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
2403.15510	Privacy-Preserving End-to-End Spoken Language Understanding	Spoken language understanding (SLU), one of the key enabling technologies for human-computer interaction in IoT devices, provides an easy-to-use user interface. Human speech can contain a lot of user-sensitive information, such as gender, identity, and sensitive content. New types of security and privacy breaches have ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	440,617
2201.04831	Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis	Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	275,212
2108.03233	Operational Learning-based Boundary Estimation in Electromagnetic Medical Imaging	Incorporating boundaries of the imaging object as a priori information to imaging algorithms can significantly improve the performance of electromagnetic medical imaging systems. To avoid overly complicating the system by using different sensors and the adverse effect of the subject's movement, a learning-based method ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	249,596
2104.02774	Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks	The cybersecurity of smart grids has become one of key problems in developing reliable modern power and energy systems. This paper introduces a non-stationary adversarial cost with a variation constraint for smart grids and enables us to investigate the problem of optimal smart grid protection against cyber attacks in ...	false	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false	false	false	228,844
2109.11834	A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification	Data augmentation (DA) aims to generate constrained and diversified data to improve classifiers in Low-Resource Classification (LRC). Previous studies mostly use a fine-tuned Language Model (LM) to strengthen the constraints but ignore the fact that the potential of diversity could improve the effectiveness of generate...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,075
2409.04834	Reducing Events to Augment Log-based Anomaly Detection Models: An Empirical Study	As software systems grow increasingly intricate, the precise detection of anomalies have become both essential and challenging. Current log-based anomaly detection methods depend heavily on vast amounts of log data leading to inefficient inference and potential misguidance by noise logs. However, the quantitative effec...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	486,532
1905.06533	Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech	The rapid population aging has stimulated the development of assistive devices that provide personalized medical support to the needies suffering from various etiologies. One prominent clinical application is a computer-assisted speech training system which enables personalized speech therapy to patients impaired by co...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	131,024
2102.04682	OTFS Signaling for Uplink NOMA of Heterogeneous Mobility Users	We investigate a coded uplink non-orthogonal multiple access (NOMA) configuration in which groups of co-channel users are modulated in accordance with orthogonal time frequency space (OTFS). We take advantage of OTFS characteristics to achieve NOMA spectrum sharing in the delay-Doppler domain between stationary and mob...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	219,190
2105.06850	Deep Learning Based RIS Channel Extrapolation with Element-grouping	Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks. In this letter, we consider the acquisition of the cascaded channels, which is a challenging task due to the massive number of passive RIS elements. To reduce the pilot overhead, we adopt the...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	235,253
2011.14899	Secure Vehicular Communications through Reconfigurable Intelligent Surfaces	Reconfigurable intelligent surfaces (RIS) is considered as a revolutionary technique to improve the wireless system performance by reconfiguring the radio wave propagation environment artificially. Motivated by the potential of RIS in vehicular networks, we analyze the secrecy outage performance of RIS-aided vehicular ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	208,922
2110.13674	C$^2$SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction	Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between sensors and processing apparatus and related computation become two major bottlenecks for seizure prediction systems due to the constrained ba...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	263,276
2202.00728	Physical Design using Differentiable Learned Simulators	Designing physical artifacts that serve a purpose - such as tools and other functional structures - is central to engineering as well as everyday human behavior. Though automating design has tremendous promise, general-purpose methods do not yet exist. Here we explore a simple, fast, and robust approach to inverse desi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,236
2409.18339	AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models	Recent advancements in Large Language Models (LLMs) have demonstrated great success in many Natural Language Processing (NLP) tasks. In addition to their cognitive intelligence, exploring their capabilities in emotional intelligence is also crucial, as it enables more natural and empathetic conversational AI. Recent st...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	492,203
2312.07631	AI-driven projection tomography with multicore fibre-optic cell rotation	Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning ran...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	415,004
2305.16695	The Search for Stability: Learning Dynamics of Strategic Publishers with Initial Documents	We study a game-theoretic information retrieval model in which strategic publishers aim to maximize their chances of being ranked first by the search engine while maintaining the integrity of their original documents. We show that the commonly used Probability Ranking Principle (PRP) ranking scheme results in an unstab...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	368,211
1701.07594	Intelligent real-time MEMS sensor fusion and calibration	This paper discusses an innovative adaptive heterogeneous fusion algorithm based on estimation of the mean square error of all variables used in real time processing. The algorithm is designed for a fusion between derivative and absolute sensors and is explained by the fusion of the 3-axial gyroscope, 3-axial accelerom...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	67,323
1409.6193	Estimating topological properties of weighted networks from limited information	A fundamental problem in studying and modeling economic and financial systems is represented by privacy issues, which put severe limitations on the amount of accessible information. Here we introduce a novel, highly nontrivial method to reconstruct the structural properties of complex weighted networks of this kind usi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	36,232
2412.05150	BIAS: A Body-based Interpretable Active Speaker Approach	State-of-the-art Active Speaker Detection (ASD) approaches heavily rely on audio and facial features to perform, which is not a sustainable approach in wild scenarios. Although these methods achieve good results in the standard AVA-ActiveSpeaker set, a recent wilder ASD dataset (WASD) showed the limitations of such mod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,709
2011.05944	Asymptotically Optimal Information-Directed Sampling	We introduce a simple and efficient algorithm for stochastic linear bandits with finitely many actions that is asymptotically optimal and (nearly) worst-case optimal in finite time. The approach is based on the frequentist information-directed sampling (IDS) framework, with a surrogate for the information gain that is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,091
2211.00172	Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography	Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. In contrast to axial strain, lateral strain, which is highly required i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,777
2209.07928	The BLue Amazon Brain (BLAB): A Modular Architecture of Services about the Brazilian Maritime Territory	We describe the first steps in the development of an artificial agent focused on the Brazilian maritime territory, a large region within the South Atlantic also known as the Blue Amazon. The "BLue Amazon Brain" (BLAB) integrates a number of services aimed at disseminating information about this region and its importanc...	false	false	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	317,947
1209.1652	Power-laws and the Conservation of Information in discrete token systems: Part 2 The role of defect	In a matching paper (arXiv:1207.5027), I proved that Conservation of Size and Information in a discrete token based system is overwhelmingly likely to lead to a power-law component size distribution with respect to the size of its unique alphabet. This was substantiated to a very high level of significance using some 5...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,456
2008.06910	Neural Descent for Visual 3D Human Pose and Shape	We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image. We rely on a recently introduced, expressivefull body statistical 3d human model, GHUM, trained end-to-end, and learn to reconstruct its pose and shape state in a self-supervised regime. Central to our m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,933
2302.00390	Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning	This paper presents a hierarchical classification system that automatically categorizes a scholarly publication using its abstract into a three-tier hierarchical label set (discipline, field, subfield) in a multi-class setting. This system enables a holistic categorization of research activities in the mentioned hierar...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	343,205
2005.01297	Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations	In this work, we propose Sum-Product-Transform Networks (SPTN), an extension of sum-product networks that uses invertible transformations as additional internal nodes. The type and placement of transformations determine properties of the resulting SPTN with many interesting special cases. Importantly, SPTN with Gaussia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,541
2310.03525	V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges	Achieving fully autonomous driving with heightened safety and efficiency depends on vehicle-to-everything (V2X) cooperative perception (CP), which allows vehicles to share perception data, thereby enhancing situational awareness and overcoming the limitations of the sensing ability of individual vehicles. V2X CP is cru...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,316
2202.13683	Estimating Model Performance on External Samples from Their Limited Statistical Characteristics	Methods that address data shifts usually assume full access to multiple datasets. In the healthcare domain, however, privacy-preserving regulations as well as commercial interests limit data availability and, as a result, researchers can typically study only a small number of datasets. In contrast, limited statistical ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,716
2305.05644	Towards Building the Federated GPT: Federated Instruction Tuning	While "instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amounts of diverse and high-quality instruction data (such as ChatGPT and GPT-4). Unfortunately, acquiring high-quality data, especially when it...	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	363,230
2208.02483	Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards	Semantic segmentation is a fundamental task for agricultural robots to understand the surrounding environments in natural orchards. The recent development of the LiDAR techniques enables the robot to acquire accurate range measurements of the view in the unstructured orchards. Compared to RGB images, 3D point clouds ha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,483
2209.13708	Falsification before Extrapolation in Causal Effect Estimation	Randomized Controlled Trials (RCTs) represent a gold standard when developing policy guidelines. However, RCTs are often narrow, and lack data on broader populations of interest. Causal effects in these populations are often estimated using observational datasets, which may suffer from unobserved confounding and select...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,996
1902.05204	TIRA: Toolbox for Interval Reachability Analysis	This paper presents TIRA, a Matlab library gathering several methods for the computation of interval over-approximations of the reachable sets for both continuous- and discrete-time nonlinear systems. Unlike other existing tools, the main strength of interval-based reachability analysis is its simplicity and scalabilit...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	121,497
cs/9701102	SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks	Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken- language systems still use a relatively brittle, hand-c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,356
2302.07492	Envisioning the Next-Gen Document Reader	People read digital documents on a daily basis to share, exchange, and understand information in electronic settings. However, current document readers create a static, isolated reading experience, which does not support users' goals of gaining more knowledge and performing additional tasks through document interaction...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	345,750
2203.17007	Vehicular Positioning and Tracking in Multipath Non-Line-of-Sight Channels	We consider the downlink transmission in a single cell multiple-input multiple-output system, in which the user equipment correspond to a vehicle moving along a given trajectory. This system utilizes millimeter wave channels characterized by multiple non-line-of-sight (NLoS) components. As it has been pointed out in se...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	289,009
1407.6245	scikit-image: Image processing in Python	scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal "Modified BSD" open source license, provides a well-documented API in the Python programming language, and is developed by an active, internatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	34,851
2302.07491	Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment	Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	345,749
2405.17187	Memorize What Matters: Emergent Scene Decomposition from Multitraverse	Humans naturally retain memories of permanent elements, while ephemeral moments often slip through the cracks of memory. This selective retention is crucial for robotic perception, localization, and mapping. To endow robots with this capability, we introduce 3D Gaussian Mapping (3DGM), a self-supervised, camera-only of...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	457,802
1812.01997	Characterizing the 2016 Russian IRA Influence Campaign	Until recently, social media were seen to promote democratic discourse on social and political issues. However, this powerful communication ecosystem has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	115,663
2108.08380	Revisiting Binary Local Image Description for Resource Limited Devices	The advent of a panoply of resource limited devices opens up new challenges in the design of computer vision algorithms with a clear compromise between accuracy and computational requirements. In this paper we present new binary image descriptors that emerge from the application of triplet ranking loss, hard negative m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,231
2107.03428	Management of Resource at the Network Edge for Federated Learning	Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	245,152
1906.06393	A Unified Framework of Constrained Robust Submodular Optimization with Applications	Robust optimization is becoming increasingly important in machine learning applications. In this paper, we study a unified framework of robust submodular optimization. We study this problem both from a minimization and maximization perspective (previous work has only focused on variants of robust submodular maximizatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	135,282
2010.12108	GPS-Denied Navigation Using SAR Images and Neural Networks	Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	202,565
1502.03258	Structural characterizations of the navigational expressiveness of relation algebras on a tree	Given a document D in the form of an unordered node-labeled tree, we study the expressiveness on D of various basic fragments of XPath, the core navigational language on XML documents. Working from the perspective of these languages as fragments of Tarski's relation algebra, we give characterizations, in terms of the s...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	40,131
2411.16217	Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding	Multiple-in-one image restoration (IR) has made significant progress, aiming to handle all types of single degraded image restoration with a single model. However, in real-world scenarios, images often suffer from combinations of multiple degradation factors. Existing multiple-in-one IR models encounter challenges rela...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,950
1911.11534	Decoupling Features and Coordinates for Few-shot RGB Relocalization	Cross-scene model adaption is crucial for camera relocalization in real scenarios. It is often preferable that a pre-learned model can be fast adapted to a novel scene with as few training samples as possible. The existing state-of-the-art approaches, however, can hardly support such few-shot scene adaption due to the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	155,154
2203.15589	On Kernelized Multi-Armed Bandits with Constraints	We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are assumed to lie in a reproducing kernel Hilbert space (RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes standard mu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	288,465
1910.04985	VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition	To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNet. Variable group convolution is introduced by VarGNet to solve the conflict between small computational cost and the unbalance of computati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	148,936
2307.15557	Dynamic algorithms for k-center on graphs	In this paper we give the first efficient algorithms for the $k$-center problem on dynamic graphs undergoing edge updates. In this problem, the goal is to partition the input into $k$ sets by choosing $k$ centers such that the maximum distance from any data point to its closest center is minimized. It is known that it ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	382,306
2007.05778	Evaluating the Potential of Drone Swarms in Nonverbal HRI Communication	Human-to-human communications are enriched with affects and emotions, conveyed, and perceived through both verbal and nonverbal communication. It is our thesis that drone swarms can be used to communicate information enriched with effects via nonverbal channels: guiding, generally interacting with, or warning a human a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	186,781
1606.04443	A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification	We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,251
2408.02766	ConDL: Detector-Free Dense Image Matching	In this work, we introduce a deep-learning framework designed for estimating dense image correspondences. Our fully convolutional model generates dense feature maps for images, where each pixel is associated with a descriptor that can be matched across multiple images. Unlike previous methods, our model is trained on s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	478,753
2402.13016	Understanding the effects of language-specific class imbalance in multilingual fine-tuning	We study the effect of one type of imbalance often present in real-life multilingual classification datasets: an uneven distribution of labels across languages. We show evidence that fine-tuning a transformer-based Large Language Model (LLM) on a dataset with this imbalance leads to worse performance, a more pronounced...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,083
2110.15245	From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence	Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning methods to a particular problem set has become an established and valuable modus operandi to advance a particular field. In this article we argue...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	263,818
2407.11854	Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection	Grammatical Error Detection (GED) methods rely heavily on human annotated error corpora. However, these annotations are unavailable in many low-resource languages. In this paper, we investigate GED in this context. Leveraging the zero-shot cross-lingual transfer capabilities of multilingual pre-trained language models,...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	473,647
1905.06836	Stability of Linear Structural Equation Models of Causal Inference	We consider the numerical stability of the parameter recovery problem in Linear Structural Equation Model ($\LSEM$) of causal inference. A long line of work starting from Wright (1920) has focused on understanding which sub-classes of $\LSEM$ allow for efficient parameter recovery. Despite decades of study, this questi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,086
2111.06676	A Reverse Jensen Inequality Result with Application to Mutual Information Estimation	The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard inequalities such as the inequality of arithmetic and geometric means or the H\"older inequality. In a probabilistic setting, the Jensen inequal...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	266,140
2409.10775	Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?	Image classification models, including convolutional neural networks (CNNs), perform well on a variety of classification tasks but struggle under conditions of partial occlusion, i.e., conditions in which objects are partially covered from the view of a camera. Methods to improve performance under occlusion, including ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	488,877
2006.06910	Hybrid Attentional Memory Network for Computational drug repositioning	Drug repositioning is designed to discover new uses of known drugs, which is an important and efficient method of drug discovery. Researchers only use one certain type of Collaborative Filtering (CF) models for drug repositioning currently, like the neighborhood based approaches which are good at mining the local infor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,607
2404.16283	Andes: Defining and Enhancing Quality-of-Experience in LLM-Based Text Streaming Services	Large language models (LLMs) are now at the core of conversational AI services such as real-time translation and chatbots, which provide live user interaction by incrementally streaming text to the user. However, existing LLM serving systems fail to provide good user experience because their optimization metrics are no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	449,423
2410.03505	Classification-Denoising Networks	Image classification and denoising suffer from complementary issues of lack of robustness or partially ignoring conditioning information. We argue that they can be alleviated by unifying both tasks through a model of the joint probability of (noisy) images and class labels. Classification is performed with a forward pa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	494,817
1106.0219	Identifying Mislabeled Training Data	This paper presents a new approach to identifying and eliminating mislabeled training instances for supervised learning. The goal of this approach is to improve classification accuracies produced by learning algorithms by improving the quality of the training data. Our approach uses a set of learning algorithms to crea...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,633
2312.10527	CoCoGen: Physically-Consistent and Conditioned Score-based Generative Models for Forward and Inverse Problems	Recent advances in generative artificial intelligence have had a significant impact on diverse domains spanning computer vision, natural language processing, and drug discovery. This work extends the reach of generative models into physical problem domains, particularly addressing the efficient enforcement of physical ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,203
2110.07698	Directed Percolation in Random Temporal Network Models with Heterogeneities	The event graph representation of temporal networks suggests that the connectivity of temporal structures can be mapped to a directed percolation problem. However, similar to percolation theory on static networks, this mapping is valid under the approximation that the structure and interaction dynamics of the temporal ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	261,085
2407.14166	On Maximum Entropy Linear Feature Inversion	We revisit the classical problem of inverting dimension-reducing linear mappings using the maximum entropy (MaxEnt) criterion. In the literature, solutions are problem-dependent, inconsistent, and use different entropy measures. We propose a new unified approach that not only specializes to the existing approaches, but...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	474,677
2402.04869	Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy	As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space. However, there is still a considerable gap in discovering and incorpo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	427,625
2301.11697	Big portfolio selection by graph-based conditional moments method	How to do big portfolio selection is very important but challenging for both researchers and practitioners. In this paper, we propose a new graph-based conditional moments (GRACE) method to do portfolio selection based on thousands of stocks or more. The GRACE method first learns the conditional quantiles and mean of s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,240
2101.00296	Tweeting for the Cause: Network analysis of UK petition sharing	Online government petitions represent a new data-rich mode of political participation. This work examines the thus far understudied dynamics of sharing petitions on social media in order to garner signatures and, ultimately, a government response. Using 20 months of Twitter data comprising over 1 million tweets linking...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	214,034
1906.07480	Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image	Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances b...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	135,605
1904.01870	Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation	Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures. Since the groundtruth depth labels are hard to obtain, recent methods try to learn depth estimation networks in an unsupervised way by exploring unsupervised cues, which are effective but less reliable than true label...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,272
1404.1592	The Power of Online Learning in Stochastic Network Optimization	In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics {\it a priori}. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	32,132
2303.07740	Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening	Under the flourishing development in performance, current image-text retrieval methods suffer from $N$-related time complexity, which hinders their application in practice. Targeting at efficiency improvement, this paper presents a simple and effective keyword-guided pre-screening framework for the image-text retrieval...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	351,362
2109.12023	Indirectly Supervised English Sentence Break Prediction Using Paragraph Break Probability Estimates	This report explores the use of paragraph break probability estimates to help predict the location of sentence breaks in English natural language text. We show that a sentence break predictor based almost solely on paragraph break probability estimates can achieve high accuracy on this task. This sentence break predict...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,128
2106.01998	Toward Explainable Users: Using NLP to Enable AI to Understand Users' Perceptions of Cyber Attacks	To understand how end-users conceptualize consequences of cyber security attacks, we performed a card sorting study, a well-known technique in Cognitive Sciences, where participants were free to group the given consequences of chosen cyber attacks into as many categories as they wished using rationales they see fit. Th...	true	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	238,691
cmp-lg/9502028	Lexical Acquisition via Constraint Solving	This paper describes a method to automatically acquire the syntactic and semantic classifications of unknown words. Our method reduces the search space of the lexical acquisition problem by utilizing both the left and the right context of the unknown word. Link Grammar provides a convenient framework in which to implem...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,289
2102.09198	Learning Continuous Exponential Families Beyond Gaussian	We address the problem of learning of continuous exponential family distributions with unbounded support. While a lot of progress has been made on learning of Gaussian graphical models, we still lack scalable algorithms for reconstructing general continuous exponential families modeling higher-order moments of the data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,696
2407.15909	A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting	Artificial Intelligence (AI) models have reached a very significant level of accuracy. While their superior performance offers considerable benefits, their inherent complexity often decreases human trust, which slows their application in high-risk decision-making domains, such as finance. The field of eXplainable AI (X...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	475,405
2001.05784	Cache-Aided Modulation for Heterogeneous Coded Caching over a Gaussian Broadcast Channel	Coded caching is an information theoretic scheme to reduce high peak hours traffic by partially prefetching files in the users local storage during low peak hours. This paper considers heterogeneous decentralized caching systems where cache of users and content library files may have distinct sizes. The server communic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,636
2305.11638	A Path to Holistic Privacy in Stream Processing Systems	The massive streams of Internet of Things (IoT) data require a timely analysis to retain data usefulness. Stream processing systems (SPSs) enable this task, deriving knowledge from the IoT data in real-time. Such real-time analytics benefits many applications but can also be used to violate user privacy, as the IoT dat...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	365,632
1111.0158	Applying Fuzzy ID3 Decision Tree for Software Effort Estimation	Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and contr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	12,863
cmp-lg/9806007	An Investigation of Transformation-Based Learning in Discourse	This paper presents results from the first attempt to apply Transformation-Based Learning to a discourse-level Natural Language Processing task. To address two limitations of the standard algorithm, we developed a Monte Carlo version of Transformation-Based Learning to make the method tractable for a wider range of pro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,881
2106.03152	Technical Report: Temporal Aggregate Representations	This technical report extends our work presented in [9] with more experiments. In [9], we tackle long-term video understanding, which requires reasoning from current and past or future observations and raises several fundamental questions. How should temporal or sequential relationships be modelled? What temporal exten...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,198
2406.10019	Group and Shuffle: Efficient Structured Orthogonal Parametrization	The increasing size of neural networks has led to a growing demand for methods of efficient fine-tuning. Recently, an orthogonal fine-tuning paradigm was introduced that uses orthogonal matrices for adapting the weights of a pretrained model. In this paper, we introduce a new class of structured matrices, which unifies...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	464,193
2310.01452	Fooling the Textual Fooler via Randomizing Latent Representations	Despite outstanding performance in a variety of NLP tasks, recent studies have revealed that NLP models are vulnerable to adversarial attacks that slightly perturb the input to cause the models to misbehave. Among these attacks, adversarial word-level perturbations are well-studied and effective attack strategies. Sinc...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	396,439
2403.17995	Semi-Supervised Image Captioning Considering Wasserstein Graph Matching	Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	441,720
2407.03056	Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation	Vision-Language Models (VLMs) demonstrate remarkable zero-shot generalization to unseen tasks, but fall short of the performance of supervised methods in generalizing to downstream tasks with limited data. Prompt learning is emerging as a parameter-efficient method for adapting VLMs, but state-of-the-art approaches req...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	470,005
2409.10707	Finite Element Modeling of Surface Traveling Wave Friction Driven for Rotary Ultrasonic Motor	Finite element modeling (FEM) is a critical tool in the design and analysis of piezoelectric devices, offering detailed numerical simulations that guide various applications. While traditionally applied to eigenfrequency analysis and time-dependent studies for predicting excitation eigenfrequencies and estimating trave...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	488,853
2012.02399	P3-LOAM: PPP/LiDAR Loosely Coupled SLAM with Accurate Covariance Estimation and Robust RAIM in Urban Canyon Environment	Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) has drawn increasing interests in autonomous driving. However, LiDAR-SLAM suffers from accumulating errors which can be significantly mitigated by Global Navigation Satellite System (GNSS). Precise Point Positioning (PPP), an accurat...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	209,754
1902.00137	Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning	In this paper, we present a new class of Markov decision processes (MDPs), called Tsallis MDPs, with Tsallis entropy maximization, which generalizes existing maximum entropy reinforcement learning (RL). A Tsallis MDP provides a unified framework for the original RL problem and RL with various types of entropy, includin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	120,327
2107.14487	Refining Labelled Systems for Modal and Constructive Logics with Applications	This thesis introduces the "method of structural refinement", which serves as a means of transforming the relational semantics of a modal and/or constructive logic into an 'economical' proof system by connecting two proof-theoretic paradigms: labelled and nested sequent calculi. The formalism of labelled sequents has b...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	248,482
1903.01669	Deep Active Localization	Active localization is the problem of generating robot actions that allow it to maximally disambiguate its pose within a reference map. Traditional approaches to this use an information-theoretic criterion for action selection and hand-crafted perceptual models. In this work we propose an end-to-end differentiable meth...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	123,311
2411.00448	ConceptFactory: Facilitate 3D Object Knowledge Annotation with Object Conceptualization	We present ConceptFactory, a novel scope to facilitate more efficient annotation of 3D object knowledge by recognizing 3D objects through generalized concepts (i.e. object conceptualization), aiming at promoting machine intelligence to learn comprehensive object knowledge from both vision and robotics aspects. This ide...	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	504,603
2303.10881	Machine Learning Automated Approach for Enormous Synchrotron X-Ray Diffraction Data Interpretation	Manual analysis of XRD data is usually laborious and time consuming. The deep neural network (DNN) based models trained by synthetic XRD patterns are proved to be an automatic, accurate, and high throughput method to analysis common XRD data collected from solid sample in ambient environment. However, it remains unknow...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,617
2210.14572	Quantifying the Loss of Acyclic Join Dependencies	Acyclic schemes posses known benefits for database design, speeding up queries, and reducing space requirements. An acyclic join dependency (AJD) is lossless with respect to a universal relation if joining the projections associated with the schema results in the original universal relation. An intuitive and standard m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	326,603
1502.05938	Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data	Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal observational data are often non-causal and occur due to confounding. The focus of this paper is to investigate incorporating information ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	40,431
1311.3365	Deriving the Qubit from Entropy Principles	The Heisenberg uncertainty principle is one of the most famous features of quantum mechanics. However, the non-determinism implied by the Heisenberg uncertainty principle --- together with other prominent aspects of quantum mechanics such as superposition, entanglement, and nonlocality --- poses deep puzzles about the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,401
1404.5190	Sparse Approximation, List Decoding, and Uncertainty Principles	We consider list versions of sparse approximation problems, where unlike the existing results in sparse approximation that consider situations with unique solutions, we are interested in multiple solutions. We introduce these problems and present the first combinatorial results on the output list size. These generalize...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,481
2408.12209	Zeroth-Order Stochastic Mirror Descent Algorithms for Minimax Excess Risk Optimization	The minimax excess risk optimization (MERO) problem is a new variation of the traditional distributionally robust optimization (DRO) problem, which achieves uniformly low regret across all test distributions under suitable conditions. In this paper, we propose a zeroth-order stochastic mirror descent (ZO-SMD) algorithm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,640
2302.10447	Mask-guided BERT for Few Shot Text Classification	Transformer-based language models have achieved significant success in various domains. However, the data-intensive nature of the transformer architecture requires much labeled data, which is challenging in low-resource scenarios (i.e., few-shot learning (FSL)). The main challenge of FSL is the difficulty of training r...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	346,815
2312.15993	Adaptive Kalman-based hybrid car following strategy using TD3 and CACC	In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and significantly improve the performance of car following. However, it is challenging for the traditional hybrid strategy based on fixed coeffi...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	418,221
1506.07230	Extensions of the I-MMSE Relation	Unveiling a fundamental link between information theory and estimation theory, the I-MMSE relation by Guo, Shamai and Verdu~\cite{gu05}, together with its numerous extensions, has great theoretical significance and various practical applications. On the other hand, its influences to date have been restricted to channel...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,494

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