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
1701.08288	Select Your Questions Wisely: For Entity Resolution With Crowd Errors	Crowdsourcing is becoming increasingly important in entity resolution tasks due to their inherent complexity such as clustering of images and natural language processing. Humans can provide more insightful information for these difficult problems compared to machine-based automatic techniques. Nevertheless, human worke...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	67,437
2205.10841	Robust Modeling and Controls for Racing on the Edge	Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of h...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	297,894
2409.15383	Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics	Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performance across species and habitats, especially in complex soundscapes. In ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	490,905
1008.4232	Online Learning in Case of Unbounded Losses Using the Follow Perturbed Leader Algorithm	In this paper the sequential prediction problem with expert advice is considered for the case where losses of experts suffered at each step cannot be bounded in advance. We present some modification of Kalai and Vempala algorithm of following the perturbed leader where weights depend on past losses of the experts. New ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	7,366
2303.05076	Pedestrian Attribute Editing for Gait Recognition and Anonymization	As a kind of biometrics, the gait information of pedestrians has attracted widespread attention from both industry and academia since it can be acquired from long distances without the cooperation of targets. In recent literature, this line of research has brought exciting chances along with alarming challenges: On the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,327
2402.00698	Time-Series Analysis Approach for Improving Energy Efficiency of a Fixed-Route Vessel in Short-Sea Shipping	Several approaches have been developed for improving the ship energy efficiency, thereby reducing operating costs and ensuring compliance with climate change mitigation regulations. Many of these approaches will heavily depend on measured data from onboard IoT devices, including operational and environmental informatio...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	425,686
2103.10621	Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement	Low-light image enhancement aims to improve an image's visibility while keeping its visual naturalness. Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the low-light image while ref...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,515
2401.05667	EsaCL: Efficient Continual Learning of Sparse Models	A key challenge in the continual learning setting is to efficiently learn a sequence of tasks without forgetting how to perform previously learned tasks. Many existing approaches to this problem work by either retraining the model on previous tasks or by expanding the model to accommodate new tasks. However, these appr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	420,868
1912.12828	ICSTrace: A Malicious IP Traceback Model for Attacking Data of Industrial Control System	Considering the attacks against industrial control system are mostly organized and premeditated actions, IP traceback is significant for the security of industrial control system. Based on the infrastructure of the Internet, we have developed a novel malicious IP traceback model-ICSTrace, without deploying any new serv...	false	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	true	158,941
2412.04261	Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier	We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models. By leveraging several years of research at Cohere F...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	514,330
2012.04573	Estimation of the Mean Function of Functional Data via Deep Neural Networks	In this work, we propose a deep neural network method to perform nonparametric regression for functional data. The proposed estimators are based on sparsely connected deep neural networks with ReLU activation function. By properly choosing network architecture, our estimator achieves the optimal nonparametric convergen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,496
2104.05508	Noether: The More Things Change, the More Stay the Same	Symmetries have proven to be important ingredients in the analysis of neural networks. So far their use has mostly been implicit or seemingly coincidental. We undertake a systematic study of the role that symmetry plays. In particular, we clarify how symmetry interacts with the learning algorithm. The key ingredient ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,755
2001.02524	LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition	In recent years, deep learning has achieved great success in many natural language processing tasks including named entity recognition. The shortcoming is that a large amount of manually-annotated data is usually required. Previous studies have demonstrated that active learning could elaborately reduce the cost of data...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	159,758
2405.10216	Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting	Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large pre-trained or foundational models across different modalities and tasks. However, its application to time series data, particularly within foundational models, remains underexplored. This paper examines the impact of LoRA on contemporary time ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	454,679
2205.04538	A Realistic Cyclist Model for SUMO Based on the SimRa Dataset	Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on simulation tools such as SUMO which allow them to study the effects of construction ch...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	295,666
2105.03743	Certified Robustness to Text Adversarial Attacks by Randomized [MASK]	Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions. However, all existing certified defense methods assume that the defenders are informed of how the adversaries generate synonyms, which is not a realistic scenario. ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	234,250
2311.02421	Digital Twins for Human-Robot Collaboration: A Future Perspective	As collaborative robot (Cobot) adoption in many sectors grows, so does the interest in integrating digital twins in human-robot collaboration (HRC). Virtual representations of physical systems (PT) and assets, known as digital twins, can revolutionize human-robot collaboration by enabling real-time simulation, monitori...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	405,437
2402.12713	Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs	Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. However, their use is challenged by intrinsic biases (e.g., risk-preference bias) and a superficial understanding of market intricacies, necessitating a thorough assessment of their financial ins...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	430,960
2406.06910	Agent-SiMT: Agent-assisted Simultaneous Machine Translation with Large Language Models	Simultaneous Machine Translation (SiMT) generates target translations while reading the source sentence. It relies on a policy to determine the optimal timing for reading sentences and generating translations. Existing SiMT methods generally adopt the traditional Transformer architecture, which concurrently determines ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	462,819
1902.08588	Towards Neural Mixture Recommender for Long Range Dependent User Sequences	Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and items over time. However, while different model architectures excel at capturing various temporal ranges or dynamics, distinct application cont...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	122,224
2008.10580	Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks	The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences. To this end, we first propose an efficient approach to convert the RNA sequences into images characterizing their base-pairing probability. As a result, classifying RNA sequences is converted to an image ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,043
1803.05815	OFDM-Autoencoder for End-to-End Learning of Communications Systems	We extend the idea of end-to-end learning of communications systems through deep neural network (NN)-based autoencoders to orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same benefits as a conventional OFDM system, namely singletap equalization and robustness again...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	92,708
2406.02047	Kinematic analysis of a parallel robot for minimally invasive surgery	The paper presents the kinematic modelling for the coupled motion of a 6-DOF surgical parallel robot PARA-SILSROB which guides a mobile platform carrying the surgical instruments, and the actuators of the sub-modules which hold these tools. To increase the surgical procedure safety, a closed form solution for the kinem...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	460,589
2309.06677	SHARM: Segmented Head Anatomical Reference Models	Reliable segmentation of anatomical tissues of human head is a major step in several clinical applications such as brain mapping, surgery planning and associated computational simulation studies. Segmentation is based on identifying different anatomical structures through labeling different tissues through medical imag...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	391,501
cs/0406047	Self-organizing neural networks in classification and image recognition	Self-organizing neural networks are used for brick finding in OPERA experiment. Self-organizing neural networks and wavelet analysis used for recognition and extraction of car numbers from images.	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	538,244
2205.05600	RLOP: RL Methods in Option Pricing from a Mathematical Perspective	Abstract In this work, we build two environments, namely the modified QLBS and RLOP models, from a mathematics perspective which enables RL methods in option pricing through replicating by portfolio. We implement the environment specifications (the source code can be found at https://github.com/owen8877/RLOP), the lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,980
2101.12369	Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection	In this paper, we study the information theoretic bounds for exact recovery in sub-hypergraph models for community detection. We define a general model called the $m-$uniform sub-hypergraph stochastic block model ($m-$ShSBM). Under the $m-$ShSBM, we use Fano's inequality to identify the region of model parameters where...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	217,543
1805.07784	Adaptive Recovery of Dictionary-sparse Signals using Binary Measurements	One-bit compressive sensing (CS) is an advanced version of sparse recovery in which the sparse signal of interest can be recovered from extremely quantized measurements. Namely, only the sign of each measurement is available to us. In many applications, the ground-truth signal is not sparse itself, but can be represent...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,935
1908.00981	Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections	One challenging aspect of the Connected and Automated Vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving beha...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	140,644
2405.20104	Object-centric Reconstruction and Tracking of Dynamic Unknown Objects using 3D Gaussian Splatting	Generalizable perception is one of the pillars of high-level autonomy in space robotics. Estimating the structure and motion of unknown objects in dynamic environments is fundamental for such autonomous systems. Traditionally, the solutions have relied on prior knowledge of target objects, multiple disparate representa...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	459,199
2410.16240	Nonlinear Magnetics Model for Permanent Magnet Synchronous Machines Capturing Saturation and Temperature Effects	This paper proposes a nonlinear magnetics model for Permanent Magnet Synchronous Machines (PMSMs) that accurately captures the effects of magnetic saturation in the machine iron and variations in rotor temperature on the permanent magnet excitation. The proposed model considers the permanent magnet as a current source ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	500,941
2004.02767	Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio	Automatic designing computationally efficient neural networks has received much attention in recent years. Existing approaches either utilize network pruning or leverage the network architecture search methods. This paper presents a new framework named network adjustment, which considers network accuracy as a function ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,336
2308.14525	Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation	Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from images, including static elements (e.g., roads) and dynamic elements (e.g., vehicles, pedestrians). However, the high cost of annotation procedures of full-supervised methods limits the capabili...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,363
2205.11460	Graph-theoretical approach to robust 3D normal extraction of LiDAR data	Low dimensional primitive feature extraction from LiDAR point clouds (such as planes) forms the basis of majority of LiDAR data processing tasks. A major challenge in LiDAR data analysis arises from the irregular nature of LiDAR data that forces practitioners to either regularize the data using some form of gridding or...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	298,150
2405.20681	No Free Lunch Theorem for Privacy-Preserving LLM Inference	Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productivity, reduce costs, and enable us to focus on more valuable tasks. Furthermore, LLMs possess the capacity to sift through extensive datasets,...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	459,469
2003.12944	Mutual Learning Network for Multi-Source Domain Adaptation	Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the setting of a single source domain, where all the labeled source data come from the same distribution. However, in practice the labeled data can come from multiple source domains with different distributions. In such scenarios, the single source ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	170,051
2110.06339	Natural Computational Architectures for Cognitive Info-Communication	Recent comprehensive overview of 40 years of research in cognitive architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core cognitive abilities in humans, but only marginally addresses biologically plausible approaches based on natural computation. This mini review presents a set of perspectives and...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	260,571
2303.08896	SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models	Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output. Existing fact-checking approaches either require access to th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	351,813
2001.09249	TiFL: A Tier-based Federated Learning System	Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	161,515
1501.07422	Pairwise Rotation Hashing for High-dimensional Features	Binary Hashing is widely used for effective approximate nearest neighbors search. Even though various binary hashing methods have been proposed, very few methods are feasible for extremely high-dimensional features often used in visual tasks today. We propose a novel highly sparse linear hashing method based on pairwis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,707
2409.02841	Historical German Text Normalization Using Type- and Token-Based Language Modeling	Historic variations of spelling poses a challenge for full-text search or natural language processing on historical digitized texts. To minimize the gap between the historic orthography and contemporary spelling, usually an automatic orthographic normalization of the historical source material is pursued. This report p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	485,847
2308.05751	Artificial-Intelligence-Based Design for Circuit Parameters of Power Converters	Parameter design is significant in ensuring a satisfactory holistic performance of power converters. Generally, circuit parameter design for power converters consists of two processes: analysis and deduction process and optimization process. The existing approaches for parameter design consist of two types: traditional...	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	384,899
1906.06765	Defending Against Adversarial Attacks Using Random Forests	As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world. Unfortunately, some recent studies show that adversarial examples, which are hard to be distinguished from real examples, can easily fool DNNs and m...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	135,403
2307.04571	Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation	Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice in decision-making processes like interactive recommendation. Offline RL faces the value overestimation problem. To address it, existing me...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	378,451
2410.22007	Survey of Load-Altering Attacks Against Power Grids: Attack Impact, Detection and Mitigation	The growing penetration of IoT devices in power grids despite its benefits, raises cyber security concerns. In particular, load-altering attacks (LAAs) targetting high-wattage IoT-controllable load devices pose serious risks to grid stability and disrupt electricity markets. This paper provides a comprehensive review o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	503,465
2204.08261	Visio-Linguistic Brain Encoding	Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding aims at constructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain encoding for si...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	292,021
2208.05399	Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion	Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e.g. difficulty in guaranteeing intra- and inter-operator repeatability. However, due to anatomical and physiological variations between patients and relative movement of anatomical substructures, it is challenging ...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	312,397
2008.02069	Data Cleansing with Contrastive Learning for Vocal Note Event Annotations	Data cleansing is a well studied strategy for cleaning erroneous labels in datasets, which has not yet been widely adopted in Music Information Retrieval. Previously proposed data cleansing models do not consider structured (e.g. time varying) labels, such as those common to music data. We propose a novel data cleansin...	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	190,518
2305.15118	Fairness in Streaming Submodular Maximization over a Matroid Constraint	Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset. If datapoints have sensitive attributes such as gender or race, it becomes important to enforce fairness to avoid bias and discrimination. This has spurred significant interest in developin...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	367,464
2010.05013	An Encoder-Decoder CNN for Hair Removal in Dermoscopic Images	The process of removing occluding hair has a relevant role in the early and accurate diagnosis of skin cancer. It consists of detecting hairs and restore the texture below them, which is sporadically occluded. In this work, we present a model based on convolutional neural networks for hair removal in dermoscopic images...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	199,962
1301.6675	A Temporal Bayesian Network for Diagnosis and Prediction	Diagnosis and prediction in some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel representation called Temporal Nod...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,469
2403.06332	Exploiting the Margin: How Capitalism Fuels AI at the Expense of Minoritized Groups	This paper explores the intricate relationship between capitalism, racial injustice, and artificial intelligence (AI), arguing that AI acts as a contemporary vehicle for age-old forms of exploitation. By linking historical patterns of racial and economic oppression with current AI practices, this study illustrates how ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	436,398
1904.04917	Novel Uncertainty Framework for Deep Learning Ensembles	Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression. Dropout, a method commonly used to improve the convergence of deep neural networks, generates an ensemble of thinned networks with extensive weight sharing. Recent studies that dropout can b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,154
2010.04762	Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data	A growing body of work shows that models exploit annotation artifacts to achieve state-of-the-art performance on standard crowdsourced benchmarks---datasets collected from crowdworkers to create an evaluation task---while still failing on out-of-domain examples for the same task. Recent work has explored the use of cou...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,853
2403.00372	HyperSDFusion: Bridging Hierarchical Structures in Language and Geometry for Enhanced 3D Text2Shape Generation	3D shape generation from text is a fundamental task in 3D representation learning. The text-shape pairs exhibit a hierarchical structure, where a general text like ``chair" covers all 3D shapes of the chair, while more detailed prompts refer to more specific shapes. Furthermore, both text and 3D shapes are inherently h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,958
2210.03123	On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing	Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typically, GNNs are constructed by incorporating alternating layers which learn transformations of graph node features, along with graph pooling ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	321,911
2312.09424	Open Domain Knowledge Extraction for Knowledge Graphs	The quality of a knowledge graph directly impacts the quality of downstream applications (e.g. the number of answerable questions using the graph). One ongoing challenge when building a knowledge graph is to ensure completeness and freshness of the graph's entities and facts. In this paper, we introduce ODKE, a scalabl...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	415,710
2105.01998	Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors	In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,700
2005.00872	How deep the machine learning can be	Today we live in the age of artificial intelligence and machine learning; from small startups to HW or SW giants, everyone wants to build machine intelligence chips, applications. The task, however, is hard: not only because of the size of the problem: the technology one can utilize (and the paradigm it is based upon) ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	175,414
2406.04299	NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise	Graph Neural Networks (GNNs) exhibit strong potential in node classification task through a message-passing mechanism. However, their performance often hinges on high-quality node labels, which are challenging to obtain in real-world scenarios due to unreliable sources or adversarial attacks. Consequently, label noise ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,610
2407.05155	Wi-Fi Beyond Communications: Experimental Evaluation of Respiration Monitoring and Motion Detection Using COTS Devices	Wi-Fi sensing has become an attractive option for non-invasive monitoring of human activities and vital signs. This paper explores the feasibility of using state-of-the-art commercial off-the-shelf (COTS) devices for Wi-Fi sensing applications, particularly respiration monitoring and motion detection. We utilize the In...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	470,846
1701.08513	Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images	Predictive coding is attractive for compression of hyperspecral images onboard of spacecrafts in light of the excellent rate-distortion performance and low complexity of recent schemes. In this letter we propose a rate control algorithm and integrate it in a lossy extension to the CCSDS-123 lossless compression recomme...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,482
2202.08187	Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey	This paper surveys recent work in the intersection of differential privacy (DP) and fairness. It reviews the conditions under which privacy and fairness may have aligned or contrasting goals, analyzes how and why DP may exacerbate bias and unfairness in decision problems and learning tasks, and describes available miti...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,795
1903.03515	Learning $\textit{Ex Nihilo}$	This paper introduces, philosophically and to a degree formally, the novel concept of learning $\textit{ex nihilo}$, intended (obviously) to be analogous to the concept of creation $\textit{ex nihilo}$. Learning $\textit{ex nihilo}$ is an agent's learning "from nothing," by the suitable employment of schemata for deduc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	123,755
2305.16338	Think Before You Act: Decision Transformers with Working Memory	Decision Transformer-based decision-making agents have shown the ability to generalize across multiple tasks. However, their performance relies on massive data and computation. We argue that this inefficiency stems from the forgetting phenomenon, in which a model memorizes its behaviors in parameters throughout trainin...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	368,032
1812.01388	Bad practices in evaluation methodology relevant to class-imbalanced problems	For research to go in the right direction, it is essential to be able to compare and quantify performance of different algorithms focused on the same problem. Choosing a suitable evaluation metric requires deep understanding of the pursued task along with all of its characteristics. We argue that in the case of applied...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	115,508
1910.03892	Fast Panoptic Segmentation Network	In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet), does not require computationally costly instance mask predictions or merging heuristics. This is achieved by casting the panoptic task into a custom dense pixel-wise classific...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	148,622
2402.11887	Generative Semi-supervised Graph Anomaly Detection	This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	430,623
1902.06843	Fusing Visual, Textual and Connectivity Clues for Studying Mental Health	With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media. Unlike the most existing efforts which study depression by analyzing te...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	121,856
2311.08265	On The Relationship Between Universal Adversarial Attacks And Sparse Representations	The prominent success of neural networks, mainly in computer vision tasks, is increasingly shadowed by their sensitivity to small, barely perceivable adversarial perturbations in image input. In this work, we aim at explaining this vulnerability through the framework of sparsity. We show the connection between adve...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	407,652
2412.18516	Generating Explanations for Autonomous Robots: a Systematic Review	Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot's ability to make its behavior understan...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	520,450
1610.04789	Bsmooth: Learning from user feedback to disambiguate query terms in interactive data retrieval	There is great interest in supporting imprecise queries (e.g., keyword search or natural language queries) over databases today. To support such queries, the database system is typically required to disambiguate parts of the user-specified query against the database, using whatever resources are intrinsically available...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	62,432
1801.01265	Improved Bounds on Lossless Source Coding and Guessing Moments via R\'enyi Measures	This paper provides upper and lower bounds on the optimal guessing moments of a random variable taking values on a finite set when side information may be available. These moments quantify the number of guesses required for correctly identifying the unknown object and, similarly to Arikan's bounds, they are expressed i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	87,702
2111.07031	Improving the Otsu Thresholding Method of Global Binarization Using Ring Theory for Ultrasonographies of Congestive Heart Failure	Ring Theory states that a ring is an algebraic structure where two binary operations can be performed among the elements addition and multiplication. Binarization is a method of image processing where values within pixels are reduced to a scale from zero to one, with zero representing the most absence of light and one ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	266,252
1802.05014	Distributional Term Set Expansion	This paper is a short empirical study of the performance of centrality and classification based iterative term set expansion methods for distributional semantic models. Iterative term set expansion is an interactive process using distributional semantics models where a user labels terms as belonging to some sought afte...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	90,360
2106.02514	The Image Local Autoregressive Transformer	Recently, AutoRegressive (AR) models for the whole image generation empowered by transformers have achieved comparable or even better performance to Generative Adversarial Networks (GANs). Unfortunately, directly applying such AR models to edit/change local image regions, may suffer from the problems of missing global ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	238,899
1907.03736	LocationSpark: In-memory Distributed Spatial Query Processing and Optimization	Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present new techniques for spatial query processing and optimization in an in-memory and distributed setu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	137,920
1808.03604	Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling	Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. Event-based modeling (EBM) is a data-driven technique to estimate the sequence in which biomarkers for a disease become abnormal based on cross-sectional data. It can...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	104,963
2104.05353	Sparse Coding Frontend for Robust Neural Networks	Deep Neural Networks are known to be vulnerable to small, adversarially crafted, perturbations. The current most effective defense methods against these adversarial attacks are variants of adversarial training. In this paper, we introduce a radically different defense trained only on clean images: a sparse coding based...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,694
2310.06457	Small-Signal Stability and SCR Enhancement of Offshore WPPs with Synchronous Condensers	Synchronous condensers (SCs) have been reported to improve the overall stability and short-circuit power of a power system. SCs are also being integrated into offshore wind power plants (WPPs) for the same reason. This paper, investigates the effect of synchronous condensers on an offshore wind power plant with grid-fo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	398,590
1810.09003	Robust Receiver Design for Non-orthogonal Multiple Access	Non-orthogonal multiple access (NOMA) has been proposed for massive connectivity in future generations of wireless communications. A dominant NOMA scheme is based on power optimization, in which decoding of target user is assumed to be perfect. In this work, rather than optimize on power domain, we are aimed to propose...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	110,958
2201.10620	Structural importance and evolution: an application to financial transaction networks	A fundamental problem in the study of networks is the identification of important nodes. This is typically achieved using centrality metrics, which rank nodes in terms of their position in the network. This approach works well for static networks, that do not change over time, but does not consider the dynamics of the ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	277,045
2408.01269	A General Framework to Boost 3D GS Initialization for Text-to-3D Generation by Lexical Richness	Text-to-3D content creation has recently received much attention, especially with the prevalence of 3D Gaussians Splatting. In general, GS-based methods comprise two key stages: initialization and rendering optimization. To achieve initialization, existing works directly apply random sphere initialization or 3D diffusi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	478,164
2112.03154	VAE based Text Style Transfer with Pivot Words Enhancement Learning	Text Style Transfer (TST) aims to alter the underlying style of the source text to another specific style while keeping the same content. Due to the scarcity of high-quality parallel training data, unsupervised learning has become a trending direction for TST tasks. In this paper, we propose a novel VAE based Text Styl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	270,102
2207.07993	Relative Position Estimation in Multi-Agent Systems Using Attitude-Coupled Range Measurements	The ability to accurately estimate the position of robotic agents relative to one another, in possibly GPS-denied environments, is crucial to execute collaborative tasks. Inter-agent range measurements are available at a low cost, due to technologies such as ultra-wideband radio. However, the task of three-dimensional ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	308,408
cs/0606017	From semiotics of hypermedia to physics of semiosis: A view from system theory	Given that theoretical analysis and empirical validation is fundamental to any model, whether conceptual or formal, it is surprising that these two tools of scientific discovery are so often ignored in the contemporary studies of communication. In this paper, we pursued the ideas of a) correcting and expanding the mode...	true	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	539,504
2007.01965	On the application of transfer learning in prognostics and health management	Advancements in sensing and computing technologies, the development of human and computer interaction frameworks, big data storage capabilities, and the emergence of cloud storage and could computing have resulted in an abundance of data in the modern industry. This data availability has encouraged researchers and indu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,585
2308.04008	Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval	Image retrieval targets to find images from a database that are visually similar to the query image. Two-stage methods following retrieve-and-rerank paradigm have achieved excellent performance, but their separate local and global modules are inefficient to real-world applications. To better trade-off retrieval efficie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,249
2409.18313	Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation	There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhorse of large-scale non-parametric knowledge; however, existing techniques do not directly transfer to the em...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	492,186
2409.15127	Boosting Healthcare LLMs Through Retrieved Context	Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, and yet, their factual inaccuracies and hallucinations limits their application, particularly in critical domains like healthcare. Context retrieval methods, by introducing relevant information as input, have emerged ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	490,766
1312.5486	Molecular communication networks with general molecular circuit receivers	In a molecular communication network, transmitters may encode information in concentration or frequency of signalling molecules. When the signalling molecules reach the receivers, they react, via a set of chemical reactions or a molecular circuit, to produce output molecules. The counts of output molecules over time is...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,239
2005.05874	Fair Resource Allocation in Optical Networks under Tidal Traffic	We propose an alpha-fair routing and spectrum allocation (RSA) framework for reconfigurable elastic optical networks under modeled tidal traffic, that is based on the maximization of the social welfare function parameterized by a scalar alpha (the inequality aversion parameter). The objective is to approximate an egali...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	176,855
2411.08892	Auto-assessment of assessment: A conceptual framework towards fulfilling the policy gaps in academic assessment practices	Education is being transformed by rapid advances in Artificial Intelligence (AI), including emerging Generative Artificial Intelligence (GAI). Such technology can significantly support academics and students by automating monotonous tasks and making personalised suggestions. However, despite the potential of the techno...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	508,055
2403.15393	Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network	The opioid epidemic, referring to the growing hospitalizations and deaths because of overdose of opioid usage and addiction, has become a severe health problem in the United States. Many strategies have been developed by the federal and local governments and health communities to combat this crisis. Among them, improvi...	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	440,529
2007.14329	On the use of GNSS for Automatic Detection of Attenuating Environments	When different radio applications share the same spectrum, the separation by attenuating material is a way to mitigate potential interference. The indoor restriction for WLAN devices in 5150-5350 MHz is an example for a regulatory measure that aims at having WLAN devices operating in an environment that provides suffic...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	189,374
2110.07572	LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing	Semantic parsing is the task of producing a structured meaning representation for natural language utterances or questions. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	261,038
1904.05426	A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages	Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to \textit{ground} their clusters as well. Grounding generally requires reference labeled data, a luxury a low-resource language might not have. In this work, we describe an approach for low-resource unsupervis...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	127,313
1911.12082	Topological Machine Learning for Multivariate Time Series	We develop a framework for analyzing multivariate time series using topological data analysis (TDA) methods. The proposed methodology involves converting the multivariate time series to point cloud data, calculating Wasserstein distances between the persistence diagrams and using the $k$-nearest neighbors algorithm ($k...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,310
2307.05888	Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment	In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MC...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	378,885
cs/0608015	Towards "Propagation = Logic + Control"	Constraint propagation algorithms implement logical inference. For efficiency, it is essential to control whether and in what order basic inference steps are taken. We provide a high-level framework that clearly differentiates between information needed for controlling propagation versus that needed for the logical sem...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	539,627

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