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
1406.7620	Joint Optimization of Spectrum Sensing and Accessing in Multiuser MISO Cognitive Networks	In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,254
2404.17251	Camera Motion Estimation from RGB-D-Inertial Scene Flow	In this paper, we introduce a novel formulation for camera motion estimation that integrates RGB-D images and inertial data through scene flow. Our goal is to accurately estimate the camera motion in a rigid 3D environment, along with the state of the inertial measurement unit (IMU). Our proposed method offers the flex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,795
1908.10357	HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation	Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	143,095
1207.2253	A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem	Flexible job shop scheduling has been noticed as an effective manufacturing system to cope with rapid development in today's competitive environment. Flexible job shop scheduling problem (FJSSP) is known as a NP-hard problem in the field of optimization. Considering the dynamic state of the real world makes this proble...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	17,368
1401.1558	The Continuity of Images by Transmission Imaging Revisited	Transmission imaging, as an important imaging technique widely used in astronomy, medical diagnosis, and biology science, has been shown in [49] quite different from reflection imaging used in our everyday life. Understanding the structures of images (the prior information) is important for designing, testing, and choo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	29,665
2411.17582	From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs	Professional networks provide invaluable entree to opportunity through referrals and introductions. A rich literature shows they also serve to entrench and even exacerbate a status quo of privilege and disadvantage. Hiring platforms, equipped with the ability to nudge link formation, provide a tantalizing opening for b...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	511,506
2308.15014	CAPS: A Practical Partition Index for Filtered Similarity Search	With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of constraints has become an area of intense interest. While the community has recently proposed several algorithms for constrained ANNS, almost...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	388,534
2103.17151	Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models	Multi-encoder models are a broad family of context-aware neural machine translation systems that aim to improve translation quality by encoding document-level contextual information alongside the current sentence. The context encoding is undertaken by contextual parameters, trained on document-level data. In this work,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	227,803
1911.08373	Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition	Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of computation. The brain-inspired spiking neural networks (SNN) closely mimic the biologi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	154,176
2405.04592	Integrating knowledge-guided symbolic regression and model-based design of experiments to automate process flow diagram development	New products must be formulated rapidly to succeed in the global formulated product market; however, key product indicators (KPIs) can be complex, poorly understood functions of the chemical composition and processing history. Consequently, scale-up must currently undergo expensive trial-and-error campaigns. To acceler...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,616
2105.03592	De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks	Machine learning techniques have been widely applied to various applications. However, they are potentially vulnerable to data poisoning attacks, where sophisticated attackers can disrupt the learning procedure by injecting a fraction of malicious samples into the training dataset. Existing defense techniques against p...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	234,193
2310.08909	Evading Community Detection via Counterfactual Neighborhood Search	Community detection techniques are useful for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often comes at the expense of potentially exposing individuals to privacy breaches by inadvertently revealing their tastes or preferences. Therefore,...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	399,594
2403.06086	Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach	Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved accurate prediction using deep neural networks. However, these end-to-end models are usually black boxes with weak interpretability and ge...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	436,291
2211.02538	An information theoretic vulnerability metric for data integrity attacks on smart grids	A novel metric that describes the vulnerability of the measurements in power systems to data integrity attacks is proposed. The new metric, coined vulnerability index (VuIx), leverages information theoretic measures to assess the attack effect on the fundamental limits of the disruption and detection tradeoff. The resu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	328,607
1410.3944	Local-set-based Graph Signal Reconstruction	Signal processing on graph is attracting more and more attentions. For a graph signal in the low-frequency subspace, the missing data associated with unsampled vertices can be reconstructed through the sampled data by exploiting the smoothness of the graph signal. In this paper, the concept of local set is introduced a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,753
1207.4452	Pareto Local Optima of Multiobjective NK-Landscapes with Correlated Objectives	In this paper, we conduct a fitness landscape analysis for multiobjective combinatorial optimization, based on the local optima of multiobjective NK-landscapes with objective correlation. In single-objective optimization, it has become clear that local optima have a strong impact on the performance of metaheuristics. H...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	17,627
2305.11648	Applying Ising Machines to Multi-objective QUBOs	Multi-objective optimisation problems involve finding solutions with varying trade-offs between multiple and often conflicting objectives. Ising machines are physical devices that aim to find the absolute or approximate ground states of an Ising model. To apply Ising machines to multi-objective problems, a weighted sum...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	365,635
1806.08782	Finding Local Minima via Stochastic Nested Variance Reduction	We propose two algorithms that can find local minima faster than the state-of-the-art algorithms in both finite-sum and general stochastic nonconvex optimization. At the core of the proposed algorithms is $\text{One-epoch-SNVRG}^+$ using stochastic nested variance reduction (Zhou et al., 2018a), which outperforms the s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,219
1909.07972	A Joint Learning and Communications Framework for Federated Learning over Wireless Networks	In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	145,832
1805.00252	Characterizing Efficient Referrals in Social Networks	Users of social networks often focus on specific areas of that network, leading to the well-known "filter bubble" effect. Connecting people to a new area of the network in a way that will cause them to become active in that area could help alleviate this effect and improve social welfare. Here we present preliminary ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	96,392
1307.1872	Intelligent Hybrid Man-Machine Translation Quality Estimation	Inferring evaluation scores based on human judgments is invaluable compared to using current evaluation metrics which are not suitable for real-time applications e.g. post-editing. However, these judgments are much more expensive to collect especially from expert translators, compared to evaluation based on indicators ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	25,668
2008.08446	A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations	Operating Earth observing satellites requires efficient planning methods that coordinate activities of multiple spacecraft. The satellite task planning problem entails selecting actions that best satisfy mission objectives for autonomous execution. Task scheduling is often performed by human operators assisted by heuri...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	192,425
2410.20274	Library Learning Doesn't: The Curious Case of the Single-Use "Library"	Advances in Large Language Models (LLMs) have spurred a wave of LLM library learning systems for mathematical reasoning. These systems aim to learn a reusable library of tools, such as formal Isabelle lemmas or Python programs that are tailored to a family of tasks. Many of these systems are inspired by the human struc...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	502,732
2305.13911	A Deep Learning Approach for Generating Soft Range Information from RF Data	Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative for highly accurate localization that gives all probable range values rather than a single estimate ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	366,757
2404.06488	Pitfalls of Conversational LLMs on News Debiasing	This paper addresses debiasing in news editing and evaluates the effectiveness of conversational Large Language Models in this task. We designed an evaluation checklist tailored to news editors' perspectives, obtained generated texts from three popular conversational models using a subset of a publicly available datase...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	445,488
1808.06041	CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network	Computer Vision for Analyzing and Classifying cells and tissues often require rigorous lab procedures and so automated Computer Vision solutions have been sought. Most work in such field usually requires Feature Extractions before the analysis of such features via Machine Learning and Machine Vision algorithms. We deve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,459
2012.06257	On Learning the Right Attention Point for Feature Enhancement	We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior works, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maxi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,050
2303.15953	Randomly Initialized Subnetworks with Iterative Weight Recycling	The Multi-Prize Lottery Ticket Hypothesis posits that randomly initialized neural networks contain several subnetworks that achieve comparable accuracy to fully trained models of the same architecture. However, current methods require that the network is sufficiently overparameterized. In this work, we propose a modifi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	354,691
1809.07306	Clustering students' open-ended questionnaire answers	Open responses form a rich but underused source of information in educational data mining and intelligent tutoring systems. One of the major obstacles is the difficulty of clustering short texts automatically. In this paper, we investigate the problem of clustering free-formed questionnaire answers. We present comparat...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	108,252
1909.09895	Efficient Learning of Distributed Linear-Quadratic Controllers	In this work, we propose a robust approach to design distributed controllers for unknown-but-sparse linear and time-invariant systems. By leveraging modern techniques in distributed controller synthesis and structured linear inverse problems as applied to system identification, we show that near-optimal distributed con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,387
2411.09204	RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation	The recovery of damaged or resected ribcage structures requires precise, custom-designed implants to restore the integrity and functionality of the thoracic cavity. Traditional implant design methods rely mainly on manual processes, making them time-consuming and susceptible to variability. In this work, we explore the...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	508,168
1802.03796	Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks	We provide theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the convex linear regression loss. We prove that the rate of convergence of an ideal curriculum learning method is monotonically increasing with the difficulty of the examples. Moreover, among all e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,067
2407.05669	Fractional Budget Allocation for Influence Maximization under General Marketing Strategies	We consider the fractional influence maximization problem, i.e., identifying users on a social network to be incentivized with potentially partial discounts to maximize the influence on the network. The larger the discount given to a user, the higher the likelihood of its activation (adopting a new product or innovatio...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	true	471,080
2209.10753	Reinforcement Learning in Computing and Network Convergence Orchestration	As computing power is becoming the core productivity of the digital economy era, the concept of Computing and Network Convergence (CNC), under which network and computing resources can be dynamically scheduled and allocated according to users' needs, has been proposed and attracted wide attention. Based on the tasks' p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	318,958
2209.05239	$\beta$-CapsNet: Learning Disentangled Representation for CapsNet by Information Bottleneck	We present a framework for learning disentangled representation of CapsNet by information bottleneck constraint that distills information into a compact form and motivates to learn an interpretable factorized capsule. In our $\beta$-CapsNet framework, hyperparameter $\beta$ is utilized to trade-off disentanglement and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	317,033
1212.5182	Performance Evaluation of an Orthogonal Frequency Division Multiplexing based Wireless Communication System with implementation of Least Mean Square Equalization technique	Orthogonal Frequency Division Multiplexing (OFDM) has recently been applied in wireless communication systems due to its high data rate transmission capability with high bandwidth efficiency and its robustness to multi-path delay. Fading is the one of the major aspect which is considered in the receiver. To cancel the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,515
2205.14859	Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever	Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss. For efficiently training recommender retrievers on modern hardwares, inbatch sampling, where the items in the mini-batch a...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	299,521
1908.11588	Generating Persuasive Visual Storylines for Promotional Videos	Video contents have become a critical tool for promoting products in E-commerce. However, the lack of automatic promotional video generation solutions makes large-scale video-based promotion campaigns infeasible. The first step of automatically producing promotional videos is to generate visual storylines, which is to ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	143,422
1912.00509	Speeding up Word Mover's Distance and its variants via properties of distances between embeddings	The Word Mover's Distance (WMD) proposed by Kusner et al. is a distance between documents that takes advantage of semantic relations among words that are captured by their embeddings. This distance proved to be quite effective, obtaining state-of-art error rates for classification tasks, but is also impracticable for l...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	155,793
2405.00946	SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters	This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources. At the heart of SparseTSF lies the Cross-Period Sparse Forecasting...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,139
2404.17791	HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots	Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This paper presents a human-inspired scene perception model to minimize the gap between human and robotic capabilities. The approach takes over f...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	450,001
2306.14066	SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models	Uncertainty quantification is crucial to decision-making. A prominent example is probabilistic forecasting in numerical weather prediction. The dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts. This is done by running many physics-based simulations under diffe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,526
2405.02677	Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing	Narratives serve as fundamental frameworks in our understanding of the world and play a crucial role in collaborative sensemaking, providing a versatile foundation for sensemaking. Framing is a subtle yet potent mechanism that influences public perception through specific word choices, shaping interpretations of report...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	451,864
2312.07792	Differentially private projection-depth-based medians	We develop $(\epsilon,\delta)$-differentially private projection-depth-based medians using the propose-test-release (PTR) and exponential mechanisms. Under general conditions on the input parameters and the population measure, (e.g. we do not assume any moment bounds), we quantify the probability the test in PTR fails,...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	415,053
1605.01824	Persistent AUV Operations Using a Robust Reactive Mission and Path Planning (RRMPP) Architecture	Providing a higher level of decision autonomy and accompanying prompt changes of an uncertain environment is a true challenge of AUVs autonomous operations. The proceeding approach introduces a robust reactive structure that accommodates an AUV's mission planning, task-time management in a top level and incorporates en...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	55,530
1912.05270	MineGAN: effective knowledge transfer from GANs to target domains with few images	One of the attractive characteristics of deep neural networks is their ability to transfer knowledge obtained in one domain to other related domains. As a result, high-quality networks can be trained in domains with relatively little training data. This property has been extensively studied for discriminative networks ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,065
2008.09794	Solution space of optimal heat pump schedules	We study the space of optimal schedules for a heat pump with thermal energy storage used in heating a residential building. We model the heating system as a Mixed Integer Linear Program with the objective to minimise the cost of heating. We generate a large number of realistic daily heat demands and calculate the optim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	192,822
1906.09557	Posterior-Guided Neural Architecture Search	The emergence of neural architecture search (NAS) has greatly advanced the research on network design. Recent proposals such as gradient-based methods or one-shot approaches significantly boost the efficiency of NAS. In this paper, we formulate the NAS problem from a Bayesian perspective. We propose explicitly estimati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	136,205
2306.10646	Referenceless User Controllable Semantic Image Synthesis	Despite recent progress in semantic image synthesis, complete control over image style remains a challenging problem. Existing methods require reference images to feed style information into semantic layouts, which indicates that the style is constrained by the given image. In this paper, we propose a model named RUCGA...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	374,306
2103.16241	Improving robustness against common corruptions with frequency biased models	CNNs perform remarkably well when the training and test distributions are i.i.d, but unseen image corruptions can cause a surprisingly large drop in performance. In various real scenarios, unexpected distortions, such as random noise, compression artefacts, or weather distortions are common phenomena. Improving perform...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,513
2302.12736	Balanced Off-Policy Evaluation for Personalized Pricing	We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand. The goal is to perform off-policy evaluation for a new personalized pricing policy that maps features to prices. Methods based on inverse propensity weighting (in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,678
2308.15502	On the Steganographic Capacity of Selected Learning Models	Machine learning and deep learning models are potential vectors for various attack scenarios. For example, previous research has shown that malware can be hidden in deep learning models. Hiding information in a learning model can be viewed as a form of steganography. In this research, we consider the general question o...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	388,707
1002.2321	Exploiting Grids for applications in Condensed Matter Physics	Grids - the collection of heterogeneous computers spread across the globe - present a new paradigm for the large scale problems in variety of fields. We discuss two representative cases in the area of condensed matter physics outlining the widespread applications of the Grids. Both the problems involve calculations bas...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	5,683
1703.00523	ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection	Our system addresses Part 1, Lesion Segmentation and Part 3, Lesion Classification of the ISIC 2017 challenge. Both algorithms make use of deep convolutional networks to achieve the challenge objective.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,178
1808.02595	A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines	Deep learning approaches have been rapidly adopted across a wide range of fields because of their accuracy and flexibility, but require large labeled training datasets. This presents a fundamental problem for applications with limited, expensive, or private data (i.e. small data), such as human pose and behavior estima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,801
1403.7317	On the Outage Probability of the Full-Duplex Interference-Limited Relay Channel	In this paper, we study the performance, in terms of the asymptotic error probability, of a user which communicates with a destination with the aid of a full-duplex in-band relay. We consider that the network is interference-limited, and interfering users are distributed as a Poisson point process. In this case, the as...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,891
2403.15856	#TeamFollowBack: Detection & Analysis of Follow Back Accounts on Social Media	Follow back accounts inflate their follower counts by engaging in reciprocal followings. Such accounts manipulate the public and the algorithms by appearing more popular than they really are. Despite their potential harm, no studies have analyzed such accounts at scale. In this study, we present the first large-scale a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	440,776
2308.05269	A Novel Self-training Approach for Low-resource Speech Recognition	In this paper, we propose a self-training approach for automatic speech recognition (ASR) for low-resource settings. While self-training approaches have been extensively developed and evaluated for high-resource languages such as English, their applications to low-resource languages like Punjabi have been limited, desp...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	384,729
2208.01815	Effidit: Your AI Writing Assistant	In this technical report, we introduce Effidit (Efficient and Intelligent Editing), a digital writing assistant that facilitates users to write higher-quality text more efficiently by using artificial intelligence (AI) technologies. Previous writing assistants typically provide the function of error checking (to detect...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	311,273
2502.01587	Verbalized Bayesian Persuasion	Information design (ID) explores how a sender influence the optimal behavior of receivers to achieve specific objectives. While ID originates from everyday human communication, existing game-theoretic and machine learning methods often model information structures as numbers, which limits many applications to toy games...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	529,931
2210.06926	Delta-Closure Structure for Studying Data Distribution	In this paper, we revisit pattern mining and study the distribution underlying a binary dataset thanks to the closure structure which is based on passkeys, i.e., minimum generators in equivalence classes robust to noise. We introduce $\Delta$-closedness, a generalization of the closure operator, where $\Delta$ measures...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	323,507
2501.13031	A Probabilistic Model for Self-Supervised Learning	Self-supervised learning (SSL) aims to find meaningful representations from unlabeled data by encoding semantic similarities through data augmentations. Despite its current popularity, theoretical insights about SSL are still scarce. For example, it is not yet known whether commonly used SSL loss functions can be relat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,532
1409.2465	Comparing Feature Detectors: A bias in the repeatability criteria, and how to correct it	Most computer vision application rely on algorithms finding local correspondences between different images. These algorithms detect and compare stable local invariant descriptors centered at scale-invariant keypoints. Because of the importance of the problem, new keypoint detectors and descriptors are constantly being ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	35,910
2002.07956	Curriculum in Gradient-Based Meta-Reinforcement Learning	Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML) have shown strong few-shot performance in supervised and reinforcement learning settings. However, specifically in the case of meta-reinforcement learning (meta-RL), we can show that gradient-based meta-learners are sensitive to task distributions...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	164,615
2411.02131	Generating the Traces You Need: A Conditional Generative Model for Process Mining Data	In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of the selected processes. However, current DL generative models are limited in their ability to adapt the learned distributions ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	505,367
2305.08192	Diffusion Models for Imperceptible and Transferable Adversarial Attack	Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space. Despite some achievements in transferability and attack success rate, the crafted adversarial examples are easily perceived by human eyes. Towards visual imperceptibility, some recent works explore unrestricted attacks without $L_p$...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	364,197
2310.10690	Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming	Student modeling is central to many educational technologies as it enables predicting future learning outcomes and designing targeted instructional strategies. However, open-ended learning domains pose challenges for accurately modeling students due to the diverse behaviors and a large space of possible misconceptions....	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	400,348
2404.10213	GaitPoint+: A Gait Recognition Network Incorporating Point Cloud Analysis and Recycling	Gait is a behavioral biometric modality that can be used to recognize individuals by the way they walk from a far distance. Most existing gait recognition approaches rely on either silhouettes or skeletons, while their joint use is underexplored. Features from silhouettes and skeletons can provide complementary informa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,000
2203.15442	Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Grounding	Visual grounding focuses on establishing fine-grained alignment between vision and natural language, which has essential applications in multimodal reasoning systems. Existing methods use pre-trained query-agnostic visual backbones to extract visual feature maps independently without considering the query information. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	288,408
2209.15415	DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness	In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of wearable data is that it is time-series data and each channel can be correlated to another one, such as x, y, z axis of accelerometer. We argue t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	320,600
2211.04154	Russian propaganda on social media during the 2022 invasion of Ukraine	The Russian invasion of Ukraine in February 2022 was accompanied by practices of information warfare, yet existing evidence is largely anecdotal while large-scale empirical evidence is lacking. Here, we analyze the spread of pro-Russian support on social media. For this, we collected N = 349,455 messages from Twitter w...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	329,153
2401.04637	Applying Large Language Models API to Issue Classification Problem	Effective prioritization of issue reports is crucial in software engineering to optimize resource allocation and address critical problems promptly. However, the manual classification of issue reports for prioritization is laborious and lacks scalability. Alternatively, many open source software (OSS) projects employ a...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	420,492
2408.00760	Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention	Conditional diffusion models have shown remarkable success in visual content generation, producing high-quality samples across various domains, largely due to classifier-free guidance (CFG). Recent attempts to extend guidance to unconditional models have relied on heuristic techniques, resulting in suboptimal generatio...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	477,962
2412.09875	Selective State Space Memory for Large Vision-Language Models	Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. However, fine-tuning these models for domain-specific applications remains a computationally intensive challenge. This paper introduces State Space Memory Integration (SSMI), a novel approach for effic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,684
2009.14738	ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks	Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three major issues: sparsity and nonlinearity capturing, residual modeling, and network smoothing. We propose Residual...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,126
2409.05385	Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models	The development of LLMs has greatly enhanced the intelligence and fluency of question answering, while the emergence of retrieval enhancement has enabled models to better utilize external information. However, the presence of noise and errors in retrieved information poses challenges to the robustness of LLMs. In this ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	486,753
2302.14523	Automatic Heteronym Resolution Pipeline Using RAD-TTS Aligners	Grapheme-to-phoneme (G2P) transduction is part of the standard text-to-speech (TTS) pipeline. However, G2P conversion is difficult for languages that contain heteronyms -- words that have one spelling but can be pronounced in multiple ways. G2P datasets with annotated heteronyms are limited in size and expensive to cre...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	348,341
1304.6245	A Two-Phase Maximum-Likelihood Sequence Estimation for Receivers with Partial CSI	The optimality of the conventional maximum likelihood sequence estimation (MLSE), also known as the Viterbi Algorithm (VA), relies on the assumption that the receiver has perfect knowledge of the channel coefficients or channel state information (CSI). However, in practical situations that fail the assumption, the MLSE...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,160
1206.2526	Analysis of Inpainting via Clustered Sparsity and Microlocal Analysis	Recently, compressed sensing techniques in combination with both wavelet and directional representation systems have been very effectively applied to the problem of image inpainting. However, a mathematical analysis of these techniques which reveals the underlying geometrical content is completely missing. In this pape...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,446
1705.03506	Towards Understanding the Impact of Crime in a Choice of a Route by a Bus Passenger	In this paper we describe a simulation platform that supports studies on the impact of crime on urban mobility. We present an example of how this can be achieved by seeking to understand the effect, on the transport system, if users of this system decide to choose optimal routes of time between origins and destinations...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	73,195
1706.00633	Towards Robust Detection of Adversarial Examples	Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples. In this paper, we present a novel training procedure and a thresholding test strategy, towards robust detection of adversarial examples. In training, we propose to minimize the reverse...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,657
2407.02937	Probing the Feasibility of Multilingual Speaker Anonymization	In speaker anonymization, speech recordings are modified in a way that the identity of the speaker remains hidden. While this technology could help to protect the privacy of individuals around the globe, current research restricts this by focusing almost exclusively on English data. In this study, we extend a state-of-...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	469,954
2206.08954	Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning	Self-supervised learning (SSL) has recently achieved tremendous empirical advancements in learning image representation. However, our understanding of the principle behind learning such a representation is still limited. This work shows that joint-embedding SSL approaches primarily learn a representation of image patch...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	303,368
2109.06737	Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning	Learning state representations enables robotic planning directly from raw observations such as images. Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space. The similarity between observations in the space of images is oft...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	255,265
2401.03932	Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes	Accurate mapping of greenhouse gas fluxes at the Earth's surface is essential for the validation and calibration of climate models. In this study, we present a framework for surface flux estimation with drones. Our approach uses data assimilation (DA) to infer fluxes from drone-based observations, and reinforcement lea...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	420,278
2401.04425	Meta-forests: Domain generalization on random forests with meta-learning	Domain generalization is a popular machine learning technique that enables models to perform well on the unseen target domain, by learning from multiple source domains. Domain generalization is useful in cases where data is limited, difficult, or expensive to collect, such as in object recognition and biomedicine. In t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	420,428
2105.09840	Semantic Security for Indoor THz-Wireless Communication	Physical-layer security (PLS) for industrial indoor terahertz (THz) wireless communication applications is considered. We use a similar model as being employed for additive white Gaussian noise (AWGN) wireless communication channels. A cell communication and a directed communication scenario are analyzed to illustrate ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	236,180
1704.07019	Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning	The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	72,275
2006.16791	Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Density	Type 2 diabetes (T2DM), one of the most prevalent chronic diseases, affects the glucose metabolism of the human body, which decreases the quantity of life and brings a heavy burden on social medical care. Patients with T2DM are more likely to suffer bone fragility fracture as diabetes affects bone mineral density (BMD)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,910
2406.05849	MAP-ADAPT: Real-Time Quality-Adaptive Semantic 3D Maps	Creating 3D semantic reconstructions of environments is fundamental to many applications, especially when related to autonomous agent operation (e.g., goal-oriented navigation or object interaction and manipulation). Commonly, 3D semantic reconstruction systems capture the entire scene in the same level of detail. Howe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	462,320
2308.05365	TriDo-Former: A Triple-Domain Transformer for Direct PET Reconstruction from Low-Dose Sinograms	To obtain high-quality positron emission tomography (PET) images while minimizing radiation exposure, various methods have been proposed for reconstructing standard-dose PET (SPET) images from low-dose PET (LPET) sinograms directly. However, current methods often neglect boundaries during sinogram-to-image reconstructi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,766
2207.02575	Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design	While much progress has been made in understanding the minimax sample complexity of reinforcement learning (RL) -- the complexity of learning on the "worst-case" instance -- such measures of complexity often do not capture the true difficulty of learning. In practice, on an "easy" instance, we might hope to achieve a c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	306,560
2407.09803	Group actions on codes in graphs	This is a chapter in a forthcoming book on completely regular codes in distance regular graphs. The chapter provides an overview, and some original results, on codes in distance regular graphs which admit symmetries via a permutation group acting on the vertices of the graph. The strongest notion of completely transiti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	472,725
2006.00195	MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning	There has been an increasing surge of interest on development of advanced Reinforcement Learning (RL) systems as intelligent approaches to learn optimal control policies directly from smart agents' interactions with the environment. Objectives: In a model-free RL method with continuous state-space, typically, the value...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	179,401
2410.22996	Semantic Enrichment of the Quantum Cascade Laser Properties in Text- A Knowledge Graph Generation Approach	A well structured collection of the various Quantum Cascade Laser (QCL) design and working properties data provides a platform to analyze and understand the relationships between these properties. By analyzing these relationships, we can gain insights into how different design features impact laser performance properti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	503,864
1107.1229	Characteristic Characteristics	While five-factor models of personality are widespread, there is still not universal agreement on this as a structural framework. Part of the reason for the lingering debate is its dependence on factor analysis. In particular, derivation or refutation of the model via other statistical means is a worthwhile project. In...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	11,176
2104.02527	Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting	We propose a novel keypoint voting scheme based on intersecting spheres, that is more accurate than existing schemes and allows for fewer, more disperse keypoints. The scheme is based upon the distance between points, which as a 1D quantity can be regressed more accurately than the 2D and 3D vector and offset quantitie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,748
2203.02982	A Survey of Implicit Discourse Relation Recognition	A discourse containing one or more sentences describes daily issues and events for people to communicate their thoughts and opinions. As sentences are normally consist of multiple text segments, correct understanding of the theme of a discourse should take into consideration of the relations in between text segments. A...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	283,927
1812.06745	Trichotomic Argumentation Representation	The Aristotelian trichotomy distinguishes three aspects of argumentation: Logos, Ethos, and Pathos. Even rich argumentation representations like the Argument Interchange Format (AIF) are only concerned with capturing the Logos aspect. Inference Anchoring Theory (IAT) adds the possibility to represent ethical requiremen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	116,679
2210.07455	Controlling Bias Exposure for Fair Interpretable Predictions	Recent work on reducing bias in NLP models usually focuses on protecting or isolating information related to a sensitive attribute (like gender or race). However, when sensitive information is semantically entangled with the task information of the input, e.g., gender information is predictive for a profession, a fair ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,718

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