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
2007.15047	Information-Theoretic Approximation to Causal Models	Inferring the causal direction and causal effect between two discrete random variables X and Y from a finite sample is often a crucial problem and a challenging task. However, if we have access to observational and interventional data, it is possible to solve that task. If X is causing Y, then it does not matter if we ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	189,551
1406.7699	Multicast Multigroup Precoding and User Scheduling for Frame-Based Satellite Communications	The present work focuses on the forward link of a broadband multibeam satellite system that aggressively reuses the user link frequency resources. Two fundamental practical challenges, namely the need to frame multiple users per transmission and the per-antenna transmit power limitations, are addressed. To this end, th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,261
2109.08634	Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?	Models designed for intelligent process automation are required to be capable of grounding user interface elements. This task of interface element grounding is centred on linking instructions in natural language to their target referents. Even though BERT and similar pre-trained language models have excelled in several...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	255,960
2405.11264	Cross-Language Assessment of Mathematical Capability of ChatGPT	This paper presents an evaluation of the mathematical capability of ChatGPT across diverse languages like Hindi, Gujarati, and Marathi. ChatGPT, based on GPT-3.5 by OpenAI, has garnered significant attention for its natural language understanding and generation abilities. However, its performance in solving mathematica...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	455,066
2303.05103	Algorithmic neutrality	Algorithms wield increasing control over our lives: over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investiga...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	350,342
2102.03406	Symbolic Behaviour in Artificial Intelligence	The ability to use symbols is the pinnacle of human intelligence, but has yet to be fully replicated in machines. Here we argue that the path towards symbolically fluent artificial intelligence (AI) begins with a reinterpretation of what symbols are, how they come to exist, and how a system behaves when it uses them. W...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	218,735
2304.10305	Feature-compatible Progressive Learning for Video Copy Detection	Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content. This paper presents our second place solutions to the Meta AI Video Similarity Challenge (VSC22), CVPR 2023. In order to compete in this challenge, we propose Feature-Compatible Progressive Learning (FCPL) f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,366
2401.00065	Accelerating Process Development for 3D Printing of New Metal Alloys	Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by convent...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	418,867
2410.16632	Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies	Reinforcement learning (RL) policies are prone to high-frequency oscillations, especially undesirable when deploying to hardware in the real-world. In this paper, we identify, categorize, and compare methods from the literature that aim to mitigate high-frequency oscillations in deep RL. We define two broad classes: lo...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	501,109
2202.06876	A Graphical Approach For Brain Haemorrhage Segmentation	Haemorrhaging of the brain is the leading cause of death in people between the ages of 15 and 24 and the third leading cause of death in people older than that. Computed tomography (CT) is an imaging modality used to diagnose neurological emergencies, including stroke and traumatic brain injury. Recent advances in Deep...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	280,364
1711.09334	In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks	In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images. In this paper, we propose an extension of the unsupervised image-to-image translation problem to multiple input setting. Given a set of paired images from mult...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,380
2312.11109	Graph Transformers for Large Graphs	Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks. However, these results have been limited to small-scale graphs, where the computational feasibility of the global attention mechanism is possible. The ne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,445
2412.06342	Tracking control of latent dynamic systems with application to spacecraft attitude control	When intelligent spacecraft or space robots perform tasks in a complex environment, the controllable variables are usually not directly available and have to be inferred from high-dimensional observable variables, such as outputs of neural networks or images. While the dynamics of these observations are highly complex,...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	515,214
2111.14125	AirSPEC: An IoT-empowered Air Quality Monitoring System integrated with a Machine Learning Framework to Detect and Predict defined Air Quality parameters	The air that surrounds us is the cardinal source of respiration of all life-forms. Therefore, it is undoubtedly vital to highlight that balanced air quality is utmost important to the respiratory health of all living beings, environmental homeostasis, and even economical equilibrium. Nevertheless, a gradual deteriorati...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	268,500
2210.14428	D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning	While combining imitation learning (IL) and reinforcement learning (RL) is a promising way to address poor sample efficiency in autonomous behavior acquisition, methods that do so typically assume that the requisite behavior demonstrations are provided by an expert that behaves optimally with respect to a task reward. ...	true	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	326,543
1811.02554	Quantizers with Parameterized Distortion Measures	In many quantization problems, the distortion function is given by the Euclidean metric to measure the distance of a source sample to any given reproduction point of the quantizer. We will in this work regard distortion functions, which are additively and multiplicatively weighted for each reproduction point resulting ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	112,616
2203.10761	Harnessing Hard Mixed Samples with Decoupled Regularizer	Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup methods have improved previous static policies effectively (e.g., linear interpolation) by maximizing target-related salient regions in mixed s...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	286,673
1004.3884	Oil Price Trackers Inspired by Immune Memory	We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term memory pool...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	6,242
2110.15588	Nash equilibrium of multi-agent graphical game with a privacy information encrypted learning algorithm	This paper studies the global Nash equilibrium problem of leader-follower multi-agent dynamics, which yields consensus with a privacy information encrypted learning algorithm. With the secure hierarchical structure, the relationship between the secure consensus problem and global Nash equilibrium is discussed under pot...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	263,929
2204.08317	An alternative approach for distributed parameter estimation under Gaussian settings	This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussian distribution. The sensor measurements at each agent are linear and corrupted with additive white Gaussian noise. Under such settings, this...	false	false	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	false	292,039
1109.6052	Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems	Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,366
2212.12799	A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models	Chemical named entity recognition (NER) models are used in many downstream tasks, from adverse drug reaction identification to pharmacoepidemiology. However, it is unknown whether these models work the same for everyone. Performance disparities can potentially cause harm rather than the intended good. This paper assess...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	338,141
2205.01954	Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem	Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordTour, unsupervised one-dimensional word embeddings. To achieve the challenging goal, we propose ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	294,784
1710.01837	Postquantum Br\`{e}gman relative entropies	We develop a new approach to construction of Br\`{e}gman relative entropies over nonreflexive Banach spaces, based on nonlinear mappings into reflexive Banach spaces. We apply it to derive few families of Br\`{e}gman relative entropies over several radially compact base normed spaces in spectral duality. In particular,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	82,071
2411.18484	SPTTE: A Spatiotemporal Probabilistic Framework for Travel Time Estimation	Accurate travel time estimation is essential for navigation and itinerary planning. While existing research employs probabilistic modeling to assess travel time uncertainty and account for correlations between multiple trips, modeling the temporal variability of multi-trip travel time distributions remains a significan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	511,889
1107.5387	Controlling wheelchairs by body motions: A learning framework for the adaptive remapping of space	Learning to operate a vehicle is generally accomplished by forming a new cognitive map between the body motions and extrapersonal space. Here, we consider the challenge of remapping movement-to-space representations in survivors of spinal cord injury, for the control of powered wheelchairs. Our goal is to facilitate th...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	11,465
2112.09407	Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks	The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. Ho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	272,137
1612.07863	Anatomy of Scholarly Information Behavior Patterns in the Wake of Academic Social Media Platforms	As more scholarly content is born digital or converted to a digital format, digital libraries are becoming increasingly vital to researchers seeking to leverage scholarly big data for scientific discovery. Although scholarly products are available in abundance-especially in environments created by the advent of social ...	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	65,997
1910.09340	Gradient Boosted Decision Tree Neural Network	In this paper we propose a method to build a neural network that is similar to an ensemble of decision trees. We first illustrate how to convert a learned ensemble of decision trees to a single neural network with one hidden layer and an input transformation. We then relax some properties of this network such as thresh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,161
1203.3584	An Accurate Arabic Root-Based Lemmatizer for Information Retrieval Purposes	In spite of its robust syntax, semantic cohesion, and less ambiguity, lemma level analysis and generation does not yet focused in Arabic NLP literatures. In the current research, we propose the first non-statistical accurate Arabic lemmatizer algorithm that is suitable for information retrieval (IR) systems. The propos...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	14,987
2402.03819	Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants	Synthetic Minority Oversampling Technique (SMOTE) is a common rebalancing strategy for handling imbalanced tabular data sets. However, few works analyze SMOTE theoretically. In this paper, we prove that SMOTE (with default parameter) tends to copy the original minority samples asymptotically. We also prove that SMOTE e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,209
1806.06827	PAC-Bayes bounds for stable algorithms with instance-dependent priors	PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a Hilbert space valued algorithm. The PAC-Bayes setting is used with a Gaussian prior centered at the expected output. Thus a novelty of our ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,770
2001.10269	Causal query in observational data with hidden variables	This paper discusses the problem of causal query in observational data with hidden variables, with the aim of seeking the change of an outcome when "manipulating" a variable while given a set of plausible confounding variables which affect the manipulated variable and the outcome. Such an "experiment on data" to estima...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	161,778
2410.19612	Shared Control with Black Box Agents using Oracle Queries	Shared control problems involve a robot learning to collaborate with a human. When learning a shared control policy, short communication between the agents can often significantly reduce running times and improve the system's accuracy. We extend the shared control problem to include the ability to directly query a coop...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	502,387
2207.03018	Partial Shape Similarity via Alignment of Multi-Metric Hamiltonian Spectra	Evaluating the similarity of non-rigid shapes with significant partiality is a fundamental task in numerous computer vision applications. Here, we propose a novel axiomatic method to match similar regions across shapes. Matching similar regions is formulated as the alignment of the spectra of operators closely related ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,688
2405.11430	MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation	Recent advancements in large language models (LLMs) have greatly improved code generation, specifically at the function level. For instance, GPT-4o has achieved a 91.0\% pass rate on HumanEval. However, this draws into question the adequacy of existing benchmarks in thoroughly assessing function-level code generation c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	455,136
2104.05547	WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding Universal Value" Documents with Soft Labels	The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind. Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL, and yet a complex task, even for experts, since the selection criteria of O...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	229,768
2203.11263	Assessing trade-offs among electrification and grid decarbonization in a clean energy transition: Application to New York State	A modeling framework is presented to investigate trade-offs among decarbonization from increased low-carbon electricity generation and electrification of heating and vehicles. The model is broadly applicable but relies on high-fidelity parameterization of existing infrastructure and anticipated electrified loads; this ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	286,851
1103.1168	An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors	This paper introduces an algorithm for the nonnegative matrix factorization-and-completion problem, which aims to find nonnegative low-rank matrices X and Y so that the product XY approximates a nonnegative data matrix M whose elements are partially known (to a certain accuracy). This problem aggregates two existing pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	9,495
2109.10404	Digital Signal Processing Using Deep Neural Networks	Currently there is great interest in the utility of deep neural networks (DNNs) for the physical layer of radio frequency (RF) communications. In this manuscript, we describe a custom DNN specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention throug...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,594
2201.10753	Interactive Image Inpainting Using Semantic Guidance	Image inpainting approaches have achieved significant progress with the help of deep neural networks. However, existing approaches mainly focus on leveraging the priori distribution learned by neural networks to produce a single inpainting result or further yielding multiple solutions, where the controllability is not ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	277,091
1906.05791	Modeling and Control of Combustion Phasing in Dual-Fuel Compression Ignition Engines	Dual fuel engines can achieve high efficiencies and low emissions but also can encounter high cylinder-to-cylinder variations on multi-cylinder engines. In order to avoid these variations, they require a more complex method for combustion phasing control such as model-based control. Since the combustion process in thes...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	135,121
1008.3222	Proofs for an Abstraction of Continuous Dynamical Systems Utilizing Lyapunov Functions	In this report proofs are presented for a method for abstracting continuous dynamical systems by timed automata. The method is based on partitioning the state space of dynamical systems with invariant sets, which form cells representing locations of the timed automata. To enable verification of the dynamical system b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	7,304
1809.07999	Multimodal Dual Attention Memory for Video Story Question Answering	We propose a video story question-answering (QA) architecture, Multimodal Dual Attention Memory (MDAM). The key idea is to use a dual attention mechanism with late fusion. MDAM uses self-attention to learn the latent concepts in scene frames and captions. Given a question, MDAM uses the second attention over these late...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	true	108,406
2306.00826	In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation	Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to the in-distribution task. The OOD detection performance when the in-distribution (ID) is ImageNet-1K is commonly being tested on a small range of test OOD datasets. We find that most of the currently used test OOD datasets, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	370,178
1403.4887	Using Entropy Estimates for DAG-Based Ontologies	Motivation: Entropy measurements on hierarchical structures have been used in methods for information retrieval and natural language modeling. Here we explore its application to semantic similarity. By finding shared ontology terms, semantic similarity can be established between annotated genes. A common procedure for ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	31,688
1805.03616	A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization	In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	97,083
2402.07436	Novel definition and quantitative analysis of branch structure with topological data analysis	While branching network structures abound in nature, their objective analysis is more difficult than expected because existing quantitative methods often rely on the subjective judgment of branch structures. This problem is particularly pronounced when dealing with images comprising discrete particles. Here we propose ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	428,718
2309.03381	Active shooter detection and robust tracking utilizing supplemental synthetic data	The increasing concern surrounding gun violence in the United States has led to a focus on developing systems to improve public safety. One approach to developing such a system is to detect and track shooters, which would help prevent or mitigate the impact of violent incidents. In this paper, we proposed detecting sho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	390,359
2412.15156	Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM	Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos. However, achieving the desired output often entails multiple revisions and iterative inference to refine user-provided prom...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	518,959
1806.06923	Implicit Quantile Networks for Distributional Reinforcement Learning	In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN. We achieve this by using quantile regression to approximate the full quantile function for the state-action return distribution. By reparameter...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	100,787
1512.02972	Get More With Less: Near Real-Time Image Clustering on Mobile Phones	Machine learning algorithms, in conjunction with user data, hold the promise of revolutionizing the way we interact with our phones, and indeed their widespread adoption in the design of apps bear testimony to this promise. However, currently, the computationally expensive segments of the learning pipeline, such as fea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	49,987
1910.08888	Monotonic Properties of Completed Aggregates in Recursive Queries	The use of aggregates in recursion enables efficient and scalable support for a wide range of BigData algorithms, including those used in graph applications, KDD applications, and ML applications, which have proven difficult to be expressed and supported efficiently in BigData systems supporting Datalog or SQL. The pro...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	150,002
2211.06745	Quadrature Control-Bounded ADCs	In this paper, the design flexibility of the control-bounded analog-to-digital converter principle is demonstrated by considering band-pass analog-to-digital conversion. We show how a low-pass control-bounded analog-to-digital converter can be translated into a band-pass version where the guaranteed stability, converte...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	330,007
1710.04177	The Social Bow Tie	Understanding tie strength in social networks, and the factors that influence it, have received much attention in a myriad of disciplines for decades. Several models incorporating indicators of tie strength have been proposed and used to quantify relationships in social networks, and a standard set of structural networ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	82,445
1806.00557	CubeSLAM: Monocular 3D Object SLAM	We present a method for single image 3D cuboid object detection and multi-view object SLAM in both static and dynamic environments, and demonstrate that the two parts can improve each other. Firstly for single image object detection, we generate high-quality cuboid proposals from 2D bounding boxes and vanishing points ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	99,332
2111.13328	Advantage of the key relay protocol over secure network coding	The key relay protocol (KRP) plays an important role in improving the performance and the security of quantum key distribution (QKD) networks. On the other hand, there is also an existing research field called secure network coding (SNC), which has similar goal and structure. We here analyze differences and similaritie...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	268,265
1510.01942	Helping Domain Experts Build Speech Translation Systems	We present a new platform, "Regulus Lite", which supports rapid development and web deployment of several types of phrasal speech translation systems using a minimal formalism. A distinguishing feature is that most development work can be performed directly by domain experts. We motivate the need for platforms of this ...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	47,672
cs/0106003	A note on radial basis function computing	This note carries three purposes involving our latest advances on the radial basis function (RBF) approach. First, we will introduce a new scheme employing the boundary knot method (BKM) to nonlinear convection-diffusion problem. It is stressed that the new scheme directly results in a linear BKM formulation of nonline...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	537,345
2304.03454	Detecting Chinese Fake News on Twitter during the COVID-19 Pandemic	The outbreak of COVID-19 has led to a global surge of Sinophobia partly because of the spread of misinformation, disinformation, and fake news on China. In this paper, we report on the creation of a novel classifier that detects whether Chinese-language social media posts from Twitter are related to fake news about Chi...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	356,816
2407.01079	On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)	We investigate the statistical and computational limits of latent Diffusion Transformers (DiTs) under the low-dimensional linear latent space assumption. Statistically, we study the universal approximation and sample complexity of the DiTs score function, as well as the distribution recovery property of the initial dat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	469,139
2405.05081	Robust deep learning from weakly dependent data	Recent developments on deep learning established some theoretical properties of deep neural networks estimators. However, most of the existing works on this topic are restricted to bounded loss functions or (sub)-Gaussian or bounded input. This paper considers robust deep learning from weakly dependent observations, wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,786
2403.19149	Topological Cycle Graph Attention Network for Brain Functional Connectivity	This study, we introduce a novel Topological Cycle Graph Attention Network (CycGAT), designed to delineate a functional backbone within brain functional graph--key pathways essential for signal transmissio--from non-essential, redundant connections that form cycles around this core structure. We first introduce a cycle...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	442,229
2012.05331	Speech Recognition for Endangered and Extinct Samoyedic languages	Our study presents a series of experiments on speech recognition with endangered and extinct Samoyedic languages, spoken in Northern and Southern Siberia. To best of our knowledge, this is the first time a functional ASR system is built for an extinct language. We achieve with Kamas language a Label Error Rate of 15\%,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	210,743
2310.17381	Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios	Uncertainty in the behavior of other traffic participants is a crucial factor in collision avoidance for automated driving; here, stochastic metrics could avoid overly conservative decisions. This paper introduces a Stochastic Model Predictive Control (SMPC) planner for emergency collision avoidance in highway scenario...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	403,111
1905.03638	Mappa Mundi: An Interactive Artistic Mind Map Generator with Artificial Imagination	We present a novel real-time, collaborative, and interactive AI painting system, Mappa Mundi, for artistic Mind Map creation. The system consists of a voice-based input interface, an automatic topic expansion module, and an image projection module. The key innovation is to inject Artificial Imagination into painting cr...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	130,241
2407.20917	How to Choose a Reinforcement-Learning Algorithm	The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an algorithm for a task at hand can be challenging. In this work, we streamline the process of choosing reinforcement-learning algorithms and a...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	477,332
1509.03907	Binary Codes and Period-2 Orbits of Sequential Dynamical Systems	Let $[K_n,f,\pi]$ be the (global) SDS map of a sequential dynamical system (SDS) defined over the complete graph $K_n$ using the update order $\pi\in S_n$ in which all vertex functions are equal to the same function $f\colon\mathbb F_2^n\to\mathbb F_2^n$. Let $\eta_n$ denote the maximum number of periodic orbits of per...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	46,873
1706.02695	Breaking Diversity Restriction: Distributed Optimal Control of Stand-alone DC Microgrids	Stand-alone direct current (DC) microgrids may belong to different owners and adopt various control strategies. This brings great challenge to its optimal operation due to the difficulty of implementing a unified control. This paper addresses the distributed optimal control of DC microgrids, which intends to break the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	75,018
2310.03325	Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning	Visual planning simulates how humans make decisions to achieve desired goals in the form of searching for visual causal transitions between an initial visual state and a final visual goal state. It has become increasingly important in egocentric vision with its advantages in guiding agents to perform daily tasks in com...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	397,235
2001.03000	Guidelines for enhancing data locality in selected machine learning algorithms	To deal with the complexity of the new bigger and more complex generation of data, machine learning (ML) techniques are probably the first and foremost used. For ML algorithms to produce results in a reasonable amount of time, they need to be implemented efficiently. In this paper, we analyze one of the means to increa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,860
1803.09362	PI Consensus Error Transformation for Adaptive Cooperative Control of Nonlinear Multi-Agent Systems	A solution is provided in this note for the adaptive consensus problem of nonlinear multi-agent systems with unknown and non-identical control directions assuming a strongly connected underlying graph topology. This is achieved with the introduction of a novel variable transformation called PI consensus error transform...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	93,481
2409.19579	Leveraging Surgical Activity Grammar for Primary Intention Prediction in Laparoscopy Procedures	Surgical procedures are inherently complex and dynamic, with intricate dependencies and various execution paths. Accurate identification of the intentions behind critical actions, referred to as Primary Intentions (PIs), is crucial to understanding and planning the procedure. This paper presents a novel framework that ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	492,748
2012.14873	Twin Neural Network Regression	We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,630
1902.07159	Edge Replacement Grammars: A Formal Language Approach for Generating Graphs	Graphs are increasingly becoming ubiquitous as models for structured data. A generative model that closely mimics the structural properties of a given set of graphs has utility in a variety of domains. Much of the existing work require that a large number of parameters, in fact exponential in size of the graphs, be est...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	121,921
2403.19862	PACC: A Passive-Arm Approach for High-Payload Collaborative Carrying with Quadruped Robots Using Model Predictive Control	In this paper, we introduce the concept of using passive arm structures with intrinsic impedance for robot-robot and human-robot collaborative carrying with quadruped robots. The concept is meant for a leader-follower task and takes a minimalist approach that focuses on exploiting the robots' payload capabilities and r...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	442,501
2309.00920	Trustworthy Distributed Average Consensus based on Locally Assessed Trust Evaluations	This paper proposes a distributed algorithm for average consensus in a multi-agent system under a fixed bidirectional communication topology, in the presence of malicious agents (nodes) that may try to influence the average consensus outcome by manipulating their updates. The proposed algorithm converges asymptotically...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	389,470
1911.03976	On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs	Variational autoencoders (VAEs) hold great potential for modelling text, as they could in theory separate high-level semantic and syntactic properties from local regularities of natural language. Practically, however, VAEs with autoregressive decoders often suffer from posterior collapse, a phenomenon where the model l...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	152,849
2301.05609	Co-manipulation of soft-materials estimating deformation from depth images	Human-robot co-manipulation of soft materials, such as fabrics, composites, and sheets of paper/cardboard, is a challenging operation that presents several relevant industrial applications. Estimating the deformation state of the co-manipulated material is one of the main challenges. Viable methods provide the indirect...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	340,399
1904.06483	Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling	We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. It is governed by a simple generative model, where the likelihood to gene...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	127,558
2204.13583	KL-Mat : Fair Recommender System via Information Geometry	Recommender system has intrinsic problems such as sparsity and fairness. Although it has been widely adopted for the past decades, research on fairness of recommendation algorithms has been largely neglected until recently. One important paradigm for resolving the issue is regularization. However, researchers have not ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	293,860
1602.00489	Real Time Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming - the Case of Safari	The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	51,588
2010.11868	Parameter Reduction in Probabilistic Critical Time Evaluation Using Sensitivity Analysis and PCA	In this paper, we discuss a method to find the most influential power system parameters to the probabilistic transient stability assessment problem---finding the probability distribution of the critical clearing time. We perform the parameter selection by employing a sensitivity analysis combined with a principal compo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	202,458
2101.06227	Deep Reinforcement Learning for Haptic Shared Control in Unknown Tasks	Recent years have shown a growing interest in using haptic shared control (HSC) in teleoperated systems. In HSC, the application of virtual guiding forces decreases the user's control effort and improves execution time in various tasks, presenting a good alternative in comparison with direct teleoperation. HSC, despite...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	215,648
2007.12540	Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference	Multi-task networks are commonly utilized to alleviate the need for a large number of highly specialized single-task networks. However, two common challenges in developing multi-task models are often overlooked in literature. First, enabling the model to be inherently incremental, continuously incorporating information...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	188,848
2303.08358	DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification	In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means that not only multi-view features are often missing, and label comp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,612
2005.07443	Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures	When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayes...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	177,284
2407.15814	Perceptions of Linguistic Uncertainty by Language Models and Humans	_Uncertainty expressions_ such as "probably" or "highly unlikely" are pervasive in human language. While prior work has established that there is population-level agreement in terms of how humans quantitatively interpret these expressions, there has been little inquiry into the abilities of language models in the same ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	475,344
2103.17236	High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling	Fabrication process variations can significantly influence the performance and yield of nano-scale electronic and photonic circuits. Stochastic spectral methods have achieved great success in quantifying the impact of process variations, but they suffer from the curse of dimensionality. Recently, low-rank tensor method...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	227,827
2502.03792	Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent Learning Rate Constraints	We demonstrate that applying an eventual decay to the learning rate (LR) in empirical risk minimization (ERM), where the mean-squared-error loss is minimized using standard gradient descent (GD) for training a two-layer neural network with Lipschitz activation functions, ensures that the resulting network exhibits a hi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,861
2405.04294	Enhancing the Efficiency and Accuracy of Underlying Asset Reviews in Structured Finance: The Application of Multi-agent Framework	Structured finance, which involves restructuring diverse assets into securities like MBS, ABS, and CDOs, enhances capital market efficiency but presents significant due diligence challenges. This study explores the integration of artificial intelligence (AI) with traditional asset review processes to improve efficiency...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	452,517
1809.01797	Describing a Knowledge Base	We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	106,892
2405.15307	Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation	Large Language Models (LLMs) driven by In-Context Learning (ICL) have significantly improved the performance of text-to-SQL. Previous methods generally employ a two-stage reasoning framework, namely 1) schema linking and 2) logical synthesis, making the framework not only effective but also interpretable. Despite these...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	456,863
2210.06908	Feature-Proxy Transformer for Few-Shot Segmentation	Few-shot segmentation (FSS) aims at performing semantic segmentation on novel classes given a few annotated support samples. With a rethink of recent advances, we find that the current FSS framework has deviated far from the supervised segmentation framework: Given the deep features, FSS methods typically use an intric...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,498
1606.03558	Universal Correspondence Network	We present a deep learning framework for accurate visual correspondences and demonstrate its effectiveness for both geometric and semantic matching, spanning across rigid motions to intra-class shape or appearance variations. In contrast to previous CNN-based approaches that optimize a surrogate patch similarity object...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	57,109
2203.06948	Continuous Time Graph Processes with Known ERGM Equilibria: Contextual Review, Extensions, and Synthesis	Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful are those whose long-term behavior converges to a graph distribution of known form. Here, we review some of the conditions for such convergence, and provide examples of novel and/or known processes that...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	285,279
2111.04316	SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning	Teaching machines to recognize a new category based on few training samples especially only one remains challenging owing to the incomprehensive understanding of the novel category caused by the lack of data. However, human can learn new classes quickly even given few samples since human can tell what discriminative fe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	265,455
2006.09719	Automatically Ranked Russian Paraphrase Corpus for Text Generation	The article is focused on automatic development and ranking of a large corpus for Russian paraphrase generation which proves to be the first corpus of such type in Russian computational linguistics. Existing manually annotated paraphrase datasets for Russian are limited to small-sized ParaPhraser corpus and ParaPlag wh...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	182,636
0712.4101	Digital Ecosystems: Stability of Evolving Agent Populations	Stability is perhaps one of the most desirable features of any engineered system, given the importance of being able to predict its response to various environmental conditions prior to actual deployment. Engineered systems are becoming ever more complex, approaching the same levels of biological ecosystems, and so the...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	1,083
2309.11478	Fictional Worlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs	We address the integration of storytelling and Large Language Models (LLMs) to develop engaging and believable Social Chatbots (SCs) in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept of story enginee...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	393,423

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