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Octopus-inspired sensorized soft arm for environmental interaction Sci. Robot. (IF 25.0) Pub Date : 2023-11-29 Zhexin Xie, Feiyang Yuan, Jiaqi Liu, Lufeng Tian, Bohan Chen, Zhongqiang Fu, Sizhe Mao, Tongtong Jin, Yun Wang, Xia He, Gang Wang, Yanru Mo, Xilun Ding, Yihui Zhang, Cecilia Laschi, Li Wen
Octopuses can whip their soft arms with a characteristic “bend propagation” motion to capture prey with sensitive suckers. This relatively simple strategy provides models for robotic grasping, controllable with a small number of inputs, and a highly deformable arm with sensing capabilities. Here, we implemented an electronics-integrated soft octopus arm (E-SOAM) capable of reaching, sensing, grasping
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Pessimistic value iteration for multi-task data sharing in Offline Reinforcement Learning Artif. Intell. (IF 14.4) Pub Date : 2023-11-20 Chenjia Bai, Lingxiao Wang, Jianye Hao, Zhuoran Yang, Bin Zhao, Zhen Wang, Xuelong Li
Offline Reinforcement Learning (RL) has shown promising results in learning a task-specific policy from a fixed dataset. However, successful offline RL often relies heavily on the coverage and quality of the given dataset. In scenarios where the dataset for a specific task is limited, a natural approach is to improve offline RL with datasets from other tasks, namely, to conduct Multi-Task Data Sharing
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Visual dexterity: In-hand reorientation of novel and complex object shapes Sci. Robot. (IF 25.0) Pub Date : 2023-11-22 Tao Chen, Megha Tippur, Siyang Wu, Vikash Kumar, Edward Adelson, Pulkit Agrawal
In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in less structured environments, which remain beyond the reach of current robots. Prior works built reorientation systems assuming one or many of the following conditions: reorienting only specific objects with simple shapes, limited range of reorientation, slow or quasi-static manipulation
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A framework for robotic excavation and dry stone construction using on-site materials Sci. Robot. (IF 25.0) Pub Date : 2023-11-22 Ryan Luke Johns, Martin Wermelinger, Ruben Mascaro, Dominic Jud, Ilmar Hurkxkens, Lauren Vasey, Margarita Chli, Fabio Gramazio, Matthias Kohler, Marco Hutter
Automated building processes that enable efficient in situ resource utilization can facilitate construction in remote locations while simultaneously offering a carbon-reducing alternative to commonplace building practices. Toward these ends, we present a robotic construction pipeline that is capable of planning and building freeform stone walls and landscapes from highly heterogeneous local materials
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Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-20 Jascha Achterberg, Danyal Akarca, D. J. Strouse, John Duncan, Duncan E. Astle
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A social network for AI Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-17
Further progress in AI may require learning algorithms to generate their own data rather than assimilate static datasets. A Perspective in this issue proposes that they could do so by interacting with other learning agents in a socially structured way.
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A social path to human-like artificial intelligence Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-17 Edgar A. Duéñez-Guzmán, Suzanne Sadedin, Jane X. Wang, Kevin R. McKee, Joel Z. Leibo
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Differentiable visual computing for inverse problems and machine learning Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-17 Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas, Krishna Murthy Jatavallabhula, Cengiz Oztireli, Tzu-Mao Li, Derek Nowrouzezahrai
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Topological structure of complex predictions Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-17 Meng Liu, Tamal K. Dey, David F. Gleich
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Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-16 Sumeer Ahmad Khan, Alberto Maillo, Vincenzo Lagani, Robert Lehmann, Narsis A. Kiani, David Gomez-Cabrero, Jesper Tegner
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Incorporating neuro-inspired adaptability for continual learning in artificial intelligence Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-16 Liyuan Wang, Xingxing Zhang, Qian Li, Mingtian Zhang, Hang Su, Jun Zhu, Yi Zhong
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A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm Sci. Robot. (IF 25.0) Pub Date : 2023-11-15 Nathaniel Dennler, Amelia Cain, Erica De Guzmann, Claudia Chiu, Carolee J. Winstein, Stefanos Nikolaidis, Maja J. Matarić
An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation
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Motion planning around obstacles with convex optimization Sci. Robot. (IF 25.0) Pub Date : 2023-11-15 Tobia Marcucci, Mark Petersen, David von Wrangel, Russ Tedrake
From quadrotors delivering packages in urban areas to robot arms moving in confined warehouses, motion planning around obstacles is a core challenge in modern robotics. Planners based on optimization can design trajectories in high-dimensional spaces while satisfying the robot dynamics. However, in the presence of obstacles, these optimization problems become nonconvex and very hard to solve, even
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How to break information cocoons Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-13 Fernando P. Santos
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Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-13 Aria Y. Wang, Kendrick Kay, Thomas Naselaris, Michael J. Tarr, Leila Wehbe
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Learning characteristics of graph neural networks predicting protein–ligand affinities Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-13 Andrea Mastropietro, Giuseppe Pasculli, Jürgen Bajorath
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Reporting electricity consumption is essential for sustainable AI Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-10 Charlotte Debus, Marie Piraud, Achim Streit, Fabian Theis, Markus Götz
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Approximately EFX allocations for indivisible chores Artif. Intell. (IF 14.4) Pub Date : 2023-11-07 Shengwei Zhou, Xiaowei Wu
In this paper, we study how to fairly allocate a set of m indivisible chores to a group of n agents, each of which has a general additive cost function on the items. Since envy-free (EF) allocations are not guaranteed to exist, we consider the notion of envy-freeness up to any item (EFX). In contrast to the fruitful results regarding the (approximation of) EFX allocations for goods, very little is
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Sound and relatively complete belief Hoare logic for statistical hypothesis testing programs Artif. Intell. (IF 14.4) Pub Date : 2023-11-10 Yusuke Kawamoto, Tetsuya Sato, Kohei Suenaga
We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately. Specifically, we define belief Hoare logic (BHL) for formalizing and reasoning about the statistical beliefs acquired via hypothesis testing. This program logic is sound and relatively complete with respect to a Kripke model for hypothesis
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Self-supervised deep learning for tracking degradation of perovskite light-emitting diodes with multispectral imaging Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-09 Kangyu Ji, Weizhe Lin, Yuqi Sun, Lin-Song Cui, Javad Shamsi, Yu-Hsien Chiang, Jiawei Chen, Elizabeth M. Tennyson, Linjie Dai, Qingbiao Li, Kyle Frohna, Miguel Anaya, Neil C. Greenham, Samuel D. Stranks
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Development of the Senseiver for efficient field reconstruction from sparse observations Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-06 Javier E. Santos, Zachary R. Fox, Arvind Mohan, Daniel O’Malley, Hari Viswanathan, Nicholas Lubbers
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Calibrated geometric deep learning improves kinase–drug binding predictions Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-06 Yunan Luo, Yang Liu, Jian Peng
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Hierarchical generative modelling for autonomous robots Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-11-02 Kai Yuan, Noor Sajid, Karl Friston, Zhibin Li
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ViTPose++: Vision Transformer for Generic Body Pose Estimation. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-11-03 Yufei Xu,Jing Zhang,Qiming Zhang,Dacheng Tao
Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for body pose estimation tasks. In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects
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Revisiting Person Re-Identification by Camera Selection. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-11-03 Yi-Xing Peng,Yuanxun Li,Wei-Shi Zheng
Person re-identification (Re-ID) is a fundamental task in visual surveillance. Given a query image of the target person, conventional Re-ID focuses on the pairwise similarities between the candidate images and the query. However, conventional Re-ID does not evaluate the consistency of the retrieval results of whether the most similar images ranked in each place contain the same person, which is risky
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Stability Analysis of Recurrent Neural Networks With Time-Varying Delay Based on a Flexible Negative-Determination Quadratic Function Method. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-11-03 Guoqiang Tan,Zhanshan Wang
This brief investigates the stability problem of recurrent neural networks (RNNs) with time-varying delay. First, by introducing some flexibility factors, a flexible negative-determination quadratic function method is proposed, which contains some existing methods and has less conservatism. Second, some integral inequalities and the flexible negative-determination quadratic function method are used
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Safe Adaptive Policy Transfer Reinforcement Learning for Distributed Multiagent Control. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-11-02 Bin Du,Wei Xie,Yang Li,Qisong Yang,Weidong Zhang,Rudy R Negenborn,Yusong Pang,Hongtian Chen
Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to mutual interference among agents. Safety concerns make an already difficult training process even harder. This study proposes a safe adaptive policy transfer RL approach for multiagent cooperative control. Specifically, a pioneer and follower off-policy policy transfer learning (PFOPT) method is presented
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Improving Adversarial Robustness Against Universal Patch Attacks Through Feature Norm Suppressing. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-11-02 Cheng Yu,Jiansheng Chen,Yu Wang,Youze Xue,Huimin Ma
Universal adversarial patch attacks, which are readily implemented, have been validated to be able to fool real-world deep convolutional neural networks (CNNs), posing a serious threat to practical computer vision systems based on CNNs. Unfortunately, current defending approaches are severely understudied facing the following problems. Patch detection-based methods suffer from dramatic performance
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The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-11-01 Taotao Li,Zhenyu Wen,Yang Long,Zhen Hong,Shilian Zheng,Li Yu,Bo Chen,Xiaoniu Yang,Ling Shao
Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown
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Back to Reality: Learning Data-Efficient 3D Object Detector With Shape Guidance. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-31 Xiuwei Xu,Ziwei Wang,Jie Zhou,Jiwen Lu
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i.e. annotations of object centers and categories). In order to remedy the information loss from box annotations to centers, our method makes use of synthetic 3D shapes to convert the position-level annotations into virtual scenes with
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Optimal Control for Constrained Discrete-Time Nonlinear Systems Based on Safe Reinforcement Learning. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-31 Lingzhi Zhang,Lei Xie,Yi Jiang,Zhishan Li,Xueqin Liu,Hongye Su
The state and input constraints of nonlinear systems could greatly impede the realization of their optimal control when using reinforcement learning (RL)-based approaches since the commonly used quadratic utility functions cannot meet the requirements of solving constrained optimization problems. This article develops a novel optimal control approach for constrained discrete-time (DT) nonlinear systems
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3-D Point Cloud Attribute Compression With p-Laplacian Embedding Graph Dictionary Learning. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-30 Xin Li,Wenrui Dai,Shaohui Li,Chenglin Li,Junni Zou,Hongkai Xiong
3-D point clouds facilitate 3-D visual applications with detailed information of objects and scenes but bring about enormous challenges to design efficient compression technologies. The irregular signal statistics and high-order geometric structures of 3-D point clouds cannot be fully exploited by existing sparse representation and deep learning based point cloud attribute compression schemes and graph
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SPLiT: Single Portrait Lighting Estimation Via a Tetrad of Face Intrinsics. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-30 Fan Fei,Yean Cheng,Yongjie Zhu,Qian Zheng,Si Li,Gang Pan,Boxin Shi
This paper proposes a novel pipeline to estimate a non-parametric environment map with high dynamic range from a single human face image. Lighting-independent and -dependent intrinsic images of the face are first estimated separately in a cascaded network. The influence of face geometry on the two lighting-dependent intrinsics, diffuse shading and specular reflection, are further eliminated by distributing
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Optimal Composite Likelihood Estimation and Prediction for Distributed Gaussian Process Modeling. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-30 Yongxiang Li,Qiang Zhou,Wei Jiang,Kwok-Leung Tsui
Large-scale Gaussian process (GP) modeling is becoming increasingly important in machine learning. However, the standard modeling method of GPs, which uses the maximum likelihood method and the best linear unbiased predictor, is designed to run on a single computer, which often has limited computing power. Therefore, there is a growing demand for approximate alternatives, such as composite likelihood
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False Correlation Reduction for Offline Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-30 Zhihong Deng,Zuyue Fu,Lingxiao Wang,Zhuoran Yang,Chenjia Bai,Tianyi Zhou,Zhaoran Wang,Jing Jiang
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems. Most existing papers only discuss defending against out-of-distribution (OOD) actions while we investigate a broader issue, the false correlations between epistemic uncertainty and decision-making, an essential factor that causes suboptimality. In this paper, we propose falSe COrrelation
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Multivariate Time Series Forecasting Using Multiscale Recurrent Networks With Scale Attention and Cross-Scale Guidance. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-30 Qiang Guo,Lexin Fang,Ren Wang,Caiming Zhang
Multivariate time series (MTS) forecasting is considered as a challenging task due to complex and nonlinear interdependencies between time steps and series. With the advance of deep learning, significant efforts have been made to model long-term and short-term temporal patterns hidden in historical information by recurrent neural networks (RNNs) with a temporal attention mechanism. Although various
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Differential Neural Network Identifier for Dynamical Systems With Time-Varying State Constraints. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-30 Ilya Nachevsky,Olga Andrianova,Isaac Chairez,Alexander Poznyak
This study presents a state nonparametric identifier based on neural networks with continuous dynamics, also known as differential neural networks (DNNs). The laws for adjusting their parameters are developed using a control barrier Lyapunov functions (BLFs). The motivation for using the BLF comes from the preliminary information of the system states, which remain in a predefined time-depending set
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Mode switching in organisms for solving explore-versus-exploit problems Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-10-26 Debojyoti Biswas, Andrew Lamperski, Yu Yang, Kathleen Hoffman, John Guckenheimer, Eric S. Fortune, Noah J. Cowan
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Design of prime-editing guide RNAs with deep transfer learning Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-10-26 Feng Liu, Shuhong Huang, Jiongsong Hu, Xiaozhou Chen, Ziguo Song, Junguo Dong, Yao Liu, Xingxu Huang, Shengqi Wang, Xiaolong Wang, Wenjie Shu
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Deep learning of causal structures in high dimensions under data limitations Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-10-26 Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee
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Regional Evaluation Study of VFTO Interference to Secondary Side Cables Based on Cloud Model and MARCOS. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-27 Yongji Wu,Wei Yan,Puliang Du,Xiaomin Gong,Mengxia Zhou
With the advent of the data era, most power secondary side equipment tends to be digitized. The power system needs more accurate numerical results to further improve its operating efficiency. Therefore, it is important to study the electromagnetic interferences of very fast transient overvoltage (VFTO) generated by gas-insulated switchgear (GIS). To protect the secondary side cable from interferences
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Feature Pyramid Fusion Network for Hyperspectral Pansharpening. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-27 Wenqian Dong,Yihan Yang,Jiahui Qu,Yunsong Li,Yufei Yang,Xiuping Jia
Hyperspectral (HS) pansharpening aims at fusing an observed HS image with a panchromatic (PAN) image, to produce an image with the high spectral resolution of the former and the high spatial resolution of the latter. Most of the existing convolutional neural networks (CNNs)-based pansharpening methods reconstruct the desired high-resolution image from the encoded low-resolution (LR) representation
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Online Sparse Representation Clustering for Evolving Data Streams. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-27 Jie Chen,Shengxiang Yang,Conor Fahy,Zhu Wang,Yinan Guo,Yingke Chen
Data stream clustering can be performed to discover the patterns underlying continuously arriving sequences of data. A number of data stream clustering algorithms for finding clusters in arbitrary shapes and handling outliers, such as density-based clustering algorithms, have been proposed. However, these algorithms are often limited in their ability to construct and merge microclusters by measuring
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Differential Game-Based Deep Reinforcement Learning in Underwater Target Hunting Task. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-27 Wei Wei,Jingjing Wang,Jun Du,Zhengru Fang,Yong Ren,C L Philip Chen
To meet requirements for real-time trajectory scheduling and distributed coordination, underwater target hunting task is challenging in terms of turbulent ocean environments and dynamic adversarial environment. Despite the existing research in game-based target hunting area, few approaches have considered dynamic environmental factors, such as sea currents, winds, and communication delay. In this article
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Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-27 Jiaojiao Li,Songcheng Du,Rui Song,Yunsong Li,Qian Du
Fusion-based spectral super-resolution aims to yield a high-resolution hyperspectral image (HR-HSI) by integrating the available high-resolution multispectral image (HR-MSI) with the corresponding low-resolution hyperspectral image (LR-HSI). With the prosperity of deep convolutional neural networks, plentiful fusion methods have made breakthroughs in reconstruction performance promotions. Nevertheless
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Gerrymandering individual fairness Artif. Intell. (IF 14.4) Pub Date : 2023-10-24 Tim Räz
Individual fairness requires that similar individuals are treated similarly. It is supposed to prevent the unfair treatment of individuals on the subgroup level and to overcome the problem that group fairness measures are susceptible to manipulation or gerrymandering. The goal of the present paper is to explore the extent to which individual fairness itself can be gerrymandered. It will be proved that
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Dual forgetting operators in the context of weakest sufficient and strongest necessary conditions Artif. Intell. (IF 14.4) Pub Date : 2023-10-23 Patrick Doherty, Andrzej Szałas
Forgetting is an important concept in knowledge representation and automated reasoning with widespread applications across a number of disciplines. A standard forgetting operator, characterized in [26] in terms of model-theoretic semantics and primarily focusing on the propositional case, opened up a new research subarea. In this paper, a new operator called weak forgetting, dual to standard forgetting
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Deformable Dynamic Sampling and Dynamic Predictable Mask Mining for Image Inpainting. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-26 Cai Shang,Yu Zeng,Shu Yang,Xu Jia,Huchuan Lu,You He
Existing image inpainting methods often produce artifacts that are caused by using vanilla convolution layers as building blocks that treat all image regions equally and generate holes at random locations with equal probability. This design does not differentiate the missing regions and valid regions in inference and does not consider the predictability of missing regions in training. To address these
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Robust Representation Learning for Power System Short-Term Voltage Stability Assessment Under Diverse Data Loss Conditions. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-26 Lipeng Zhu,Weijia Wen,Yinpeng Qu,Feifan Shen,Jiayong Li,Yue Song,Tao Liu
With the help of neural network-based representation learning, significant progress has been recently made in data-driven online dynamic stability assessment (DSA) of complex electric power systems. However, without sufficient attention to diverse data loss conditions in practice, the existing data-driven DSA solutions' performance could be largely degraded due to practical defective input data. To
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Phy-Taylor: Partially Physics-Knowledge-Enhanced Deep Neural Networks via NN Editing. IEEE Trans. Neural Netw. Learn. Syst. (IF 10.4) Pub Date : 2023-10-26 Yanbing Mao,Yuliang Gu,Lui Sha,Huajie Shao,Qixin Wang,Tarek Abdelzaher
Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To address this challenge, we propose a physics-knowledge-enhanced DNN framework called Phy-Taylor, accelerating learning-compliant representations with physics knowledge. The Phy-Taylor framework makes two key contributions;
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Neural scaling of deep chemical models Nat. Mach. Intell. (IF 23.8) Pub Date : 2023-10-23 Nathan C. Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez-Bombarelli, Connor W. Coley, Vijay Gadepally
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Touching reality: Bridging the user-researcher divide in upper-limb prosthetics. Sci. Robot. (IF 25.0) Pub Date : 2023-10-25 J D Brown,E Battaglia,S Engdahl,G Levay,A C Parks,E Skinner,M K O'Malley
Realistically improving upper-limb prostheses is only possible if we listen to users' actual technological needs.
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Assistive robotics should seamlessly integrate humans and robots. Sci. Robot. (IF 25.0) Pub Date : 2023-10-25 Douglas Weber,Amos Matsiko
Better integration of assistive robots with humans and adoption of a user-centric approach in their development will improve performance.
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The role of collaborative robotics in assistive and rehabilitation applications. Sci. Robot. (IF 25.0) Pub Date : 2023-10-25 Monroe Kennedy
Collaborative robotics principles and advancements may transform the field of assistive and rehabilitation robotics.
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A powered prosthesis supports weight-bearing stand-to-sit transitions. Sci. Robot. (IF 25.0) Pub Date : 2023-10-25 Amos Matsiko
Above-knee amputees were capable of weight-bearing symmetry during stand-to-sit transitions when using a powered prosthesis.
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Effect of hip abduction assistance on metabolic cost and balance during human walking Sci. Robot. (IF 25.0) Pub Date : 2023-10-25 Juneil Park, Kimoon Nam, Juseok Yun, JunYoung Moon, JaeWook Ryu, Sungjin Park, Seungtae Yang, Alireza Nasirzadeh, Woochul Nam, Sruthi Ramadurai, Myunghee Kim, Giuk Lee
The use of wearable robots to provide walking assistance has rapidly grown over the past decade, with notable advances made in robot design and control methods toward reducing physical effort while performing an activity. The reduction in walking effort has mainly been achieved by assisting forward progression in the sagittal plane. Human gait, however, is a complex movement that combines motions in
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Learning Hierarchical Modular Networks for Video Captioning. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-25 Guorong Li,Hanhua Ye,Yuankai Qi,Shuhui Wang,Laiyun Qing,Qingming Huang,Ming-Hsuan Yang
Video captioning aims to generate natural language descriptions for a given video clip. Existing methods mainly focus on end-to-end representation learning via word-by-word comparison between predicted captions and ground-truth texts. Although significant progress has been made, such supervised approaches neglect semantic alignment between visual and linguistic entities, which may negatively affect
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A Theoretical Analysis of DeepWalk and Node2vec for Exact Recovery of Community Structures in Stochastic Blockmodels. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-25 Yichi Zhang,Minh Tang
Random-walk-based network embedding algorithms like DeepWalk and node2vec are widely used to obtain Euclidean representation of the nodes in a network prior to performing downstream inference tasks. However, despite their impressive empirical performance, there is a lack of theoretical results explaining their large-sample behavior. In this paper, we study node2vec and DeepWalk through the perspective
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Mitigating Confounding Bias in Practical Recommender Systems With Partially Inaccessible Exposure Status. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-25 Tianwei Cao,Qianqian Xu,Zhiyong Yang,Qingming Huang
To improve user experience, recommender systems have been widely used on many online platforms. In these systems, recommendation models are typically learned from positive/negative feedback that are collected automatically. Notably, recommender systems are a little different from general supervised learning tasks. In recommender systems, there are some factors (e.g. previous recommendation models or
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A Theoretical Analysis of Density Peaks Clustering and the Component-wise Peak-Finding Algorithm. IEEE Trans. Pattern Anal. Mach. Intell. (IF 23.6) Pub Date : 2023-10-25 Joshua Tobin,Mimi Zhang
Density peaks clustering detects modes as points with high density and large distance to points of higher density. Each non-mode point is assigned to the same cluster as its nearest neighbor of higher density. Density peaks clustering has proved capable in applications, yet little work has been done to understand its theoretical properties or the characteristics of the clusterings it produces. Here