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
2105.11816	Public Transportation Demand Analysis: A Case Study of Metropolitan Lagos	Modelling, simulation, and forecasting offer a means of facilitating better planning and decision-making. These quantitative approaches can add value beyond traditional methods that do not rely on data and are particularly relevant for public transportation. Lagos is experiencing rapid urbanization and currently has a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,830
1701.03313	Information-Theoretic Analysis of Refractory Effects in the P300 Speller	The P300 speller is a brain-computer interface that enables people with neuromuscular disorders to communicate based on eliciting event-related potentials (ERP) in electroencephalography (EEG) measurements. One challenge to reliable communication is the presence of refractory effects in the P300 ERP that induces tempor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,686
2406.16662	Adaptive Coding for Two-Way Wiretap Channel under Strong Secrecy	This paper studies adaptive coding for the two-way wiretap channel. Especially, the strong secrecy metric is of our interest that is defined by the information leakage of transmitted messages to the eavesdropper. First, we consider an adaptive coding, the construction of which is based on running the well studied non-a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	467,216
2310.19858	iGEM: a model system for team science and innovation	Teams are a primary source of innovation in science and technology. Rather than examining the lone genius, scholarly and policy attention has shifted to understanding how team interactions produce new and useful ideas. Yet the organizational roots of innovation remain unclear, in part because of the limitations of curr...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	404,168
1911.06816	QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images	Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	153,627
2102.12179	Multichannel LSTM-CNN for Telugu Technical Domain Identification	With the instantaneous growth of text information, retrieving domain-oriented information from the text data has a broad range of applications in Information Retrieval and Natural language Processing. Thematic keywords give a compressed representation of the text. Usually, Domain Identification plays a significant role...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	221,646
2005.03692	A Systematic Assessment of Syntactic Generalization in Neural Language Models	While state-of-the-art neural network models continue to achieve lower perplexity scores on language modeling benchmarks, it remains unknown whether optimizing for broad-coverage predictive performance leads to human-like syntactic knowledge. Furthermore, existing work has not provided a clear picture about the model p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	176,225
2409.10525	"Is This It?": Towards Ecologically Valid Benchmarks for Situated Collaboration	We report initial work towards constructing ecologically valid benchmarks to assess the capabilities of large multimodal models for engaging in situated collaboration. In contrast to existing benchmarks, in which question-answer pairs are generated post hoc over preexisting or synthetic datasets via templates, human an...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	488,775
1703.00177	Optical Flow-based 3D Human Motion Estimation from Monocular Video	We present a generative method to estimate 3D human motion and body shape from monocular video. Under the assumption that starting from an initial pose optical flow constrains subsequent human motion, we exploit flow to find temporally coherent human poses of a motion sequence. We estimate human motion by minimizing th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,123
2209.01161	Reconstructing editable prismatic CAD from rounded voxel models	Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications. In this work, we introduce a novel neural network architecture to solve this challenging task and approximate a smoothed signed distance function with an editable, constrained, prismati...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	315,792
2009.06483	Unsupervised Domain Adaptation by Uncertain Feature Alignment	Unsupervised domain adaptation (UDA) deals with the adaptation of models from a given source domain with labeled data to an unlabeled target domain. In this paper, we utilize the inherent prediction uncertainty of a model to accomplish the domain adaptation task. The uncertainty is measured by Monte-Carlo dropout and u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,659
2003.06536	The p-AAA algorithm for data driven modeling of parametric dynamical systems	The AAA algorithm has become a popular tool for data-driven rational approximation of single variable functions, such as transfer functions of a linear dynamical system. In the setting of parametric dynamical systems appearing in many prominent applications, the underlying (transfer) function to be modeled is a multiva...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	168,147
2309.15803	ANNCRIPS: Artificial Neural Networks for Cancer Research In Prediction & Survival	Prostate cancer is a prevalent malignancy among men aged 50 and older. Current diagnostic methods primarily rely on blood tests, PSA:Prostate-Specific Antigen levels, and Digital Rectal Examinations (DRE). However, these methods suffer from a significant rate of false positive results. This study focuses on the develop...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	395,124
2406.05410	MLLM-SR: Conversational Symbolic Regression base Multi-Modal Large Language Models	Formulas are the language of communication between humans and nature. It is an important research topic of artificial intelligence to find expressions from observed data to reflect the relationship between each variable in the data, which is called a symbolic regression problem. The existing symbolic regression methods...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	462,122
2312.07696	Real-time Network Intrusion Detection via Decision Transformers	Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e.g., network intrusion detection from a sequence of arriving packets. Existing approaches like reinforcement learning may not be suitable for such cybersecurity decision p...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	415,019
2302.05829	Tighter PAC-Bayes Bounds Through Coin-Betting	We consider the problem of estimating the mean of a sequence of random elements $f(X_1, \theta)$ $, \ldots, $ $f(X_n, \theta)$ where $f$ is a fixed scalar function, $S=(X_1, \ldots, X_n)$ are independent random variables, and $\theta$ is a possibly $S$-dependent parameter. An example of such a problem would be to estim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,184
1811.02701	Proceedings of the 2018 Workshop on Compositional Approaches in Physics, NLP, and Social Sciences	The ability to compose parts to form a more complex whole, and to analyze a whole as a combination of elements, is desirable across disciplines. This workshop bring together researchers applying compositional approaches to physics, NLP, cognitive science, and game theory. Within NLP, a long-standing aim is to represent...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	112,666
2207.04380	Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions	The privacy loss distribution (PLD) provides a tight characterization of the privacy loss of a mechanism in the context of differential privacy (DP). Recent work has shown that PLD-based accounting allows for tighter $(\varepsilon, \delta)$-DP guarantees for many popular mechanisms compared to other known methods. A ke...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	307,181
1707.05972	Drone-based Object Counting by Spatially Regularized Regional Proposal Network	Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed came...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,332
1802.05155	A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization	Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning, e.g., training deep neural networks, variational Bayesian inference, and etc. Despite its empirical success, there is still a lack of theoretical understanding of convergence proper...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,388
2107.12594	On the generalized Hamming weights of hyperbolic codes	A hyperbolic code is an evaluation code that improves a Reed-Muller because the dimension increases while the minimum distance is not penalized. We give the necessary and sufficient conditions, based on the basic parameters of the Reed-Muller, to determine whether a Reed-Muller coincides with a hyperbolic code. Given a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	247,943
2305.04609	SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation	Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	362,839
2104.11403	Low Pass Filter for Anti-aliasing in Temporal Action Localization	In temporal action localization methods, temporal downsampling operations are widely used to extract proposal features, but they often lead to the aliasing problem, due to lacking consideration of sampling rates. This paper aims to verify the existence of aliasing in TAL methods and investigate utilizing low pass filte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,898
2201.10792	On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR	End-to-end automatic speech recognition (ASR) has achieved promising results. However, most existing end-to-end ASR methods neglect the use of specific language characteristics. For Mandarin Chinese ASR tasks, there exist mutual promotion relationship between Pinyin and Character where Chinese characters can be romaniz...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	277,106
2407.19231	Alleviating Over-Smoothing via Aggregation over Compact Manifolds	Graph neural networks (GNNs) have achieved significant success in various applications. Most GNNs learn the node features with information aggregation of its neighbors and feature transformation in each layer. However, the node features become indistinguishable after many layers, leading to performance deterioration: a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,697
2206.06481	RigNeRF: Fully Controllable Neural 3D Portraits	Volumetric neural rendering methods, such as neural radiance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not support the editing of objects, such as a human head, within a scene. In this work, we propose RigNeRF, a system that goes beyond just novel view ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,388
2502.10764	Learning to Explain Air Traffic Situation	Understanding how air traffic controllers construct a mental 'picture' of complex air traffic situations is crucial but remains a challenge due to the inherently intricate, high-dimensional interactions between aircraft, pilots, and controllers. Previous work on modeling the strategies of air traffic controllers and th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	534,038
2411.07942	Towards Low-bit Communication for Tensor Parallel LLM Inference	Tensor parallelism provides an effective way to increase server large language model (LLM) inference efficiency despite adding an additional communication cost. However, as server LLMs continue to scale in size, they will need to be distributed across more devices, magnifying the communication cost. One way to approach...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	507,728
2011.04717	Real-time Locational Marginal Price Forecasting Using Generative Adversarial Network	In this paper, we propose a model-free unsupervised learning approach to forecast real-time locational marginal prices (RTLMPs) in wholesale electricity markets. By organizing system-wide hourly RTLMP data into a 3-dimensional (3D) tensor consisting of a series of time-indexed matrices, we formulate the RTLMP forecasti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,658
2305.02200	Deep Graph Representation Learning and Optimization for Influence Maximization	Influence maximization (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. Researchers have made great progress in designing various traditional methods, and their theoretical design and performance gain are close to a limit. In the past few ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,949
1309.3842	Estimation of intrinsic volumes from digital grey-scale images	Local algorithms are common tools for estimating intrinsic volumes from black-and-white digital images. However, these algorithms are typically biased in the design based setting, even when the resolution tends to infinity. Moreover, images recorded in practice are most often blurred grey-scale images rather than black...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	27,050
1907.00236	Streaming Quantiles Algorithms with Small Space and Update Time	Approximating quantiles and distributions over streaming data has been studied for roughly two decades now. Recently, Karnin, Lang, and Liberty proposed the first asymptotically optimal algorithm for doing so. This manuscript complements their theoretical result by providing a practical variants of their algorithm with...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	136,974
1702.00694	Integrating Soft Robotics with ROS - A hybrid pick and place arm	Soft robotic systems present a variety of new opportunities for solving complex problems. The use of soft robotic grippers, for example, can simplify the complexity in tasks such as the of grasping irregular and delicate objects. Adoption of soft robotics by academia and industry, however, has been slow and this is, in...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	67,690
2502.04758	Differential Privacy of Quantum and Quantum-Inspired-Classical Recommendation Algorithms	We analyze the DP (differential privacy) properties of the quantum recommendation algorithm and the quantum-inspired-classical recommendation algorithm. We discover that the quantum recommendation algorithm is a privacy curating mechanism on its own, requiring no external noise, which is different from traditional diff...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	531,308
1407.2875	Quantum Dynamics, Minkowski-Hilbert space, and A Quantum Stochastic Duhamel Principle	In this paper we shall re-visit the well-known Schr\"odinger and Lindblad dynamics of quantum mechanics. However, these equations may be realized as the consequence of a more general, underlying dynamical process. In both cases we shall see that the evolution of a quantum state $P_\psi=\varrho(0)$ has the not so well-k...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,571
1912.11221	FDD Massive MIMO Uplink and Downlink Channel Reciprocity Properties: Full or Partial Reciprocity?	One challenge for FDD massive MIMO communication system is how to obtain the downlink channel state information (CSI) at the base station. Except for traditional codebook feedback through uplink pilot transmission, some channel reciprocity properties can be utilized through uplink channel estimation and channel paramet...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	158,516
2005.09996	Heterogeneous Susceptibilities in Social Influence Models	Network autocorrelation models are widely used to evaluate the impact of social influence on some variable of interest. This is a large class of models that parsimoniously accounts for how one's neighbors influence one's own behaviors or opinions by incorporating the network adjacency matrix into the joint distribution...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	178,054
2011.04803	Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering	Standard first-order stochastic optimization algorithms base their updates solely on the average mini-batch gradient, and it has been shown that tracking additional quantities such as the curvature can help de-sensitize common hyperparameters. Based on this intuition, we explore the use of exact per-sample Hessian-vect...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,696
1504.00580	Quantum image classification using principal component analysis	We present a novel quantum algorithm for classification of images. The algorithm is constructed using principal component analysis and von Neuman quantum measurements. In order to apply the algorithm we present a new quantum representation of grayscale images.	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	41,716
2012.03108	Generating Synthetic Multispectral Satellite Imagery from Sentinel-2	Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications. Building supervised machine learning models based on these imagery, however, may require ground reference labels which are not available at global scale. Here, we propose a generative model to...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	209,991
1712.07733	A Unified Asymptotic Analysis of Area Spectral Efficiency in Ultradense Cellular Networks	This paper studies the asymptotic properties of average area spectral efficiency (ASE) of a downlink cellular network in the limit of very dense base station (BS) and user densities. This asymptotic analysis relies on three assumptions: (1) interference is treated as noise; (2) the BS locations are drawn from a Poisson...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	87,087
1804.08144	Union bound for quantum information processing	In this paper, we prove a quantum union bound that is relevant when performing a sequence of binary-outcome quantum measurements on a quantum state. The quantum union bound proved here involves a tunable parameter that can be optimized, and this tunable parameter plays a similar role to a parameter involved in the Haya...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	95,698
2405.16551	GPU Based Differential Evolution: New Insights and Comparative Study	Differential Evolution (DE) is a highly successful population based global optimisation algorithm, commonly used for solving numerical optimisation problems. However, as the complexity of the objective function increases, the wall-clock run-time of the algorithm suffers as many fitness function evaluations must take pl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	457,486
2202.10745	Improving Systematic Generalization Through Modularity and Augmentation	Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neural network learning. In this work, we investigate how two well-known modeling principles -- modularity and data augmentation -- affect systematic generalization of n...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	281,648
1412.8340	On the Smallest Eigenvalue of General correlated Gaussian Matrices	This paper investigates the behaviour of the spectrum of generally correlated Gaussian random matrices whose columns are zero-mean independent vectors but have different correlations, under the specific regime where the number of their columns and that of their rows grow at infinity with the same pace. This work is, in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,909
1110.0207	Analysing complexity of XML Schemas in geospatial web services	XML Schema is the language used to define the structure of messages exchanged between OGC-based web service clients and providers. The size of these schemas has been growing with time, reaching a state that makes its understanding and effective application a hard task. A first step to cope with this situation is to pro...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	12,442
2501.07746	A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks	The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide valuable insights for improving communication and understanding of human behaviors. Despite significant advancements in Affective Computing, th...	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	false	false	524,493
2301.08245	Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces	Estimating depth from images nowadays yields outstanding results, both in terms of in-domain accuracy and generalization. However, we identify two main challenges that remain open in this field: dealing with non-Lambertian materials and effectively processing high-resolution images. Purposely, we propose a novel datase...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	341,147
2104.11645	Software-Defined Edge Computing: A New Architecture Paradigm to Support IoT Data Analysis	The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the cur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	231,979
2407.00119	Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations	The task of multi-modal emotion recognition in conversation (MERC) aims to analyze the genuine emotional state of each utterance based on the multi-modal information in the conversation, which is crucial for conversation understanding. Existing methods focus on using graph neural networks (GNN) to model conversational ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	468,735
2006.07565	Line-of-Sight MIMO for High Capacity Millimeter Wave Backhaul in FDD Systems	Wireless backhaul is considered to be the key part of the future wireless network with dense small cell traffic and high capacity demand. In this paper, we focus on the design of a high spectral efficiency line-of-sight (LoS) multiple-input multiple-output (MIMO) system for millimeter wave (mmWave) backhaul using dual-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	181,854
2408.11146	Swim till You Sink: Computing the Limit of a Game	During 2023, two interesting results were proven about the limit behavior of game dynamics: First, it was shown that there is a game for which no dynamics converges to the Nash equilibria. Second, it was shown that the sink equilibria of a game adequately capture the limit behavior of natural game dynamics. These two r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	482,156
1608.05766	On Nonconvex Decentralized Gradient Descent	Consensus optimization has received considerable attention in recent years. A number of decentralized algorithms have been proposed for {convex} consensus optimization. However, to the behaviors or consensus \emph{nonconvex} optimization, our understanding is more limited. When we lose convexity, we cannot hope our a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	60,021
2109.05702	Covert queueing problem with a Markovian statistic	Based on the covert communication framework, we consider a covert queueing problem that has a Markovian statistic. Willie jobs arrive according to a Poisson process and require service from server Bob. Bob does not have a queue for jobs to wait and hence when the server is busy, arriving Willie jobs are lost. Willie an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	254,905
2409.11432	A hybrid solution for 2-UAV RAN slicing	It's possible to distribute the Internet to users via drones. However it is then necessary to place the drones according to the positions of the users. Moreover, the 5th Generation (5G) New Radio (NR) technology is designed to accommodate a wide range of applications and industries. The NGNM 5G White Paper \cite{5gwhit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	489,155
1412.5263	Graph Analytics using the Vertica Relational Database	Graph analytics is becoming increasingly popular, with a deluge of new systems for graph analytics having been proposed in the past few years. These systems often start from the assumption that a new storage or query processing system is needed, in spite of graph data being often collected and stored in a relational da...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	38,469
2202.02491	Distributed Learning With Sparsified Gradient Differences	A very large number of communications are typically required to solve distributed learning tasks, and this critically limits scalability and convergence speed in wireless communications applications. In this paper, we devise a Gradient Descent method with Sparsification and Error Correction (GD-SEC) to improve the comm...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	278,833
2107.08567	Structural Design Recommendations in the Early Design Phase using Machine Learning	Structural engineering knowledge can be of significant importance to the architectural design team during the early design phase. However, architects and engineers do not typically work together during the conceptual phase; in fact, structural engineers are often called late into the process. As a result, updates in th...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,765
2105.00573	Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks	End-to-end approaches for sequence tasks are becoming increasingly popular. Yet for complex sequence tasks, like speech translation, systems that cascade several models trained on sub-tasks have shown to be superior, suggesting that the compositionality of cascaded systems simplifies learning and enables sophisticated ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	233,259
2411.11079	Electrostatic Force Regularization for Neural Structured Pruning	The demand for deploying deep convolutional neural networks (DCNNs) on resource-constrained devices for real-time applications remains substantial. However, existing state-of-the-art structured pruning methods often involve intricate implementations, require modifications to the original network architectures, and nece...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,911
1307.1387	Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm	Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using S...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	25,622
2412.03307	Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions	Demand for bike sharing is impacted by various factors, such as weather conditions, events, and the availability of other transportation modes. This impact remains elusive due to the complex interdependence of these factors or locationrelated user behavior variations. It is also not clear which factor is additional inf...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	513,909
2010.05324	Multilingual Offensive Language Identification with Cross-lingual Embeddings	Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g. hate speech, cyberbulling, and cyberaggression). The clear majority of these studies dea...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	200,073
1304.2749	Evidential Reasoning in Image Understanding	In this paper, we present some results of evidential reasoning in understanding multispectral images of remote sensing systems. The Dempster-Shafer approach of combination of evidences is pursued to yield contextual classification results, which are compared with previous results of the Bayesian context free classifica...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	23,758
1011.0492	Multiscale Bone Remodelling with Spatial P Systems	Many biological phenomena are inherently multiscale, i.e. they are characterized by interactions involving different spatial and temporal scales simultaneously. Though several approaches have been proposed to provide "multilayer" models, only Complex Automata, derived from Cellular Automata, naturally embed spatial inf...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,109
1609.09270	Pano2CAD: Room Layout From A Single Panorama Image	This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360-degree panorama image. Assuming Manhattan World geometry, we formulate the task as a Bayesian inference problem in which we estimate positions and orientations of walls and objects. The method combines surface...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,700
2211.06770	MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning	While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very limited due to their very high computational complexity. In this paper, we present a novel MicroISP model designed specifically for edge de...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	330,019
2306.00310	Prompt Algebra for Task Composition	We investigate whether prompts learned independently for different tasks can be later combined through prompt algebra to obtain a model that supports composition of tasks. We consider Visual Language Models (VLM) with prompt tuning as our base classifier and formally define the notion of prompt algebra. We propose cons...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	369,956
2206.10909	Model-Driven Deep Learning-Based MIMO-OFDM Detector: Design, Simulation, and Experimental Results	Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth cons...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	304,080
2501.03545	Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text Generation	This paper presents ICAT, an evaluation framework for measuring coverage of diverse factual information in long-form text generation. ICAT breaks down a long output text into a list of atomic claims and not only verifies each claim through retrieval from a (reliable) knowledge source, but also computes the alignment be...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	522,915
0708.4214	High Rate Single-Symbol Decodable Precoded DSTBCs for Cooperative Networks	Distributed Orthogonal Space-Time Block Codes (DOSTBCs) achieving full diversity order and single-symbol ML decodability have been introduced recently for cooperative networks and an upper-bound on the maximal rate of such codes along with code constructions has been presented. In this report, we introduce a new class ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	615
2406.18575	Research on Driver Facial Fatigue Detection Based on Yolov8 Model	In a society where traffic accidents frequently occur, fatigue driving has emerged as a grave issue. Fatigue driving detection technology, especially those based on the YOLOv8 deep learning model, has seen extensive research and application as an effective preventive measure. This paper discusses in depth the methods a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	468,084
1607.04731	Weakly supervised object detection using pseudo-strong labels	Object detection is an import task of computer vision.A variety of methods have been proposed,but methods using the weak labels still do not have a satisfactory result.In this paper,we propose a new framework that using the weakly supervised method's output as the pseudo-strong labels to train a strongly supervised mod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,650
2407.00896	Channel Modeling Aided Dataset Generation for AI-Enabled CSI Feedback: Advances, Challenges, and Solutions	The AI-enabled autoencoder has demonstrated great potential in channel state information (CSI) feedback in frequency division duplex (FDD) multiple input multiple output (MIMO) systems. However, this method completely changes the existing feedback strategies, making it impractical to deploy in recent years. To address ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	469,045
1704.04800	Non-parametric Impedance based Stability and Controller Bandwidth Extraction from Impedance Measurements of HVDC-connected Wind Farms	Impedance measurements have been widely used with the Nyquist plot to estimate the stability of interconnected power systems. Being a black-box method for equivalent and aggregated impedance estimation, its use for the identification of sub-components bandwidth is not a straightforward task. This paper proposes a simpl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	71,895
2406.15791	Wireless MapReduce Arrays for Coded Distributed Computing	We consider a wireless distributed computing system based on the MapReduce framework, which consists of three phases: \textit{Map}, \textit{Shuffle}, and \textit{Reduce}. The system consists of a set of distributed nodes assigned to compute arbitrary output functions depending on a file library. The computation of the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	466,869
2308.12831	EFormer: Enhanced Transformer towards Semantic-Contour Features of Foreground for Portraits Matting	The portrait matting task aims to extract an alpha matte with complete semantics and finely-detailed contours. In comparison to CNN-based approaches, transformers with self-attention module have a better capacity to capture long-range dependencies and low-frequency semantic information of a portrait. However, the recen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,684
2302.13960	Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition	We develop novel methodology for active feature acquisition (AFA), the study of how to sequentially acquire a dynamic (on a per instance basis) subset of features that minimizes acquisition costs whilst still yielding accurate predictions. The AFA framework can be useful in a myriad of domains, including health care ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	348,109
2005.07310	Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models	Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. Models such as ViLBERT, LXMERT and UNITER have significantly lifted state of the art across a wide range of V+L benchmarks with joint image-text pre-training. However, little is known about the inner mechanis...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	177,248
2201.01388	End-to-End Autoencoder Communications with Optimized Interference Suppression	An end-to-end communications system based on Orthogonal Frequency Division Multiplexing (OFDM) is modeled as an autoencoder (AE) for which the transmitter (coding and modulation) and receiver (demodulation and decoding) are represented as deep neural networks (DNNs) of the encoder and decoder, respectively. This AE com...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	true	274,233
2210.00489	Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation	We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, which jointly encodes semantics with appearance and geometry. The core of our method is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,883
2411.05960	A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra	Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,906
2310.19802	Stochastic Thermodynamics of Learning Parametric Probabilistic Models	We have formulated a family of machine learning problems as the time evolution of Parametric Probabilistic Models (PPMs), inherently rendering a thermodynamic process. Our primary motivation is to leverage the rich toolbox of thermodynamics of information to assess the information-theoretic content of learning a probab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,138
2410.23831	FRoundation: Are Foundation Models Ready for Face Recognition?	Foundation models are predominantly trained in an unsupervised or self-supervised manner on highly diverse and large-scale datasets, making them broadly applicable to various downstream tasks. In this work, we investigate for the first time whether such models are suitable for the specific domain of face recognition (F...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,204
2203.15392	Efficient Hybrid Network: Inducting Scattering Features	Recent work showed that hybrid networks, which combine predefined and learnt filters within a single architecture, are more amenable to theoretical analysis and less prone to overfitting in data-limited scenarios. However, their performance has yet to prove competitive against the conventional counterparts when suffici...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,384
1902.01889	Analyzing and Improving Representations with the Soft Nearest Neighbor Loss	We explore and expand the $\textit{Soft Nearest Neighbor Loss}$ to measure the $\textit{entanglement}$ of class manifolds in representation space: i.e., how close pairs of points from the same class are relative to pairs of points from different classes. We demonstrate several use cases of the loss. As an analytical to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,754
2206.07944	Distributed Online Private Learning of Convex Nondecomposable Objectives	We deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objectives are considered. Under this setting, each node only controls a part of the global decision, and the goal of all nodes is to collaboratively minimize the global cost ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	302,944
1901.08710	When Can Neural Networks Learn Connected Decision Regions?	Previous work has questioned the conditions under which the decision regions of a neural network are connected and further showed the implications of the corresponding theory to the problem of adversarial manipulation of classifiers. It has been proven that for a class of activation functions including leaky ReLU, neur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,558
2309.14660	CoFiI2P: Coarse-to-Fine Correspondences for Image-to-Point Cloud Registration	Image-to-point cloud (I2P) registration is a fundamental task for robots and autonomous vehicles to achieve cross-modality data fusion and localization. Current I2P registration methods primarily focus on estimating correspondences at the point or pixel level, often neglecting global alignment. As a result, I2P matchin...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	394,692
2303.02314	Virtual Sparse Convolution for Multimodal 3D Object Detection	Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, introducing a huge amount of redundant computation during detection. Meanwhile, noises brought by inacc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	349,301
2206.01904	Soft Adversarial Training Can Retain Natural Accuracy	Adversarial training for neural networks has been in the limelight in recent years. The advancement in neural network architectures over the last decade has led to significant improvement in their performance. It sparked an interest in their deployment for real-time applications. This process initiated the need to unde...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	300,660
2405.18033	RT-GS2: Real-Time Generalizable Semantic Segmentation for 3D Gaussian Representations of Radiance Fields	Gaussian Splatting has revolutionized the world of novel view synthesis by achieving high rendering performance in real-time. Recently, studies have focused on enriching these 3D representations with semantic information for downstream tasks. In this paper, we introduce RT-GS2, the first generalizable semantic segmenta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,245
2004.08572	Automatic Grading of Knee Osteoarthritis on the Kellgren-Lawrence Scale from Radiographs Using Convolutional Neural Networks	The severity of knee osteoarthritis is graded using the 5-point Kellgren-Lawrence (KL) scale where healthy knees are assigned grade 0, and the subsequent grades 1-4 represent increasing severity of the affliction. Although several methods have been proposed in recent years to develop models that can automatically predi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	173,104
2402.10184	Reward Generalization in RLHF: A Topological Perspective	Existing alignment methods share a common topology of information flow, where reward information is collected from humans, modeled with preference learning, and used to tune language models. However, this shared topology has not been systematically characterized, nor have its alternatives been thoroughly explored, leav...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	429,857
2402.12101	Design and Performance of Enhanced Spread Spectrum Aloha for Unsourced Multiple Access	We analyze the performance of enhanced spread spectrum Aloha (E-SSA) in the framework of unsourced multiple access (UMAC). The asynchronous, unframed transmission of E-SSA is modified to enable a direct comparison with framed UMAC schemes and with Polyanskiy's achievability bound. The design of E-SSA is tailored to the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	430,715
2208.03763	Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation	The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over the last few years, almost all existing SGG models follow the same training paradigm: they treat both object and predicate classification in S...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,890
2501.07276	Bridging Smart Meter Gaps: A Benchmark of Statistical, Machine Learning and Time Series Foundation Models for Data Imputation	The integrity of time series data in smart grids is often compromised by missing values due to sensor failures, transmission errors, or disruptions. Gaps in smart meter data can bias consumption analyses and hinder reliable predictions, causing technical and economic inefficiencies. As smart meter data grows in volume ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	524,338
1411.5923	Stability and disturbance attenuation for a switched Markov jump linear system	We address a class of Markov jump linear systems that are characterized by the underlying Markov process being time-inhomogeneous with a priori unknown transition probabilities. Necessary and sufficient conditions for uniform stochastic stability and uniform stochastic disturbance attenuation are reported. In both case...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	37,780
2411.12901	Signformer is all you need: Towards Edge AI for Sign Language	Sign language translation, especially in gloss-free paradigm, is confronting a dilemma of impracticality and unsustainability due to growing resource-intensive methodologies. Contemporary state-of-the-arts (SOTAs) have significantly hinged on pretrained sophiscated backbones such as Large Language Models (LLMs), embedd...	true	false	false	false	false	false	true	false	true	false	false	true	false	true	false	false	false	false	509,590
2409.02647	Learning-Based Error Detection System for Advanced Vehicle Instrument Cluster Rendering	The automotive industry is currently expanding digital display options with every new model that comes onto the market. This entails not just an expansion in dimensions, resolution, and customization choices, but also the capability to employ novel display effects like overlays while assembling the content of the displ...	true	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	485,780

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