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
2401.04398	Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding	Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both free-form questions an...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,420
2203.10698	A Policy Driven AI-Assisted PoW Framework	Proof of Work (PoW) based cyberdefense systems require incoming network requests to expend effort solving an arbitrary mathematical puzzle. Current state of the art is unable to differentiate between trustworthy and untrustworthy connections, requiring all to solve complex puzzles. In this paper, we introduce an Artifi...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	286,646
2212.05546	Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans	Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	335,817
1108.5395	Noise Covariance Properties in Dual-Tree Wavelet Decompositions	Dual-tree wavelet decompositions have recently gained much popularity, mainly due to their ability to provide an accurate directional analysis of images combined with a reduced redundancy. When the decomposition of a random process is performed -- which occurs in particular when an additive noise is corrupting the sign...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	11,829
2109.00074	Effectiveness of Deep Networks in NLP using BiDAF as an example architecture	Question Answering with NLP has progressed through the evolution of advanced model architectures like BERT and BiDAF and earlier word, character, and context-based embeddings. As BERT has leapfrogged the accuracy of models, an element of the next frontier can be the introduction of deep networks and an effective way to...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	252,989
2410.02618	Achieving Fairness in Predictive Process Analytics via Adversarial Learning (Extended Version)	Predictive business process analytics has become important for organizations, offering real-time operational support for their processes. However, these algorithms often perform unfair predictions because they are based on biased variables (e.g., gender or nationality), namely variables embodying discrimination. This p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	494,358
1709.10507	Vision-based deep execution monitoring	Execution monitor of high-level robot actions can be effectively improved by visual monitoring the state of the world in terms of preconditions and postconditions that hold before and after the execution of an action. Furthermore a policy for searching where to look at, either for verifying the relations that specify t...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	81,795
2010.02168	Identification of Anomalous Diffusion Sources by Unsupervised Learning	Fractional Brownian motion (fBm) is a ubiquitous diffusion process in which the memory effects of the stochastic transport result in the mean squared particle displacement following a power law, $\langle {\Delta r}^2 \rangle \sim t^{\alpha}$, where the diffusion exponent $\alpha$ characterizes whether the transport is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,924
2107.07842	A Survey of Knowledge Graph Embedding and Their Applications	Knowledge Graph embedding provides a versatile technique for representing knowledge. These techniques can be used in a variety of applications such as completion of knowledge graph to predict missing information, recommender systems, question answering, query expansion, etc. The information embedded in Knowledge graph ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	246,545
2304.13863	Ensoul: A framework for the creation of self organizing intelligent ultra low power systems (SOULS) through evolutionary enerstatic networks	Ensoul is a framework proposed for the purpose of creating technologies that create more technologies through the combined use of networks, and nests, of energy homeostatic (enerstatic) loops and open-ended evolutionary techniques. Generative technologies developed by such an approach serve as both simple, yet insightf...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	360,735
2108.04938	BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis	Vision-and-language(V&L) models take image and text as input and learn to capture the associations between them. Prior studies show that pre-trained V&L models can significantly improve the model performance for downstream tasks such as Visual Question Answering (VQA). However, V&L models are less effective when applie...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	250,156
2204.00642	Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents	Chemical Mass Casualty Incidents (MCI) place a heavy burden on hospital staff and resources. Machine Learning (ML) tools can provide efficient decision support to caregivers. However, ML models require large volumes of data for the most accurate results, which is typically not feasible in the chaotic nature of a chemic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	289,332
2202.06387	Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments	Neural scaling laws define a predictable relationship between a model's parameter count and its performance after training in the form of a power law. However, most research to date has not explicitly investigated whether scaling laws can be used to accelerate model development. In this work, we perform such an empiric...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	280,203
1607.02766	Mobile Internet of Things: Can UAVs Provide an Energy-Efficient Mobile Architecture?	In this paper, the optimal trajectory and deployment of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum energy consumption, a new a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	58,413
2103.14675	Synthesis of Compositional Animations from Textual Descriptions	"How can we animate 3D-characters from a movie script or move robots by simply telling them what we would like them to do?" "How unstructured and complex can we make a sentence and still generate plausible movements from it?" These are questions that need to be answered in the long-run, as the field is still in its inf...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	226,935
1910.10238	Robust Neural Machine Translation for Clean and Noisy Speech Transcripts	Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech recognition (ASR) system. In this paper, we study ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	150,423
2410.01103	Approximately Aligned Decoding	It is common to reject undesired outputs of Large Language Models (LLMs); however, current methods to do so require an excessive amount of computation, or severely distort the distribution of outputs. We present a method to balance the distortion of the output distribution with computational efficiency, allowing for th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	493,612
2208.14384	Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data	Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	315,308
2012.06057	Interdisciplinary Approaches to Understanding Artificial Intelligence's Impact on Society	Innovations in AI have focused primarily on the questions of "what" and "how"-algorithms for finding patterns in web searches, for instance-without adequate attention to the possible harms (such as privacy, bias, or manipulation) and without adequate consideration of the societal context in which these systems operate....	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	210,980
2304.01893	Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion	We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals. We draw on recent advances in guided diffusion modeling to achieve test-time controllability of trajectories, which is normally only associated with rule-based systems. Our ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	356,241
1106.5524	Robust network community detection using balanced propagation	Label propagation has proven to be an extremely fast method for detecting communities in large complex networks. Furthermore, due to its simplicity, it is also currently one of the most commonly adopted algorithms in the literature. Despite various subsequent advances, an important issue of the algorithm has not yet be...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	11,036
1806.10969	Performance of Massive MIMO Self-Backhauling for Ultra-Dense Small Cell Deployments	A key aspect of the fifth-generation wireless communication network will be the integration of different services and technologies to provide seamless connectivity. In this paper, we consider using massive multiple-input multiple-output (mMIMO) to provide backhaul links to a dense deployment of self-backhauling (s-BH) ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	101,633
1902.02907	Source Traces for Temporal Difference Learning	This paper motivates and develops source traces for temporal difference (TD) learning in the tabular setting. Source traces are like eligibility traces, but model potential histories rather than immediate ones. This allows TD errors to be propagated to potential causal states and leads to faster generalization. Source ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	120,973
2409.13936	High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data	High-resolution flood probability maps are essential for addressing the limitations of existing flood risk assessment approaches but are often limited by the availability of historical event data. Also, producing simulated data needed for creating probabilistic flood maps using physics-based models involves significant...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	490,242
2409.10721	A Missing Data Imputation GAN for Character Sprite Generation	Creating and updating pixel art character sprites with many frames spanning different animations and poses takes time and can quickly become repetitive. However, that can be partially automated to allow artists to focus on more creative tasks. In this work, we concentrate on creating pixel art character sprites in a ta...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	488,859
2209.14515	Effect of the Dynamics of a Horizontally Wobbling Mass on Biped Walking Performance	We have developed biped robots with a passive dynamic walking mechanism. This study proposes a compass model with a wobbling mass connected to the upper body and oscillating in the horizontal direction to clarify the influence of the horizontal dynamics of the upper body on bipedal walking. The limit cycles of the mode...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	320,276
1103.2545	On essentially conditional information inequalities	In 1997, Z.Zhang and R.W.Yeung found the first example of a conditional information inequality in four variables that is not "Shannon-type". This linear inequality for entropies is called conditional (or constraint) since it holds only under condition that some linear equations are satisfied for the involved entropies....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	9,593
2105.10305	Correlated Input-Dependent Label Noise in Large-Scale Image Classification	Large scale image classification datasets often contain noisy labels. We take a principled probabilistic approach to modelling input-dependent, also known as heteroscedastic, label noise in these datasets. We place a multivariate Normal distributed latent variable on the final hidden layer of a neural network classifie...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	236,346
1908.11526	MVS^2: Deep Unsupervised Multi-view Stereo with Multi-View Symmetry	The success of existing deep-learning based multi-view stereo (MVS) approaches greatly depends on the availability of large-scale supervision in the form of dense depth maps. Such supervision, while not always possible, tends to hinder the generalization ability of the learned models in never-seen-before scenarios. In ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,404
2311.08910	Progressive Feedback-Enhanced Transformer for Image Forgery Localization	Blind detection of the forged regions in digital images is an effective authentication means to counter the malicious use of local image editing techniques. Existing encoder-decoder forensic networks overlook the fact that detecting complex and subtle tampered regions typically requires more feedback information. In th...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	407,914
2406.05897	InfoGaussian: Structure-Aware Dynamic Gaussians through Lightweight Information Shaping	3D Gaussians, as a low-level scene representation, typically involve thousands to millions of Gaussians. This makes it difficult to control the scene in ways that reflect the underlying dynamic structure, where the number of independent entities is typically much smaller. In particular, it can be challenging to animate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	462,342
0905.4761	Optimizing XML Compression	The eXtensible Markup Language (XML) provides a powerful and flexible means of encoding and exchanging data. As it turns out, its main advantage as an encoding format (namely, its requirement that all open and close markup tags are present and properly balanced) yield also one of its main disadvantages: verbosity. XML-...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	3,794
2005.08104	Single-Stage Semantic Segmentation from Image Labels	Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. with only image-level labels available for training. However, this has come at the cost of increased model complexity and sophisticated multi-stage training procedures. This is in...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	177,507
2404.18101	Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach	Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficiency attained by the acquired model. The training of supervised learning algorithms inherently adheres to predetermined loss functions during ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	450,128
2407.10811	GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents	Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation. The industry's observable input is much more limit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	473,139
2106.15274	Autonomous Driving Implementation in an Experimental Environment	Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the e...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	243,677
2203.09135	Co-visual pattern augmented generative transformer learning for automobile geo-localization	Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions. Cross-view geo-localization (CVGL), which aims to estimate the geographical location of the ground-level camera by matching against enormous geo-tagged aeri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,042
2205.07636	A review of ontologies for smart and continuous commissioning	Smart and continuous commissioning (SCCx) of buildings can result in a significant reduction in the gap between design and operational performance. Ontologies play an important role in SCCx as they facilitate data readability and reasoning by machines. A better understanding of ontologies is required in order to develo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	296,671
2207.05050	A Federated Cox Model with Non-Proportional Hazards	Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model, which is widely used in clinical practice. Neural networks, however, typically rely on data that are centrally available, whereas healthcare data are frequently held in secure silos. We present a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	307,402
1903.06753	Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis	The demand of artificial intelligent adoption for condition-based maintenance strategy is astonishingly increased over the past few years. Intelligent fault diagnosis is one critical topic of maintenance solution for mechanical systems. Deep learning models, such as convolutional neural networks (CNNs), have been succe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	124,453
2302.04046	Rover: An online Spark SQL tuning service via generalized transfer learning	Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configurations, where the optimal ones vary with the executed workloads. Among various alternatives for Spark SQL tuning, Bayesian optimization (BO) ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	344,568
1607.00695	Can we reach Pareto optimal outcomes using bottom-up approaches?	Traditionally, researchers in decision making have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc....	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	58,126
1410.8034	Latent Feature Based FM Model For Rating Prediction	Rating Prediction is a basic problem in Recommender System, and one of the most widely used method is Factorization Machines(FM). However, traditional matrix factorization methods fail to utilize the benefit of implicit feedback, which has been proved to be important in Rating Prediction problem. In this work, we consi...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	37,126
2211.14058	Cross-Domain Ensemble Distillation for Domain Generalization	Domain generalization is the task of learning models that generalize to unseen target domains. We propose a simple yet effective method for domain generalization, named cross-domain ensemble distillation (XDED), that learns domain-invariant features while encouraging the model to converge to flat minima, which recently...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	332,705
1206.4504	Revisiting Timed Specification Theories: A Linear-Time Perspective	We consider the setting of component-based design for real-time systems with critical timing constraints. Based on our earlier work, we propose a compositional specification theory for timed automata with I/O distinction, which supports substitutive refinement. Our theory provides the operations of parallel composition...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	16,643
2409.17294	Schr\"odinger bridge based deep conditional generative learning	Conditional generative models represent a significant advancement in the field of machine learning, allowing for the controlled synthesis of data by incorporating additional information into the generation process. In this work we introduce a novel Schr\"odinger bridge based deep generative method for learning conditio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,727
2412.02171	Underload: Defending against Latency Attacks for Object Detectors on Edge Devices	Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbations in the system inputs, which were generally known as misclassifyin...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	513,416
2407.08153	Lifelong Histopathology Whole Slide Image Retrieval via Distance Consistency Rehearsal	Content-based histopathological image retrieval (CBHIR) has gained attention in recent years, offering the capability to return histopathology images that are content-wise similar to the query one from an established database. However, in clinical practice, the continuously expanding size of WSI databases limits the pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,037
1304.6810	Inference and learning in probabilistic logic programs using weighted Boolean formulas	Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals giv...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	24,204
2402.03310	V-IRL: Grounding Virtual Intelligence in Real Life	There is a sensory gulf between the Earth that humans inhabit and the digital realms in which modern AI agents are created. To develop AI agents that can sense, think, and act as flexibly as humans in real-world settings, it is imperative to bridge the realism gap between the digital and physical worlds. How can we emb...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	426,954
2402.07647	GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants	We tackle the challenge of building real-world multimodal assistants for complex real-world tasks. We describe the practicalities and challenges of developing and deploying GRILLBot, a leading (first and second prize winning in 2022 and 2023) system deployed in the Alexa Prize TaskBot Challenge. Building on our Open As...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	428,799
1503.01105	ROSA: Robust sparse adaptive channel estimation in the presence of impulsive noises	Based on the assumption of Gaussian noise model, conventional adaptive filtering algorithms for reconstruction sparse channels were proposed to take advantage of channel sparsity due to the fact that broadband wireless channels usually have the sparse nature. However, state-of-the-art algorithms are vulnerable to deter...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,785
1804.06732	DPRed: Making Typical Activation and Weight Values Matter In Deep Learning Computing	We show that selecting a single data type (precision) for all values in Deep Neural Networks, even if that data type is different per layer, amounts to worst case design. Much shorter data types can be used if we target the common case by adjusting the precision at a much finer granularity. We propose Dynamic Precision...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	95,368
2502.10307	SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models	Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems is essential for efficien...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	533,812
2001.07522	Engineering AI Systems: A Research Agenda	Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quali...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	161,057
2312.05187	Seamless: Multilingual Expressive and Streaming Speech Translation	Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this work, we introduce a family of models that enable end-to-end expressive and multilingual translations in a streaming fashion. First, we contri...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	413,976
2003.11547	A Survey on Trajectory Data Management, Analytics, and Learning	Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this survey, we comprehensively review recent research trends in trajector...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	169,640
1308.5332	An Integrated Framework for Diagnosis and Prognosis of Hybrid Systems	Complex systems are naturally hybrid: their dynamic behavior is both continuous and discrete. For these systems, maintenance and repair are an increasing part of the total cost of final product. Efficient diagnosis and prognosis techniques have to be adopted to detect, isolate and anticipate faults. This paper presents...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	26,627
2105.13502	Unsupervised Domain Adaptation of Object Detectors: A Survey	Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performanc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	237,320
2411.02831	Enhancing EmoBot: An In-Depth Analysis of User Satisfaction and Faults in an Emotion-Aware Chatbot	The research community has traditionally shown a keen interest in emotion modeling, with a notable emphasis on the detection aspect. In contrast, the exploration of emotion generation has received less attention.This study delves into an existing state-of-the-art emotional chatbot, EmoBot, designed for generating emoti...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	505,681
2004.11451	War of the Hashtags: Trending New Hashtags to Override Critical Topics in Social Media	Hashtags play a cardinal role in the classification of topics over social media. A sudden burst on the usage of certain hashtags, representing specific topics, give rise to trending topics. Trending topics can be immensely useful as it can spark a discussion on a particular subject. However, it can also be used to supp...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	173,908
2406.01636	COVID-19: post infection implications in different age groups, mechanism, diagnosis, effective prevention, treatment, and recommendations	SARS-CoV-2, the highly contagious pathogen responsible for the COVID-19 pandemic, has persistent effects that begin four weeks after initial infection and last for an undetermined duration. These chronic effects are more harmful than acute ones. This review explores the long-term impact of the virus on various human or...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	460,410
2111.05160	Optimal Rate-Distortion-Leakage Tradeoff for Single-Server Information Retrieval	Private information retrieval protocols guarantee that a user can privately and losslessly retrieve a single file from a database stored across multiple servers. In this work, we propose to simultaneously relax the conditions of perfect retrievability and privacy in order to obtain improved download rates when all file...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	265,711
2401.05800	Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values	The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial process control. However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	420,916
2402.11487	Visual Concept-driven Image Generation with Text-to-Image Diffusion Model	Text-to-image (TTI) diffusion models have demonstrated impressive results in generating high-resolution images of complex and imaginative scenes. Recent approaches have further extended these methods with personalization techniques that allow them to integrate user-illustrated concepts (e.g., the user him/herself) usin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	430,418
1307.6927	Secret Key Cryptosystem based on Polar Codes over Binary Erasure Channel	This paper proposes an efficient secret key cryptosystem based on polar codes over Binary Erasure Channel. We introduce a method, for the first time to our knowledge, to hide the generator matrix of the polar codes from an attacker. In fact, our main goal is to achieve secure and reliable communication using finite-len...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	26,055
1703.01973	Batched High-dimensional Bayesian Optimization via Structural Kernel Learning	Optimization of high-dimensional black-box functions is an extremely challenging problem. While Bayesian optimization has emerged as a popular approach for optimizing black-box functions, its applicability has been limited to low-dimensional problems due to its computational and statistical challenges arising from high...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	69,468
2407.18114	Unsupervised Training of Neural Cellular Automata on Edge Devices	The disparity in access to machine learning tools for medical imaging across different regions significantly limits the potential for universal healthcare innovation, particularly in remote areas. Our research addresses this issue by implementing Neural Cellular Automata (NCA) training directly on smartphones for acces...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,251
2310.09877	Statistical inference using machine learning and classical techniques based on accumulated local effects (ALE)	Accumulated Local Effects (ALE) is a model-agnostic approach for global explanations of the results of black-box machine learning (ML) algorithms. There are at least three challenges with conducting statistical inference based on ALE: ensuring the reliability of ALE analyses, especially in the context of small datasets...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	399,987
2207.07429	Continual Learning For On-Device Environmental Sound Classification	Continuously learning new classes without catastrophic forgetting is a challenging problem for on-device environmental sound classification given the restrictions on computation resources (e.g., model size, running memory). To address this issue, we propose a simple and efficient continual learning method. Our method s...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	308,203
2210.08363	Data-Efficient Augmentation for Training Neural Networks	Data augmentation is essential to achieve state-of-the-art performance in many deep learning applications. However, the most effective augmentation techniques become computationally prohibitive for even medium-sized datasets. To address this, we propose a rigorous technique to select subsets of data points that when au...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	324,115
2404.01288	Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided	Large language models (LLMs) have offered new opportunities for emotional support, and recent work has shown that they can produce empathic responses to people in distress. However, long-term mental well-being requires emotional self-regulation, where a one-time empathic response falls short. This work takes a first st...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	443,355
2309.10172	Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach	Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally intractable. In this study, we demonstrate a neural network approach motivated by Enhanced Super-Resolution Generative Adver...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	392,889
1307.0339	Syntactic sensitive complexity for symbol-free sequence	This work uses the L-system to construct a tree structure for the text sequence and derives its complexity. It serves as a measure of structural complexity of the text. It is applied to anomaly detection in data transmission.	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,545
2411.08618	Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach	In power systems, unpredictable events like extreme weather, equipment failures, and cyberattacks present significant challenges to ensuring safety and reliability. Ensuring resilience in the face of these uncertainties is crucial for reliable and efficient operations. This paper presents a tri-level optimization appro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	507,953
0909.4409	Clustering with Obstacles in Spatial Databases	Clustering large spatial databases is an important problem, which tries to find the densely populated regions in a spatial area to be used in data mining, knowledge discovery, or efficient information retrieval. However most algorithms have ignored the fact that physical obstacles such as rivers, lakes, and highways ex...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	4,554
1612.08169	Unsupervised Video Segmentation via Spatio-Temporally Nonlocal Appearance Learning	Video object segmentation is challenging due to the factors like rapidly fast motion, cluttered backgrounds, arbitrary object appearance variation and shape deformation. Most existing methods only explore appearance information between two consecutive frames, which do not make full use of the usefully long-term nonloca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,036
2311.08682	Enhancing Recommender System Performance by Histogram Equalization	Recommender system has been researched for decades with millions of different versions of algorithms created in the industry. In spite of the huge amount of work spent on the field, there are many basic questions to be answered in the field. The most fundamental question to be answered is the accuracy problem, and in r...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	407,826
2110.05481	Which Samples Should be Learned First: Easy or Hard?	An effective weighting scheme for training samples is essential for learning tasks. Numerous weighting schemes have been proposed. Some schemes take the easy-first mode, whereas some others take the hard-first one. Naturally, an interesting yet realistic question is raised. Which samples should be learned first given a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,295
1801.06391	Decoupling schemes for predicting compressible fluid flows	Numerical simulation of compressible fluid flows is performed using the Euler equations. They include the scalar advection equation for the density, the vector advection equation for the velocity and a given pressure dependence on the density. An approximate solution of an initial--boundary value problem is calculated ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	88,598
2106.05190	DPER: Efficient Parameter Estimation for Randomly Missing Data	The missing data problem has been broadly studied in the last few decades and has various applications in different areas such as statistics or bioinformatics. Even though many methods have been developed to tackle this challenge, most of those are imputation techniques that require multiple iterations through the data...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,002
2303.12861	Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT	Breast cancer is the most prevalent cancer among women worldwide, and early detection is crucial for reducing its mortality rate and improving quality of life. Dedicated breast computed tomography (CT) scanners offer better image quality than mammography and tomosynthesis in general but at higher radiation dose. To ena...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	353,435
1009.0282	Empirical processes, typical sequences and coordinated actions in standard Borel spaces	This paper proposes a new notion of typical sequences on a wide class of abstract alphabets (so-called standard Borel spaces), which is based on approximations of memoryless sources by empirical distributions uniformly over a class of measurable "test functions." In the finite-alphabet case, we can take all uniformly b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,444
2002.08583	Regret Minimization in Stochastic Contextual Dueling Bandits	We consider the problem of stochastic $K$-armed dueling bandit in the contextual setting, where at each round the learner is presented with a context set of $K$ items, each represented by a $d$-dimensional feature vector, and the goal of the learner is to identify the best arm of each context sets. However, unlike the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,805
2303.03825	A Reachability Tree-Based Algorithm for Robot Task and Motion Planning	This paper presents a novel algorithm for robot task and motion planning (TAMP) problems by utilizing a reachability tree. While tree-based algorithms are known for their speed and simplicity in motion planning (MP), they are not well-suited for TAMP problems that involve both abstracted and geometrical state variables...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	349,863
2106.10502	JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs	Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. To tackle ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	242,045
2412.05355	MotionShop: Zero-Shot Motion Transfer in Video Diffusion Models with Mixture of Score Guidance	In this work, we propose the first motion transfer approach in diffusion transformer through Mixture of Score Guidance (MSG), a theoretically-grounded framework for motion transfer in diffusion models. Our key theoretical contribution lies in reformulating conditional score to decompose motion score and content score i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	514,806
1705.01371	Weakly-supervised Visual Grounding of Phrases with Linguistic Structures	We propose a weakly-supervised approach that takes image-sentence pairs as input and learns to visually ground (i.e., localize) arbitrary linguistic phrases, in the form of spatial attention masks. Specifically, the model is trained with images and their associated image-level captions, without any explicit region-to-p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	72,836
2401.07518	Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends	Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves the applications in domains of healthcare, commerce, education and so on. Particularly, NLP has been widely applied to the education domain and its applications have enormous potential to help teachi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	421,571
2010.05250	Domain Agnostic Learning for Unbiased Authentication	Authentication is the task of confirming the matching relationship between a data instance and a given identity. Typical examples of authentication problems include face recognition and person re-identification. Data-driven authentication could be affected by undesired biases, i.e., the models are often trained in one ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	200,043
2202.00264	Graph-based Neural Acceleration for Nonnegative Matrix Factorization	We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite graphs that is well-known in certain fields, e.g., sparse linear algebra, but has not yet been exploited to design graph neural networks for matrix computations. We f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	278,085
2108.06207	Disentangling Hate in Online Memes	Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry r...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	250,533
2105.07768	Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search	In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of sparse single-snapshot input measurements, in the case where data-augmentation by side deterministic simulations cannot be performed. The approac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,553
1409.4762	Source and Channel Optimal Rate LDPC Code Design for one Sender in BE-MAC with Source Correlation	In this paper, we present an extension of the semidefinite programming formulation of the optimal rate code design in single link Binary Erasure Channel (BEC) proposed by the authors to the Binary Erasure Multiple Access Channel (BE-MAC) with two sources correlation. This new way can be easily extended to the multiple ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,108
1807.03337	Optimum Transmission Delay for Function Computation in NFV-based Networks: the role of Network Coding and Redundant Computing	In this paper, we study the problem of delay minimization in NFV-based networks. In such systems, the ultimate goal of any request is to compute a sequence of functions in the network, where each function can be computed at only a specific subset of network nodes. In conventional approaches, for each function, we choos...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	102,491
2310.11965	Filling in the Gaps: Efficient Event Coreference Resolution using Graph Autoencoder Networks	We introduce a novel and efficient method for Event Coreference Resolution (ECR) applied to a lower-resourced language domain. By framing ECR as a graph reconstruction task, we are able to combine deep semantic embeddings with structural coreference chain knowledge to create a parameter-efficient family of Graph Autoen...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	400,852
1907.13276	Are Outlier Detection Methods Resilient to Sampling?	Outlier detection is a fundamental task in data mining and has many applications including detecting errors in databases. While there has been extensive prior work on methods for outlier detection, modern datasets often have sizes that are beyond the ability of commonly used methods to process the data within a reasona...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	140,332
1509.07714	Cyclic codes from the first class two-prime Whiteman's generalized cyclotomic sequence with order 6	Binary Whiteman's cyclotomic sequences of orders 2 and 4 have a number of good randomness properties. In this paper, we compute the autocorrelation values and linear complexity of the first class two-prime Whiteman's generalized cyclotomic sequence (WGCS-I) of order $d=6$. Our results show that the autocorrelation valu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	47,284
2101.00307	Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks	Quantifying the topological similarities of different parts of urban road networks (URNs) enables us to understand the urban growth patterns. While conventional statistics provide useful information about characteristics of either a single node's direct neighbors or the entire network, such metrics fail to measure the ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,038
1908.07235	Density estimation in representation space to predict model uncertainty	Deep learning models frequently make incorrect predictions with high confidence when presented with test examples that are not well represented in their training dataset. We propose a novel and straightforward approach to estimate prediction uncertainty in a pre-trained neural network model. Our method estimates the tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	142,242

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