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
1903.04392	A Hybrid Controller for Obstacle Avoidance in an n-dimensional Euclidean Space	For a vehicle moving in an $n$-dimensional Euclidean space, we present a construction of a hybrid feedback that guarantees both global asymptotic stabilization of a reference position and avoidance of an obstacle corresponding to a bounded spherical region. The proposed hybrid control algorithm switches between two mod...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	123,966
1606.06997	On the uniqueness and stability of dictionaries for sparse representation of noisy signals	Learning optimal dictionaries for sparse coding has exposed characteristic sparse features of many natural signals. However, universal guarantees of the stability of such features in the presence of noise are lacking. Here, we provide very general conditions guaranteeing when dictionaries yielding the sparsest encoding...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,640
1506.07240	Convolution and Product Theorem for the Special Affine Fourier Transform	The Special Affine Fourier Transform or the SAFT generalizes a number of well known unitary transformations as well as signal processing and optics related mathematical operations. Unlike the Fourier transform, the SAFT does not work well with the standard convolution operation. Recently, Q. Xiang and K. Y. Qin intro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,497
1904.01554	Learning Algorithms via Neural Logic Networks	We propose a novel learning paradigm for Deep Neural Networks (DNN) by using Boolean logic algebra. We first present the basic differentiable operators of a Boolean system such as conjunction, disjunction and exclusive-OR and show how these elementary operators can be combined in a simple and meaningful way to form Neu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	126,169
1301.0564	Iterative Join-Graph Propagation	The paper presents an iterative version of join-tree clustering that applies the message passing of join-tree clustering algorithm to join-graphs rather than to join-trees, iteratively. It is inspired by the success of Pearl's belief propagation algorithm as an iterative approximation scheme on one hand, and by a recen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	20,746
2402.09173	Optimal and Efficient Algorithms for Decentralized Online Convex Optimization	We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using only local computations and communications. Previous studies have established $O(n^{5/4}\rho^{-1/2}\sqrt{T})$ and ${O}(n^{3/2}\rho^{-1}\log T)$ regret boun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,401
2305.18978	IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics	Aiding humans with scientific designs is one of the most exciting of artificial intelligence (AI) and machine learning (ML), due to their potential for the discovery of new drugs, design of new materials and chemical compounds, etc. However, scientific design typically requires complex domain knowledge that is not fami...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	369,327
1206.6391	Gaussian Process Quantile Regression using Expectation Propagation	Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,926
2501.05360	On Corrigibility and Alignment in Multi Agent Games	Corrigibility of autonomous agents is an under explored part of system design, with previous work focusing on single agent systems. It has been suggested that uncertainty over the human preferences acts to keep the agents corrigible, even in the face of human irrationality. We present a general framework for modelling ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	523,549
2010.02744	Stepwise Extractive Summarization and Planning with Structured Transformers	We propose encoder-centric stepwise models for extractive summarization using structured transformers -- HiBERT and Extended Transformers. We enable stepwise summarization by injecting the previously generated summary into the structured transformer as an auxiliary sub-structure. Our models are not only efficient in mo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,150
2104.01948	Robust Trust Region for Weakly Supervised Segmentation	Acquisition of training data for the standard semantic segmentation is expensive if requiring that each pixel is labeled. Yet, current methods significantly deteriorate in weakly supervised settings, e.g. where a fraction of pixels is labeled or when only image-level tags are available. It has been shown that regulariz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,532
1302.1531	Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions	Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expressed locally in Bayesian networks through convex sets of distributions. Two approaches for combination of local models are considered. The first...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,832
2209.00642	Lip-to-Speech Synthesis for Arbitrary Speakers in the Wild	In this work, we address the problem of generating speech from silent lip videos for any speaker in the wild. In stark contrast to previous works, our method (i) is not restricted to a fixed number of speakers, (ii) does not explicitly impose constraints on the domain or the vocabulary and (iii) deals with videos that ...	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	315,645
2207.02862	Verifying the Union of Manifolds Hypothesis for Image Data	Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the manifold hypothesis, which states that the data lies on an unknown manifo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	306,647
2102.10934	Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks	We study the problem of incorporating prior knowledge into a deep Transformer-based model,i.e.,Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks. By probing and analyzing what BERT has already known when solving this task, we obtain better unde...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	221,264
2007.15068	Unselfie: Translating Selfies to Neutral-pose Portraits in the Wild	Due to the ubiquity of smartphones, it is popular to take photos of one's self, or "selfies." Such photos are convenient to take, because they do not require specialized equipment or a third-party photographer. However, in selfies, constraints such as human arm length often make the body pose look unnatural. To address...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,555
1104.5384	Chance-constrained Model Predictive Control for Multi-Agent Systems	We consider stochastic model predictive control of a multi-agent systems with constraints on the probabilities of inter-agent collisions. We first study a sample-based approximation of the collision probabilities and use this approximation to formulate constraints for the stochastic control problem. This approximation ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	10,155
1707.05000	In-Order Transition-based Constituent Parsing	Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, re...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	77,146
2009.10823	Ants, robots, humans: a self-organizing, complex systems modeling approach	Most of the grand challenges of humanity today involve complex agent-based systems, such as epidemiology, economics or ecology. However, remains as a pending task the challenge of identifying the general principles underlying their self-organizing capabilities. This article presents a novel modeling approach, capable t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	196,993
2409.15241	Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping	Given the popularity of generative AI, Large Language Models (LLMs) often consume hundreds or thousands of GPUs for parallelizing and accelerating the training process. Communication overhead becomes more pronounced when training LLMs at scale. To eliminate communication overhead in distributed LLM training, we propose...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	490,815
2002.07257	An Networked HIL Simulation System for Modeling Large-scale Power Systems	This paper presents a network hardware-in-the-loop (HIL) simulation system for modeling large-scale power systems. Researchers have developed many HIL test systems for power systems in recent years. Those test systems can model both microsecond-level dynamic responses of power electronic systems and millisecond-level t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	164,413
1310.0873	Phase Retrieval for Sparse Signals	The aim of this paper is to build up the theoretical framework for the recovery of sparse signals from the magnitude of the measurement. We first investigate the minimal number of measurements for the success of the recovery of sparse signals without the phase information. We completely settle the minimality question f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,524
2007.06606	Power, Preferment, and Patronage: Catholic Bishops, Social Networks, and the Affair(s) of Ex-Cardinal McCarrick	Social Network Analysis (SNA) has shed powerful light on cultures where the influence of patronage, preferment, and reciprocal obligations are traditionally important. Accordingly, we argue here that episcopal appointments, culture, and governance within the Catholic Church are ideal topics for SNA interrogation. We an...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	187,056
2404.08458	On the Independence Assumption in Neurosymbolic Learning	State-of-the-art neurosymbolic learning systems use probabilistic reasoning to guide neural networks towards predictions that conform to logical constraints over symbols. Many such systems assume that the probabilities of the considered symbols are conditionally independent given the input to simplify learning and reas...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	446,251
2012.13455	Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins	Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjects in a broad range of severity and is assessed in clinical trials with multiple cognitive and functional instruments. As clinical trials in AD increasingly focus on earlier stages of the disease, especially Mild Cognitive Impairment (MCI), the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,223
2502.12388	Achieving Upper Bound Accuracy of Joint Training in Continual Learning	Continual learning has been an active research area in machine learning, focusing on incrementally learning a sequence of tasks. A key challenge is catastrophic forgetting (CF), and most research efforts have been directed toward mitigating this issue. However, a significant gap remains between the accuracy achieved by...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	534,838
2002.10716	Understanding and Mitigating the Tradeoff Between Robustness and Accuracy	Adversarial training augments the training set with perturbations to improve the robust error (over worst-case perturbations), but it often leads to an increase in the standard error (on unperturbed test inputs). Previous explanations for this tradeoff rely on the assumption that no predictor in the hypothesis class ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,495
1602.02159	Daleel: Simplifying Cloud Instance Selection Using Machine Learning	Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules, each customer has a specific set of constraints (e.g. budget) and applica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	51,802
2502.12148	HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation	The remarkable success of the autoregressive paradigm has made significant advancement in Multimodal Large Language Models (MLLMs), with powerful models like Show-o, Transfusion and Emu3 achieving notable progress in unified image understanding and generation. For the first time, we uncover a common phenomenon: the und...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	534,712
2206.00580	Dog nose print matching with dual global descriptor based on Contrastive Learning	Recent studies in biometric-based identification tasks have shown that deep learning methods can achieve better performance. These methods generally extract the global features as descriptor to represent the original image. Nonetheless, it does not perform well for biometric identification under fine-grained tasks. The...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,177
2110.12416	Sentence Punctuation for Collaborative Commentary Generation in Esports Live-Streaming	To solve the existing sentence punctuation problem for collaborative commentary generation in Esports live-streaming, this paper presents two strategies for sentence punctuation for text sequences of game commentary, that is, punctuating sentences by two or three text sequence(s) originally punctuated by Youtube to obt...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	262,841
2106.11075	EML Online Speech Activity Detection for the Fearless Steps Challenge Phase-III	Speech Activity Detection (SAD), locating speech segments within an audio recording, is a main part of most speech technology applications. Robust SAD is usually more difficult in noisy conditions with varying signal-to-noise ratios (SNR). The Fearless Steps challenge has recently provided such data from the NASA Apoll...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	242,267
cmp-lg/9609001	Corrections and Higher-Order Unification	We propose an analysis of corrections which models some of the requirements corrections place on context. We then show that this analysis naturally extends to the interaction of corrections with pronominal anaphora on the one hand, and (in)definiteness on the other. The analysis builds on previous unification--based ap...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,657
2411.13628	MambaDETR: Query-based Temporal Modeling using State Space Model for Multi-View 3D Object Detection	Utilizing temporal information to improve the performance of 3D detection has made great progress recently in the field of autonomous driving. Traditional transformer-based temporal fusion methods suffer from quadratic computational cost and information decay as the length of the frame sequence increases. In this paper...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,871
1904.09889	A programmable actuator for combined motion and connection and its application to modular robot	This paper proposes a new type of actuator at millimeter scale, which is based on Simplified Electro-Permanent (SEP) magnets. The new actuator can achieve connection and smooth motion by controlling the polarity of SEP magnets. Analyses based on numerical simulation are used to design a prototype. A dead-time controlla...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	128,510
1306.6311	Fast Software Polar Decoders	Among error-correcting codes, polar codes are the first to provably achieve channel capacity with an explicit construction. In this work, we present software implementations of a polar decoder that leverage the capabilities of modern general-purpose processors to achieve an information throughput in excess of 200 Mbps,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,478
1502.00196	Optimal V2G Scheduling of Electric Vehicles and Unit Commitment using Chemical Reaction Optimization	An electric vehicle (EV) may be used as energy storage which allows the bi-directional electricity flow between the vehicle's battery and the electric power grid. In order to flatten the load profile of the electricity system, EV scheduling has become a hot research topic in recent years. In this paper, we propose a ne...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	39,786
1809.03135	A Linear Approach to Fault Analysis and Intervention in Boolean Systems	The mutations of a complex systemic disease like cancer can be modeled as stuck-at faults in the Boolean system paradigm. For a class of multiple faults, the fault identification is exceptionally significant under the incomplete access of all the underlying proteins of the system. A comprehensive linear framework has b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	107,243
1910.01990	Detecting Deception in Political Debates Using Acoustic and Textual Features	We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates....	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	148,094
1903.03272	A Survey of Visuo-Haptic Simulation in Surgical Training	Surgeons must accomplish complex technical and intellectual tasks that can generate unexpected and serious challenges with little or no room for error. In the last decade, computer simulations have played an increasing role in surgical training, pre-operative planning, and biomedical research. Specifically, visuo-hapti...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	123,691
2006.07493	Bayesian Additive Regression Trees with Model Trees	Bayesian Additive Regression Trees (BART) is a tree-based machine learning method that has been successfully applied to regression and classification problems. BART assumes regularisation priors on a set of trees that work as weak learners and is very flexible for predicting in the presence of non-linearity and high-or...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,813
2306.11681	MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models	Structure-based molecular ML (SBML) models can be highly sensitive to input geometries and give predictions with large variance. We present an approach to mitigate the challenge of selecting conformations for such models by generating conformers that explicitly minimize predictive uncertainty. To achieve this, we compu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,671
2502.05806	Divide-and-Conquer: Tree-structured Strategy with Answer Distribution Estimator for Goal-Oriented Visual Dialogue	Goal-oriented visual dialogue involves multi-round interaction between artificial agents, which has been of remarkable attention due to its wide applications. Given a visual scene, this task occurs when a Questioner asks an action-oriented question and an Answerer responds with the intent of letting the Questioner know...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,786
1705.03634	4d isip: 4d implicit surface interest point detection	In this paper, we propose a new method to detect 4D spatiotemporal interest points though an implicit surface, we refer to as the 4D-ISIP. We use a 3D volume which has a truncated signed distance function(TSDF) for every voxel to represent our 3D object model. The TSDF represents the distance between the spatial points...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,218
1612.04059	Parameter Estimation Under Model Uncertainties by Iterative Covariance Approximation	We propose a novel iterative algorithm for estimating a deterministic but unknown parameter vector in the presence of model uncertainties. This iterative algorithm is based on a system model where an overall noise term describes both, the measurement noise and the noise resulting from the model uncertainties. This over...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,474
2207.02468	Re-weighting Negative Samples for Model-Agnostic Matching	Recommender Systems (RS), as an efficient tool to discover users' interested items from a very large corpus, has attracted more and more attention from academia and industry. As the initial stage of RS, large-scale matching is fundamental yet challenging. A typical recipe is to learn user and item representations with ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	306,531
2010.08532	Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement	Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain better generalization, using the starting point as the reference (SPAR), either through weights or features, has been successfully applied to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	201,206
2406.07177	TernaryLLM: Ternarized Large Language Model	Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computational costs and memory requirements. Ternarization, an extreme form of quantization, offers a solution by reducing memory usage and enabling energy-efficient floating-point...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,938
2311.08391	A Material Lens on Coloniality in NLP	Coloniality, the continuation of colonial harms beyond "official" colonization, has pervasive effects across society and scientific fields. Natural Language Processing (NLP) is no exception to this broad phenomenon. In this work, we argue that coloniality is implicitly embedded in and amplified by NLP data, algorithms,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,709
2401.02937	Locally Adaptive Neural 3D Morphable Models	We present the Locally Adaptive Morphable Model (LAMM), a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes. We train our architecture following a simple self-supervised training scheme in which input displacements over a set of sparse control vertices are used to overwrite t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,894
2011.12454	Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer	Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of primary interest. In computer vision, learning from long tailed datasets is a recurring theme, especially for natural image datasets. While ex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,163
2208.05067	Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes	In this paper, we propose a novel object-level mapping system that can simultaneously segment, track, and reconstruct objects in dynamic scenes. It can further predict and complete their full geometries by conditioning on reconstructions from depth inputs and a category-level shape prior with the aim that completed obj...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	312,305
2108.09663	SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation	3D detection plays an indispensable role in environment perception. Due to the high cost of commonly used LiDAR sensor, stereo vision based 3D detection, as an economical yet effective setting, attracts more attention recently. For these approaches based on 2D images, accurate depth information is the key to achieve 3D...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,679
2306.09121	The Split Matters: Flat Minima Methods for Improving the Performance of GNNs	When training a Neural Network, it is optimized using the available training data with the hope that it generalizes well to new or unseen testing data. At the same absolute value, a flat minimum in the loss landscape is presumed to generalize better than a sharp minimum. Methods for determining flat minima have been mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,686
2301.02166	Identification of lung nodules CT scan using YOLOv5 based on convolution neural network	Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,431
2204.11545	LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback	Pseudo-relevance feedback (PRF) has proven to be an effective query reformulation technique to improve retrieval accuracy. It aims to alleviate the mismatch of linguistic expressions between a query and its potential relevant documents. Existing PRF methods independently treat revised queries originating from the same ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	293,189
1005.4285	Local Minima of a Quadratic Binary Functional with Quasi-Hebbian Connection Matrix	The local minima of a quadratic functional depending on binary variables are discussed. An arbitrary connection matrix can be presented in the form of quasi-Hebbian expansion where each pattern is supplied with its own individual weight. For such matrices statistical physics methods allow one to derive an equation desc...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	6,552
2007.07702	Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection	Terrain relative navigation can improve the precision of a spacecraft's position estimate by detecting global features that act as supplementary measurements to correct for drift in the inertial navigation system. This paper presents a system that uses a convolutional neural network (CNN) and image processing methods t...	false	false	false	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	187,414
1105.1421	An Empirical Investigation on Important Subgraphs in Cooperation-Competition networks	Subgraphs are very important for understanding structure and function of complex networks. Dyad and triad are the elementary subgraphs. We focus on the distribution of their act degree defined as the number of activities, events or organizations they join, which indicates the importance of the subgraphs. The empirical ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	10,280
2404.00463	Addressing Both Statistical and Causal Gender Fairness in NLP Models	Statistical fairness stipulates equivalent outcomes for every protected group, whereas causal fairness prescribes that a model makes the same prediction for an individual regardless of their protected characteristics. Counterfactual data augmentation (CDA) is effective for reducing bias in NLP models, yet models traine...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	442,937
2409.16974	Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions	There have been rapid advancements in the capabilities of large language models (LLMs) in recent years, greatly revolutionizing the field of natural language processing (NLP) and artificial intelligence (AI) to understand and interact with human language. Therefore, in this work, we conduct a systematic investigation o...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	491,602
1806.02457	Reference Model of Multi-Entity Bayesian Networks for Predictive Situation Awareness	During the past quarter-century, situation awareness (SAW) has become a critical research theme, because of its importance. Since the concept of SAW was first introduced during World War I, various versions of SAW have been researched and introduced. Predictive Situation Awareness (PSAW) focuses on the ability to predi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	99,788
2107.06115	A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization	Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven approach for adaptive traffic signal control in complex urban traffic networks. Although the development of deep neural networks (DNN) further enh...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	245,989
1006.0544	Capacity scaling law by multiuser diversity in cognitive radio systems	This paper analyzes the multiuser diversity gain in a cognitive radio (CR) system where secondary transmitters opportunistically utilize the spectrum licensed to primary users only when it is not occupied by the primary users. To protect the primary users from the interference caused by the missed detection of primary ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,657
1606.02739	Efficient collective influence maximization in cascading processes with first-order transitions	In social networks, the collective behavior of large populations can be shaped by a small set of influencers through a cascading process induced by "peer pressure". For large-scale networks, efficient identification of multiple influential spreaders with a linear algorithm in threshold models that exhibit a first-order...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	57,008
1902.06085	DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet	Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. In the course of post-treatment magnetic resonance imaging (MRI), PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM. So, these similarities pose chall...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,679
2105.11634	Robust Principal Component Analysis Using a Novel Kernel Related with the L1-Norm	We consider a family of vector dot products that can be implemented using sign changes and addition operations only. The dot products are energy-efficient as they avoid the multiplication operation entirely. Moreover, the dot products induce the $\ell_1$-norm, thus providing robustness to impulsive noise. First, we ana...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,768
2303.08863	Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels	Image-to-image reconstruction problems with free or inexpensive metadata in the form of class labels appear often in biological and medical image domains. Existing text-guided or style-transfer image-to-image approaches do not translate to datasets where additional information is provided as discrete classes. We introd...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	351,797
2411.18933	Efficient Track Anything	Segment Anything Model 2 (SAM 2) has emerged as a powerful tool for video object segmentation and tracking anything. Key components of SAM 2 that drive the impressive video object segmentation performance include a large multistage image encoder for frame feature extraction and a memory mechanism that stores memory con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,057
2406.10486	Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?	We examine whether large language models (LLMs) exhibit race- and gender-based name discrimination in hiring decisions, similar to classic findings in the social sciences (Bertrand and Mullainathan, 2004). We design a series of templatic prompts to LLMs to write an email to a named job applicant informing them of a hir...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	464,423
1904.03090	Eigenvalue distribution of nonlinear models of random matrices	This paper is concerned with the asymptotic empirical eigenvalue distribution of a non linear random matrix ensemble. More precisely we consider $M= \frac{1}{m} YY^*$ with $Y=f(WX)$ where $W$ and $X$ are random rectangular matrices with i.i.d. centered entries. The function $f$ is applied pointwise and can be seen as a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,606
2409.11951	GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations	Real-time rendering of human head avatars is a cornerstone of many computer graphics applications, such as augmented reality, video games, and films, to name a few. Recent approaches address this challenge with computationally efficient geometry primitives in a carefully calibrated multi-view setup. Albeit producing ph...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	489,374
2105.03153	Pairwise Fairness for Ordinal Regression	We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor has the form of a threshold model, composed of a scoring function and a set o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,056
2211.13208	On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation	Sample-efficient offline reinforcement learning (RL) with linear function approximation has recently been studied extensively. Much of prior work has yielded the minimax-optimal bound of $\tilde{\mathcal{O}}(\frac{1}{\sqrt{K}})$, with $K$ being the number of episodes in the offline data. In this work, we seek to unders...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	332,385
2403.10951	TVIM: Thermo-Active Variable Impedance Module: Evaluating Shear-Mode Capabilities of Polycaprolactone	In this work, we introduce an advanced thermo-active variable impedance module which builds upon our previous innovation in thermal-based impedance adjustment for actuation systems. Our initial design harnessed the temperature-responsive, viscoelastic properties of Polycaprolactone (PCL) to modulate stiffness and dampi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	438,448
2203.05187	Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection	The food packaging industry handles an immense variety of food products with wide-ranging shapes and sizes, even within one kind of food. Menus are also diverse and change frequently, making automation of pick-and-place difficult. A popular approach to bin-picking is to first identify each piece of food in the tray by ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	284,736
2210.13778	IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension	Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020; Wilie et al., 2020). However, most MRC datasets only have answerable question type, overlooking the importance of unanswerable question...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	326,305
2210.08046	Differentiable Hybrid Traffic Simulation	We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated into a neural network for traffic control and flow optimization. This is the first differentiable traffic simulator for macroscopic and hyb...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	323,958
2207.14745	Collision detection and identification for a legged manipulator	To safely deploy legged robots in the real world it is necessary to provide them with the ability to reliably detect unexpected contacts and accurately estimate the corresponding contact force. In this paper, we propose a collision detection and identification pipeline for a quadrupedal manipulator. We first introduce ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	310,696
2405.01925	A Modular, Tendon Driven Variable Stiffness Manipulator with Internal Routing for Improved Stability and Increased Payload Capacity	Stability and reliable operation under a spectrum of environmental conditions is still an open challenge for soft and continuum style manipulators. The inability to carry sufficient load and effectively reject external disturbances are two drawbacks which limit the scale of continuum designs, preventing widespread adop...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	451,562
2308.00285	Predictive Modeling through Hyper-Bayesian Optimization	Model selection is an integral problem of model based optimization techniques such as Bayesian optimization (BO). Current approaches often treat model selection as an estimation problem, to be periodically updated with observations coming from the optimization iterations. In this paper, we propose an alternative way to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	382,888
2011.13917	Task Programming: Learning Data Efficient Behavior Representations	Specialized domain knowledge is often necessary to accurately annotate training sets for in-depth analysis, but can be burdensome and time-consuming to acquire from domain experts. This issue arises prominently in automated behavior analysis, in which agent movements or actions of interest are detected from video track...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	208,617
1504.05679	On Two-Pair Two-Way Relay Channel with an Intermittently Available Relay	When multiple users share the same resource for physical layer cooperation such as relay terminals in their vicinities, this shared resource may not be always available for every user, and it is critical for transmitting terminals to know whether other users have access to that common resource in order to better utiliz...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,306
2012.15405	From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems	Existing communication systems are mainly built based on Shannon's information theory which deliberately ignores the semantic aspects of communication. The recent iteration of wireless technology, the so-called 5G and beyond, promises to support a plethora of services enabled by carefully tailored network capabilities ...	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	true	213,769
2404.12139	Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models	Vision-Language Pre-training (VLP) models like CLIP have achieved remarkable success in computer vision and particularly demonstrated superior robustness to distribution shifts of 2D images. However, their robustness under 3D viewpoint variations is still limited, which can hinder the development for real-world applica...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,753
0908.1076	Multi-Agent Model Predictive Control: A Survey	In this report we define characteristic control design elements and show how conventional single-agent MPC implements these. We survey recent literature on multi-agent MPC and discuss how this literature deals with decomposition, problem assignment, and cooperation.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	4,242
cs/0702008	MMSE Optimal Algebraic Space-Time Codes	Design of Space-Time Block Codes (STBCs) for Maximum Likelihood (ML) reception has been predominantly the main focus of researchers. However, the ML decoding complexity of STBCs becomes prohibitive large as the number of transmit and receive antennas increase. Hence it is natural to resort to a suboptimal reception tec...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,124
2411.16946	Lens Distortion Encoding System Version 1.0	Lens Distortion Encoding System (LDES) allows for a distortion-accurate workflow, with a seamless interchange of high quality motion picture images regardless of the lens source. This system is similar in a concept to the Academy Color Encoding System (ACES), but for distortion. Presented solution is fully compatible w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	511,243
2209.04745	Optimization of the fluid model of scheduling: local predictions	In this research a continuous model for resource allocations in a queuing system is considered and a local prediction on the system behavior is developed. As a result we obtain a set of possible cases, some of which lead to quite clear optimization problems. Currently, the main result of this research direction is an a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	316,871
1903.06923	Spatiotemporal Feature Learning for Event-Based Vision	Unlike conventional frame-based sensors, event-based visual sensors output information through spikes at a high temporal resolution. By only encoding changes in pixel intensity, they showcase a low-power consuming, low-latency approach to visual information sensing. To use this information for higher sensory tasks like...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,493
2203.11321	Alarm-Based Root Cause Analysis in Industrial Processes Using Deep Learning	Alarm management systems have become indispensable in modern industry. Alarms inform the operator of abnormal situations, particularly in the case of equipment failures. Due to the interconnections between various parts of the system, each fault can affect other sections of the system operating normally. As a result, t...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	286,868
2502.09376	LoRA Training Provably Converges to a Low-Rank Global Minimum or It Fails Loudly (But it Probably Won't Fail)	Low-rank adaptation (LoRA) has become a standard approach for fine-tuning large foundation models. However, our theoretical understanding of LoRA remains limited as prior analyses of LoRA's training dynamics either rely on linearization arguments or consider highly simplified setups. In this work, we analyze the LoRA l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,430
2209.08614	Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features	Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood for applications in education, healthcare, and entertainment, among others. Deep convolutional neural networks show promising results in classifying facial expressions of adults. However, classifier model...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	318,190
2311.08687	An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease Phenotyping	Diabetic eye disease is a major cause of blindness worldwide. The ability to monitor relevant clinical trajectories and detect lapses in care is critical to managing the disease and preventing blindness. Alas, much of the information necessary to support these goals is found only in the free text of the electronic medi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	407,829
2211.13428	Real-Time Marker Localization Learning for GelStereo Tactile Sensing	Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile se...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	332,468
2502.04115	A Neural Network-based Multi-timestep Command Governor for Nonlinear Systems with Constraints	The multi-timestep command governor (MCG) is an add-on algorithm that enforces constraints by modifying, at each timestep, the reference command to a pre-stabilized control system. The MCG can be interpreted as a Model-Predictive Control scheme operating on the reference command. The implementation of MCG on nonlinear ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	530,989
2306.10128	Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis	Self-attention mechanisms are commonly included in a convolutional neural networks to achieve an improved efficiency performance balance. However, adding self-attention mechanisms adds additional hyperparameters to tune for the application at hand. In this work we propose a novel type of DNN analysis called Multi-Scale...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	374,114
2302.09915	TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training	Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	346,620
1812.04994	Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity	As societies around the world are ageing, the number of Alzheimer's disease (AD) patients is rapidly increasing. To date, no low-cost, non-invasive biomarkers have been established to advance the objectivization of AD diagnosis and progression assessment. Here, we utilize Bayesian neural networks to develop a multivari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,329
2109.09051	On Infinite Families of Narrow-Sense Antiprimitive BCH Codes Admitting 3-Transitive Automorphism Groups and their Consequences	The Bose-Chaudhuri-Hocquenghem (BCH) codes are a well-studied subclass of cyclic codes that have found numerous applications in error correction and notably in quantum information processing. A subclass of attractive BCH codes is the narrow-sense BCH codes over the Galois field $\mathrm{GF}(q)$ with length $q+1$, which...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	256,137

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