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
2210.10639	Robot Navigation with Reinforcement Learned Path Generation and Fine-Tuned Motion Control	In this paper, we propose a novel reinforcement learning (RL) based path generation (RL-PG) approach for mobile robot navigation without a prior exploration of an unknown environment. Multiple predictive path points are dynamically generated by a deep Markov model optimized using RL approach for robot to track. To ensu...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	325,003
1908.11820	Learning Rich Representations For Structured Visual Prediction Tasks	We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps. Our key idea is to map small image elements to feature representations extracted from a sequence of nested regions of increasing spatial exten...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	143,479
2104.09995	Review of end-to-end speech synthesis technology based on deep learning	As an indispensable part of modern human-computer interaction system, speech synthesis technology helps users get the output of intelligent machine more easily and intuitively, thus has attracted more and more attention. Due to the limitations of high complexity and low efficiency of traditional speech synthesis techno...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	231,428
2101.02634	Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective	In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mobility is a sequential decision-making process dependent on the users' dynamic interests. With accurate user profiles, the predictive model ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	214,686
1810.07561	Modelling project failure and its mitigation in a time-stamped network of interrelated tasks	Resolving major societal challenges, such as stagnated economic growth or wasted resources, heavily relies on successful project delivery. However, projects are notoriously hard to deliver successfully, partly due to their interconnected nature which makes them prone to cascading failures. We deploy a model of cascadin...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	110,663
2007.07686	Relative Pose Estimation of Calibrated Cameras with Known $\mathrm{SE}(3)$ Invariants	The $\mathrm{SE}(3)$ invariants of a pose include its rotation angle and screw translation. In this paper, we present a complete comprehensive study of the relative pose estimation problem for a calibrated camera constrained by known $\mathrm{SE}(3)$ invariant, which involves 5 minimal problems in total. These problems...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	187,407
2203.12751	ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues	Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations. In this paper, we propose the design and rationale of the ThingTalk formal representation, and how the design improves the development of transactional task-oriented agents. ThingTalk is built on four...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	287,379
1410.4256	Anatomy of a Crash	Transportation networks constitute a critical infrastructure enabling the transfers of passengers and goods, with a significant impact on the economy at different scales. Transportation modes, whether air, road or rail, are coupled and interdependent. The frequent occurrence of perturbations on one or several modes dis...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	36,781
2301.00163	A Survey about Acquisition System Design for Myoelectric Prosthesis	According to the World Health Organization (WHO), 30 million people are in need of prosthetic and orthotic devices. Some people are born with this limb loss, while others lose limbs due to diseases such as Cancer, diabetes, and work accidents. Additionally, limb amputation is among the most severe and heavily reported ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	338,821
2205.15265	Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?	Technological and computational advances continuously drive forward the broad field of deep learning. In recent years, the derivation of quantities describing theuncertainty in the prediction - which naturally accompanies the modeling process - has sparked general interest in the deep learning community. Often neglecte...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	299,661
1111.4825	Chebyshev Polynomials in Distributed Consensus Applications	In this paper we analyze the use of Chebyshev polynomials in distributed consensus applications. We study the properties of these polynomials to propose a distributed algorithm that reaches the consensus in a fast way. The algorithm is expressed in the form of a linear iteration and, at each step, the agents only requi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	true	13,112
2411.02817	Conditional Vendi Score: An Information-Theoretic Approach to Diversity Evaluation of Prompt-based Generative Models	Text-conditioned generation models are commonly evaluated based on the quality of the generated data and its alignment with the input text prompt. On the other hand, several applications of prompt-based generative models require sufficient diversity in the generated data to ensure the models' capability of generating i...	false	false	false	false	true	false	true	false	false	true	false	true	false	false	false	false	false	false	505,674
2310.08678	Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams	Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models. This study aims at assessing the financial reasoning capabilities of LLMs. We leverage mock exam questions of the Charte...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	399,481
2102.08928	Synthesizing multi-layer perceptron network with ant lion, biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings	The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	220,616
1507.03269	Tensor principal component analysis via sum-of-squares proofs	We study a statistical model for the tensor principal component analysis problem introduced by Montanari and Richard: Given a order-$3$ tensor $T$ of the form $T = \tau \cdot v_0^{\otimes 3} + A$, where $\tau \geq 0$ is a signal-to-noise ratio, $v_0$ is a unit vector, and $A$ is a random noise tensor, the goal is to re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	45,067
1812.05463	Measured Channel Hardening in an Indoor Multiband Scenario	A study of channel hardening in a large-scale antenna system has been carried out by means of indoor channel measurements over four frequency bands, namely 1.472 GHz, 2.6 GHz, 3.82 GHz and 4.16 GHz. NTNU's Reconfigurable Radio Network Platform has been used to record the channel estimates for 40 single user non-line of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	116,417
2405.07976	Localized Adaptive Risk Control	Adaptive Risk Control (ARC) is an online calibration strategy based on set prediction that offers worst-case deterministic long-term risk control, as well as statistical marginal coverage guarantees. ARC adjusts the size of the prediction set by varying a single scalar threshold based on feedback from past decisions. I...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	453,934
2010.12698	Stabilizing Transformer-Based Action Sequence Generation For Q-Learning	Since the publication of the original Transformer architecture (Vaswani et al. 2017), Transformers revolutionized the field of Natural Language Processing. This, mainly due to their ability to understand timely dependencies better than competing RNN-based architectures. Surprisingly, this architecture change does not a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,807
1503.07241	GraphMat: High performance graph analytics made productive	Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	41,454
2307.06810	Spatio-Temporal Calibration for Omni-Directional Vehicle-Mounted Event Cameras	We present a solution to the problem of spatio-temporal calibration for event cameras mounted on an onmi-directional vehicle. Different from traditional methods that typically determine the camera's pose with respect to the vehicle's body frame using alignment of trajectories, our approach leverages the kinematic corre...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	379,180
1506.00685	Model-based reinforcement learning for infinite-horizon approximate optimal tracking	This paper provides an approximate online adaptive solution to the infinite-horizon optimal tracking problem for control-affine continuous-time nonlinear systems with unknown drift dynamics. Model-based reinforcement learning is used to relax the persistence of excitation condition. Model-based reinforcement learning i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	43,697
1608.06349	Five dimensions of reasoning in the wild	Reasoning does not work well when done in isolation from its significance, both to the needs and interests of an agent and with respect to the wider world. Moreover, those issues may best be handled with a new sort of data structure that goes beyond the knowledge base and incorporates aspects of perceptual knowledge an...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	60,104
1106.4987	The Cosparse Analysis Model and Algorithms	After a decade of extensive study of the sparse representation synthesis model, we can safely say that this is a mature and stable field, with clear theoretical foundations, and appealing applications. Alongside this approach, there is an analysis counterpart model, which, despite its similarity to the synthesis altern...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	10,980
2006.11693	Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020	This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020. Our approach follows a two-stage pipeline: first, we extract a set of temporal event proposals; then we propose a multi-event captioning model to capture the event-level temporal relati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	183,335
2402.11125	See Spot Guide: Accessible Interfaces for an Assistive Quadruped Robot	While there is no replacement for the learned expertise, devotion, and social benefits of a guide dog, there are cases in which a robot navigation assistant could be helpful for individuals with blindness or low vision (BLV). This study investigated the potential for an industrial agile robot to perform guided navigati...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	430,245
2107.11412	Using Deep Learning Techniques and Inferential Speech Statistics for AI Synthesised Speech Recognition	The recent developments in technology have re-warded us with amazing audio synthesis models like TACOTRON and WAVENETS. On the other side, it poses greater threats such as speech clones and deep fakes, that may go undetected. To tackle these alarming situations, there is an urgent need to propose models that can help d...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	247,578
2410.15212	Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms	The relentless pursuit of technological advancements has ushered in a new era where artificial intelligence (AI) is not only a powerful tool but also a critical economic driver. At the forefront of this transformation is Generative AI, which is catalyzing a paradigm shift across industries. Deep generative models, an i...	false	true	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	500,420
2105.13939	Efficient Online-Bandit Strategies for Minimax Learning Problems	Several learning problems involve solving min-max problems, e.g., empirical distributional robust learning or learning with non-standard aggregated losses. More specifically, these problems are convex-linear problems where the minimization is carried out over the model parameters $w\in\mathcal{W}$ and the maximization ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	237,461
1912.06875	Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator	Multi-agent reinforcement learning has been successfully applied to a number of challenging problems. Despite these empirical successes, theoretical understanding of different algorithms is lacking, primarily due to the curse of dimensionality caused by the exponential growth of the state-action space with the number o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	157,451
1611.00277	Joint Antenna Selection and Spatial Switching for Energy Efficient MIMO SWIPT System	In this paper, we investigate joint antenna selection and spatial switching (SS) for quality-of-service (QoS)-constrained energy efficiency (EE) optimization in a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system. A practical linear power model taking into account...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,200
2205.02003	Multi-subgoal Robot Navigation in Crowds with History Information and Interactions	Robot navigation in dynamic environments shared with humans is an important but challenging task, which suffers from performance deterioration as the crowd grows. In this paper, multi-subgoal robot navigation approach based on deep reinforcement learning is proposed, which can reason about more comprehensive relationsh...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	294,802
2310.12299	Instantaneous Frequency Estimation in Unbalanced Systems Using Affine Differential Geometry	The paper discusses the relationships between electrical and affine differential geometry quantities, establishing a link between frequency and time derivatives of voltage, through the utilization of affine geometric invariants. Based on this link, a new instantaneous frequency estimation formula is proposed, which is ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	400,964
2107.00594	Pretext Tasks selection for multitask self-supervised speech representation learning	Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task. In audio/speech signal processing, a wide range of features where engineered through decades of research efforts. As it turns out, learni...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,207
2101.00693	Neural Networks for Keyword Spotting on IoT Devices	We explore Neural Networks (NNs) for keyword spotting (KWS) on IoT devices like smart speakers and wearables. Since we target to execute our NN on a constrained memory and computation footprint, we propose a CNN design that. (i) uses a limited number of multiplies. (ii) uses a limited number of model parameters.	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	214,169
2008.01449	Prior Guided Feature Enrichment Network for Few-Shot Segmentation	State-of-the-art semantic segmentation methods require sufficient labeled data to achieve good results and hardly work on unseen classes without fine-tuning. Few-shot segmentation is thus proposed to tackle this problem by learning a model that quickly adapts to new classes with a few labeled support samples. Theses fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,319
1903.08755	Using Ego-Clusters to Measure Network Effects at LinkedIn	A network effect is said to take place when a new feature not only impacts the people who receive it, but also other users of the platform, like their connections or the people who follow them. This very common phenomenon violates the fundamental assumption underpinning nearly all enterprise experimentation systems, th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	124,896
2410.11419	GS^3: Efficient Relighting with Triple Gaussian Splatting	We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex appearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance func...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	498,555
1204.3481	Crowdsourcing Collective Emotional Intelligence	One of the hallmarks of emotional intelligence is the ability to regulate emotions. Research suggests that cognitive reappraisal - a technique that involves reinterpreting the meaning of a thought or situation - can down-regulate negative emotions, without incurring significant psychological or physiological costs. Hab...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	15,500
2402.17129	Side Information-Driven Session-based Recommendation: A Survey	The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance. In this survey, we thoroughly review the side informatio...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	432,849
2409.12965	Optical training of large-scale Transformers and deep neural networks with direct feedback alignment	Modern machine learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly considered for inference but, to date, remain mostly limited to relatively basic tasks. Simultaneously, the problem of training deep and c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	489,793
2502.11149	Large Language-Geometry Model: When LLM meets Equivariance	Accurately predicting 3D structures and dynamics of physical systems is crucial in scientific applications. Existing approaches that rely on geometric Graph Neural Networks (GNNs) effectively enforce $\mathrm{E}(3)$-equivariance, but they often fall in leveraging extensive broader information. While direct application ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,219
2405.05409	Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing	Transformers have shown impressive capabilities across various tasks, but their performance on compositional problems remains a topic of debate. In this work, we investigate the mechanisms of how transformers behave on unseen compositional tasks. We discover that the parameter initialization scale plays a critical role...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,901
2408.17377	NDP: Next Distribution Prediction as a More Broad Target	Large language models (LLMs) trained on next-token prediction (NTP) paradigm have demonstrated powerful capabilities. However, the existing NTP paradigm contains several limitations, particularly related to planned task complications and error propagation during inference. In our work, we extend the critique of NTP, hi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	484,683
2407.00998	Opportunities for Shape-based Optimization of Link Traversal Queries	Data on the web is naturally unindexed and decentralized. Centralizing web data, especially personal data, raises ethical and legal concerns. Yet, compared to centralized query approaches, decentralization-friendly alternatives such as Link Traversal Query Processing (LTQP) are significantly less performant and underst...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	469,104
2210.06223	Latency-aware Spatial-wise Dynamic Networks	Spatial-wise dynamic convolution has become a promising approach to improving the inference efficiency of deep networks. By allocating more computation to the most informative pixels, such an adaptive inference paradigm reduces the spatial redundancy in image features and saves a considerable amount of unnecessary comp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,180
2311.11634	Self-orthogonal codes from $p$-divisible codes	Self-orthogonal codes are an important subclass of linear codes which have nice applications in quantum codes and lattices. It is known that a binary linear code is self-orthogonal if its every codeword has weight divisible by four, and a ternary linear code is self-orthogonal if and only if its every codeword has weig...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	409,022
1805.07486	A Tunable Base Station Cooperation Scheme for Poisson Cellular Networks	We propose a tunable location-dependent base station (BS) cooperation scheme by partitioning the plane into three regions: the cell centers, cell edges and cell corners. The area fraction of each region is tuned by the cooperation level $\gamma$ ranging from 0 to 1. Depending on the region a user resides in, he/she rec...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,842
2410.03368	Latent Abstractions in Generative Diffusion Models	In this work we study how diffusion-based generative models produce high-dimensional data, such as an image, by implicitly relying on a manifestation of a low-dimensional set of latent abstractions, that guide the generative process. We present a novel theoretical framework that extends NLF, and that offers a unique pe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	494,746
1709.06129	When is a Convolutional Filter Easy To Learn?	We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	81,032
1805.11233	Retraining-Based Iterative Weight Quantization for Deep Neural Networks	Model compression has gained a lot of attention due to its ability to reduce hardware resource requirements significantly while maintaining accuracy of DNNs. Model compression is especially useful for memory-intensive recurrent neural networks because smaller memory footprint is crucial not only for reducing storage re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,873
2006.14622	Resilience in urban networked infrastructure: the case of Water Distribution Systems	Resilience is meant as the capability of a networked infrastructure to provide its service even if some components fail: in this paper we focus on how resilience depends both on net-wide measures of connectivity and the role of a single component. This paper has two objectives: first to show how a set of global measure...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	184,283
1902.01878	Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning	Deep learning model developers often use cloud GPU resources to experiment with large data and models that need expensive setups. However, this practice raises privacy concerns. Adversaries may be interested in: 1) personally identifiable information or objects encoded in the training images, and 2) the models trained ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	120,750
2411.06805	AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant	The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hallucination". Initial retrieval-augmented generation (RAG) methods like the "Retrieve-Read" framework was inadequate for complex reasoning ta...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	507,281
2307.06335	Neural Free-Viewpoint Relighting for Glossy Indirect Illumination	Precomputed Radiance Transfer (PRT) remains an attractive solution for real-time rendering of complex light transport effects such as glossy global illumination. After precomputation, we can relight the scene with new environment maps while changing viewpoint in real-time. However, practical PRT methods are usually lim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	379,038
1910.05852	Implicit competitive regularization in GANs	To improve the stability of GAN training we need to understand why they can produce realistic samples. Presently, this is attributed to properties of the divergence obtained under an optimal discriminator. This argument has a fundamental flaw: If we do not impose regularity of the discriminator, it can exploit visually...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,188
2410.01736	Recursive Abstractive Processing for Retrieval in Dynamic Datasets	Recent retrieval-augmented models enhance basic methods by building a hierarchical structure over retrieved text chunks through recursive embedding, clustering, and summarization. The most relevant information is then retrieved from both the original text and generated summaries. However, such approaches face limitatio...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	493,918
2406.06470	GKAN: Graph Kolmogorov-Arnold Networks	We introduce Graph Kolmogorov-Arnold Networks (GKAN), an innovative neural network architecture that extends the principles of the recently proposed Kolmogorov-Arnold Networks (KAN) to graph-structured data. By adopting the unique characteristics of KANs, notably the use of learnable univariate functions instead of fix...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	462,593
2303.15299	Resilient Output Consensus Control of Heterogeneous Multi-agent Systems against Byzantine Attacks: A Twin Layer Approach	This paper studies the problem of cooperative control of heterogeneous multi-agent systems (MASs) against Byzantine attacks. The agent affected by Byzantine attacks sends different wrong values to all neighbors while applying wrong input signals for itself, which is aggressive and difficult to be defended. Inspired by ...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	354,442
1605.07785	Geometry-aware stationary subspace analysis	In many real-world applications data exhibits non-stationarity, i.e., its distribution changes over time. One approach to handling non-stationarity is to remove or minimize it before attempting to analyze the data. In the context of brain computer interface (BCI) data analysis this may be done by means of stationary su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	56,344
0806.3115	Using rational numbers to key nested sets	This report details the generation and use of tree node ordering keys in a single relational database table. The keys for each node are calculated from the keys of its parent, in such a way that the sort order places every node in the tree before all of its descendants and after all siblings having a lower index. The c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	1,941
1911.06463	Flexible Functional Split and Power Control for Energy Harvesting Cloud Radio Access Networks	Functional split is a promising technique to flexibly balance the processing cost at remote ends and the fronthaul rate in cloud radio access networks (C-RAN). By harvesting renewable energy, remote radio units (RRUs) can save grid power and be flexibly deployed. However, the randomness of energy arrival poses a major ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	153,542
2311.16588	Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation	This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation. Ascle is tailored for biomedical researchers and healthcare professionals with an easy-to-use, all-in-one solution that requires minimal programming expertise. For the first time, Ascle evaluates an...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	410,972
2312.05799	SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution	Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task. Recent image guided DSR approaches mainly focus on spatial domain to rebuild depth structure. However, since the structure of LR depth is usually blurry, only conside...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,247
1804.01349	The role of geography in the complex diffusion of innovations	The urban-rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	94,211
2403.19098	GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving	Modeling complicated interactions among the ego-vehicle, road agents, and map elements has been a crucial part for safety-critical autonomous driving. Previous works on end-to-end autonomous driving rely on the attention mechanism for handling heterogeneous interactions, which fails to capture the geometric priors and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	442,198
2012.15416	Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation	Large pre-trained language models are capable of generating realistic text. However, controlling these models so that the generated text satisfies lexical constraints, i.e., contains specific words, is a challenging problem. Given that state-of-the-art language models are too large to be trained from scratch in a manag...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	213,775
2006.14983	Solution of matching equations of IDA-PBC by Pfaffian differential equations	Finding the general solution of partial differential equations (PDEs) is essential for controller design in newly developed methods. Interconnection and damping assignment passivity based control (IDA-PBC) is one of such methods in which the solution to corresponding PDEs which are called matching equations, is needed ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	184,383
2102.07756	Timely Transmissions Using Optimized Variable Length Coding	A status updating system is considered in which a variable length code is used to transmit messages to a receiver over a noisy channel. The goal is to optimize the codewords lengths such that successfully-decoded messages are timely. That is, such that the age-of-information (AoI) at the receiver is minimized. A hybrid...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	220,210
2404.19356	A Concept for Semi-Automatic Configuration of Sufficiently Valid Simulation Setups for Automated Driving Systems	As simulation is increasingly used in scenario-based approaches to test Automated Driving Systems, the credibility of simulation results is a major concern. Arguably, credibility depends on the validity of the simulation setup and simulation models. When selecting appropriate simulation models, a trade-off must be made...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	450,611
2405.19833	KITRO: Refining Human Mesh by 2D Clues and Kinematic-tree Rotation	2D keypoints are commonly used as an additional cue to refine estimated 3D human meshes. Current methods optimize the pose and shape parameters with a reprojection loss on the provided 2D keypoints. Such an approach, while simple and intuitive, has limited effectiveness because the optimal solution is hard to find in a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	459,089
2001.06630	RCELF: A Residual-based Approach for Influence Maximization Problem	Influence Maximization Problem (IMP) is selecting a seed set of nodes in the social network to spread the influence as widely as possible. It has many applications in multiple domains, e.g., viral marketing is frequently used for new products or activities advertisements. While it is a classic and well-studied problem ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	true	160,843
2204.14093	Learning Localization-aware Target Confidence for Siamese Visual Tracking	Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by the classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,050
1806.07011	VirtualHome: Simulating Household Activities via Programs	In this paper, we are interested in modeling complex activities that occur in a typical household. We propose to use programs, i.e., sequences of atomic actions and interactions, as a high level representation of complex tasks. Programs are interesting because they provide a non-ambiguous representation of a task, and ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	100,818
2308.10531	SRFormer: Text Detection Transformer with Incorporated Segmentation and Regression	Existing techniques for text detection can be broadly classified into two primary groups: segmentation-based and regression-based methods. Segmentation models offer enhanced robustness to font variations but require intricate post-processing, leading to high computational overhead. Regression-based methods undertake in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,775
2408.05577	Camera Perspective Transformation to Bird's Eye View via Spatial Transformer Model for Road Intersection Monitoring	Road intersection monitoring and control research often utilize bird's eye view (BEV) simulators. In real traffic settings, achieving a BEV akin to that in a simulator necessitates the deployment of drones or specific sensor mounting, which is neither feasible nor practical. Consequently, traffic intersection managemen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,849
2003.00882	The perceptual boost of visual attention is task-dependent in naturalistic settings	Top-down attention allows people to focus on task-relevant visual information. Is the resulting perceptual boost task-dependent in naturalistic settings? We aim to answer this with a large-scale computational experiment. First, we design a collection of visual tasks, each consisting of classifying images from a chosen ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	166,477
1207.1370	On Bayesian Network Approximation by Edge Deletion	We consider the problem of deleting edges from a Bayesian network for the purpose of simplifying models in probabilistic inference. In particular, we propose a new method for deleting network edges, which is based on the evidence at hand. We provide some interesting bounds on the KL-divergence between original and appr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	17,253
1804.05497	Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA	With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	95,092
2309.07981	Efficiently Identifying Hotspots in a Spatially Varying Field with Multiple Robots	In this paper, we present algorithms to identify environmental hotspots using mobile sensors. We examine two approaches: one involving a single robot and another using multiple robots coordinated through a decentralized robot system. We introduce an adaptive algorithm that does not require precise knowledge of Gaussian...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	391,983
1405.4828	Securing SMS Based One Time Password Technique from Man in the Middle Attack	Security of financial transaction in e-commerce is difficult to implement and there is a risk that users confidential data over the internet may be accessed by hackers. Unfortunately, interacting with an online service such as a banking web application often requires certain degree of technical sophistication that not ...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	33,213
2312.03603	Voltage Restoration in MVDC Shipboard Microgrids with Economic Nonlinear Model Predictive Control	Future Naval Microgrids (MGs) will include hybrid energy storage systems (ESS), including battery and supercapacitors to respond to emerging constant power loads (CPLs) and fluctuating pulsed power loads (PPLs). Voltage regulation of naval microgrids and power sharing among these resources become critical for success o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	413,320
2309.09756	Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving	Reinforcement Learning (RL) has the potential to surpass human performance in driving without needing any expert supervision. Despite its promise, the state-of-the-art in sensorimotor self-driving is dominated by imitation learning methods due to the inherent shortcomings of RL algorithms. Nonetheless, RL agents are ab...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	392,734
1802.04923	Beamforming with Multiple One-Bit Wireless Transceivers	Classical beamforming techniques rely on highly linear transmitters and receivers to allow phase-coherent combining at the transmitter and receiver. The transmitter uses beamforming to steer signal power towards the receiver, and the receiver uses beamforming to gather and coherently combine the signals from multiple r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	90,338
1606.05027	Learning Optimal Interventions	Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,346
2501.03172	GLiREL -- Generalist Model for Zero-Shot Relation Extraction	We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification. Inspired by recent advancements in zero-shot named entity recognition, this work presents an approach to efficiently and accurately predict zero-sh...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	522,782
2201.11094	SCAI-QReCC Shared Task on Conversational Question Answering	Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search. SCAI'21 was organised as an independent on-line event and featured a shared task on conversational question answering. Since all of the participant teams ex...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	277,179
1807.10806	Gated Fusion Network for Joint Image Deblurring and Super-Resolution	Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,032
1609.06779	A Novel GPU-based Parallel Implementation Scheme and Performance Analysis of Robot Forward Dynamics Algorithms	We propose a novel unifying scheme for parallel implementation of articulated robot dynamics algorithms. It is based on a unified Lie group notation for deriving the equations of motion of articulated robots, where various well-known forward algorithms differ only by their joint inertia matrix inversion strategies. Thi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	61,344
2404.19630	Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction	The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP. However, among these leading DL models, there is a wide variance in both the training settings and ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	450,716
2002.12798	Optimizing Memory-Access Patterns for Deep Learning Accelerators	Deep learning (DL) workloads are moving towards accelerators for faster processing and lower cost. Modern DL accelerators are good at handling the large-scale multiply-accumulate operations that dominate DL workloads; however, it is challenging to make full use of the compute power of an accelerator since the data must...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	166,140
1902.01224	Estimating the Mixing Time of Ergodic Markov Chains	We address the problem of estimating the mixing time $t_{\mathsf{mix}}$ of an arbitrary ergodic finite-state Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2019], who left the general case as an open problem. In the reversible case, the analysis is greatly facilita...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,614
2001.08012	A Real-Time Approach for Chance-Constrained Motion Planning with Dynamic Obstacles	Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surfac...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	161,180
2312.03543	GPT-4 Enhanced Multimodal Grounding for Autonomous Driving: Leveraging Cross-Modal Attention with Large Language Models	In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed to address visual grounding in AVs.Our Context-Aware Visual Grounding (...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	413,300
1912.03896	Explicit Group Sparse Projection with Applications to Deep Learning and NMF	We design a new sparse projection method for a set of vectors that guarantees a desired average sparsity level measured leveraging the popular Hoyer measure (an affine function of the ratio of the $\ell_1$ and $\ell_2$ norms). Existing approaches either project each vector individually or require the use of a regulariz...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,721
2308.03495	Balanced Face Dataset: Guiding StyleGAN to Generate Labeled Synthetic Face Image Dataset for Underrepresented Group	For a machine learning model to generalize effectively to unseen data within a particular problem domain, it is well-understood that the data needs to be of sufficient size and representative of real-world scenarios. Nonetheless, real-world datasets frequently have overrepresented and underrepresented groups. One solut...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	384,059
1902.02204	A NURBS-based Inverse Analysis for Reconstruction of Nonlinear Deformations of Thin Shell Structures	This article presents original work combining a NURBS-based inverse analysis with both kinematic and constitutive nonlinearities to recover the applied loads and deformations of thin shell structures. The inverse formulation is tackled by gradient-based optimization algorithms based on computed and measured displacemen...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	120,831
1909.12644	On a convergence property of a geometrical algorithm for statistical manifolds	In this paper, we examine a geometrical projection algorithm for statistical inference. The algorithm is based on Pythagorean relation and it is derivative-free as well as representation-free that is useful in nonparametric cases. We derive a bound of learning rate to guarantee local convergence. In special cases of m-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,182
2402.01695	Language-Guided World Models: A Model-Based Approach to AI Control	This paper introduces the concept of Language-Guided World Models (LWMs) -- probabilistic models that can simulate environments by reading texts. Agents equipped with these models provide humans with more extensive and efficient control, allowing them to simultaneously alter agent behaviors in multiple tasks via natura...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	426,148
0910.0928	BioDiVinE: A Framework for Parallel Analysis of Biological Models	In this paper a novel tool BioDiVinEfor parallel analysis of biological models is presented. The tool allows analysis of biological models specified in terms of a set of chemical reactions. Chemical reactions are transformed into a system of multi-affine differential equations. BioDiVinE employs techniques for finite d...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	4,643
1611.01390	Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures	A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The present study details the complete formulation of such a generative model intended to probe visual motion perception with a dynamic ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,361

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