs are seeking opportunities on working with blockchain systems and cryptocurrencies. A critical challenge for these actors is to identify the most suitable environment to start or evolve their businesses. In general, the question i...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	216,520
2204.05682	A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive with Supervised Learning in Standard Spiking Neural Networks	Memristive devices are a class of circuit elements that shows great promise as future building block for brain-inspired computing. One influential view in theoretical neuroscience sees the brain as a function-computing device: given input signals, the brain applies a function in order to generate new internal states an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	291,111
2209.13355	Algorithms for Large-scale Network Analysis and the NetworKit Toolkit	The abundance of massive network data in a plethora of applications makes scalable analysis algorithms and software tools necessary to generate knowledge from such data in reasonable time. Addressing scalability as well as other requirements such as good usability and a rich feature set, the open-source software Networ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	319,861
2405.17768	Revisiting the Message Passing in Heterophilous Graph Neural Networks	Graph Neural Networks (GNNs) have demonstrated strong performance in graph mining tasks due to their message-passing mechanism, which is aligned with the homophily assumption that adjacent nodes exhibit similar behaviors. However, in many real-world graphs, connected nodes may display contrasting behaviors, termed as h...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,106
2307.07125	CeRF: Convolutional Neural Radiance Fields for New View Synthesis with Derivatives of Ray Modeling	In recent years, novel view synthesis has gained popularity in generating high-fidelity images. While demonstrating superior performance in the task of synthesizing novel views, the majority of these methods are still based on the conventional multi-layer perceptron for scene embedding. Furthermore, light field models ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	379,290
2411.07763	Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows	Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics. We introduce Spider 2.0, an evaluation framework comprising 632 real-world text-to-SQL workflow p...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	true	false	507,670
2409.15687	A Comprehensive Evaluation of Large Language Models on Mental Illnesses	Large language models have shown promise in various domains, including healthcare. In this study, we conduct a comprehensive evaluation of LLMs in the context of mental health tasks using social media data. We explore the zero-shot (ZS) and few-shot (FS) capabilities of various LLMs, including GPT-4, Llama 3, Gemini, a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	491,011
2409.04415	Improved Parallel Algorithm for Non-Monotone Submodular Maximization under Knapsack Constraint	This work proposes an efficient parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem over the ground set of size $n$. Our algorithm improves the best approximation factor of the existing parallel one from $8+\epsilon$ to $7+\epsilon$ with $O(\log n)$ adaptive complexity. The...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	486,384
2412.15004	Large Language Models and Code Security: A Systematic Literature Review	Large Language Models (LLMs) have emerged as powerful tools for automating various programming tasks, including security-related ones, such as detecting and fixing vulnerabilities. Despite their promising capabilities, when required to produce or modify pre-existing code, LLMs could introduce vulnerabilities unbeknown ...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	518,920
2307.15150	R-Block: Regularized Block of Dropout for convolutional networks	Dropout as a regularization technique is widely used in fully connected layers while is less effective in convolutional layers. Therefore more structured forms of dropout have been proposed to regularize convolutional networks. The disadvantage of these methods is that the randomness introduced causes inconsistency bet...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	382,171
2112.10727	Learning Physics Properties of Fabrics and Garments with a Physics Similarity Neural Network	In this paper, we propose to predict the physics parameters of real fabrics and garments by learning their physics similarities between simulated fabrics via a Physics Similarity Network (PhySNet). For this, we estimate wind speeds generated by an electric fan and the area weight to predict bending stiffness of simulat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,511
2110.11281	Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks	Modelling the impact of a material's mesostructure on device level performance typically requires access to 3D image data containing all the relevant information to define the geometry of the simulation domain. This image data must include sufficient contrast between phases to distinguish each material, be of high enou...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	262,424
1903.10420	Development and verification of a simulation for leveraging results of a human subjects programming experiment	Quantitatively evaluating and comparing the performance of robotic solutions that are designed to work under a variety of conditions is inherently challenging because they need to be evaluated under numerous precisely repeatable conditions Manually acquiring this data is time consuming and imprecise. A deterministic si...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	125,268
2407.08023	Hybrid Structure-from-Motion and Camera Relocalization for Enhanced Egocentric Localization	We built our pipeline EgoLoc-v1, mainly inspired by EgoLoc. We propose a model ensemble strategy to improve the camera pose estimation part of the VQ3D task, which has been proven to be essential in previous work. The core idea is not only to do SfM for egocentric videos but also to do 2D-3D matching between existing 3...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,976
2004.08346	An integrated light management system with real-time light measurement and human perception	Illumination is important for well-being, productivity and safety across several environments, including offices, retail shops and industrial warehouses. Current techniques for setting up lighting require extensive and expert support and need to be repeated if the scene changes. Here we propose the first fully-automate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,039
1911.08895	Demystifying TasNet: A Dissecting Approach	In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradually replacing components of an utterance-level permutation invari...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	154,343
2006.00345	Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications	A combinatory approach of two well-known fields: deep learning and semi supervised learning is presented, to tackle the land cover identification problem. The proposed methodology demonstrates the impact on the performance of deep learning models, when SSL approaches are used as performance functions during training. O...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	179,443
2307.13008	Adaptation of Whisper models to child speech recognition	Automatic Speech Recognition (ASR) systems often struggle with transcribing child speech due to the lack of large child speech datasets required to accurately train child-friendly ASR models. However, there are huge amounts of annotated adult speech datasets which were used to create multilingual ASR models, such as Wh...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	381,457
1910.09998	Learning Resilient Behaviors for Navigation Under Uncertainty	Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not been widely deployed in real-world applications, especially in these safety-critical tasks (e.g., autonomous driving). One of the reasons is...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	150,361
2212.14427	Efficient Movie Scene Detection using State-Space Transformers	The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition models, which are t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,611
2104.11520	Modeling long-term interactions to enhance action recognition	In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,933
2308.02525	Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?	Self-supervised learning in computer vision aims to leverage the inherent structure and relationships within data to learn meaningful representations without explicit human annotation, enabling a holistic understanding of visual scenes. Robustness in vision machine learning ensures reliable and consistent performance, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,662
2410.17527	Adaptive coupling of peridynamic and classical continuum mechanical models driven by broken bond/strength criteria for structural dynamic failure	Peridynamics (PD) is widely used to simulate structural failure. However, PD models are time-consuming. To improve the computational efficiency, we developed an adaptive coupling model between PD and classical continuum mechanics (PD-CCM) based on the Morphing method [1], driven by the broken bond or strength criteria....	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	501,506
2412.17580	Quantum Time-Series Learning with Evolutionary Algorithms	Variational quantum circuits have arisen as an important method in quantum computing. A crucial step of it is parameter optimization, which is typically tackled through gradient-descent techniques. We advantageously explore instead the use of evolutionary algorithms for such optimization, specifically for time-series f...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	520,035
1601.00955	Optimally Pruning Decision Tree Ensembles With Feature Cost	We consider the problem of learning decision rules for prediction with feature budget constraint. In particular, we are interested in pruning an ensemble of decision trees to reduce expected feature cost while maintaining high prediction accuracy for any test example. We propose a novel 0-1 integer program formulation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,695
2109.00310	Analytic estimation of the MMC sub-module capacitor voltage ripple for balanced and unbalanced AC grid conditions	In this paper, a mathematical expression to define the maximum and minimum voltage ripples of the modular multilevel converter (MMC) sub-module (SM) capacitors is proposed. Using the arm averaged model of the MMC, the instantaneous power for the upper and lower arms of the converter is obtained, giving the basis to des...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	253,071
2406.02557	EVAN: Evolutional Video Streaming Adaptation via Neural Representation	Adaptive bitrate (ABR) using conventional codecs cannot further modify the bitrate once a decision has been made, exhibiting limited adaptation capability. This may result in either overly conservative or overly aggressive bitrate selection, which could cause either inefficient utilization of the network bandwidth or f...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	460,827
2409.11156	On Performance of Distributed RIS-aided Communication in Random Networks	This paper evaluates the geometrically averaged performance of a wireless communication network assisted by a multitude of distributed reconfigurable intelligent surfaces (RISs), where the RIS locations are randomly dropped obeying a homogeneous Poisson point process. By exploiting stochastic geometry and then averagin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	489,033
2301.09125	Selecting a suitable Parallel Label-propagation based algorithm for Disjoint Community Detection	Community detection is an essential task in network analysis as it helps identify groups and patterns within a network. High-speed community detection algorithms are necessary to analyze large-scale networks in a reasonable amount of time. Researchers have made significant contributions in the development of high-speed...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	341,410
2311.18064	GELDA: A generative language annotation framework to reveal visual biases in datasets	Bias analysis is a crucial step in the process of creating fair datasets for training and evaluating computer vision models. The bottleneck in dataset analysis is annotation, which typically requires: (1) specifying a list of attributes relevant to the dataset domain, and (2) classifying each image-attribute pair. Whil...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,541
1802.06214	A New De-blurring Technique for License Plate Images with Robust Length Estimation	Recognizing a license plate clearly while seeing a surveillance camera snapshot is often important in cases where the troublemaker vehicle(s) have to be identified. In many real world situations, these images are blurred due to fast motion of the vehicle and cannot be recognized by the human eye. For this kind of blurr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,617
2212.05061	Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery	Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,666
1811.09271	Distributed Gradient Descent with Coded Partial Gradient Computations	Coded computation techniques provide robustness against straggling servers in distributed computing, with the following limitations: First, they increase decoding complexity. Second, they ignore computations carried out by straggling servers; and they are typically designed to recover the full gradient, and thus, canno...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	114,216
2407.16092	Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search	Coalition formation is a key capability in multi-agent systems. An important problem in coalition formation is coalition structure generation: partitioning agents into coalitions to optimize the social welfare. This is a challenging problem that has been the subject of active research for the past three decades. In thi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	475,449
2305.15313	Greedy Poisson Rejection Sampling	One-shot channel simulation is a fundamental data compression problem concerned with encoding a single sample from a target distribution $Q$ using a coding distribution $P$ using as few bits as possible on average. Algorithms that solve this problem find applications in neural data compression and differential privacy ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	367,561
2311.10026	Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning	In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment. Motivated by this necessity, we present...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	408,390
2401.17732	High-performance Racing on Unmapped Tracks using Local Maps	Map-based methods for autonomous racing estimate the vehicle's location, which is used to follow a high-level plan. While map-based optimisation methods demonstrate high-performance results, they are limited by requiring a map of the environment. In contrast, mapless methods can operate in unmapped contexts since they ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	425,301
2307.12280	Downstream-agnostic Adversarial Examples	Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,208
2306.09996	Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering	In this paper, we explore effective prompting techniques to enhance zero- and few-shot Visual Question Answering (VQA) performance in contemporary Vision-Language Models (VLMs). Central to our investigation is the role of question templates in guiding VLMs to generate accurate answers. We identify that specific templat...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	374,048
2408.16707	Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer	The significant fluctuations in stock index prices in recent years highlight the critical need for accurate forecasting to guide investment and financial strategies. This study introduces a novel composite forecasting framework that integrates variational mode decomposition (VMD), PatchTST, and adaptive scale-weighted ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	484,422
2210.15865	Completely Heterogeneous Federated Learning	Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i.i.d. labels scenarios. Existing FL methods fail to handle the above three constraints at the same time, and the level of privacy protection needs to be lowered (e.g., the model architecture and data category distributi...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	327,115
2411.00656	Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees	This paper focuses on the system identification of an important class of nonlinear systems: linearly parameterized nonlinear systems, which enjoys wide applications in robotics and other mechanical systems. We consider two system identification methods: least-squares estimation (LSE), which is a point estimation method...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	504,687
2003.03666	Multi-task Learning Based Neural Bridging Reference Resolution	We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	167,308
2003.05626	Understanding Crowd Flow Movements Using Active-Langevin Model	Crowd flow describes the elementary group behavior of crowds. Understanding the dynamics behind these movements can help to identify various abnormalities in crowds. However, developing a crowd model describing these flows is a challenging task. In this paper, a physics-based model is proposed to describe the movements...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	167,905
1706.09249	Logics and practices of transparency and opacity in real-world applications of public sector machine learning	Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountability in these domains, and amidst accusations of intentional or unintentional bias, there have been increased calls for transparency of these technologies. Few, however, h...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	76,108
2310.02391	SE(3)-Stochastic Flow Matching for Protein Backbone Generation	The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3\mathrm{D}$ rigid motions -- i.e. the group $\tex...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	396,825
cs/0610153	Most Programs Stop Quickly or Never Halt	Since many real-world problems arising in the fields of compiler optimisation, automated software engineering, formal proof systems, and so forth are equivalent to the Halting Problem--the most notorious undecidable problem--there is a growing interest, not only academically, in understanding the problem better and in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,828
2003.13561	On Biased Random Walks, Corrupted Intervals, and Learning Under Adversarial Design	We tackle some fundamental problems in probability theory on corrupted random processes on the integer line. We analyze when a biased random walk is expected to reach its bottommost point and when intervals of integer points can be detected under a natural model of noise. We apply these results to problems in learning ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,243
2007.05086	Boundary thickness and robustness in learning models	Robustness of machine learning models to various adversarial and non-adversarial corruptions continues to be of interest. In this paper, we introduce the notion of the boundary thickness of a classifier, and we describe its connection with and usefulness for model robustness. Thick decision boundaries lead to improved ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,560
2404.18598	Anywhere: A Multi-Agent Framework for Reliable and Diverse Foreground-Conditioned Image Inpainting	Recent advancements in image inpainting, particularly through diffusion modeling, have yielded promising outcomes. However, when tested in scenarios involving the completion of images based on the foreground objects, current methods that aim to inpaint an image in an end-to-end manner encounter challenges such as "over...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	450,326
2103.02892	Data-Based System Analysis and Control of Flat Nonlinear Systems	Willems et al. showed that all input-output trajectories of a discrete-time linear time-invariant system can be obtained using linear combinations of time shifts of a single, persistently exciting, input-output trajectory of that system. In this paper, we extend this result to the class of discrete-time single-input si...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	223,100
2301.08606	Data Augmentation for Modeling Human Personality: The Dexter Machine	Modeling human personality is important for several AI challenges, from the engineering of artificial psychotherapists to the design of persona bots. However, the field of computational personality analysis heavily relies on labeled data, which may be expensive, difficult or impossible to get. This problem is amplified...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	341,243
2410.08469	Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP	A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in translating textual input into an embedding space shared with images, thereby facilitating the interpretative analysis of vision tasks through natural language. Despite the varying significance of different textual elements within a s...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	497,130
1904.02749	Learning to Cluster Faces on an Affinity Graph	Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which would involve prohibitive annotation cost. Hence, exploiting unlabeled data becomes an appealing alternative. Recent works have shown that clu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	126,510
1104.0553	Determining Relevance of Accesses at Runtime (Extended Version)	Consider the situation where a query is to be answered using Web sources that restrict the accesses that can be made on backend relational data by requiring some attributes to be given as input of the service. The accesses provide lookups on the collection of attributes values that match the binding. They can differ in...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	9,858
2208.09830	Representation Learning with Graph Neural Networks for Speech Emotion Recognition	Learning expressive representation is crucial in deep learning. In speech emotion recognition (SER), vacuum regions or noises in the speech interfere with expressive representation learning. However, traditional RNN-based models are susceptible to such noise. Recently, Graph Neural Network (GNN) has demonstrated its ef...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,852
1807.00050	Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction	Online social networks (OSN) are one of the most popular forms of modern communication and among the best known is Facebook. Information about the connection between users on the OSN is often very scarce. It's only known if users are connected, while the intensity of the connection is unknown. The aim of the research d...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	101,752
2408.01570	On Validation of Search & Retrieval of Tissue Images in Digital Pathology	Medical images play a crucial role in modern healthcare by providing vital information for diagnosis, treatment planning, and disease monitoring. Fields such as radiology and pathology rely heavily on accurate image interpretation, with radiologists examining X-rays, CT scans, and MRIs to diagnose conditions from fract...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	478,279
2412.15664	SCENIC: Scene-aware Semantic Navigation with Instruction-guided Control	Synthesizing natural human motion that adapts to complex environments while allowing creative control remains a fundamental challenge in motion synthesis. Existing models often fall short, either by assuming flat terrain or lacking the ability to control motion semantics through text. To address these limitations, we i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,224
2103.13262	FastMoE: A Fast Mixture-of-Expert Training System	Mixture-of-Expert (MoE) presents a strong potential in enlarging the size of language model to trillions of parameters. However, training trillion-scale MoE requires algorithm and system co-design for a well-tuned high performance distributed training system. Unfortunately, the only existing platform that meets the req...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	226,436
1706.04156	Gradient descent GAN optimization is locally stable	Despite the growing prominence of generative adversarial networks (GANs), optimization in GANs is still a poorly understood topic. In this paper, we analyze the "gradient descent" form of GAN optimization i.e., the natural setting where we simultaneously take small gradient steps in both generator and discriminator par...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	75,289
2012.13546	Distributional Ground Truth: Non-Redundant Crowdsourcing Data Quality Control in UI Labeling Tasks	HCI increasingly employs Machine Learning and Image Recognition, in particular for visual analysis of user interfaces (UIs). A popular way for obtaining human-labeled training data is Crowdsourcing, typically using the quality control methods ground truth and majority consensus, which necessitate redundancy in the outc...	true	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	213,246
2403.08377	Learning to Describe for Predicting Zero-shot Drug-Drug Interactions	Adverse drug-drug interactions~(DDIs) can compromise the effectiveness of concurrent drug administration, posing a significant challenge in healthcare. As the development of new drugs continues, the potential for unknown adverse effects resulting from DDIs becomes a growing concern. Traditional computational methods fo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	437,318
2405.10302	Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift	As machine learning models are increasingly deployed in dynamic environments, it becomes paramount to assess and quantify uncertainties associated with distribution shifts. A distribution shift occurs when the underlying data-generating process changes, leading to a deviation in the model's performance. The prediction ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	454,712
cs/0508023	Software Libraries and Their Reuse: Entropy, Kolmogorov Complexity, and Zipf's Law	We analyze software reuse from the perspective of information theory and Kolmogorov complexity, assessing our ability to ``compress'' programs by expressing them in terms of software components reused from libraries. A common theme in the software reuse literature is that if we can only get the right environment in pla...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	538,859
2012.00143	Task Allocation for Asynchronous Mobile Edge Learning with Delay and Energy Constraints	This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-constrained wireless edge network. The optimization is done such that the portion ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	209,035
1605.00251	A vector-contraction inequality for Rademacher complexities	The contraction inequality for Rademacher averages is extended to Lipschitz functions with vector-valued domains, and it is also shown that in the bounding expression the Rademacher variables can be replaced by arbitrary iid symmetric and sub-gaussian variables. Example applications are given for multi-category learnin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	55,317
2402.07875	Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States	In modern machine learning, models can often fit training data in numerous ways, some of which perform well on unseen (test) data, while others do not. Remarkably, in such cases gradient descent frequently exhibits an implicit bias that leads to excellent performance on unseen data. This implicit bias was extensively s...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	428,878
1307.3581	Image color transfer to evoke different emotions based on color combinations	In this paper, a color transfer framework to evoke different emotions for images based on color combinations is proposed. The purpose of this color transfer is to change the "look and feel" of images, i.e., evoking different emotions. Colors are confirmed as the most attractive factor in images. In addition, various st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	25,812
2303.17569	Iterative Prompt Learning for Unsupervised Backlit Image Enhancement	We propose a novel unsupervised backlit image enhancement method, abbreviated as CLIP-LIT, by exploring the potential of Contrastive Language-Image Pre-Training (CLIP) for pixel-level image enhancement. We show that the open-world CLIP prior not only aids in distinguishing between backlit and well-lit images, but also ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,253
2201.11620	Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study	Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this work, we explore the domain generalization of de...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	277,344
1712.01328	Learning User Intent from Action Sequences on Interactive Systems	Interactive systems have taken over the web and mobile space with increasing participation from users. Applications across every marketing domain can now be accessed through mobile or web where users can directly perform certain actions and reach a desired outcome. Actions of user on a system, though, can be representa...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	86,082
1612.00686	Identifying and Categorizing Anomalies in Retinal Imaging Data	The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabul...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	64,934
2111.05008	Misspecified Gaussian Process Bandit Optimization	We consider the problem of optimizing a black-box function based on noisy bandit feedback. Kernelized bandit algorithms have shown strong empirical and theoretical performance for this problem. They heavily rely on the assumption that the model is well-specified, however, and can fail without it. Instead, we introduce ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,673
2407.06688	Universal Multi-view Black-box Attack against Object Detectors via Layout Optimization	Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly focus on white-box attacks and are merely valid at a specific viewpoint, while the universal multi-view black-box attack is less explored, lim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,497
2009.05252	Novel and Effective CNN-Based Binarization for Historically Degraded As-built Drawing Maps	Binarizing historically degraded as-built drawing (HDAD) maps is a new challenging job, especially in terms of removing the three artifacts, namely noise, the yellowing areas, and the folded lines, while preserving the foreground components well. In this paper, we first propose a semi-automatic labeling method to creat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,272
1306.6375	Metaheuristics in Flood Disaster Management and Risk Assessment	A conceptual area is divided into units or barangays, each was allowed to evolve under a physical constraint. A risk assessment method was then used to identify the flood risk in each community using the following risk factors: the area's urbanized area ratio, literacy rate, mortality rate, poverty incidence, radio/TV ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,480
2207.05685	PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners	Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature. To fill this gap, we propose the first PAC-Bayesian adaptation bounds for multiclass learners. We facilitate ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	307,625
2004.03287	A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products	Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as supply chain monitoring and market research. However, there is a decided lack of annotated corpora and annotation guidelines in this domain. I...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,505
1606.05491	Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings	We present a natural language generator based on the sequence-to-sequence approach that can be trained to produce natural language strings as well as deep syntax dependency trees from input dialogue acts, and we use it to directly compare two-step generation with separate sentence planning and surface realization stage...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	57,416
2108.12144	Lyra: A Benchmark for Turducken-Style Code Generation	Recently, neural techniques have been used to generate source code automatically. While promising for declarative languages, these approaches achieve much poorer performance on datasets for imperative languages. Since a declarative language is typically embedded in an imperative language (i.e., the turducken-style prog...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	252,407
2409.12376	Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition	In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development. In recent years, with the gradual promotion of green energy transformation and low-carbon de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	489,552

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