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arXiv 2022年

作者： Zheng, Kaizhong Yu, Shujian Li, Baojuan Jenssen, Robert Chen, Badong National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China The Department of Computer Science Vrije Universiteit Amsterdam Amsterdam and the Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The Machine Learning Group UiT - Arctic University of Norway Tromsø Norway The School of Biomedical Engineering Fourth Military Medical University Xi’an China

Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls are developed to identify brain markers. However, existing machine learning-based diagnostic models are prone to over-fitting (due to insufficient training samples) and perform poorly in new test environment. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against 3 baselines and 7 state-of-the-art brain network classification methods on three psychiatric datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers which are consistent to clinical and neuroimaging findings. The source code and implementation details of BrainIB are freely available at GitHub repository (https://***/SJYuCNEL/brain-and-Information-Bottleneck/). Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Analysis of behavioral characteristics during prolonged cognitive load 5

Analysis of behavioral characteristics during prolonged cogn...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Kuc, Alexander Grubov, Vadim Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia Center of New Cardiological Informational Technologies Scientific Research Institute of Cardiology Saratov State Medical University Saratov Russia

ISBN: (纸本)9781665442824

In the present work we studied human behavioral characteristics during prolonged cognitive activity. For this purpose we conducted experiment with interpretation of consistently presented ambiguous visual stimuli with different levels of ambiguity. As behavioral characteristic we chose the correctness of ambiguous stimuli interpretation and we showed, that number of errors changes significantly through the experiment. © 2021 IEEE

关键词： Behavioral research

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Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent

arXiv

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arXiv 2025年

作者： Liu, Zhiyu Han, Zhi Tang, Yandong Zhang, Hai Tang, Shaojie Wang, Yao State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Statistics Northwest University Xi’an710000 China Department of Management Science and Systems State University of New York Buffalo United States Center for Intelligent Decision-making and Machine Learning School of Management Xi’an Jiaotong University Xi’an710049 China

We consider the noisy matrix sensing problem in the over-parameterization setting, where the estimated rank r is larger than the true rank r★. Specifically, our main objective is to recover a matrix X★ ∈ Rn1×n2 with rank r★ from noisy measurements using an over-parameterized factorized form LRT, where L ∈ n1×r, R ∈ n2×r and min{n1, n2} ≥ r > r★, with the true rank r★ being unknown. Recently, preconditioning methods have been proposed to accelerate the convergence of matrix sensing problem compared to vanilla gradient descent, incorporating preconditioning terms (LTL + λI)−1 and (RTR + λI)−1 into the original gradient. However, these methods require careful tuning of the damping parameter λ and are sensitive to initial points and step size. To address these limitations, we propose the alternating preconditioned gradient descent (APGD) algorithm, which alternately updates the two factor matrices, eliminating the need for the damping parameter and enabling faster convergence with larger step sizes. We theoretically prove that APGD achieves near-optimal error convergence at a linear rate, starting from arbitrary random initializations. Through extensive experiments, we validate our theoretical results and demonstrate that APGD outperforms other methods, achieving the fastest convergence rate. Notably, both our theoretical analysis and experimental results illustrate that APGD does not rely on the initialization procedure, making it more practical and versatile. Copyright © 2025, The Authors. All rights reserved.

关键词： Damping

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Object Segmentation based on Anti-disturb Network in Ecological Surveillance

Object Segmentation based on Anti-disturb Network in Ecologi...

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第33届中国控制与决策会议

作者： Wan Dai Hongpeng Wang Chongshan Fan Yunwei Xin College of Computer Science Nankai University Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Nankai University Software College Nankai University Research Laboratory of Machine Learning and Pervasive Computing Nankai University

Visual tele-observation is an effective way for ecological *** from pedestrian or rigid body detection,wildlife recognition in natural scenes face more complex problems,such as the similarity of background and foreground,wild background clutter,deformation,and other factors,which will lead to a reduction in recognition *** this paper,We propose a novel segmentation network that is named T-S network to segment background and *** network mainly consists of two parts:one is a tracking *** the context similarity of images in the data set,the purpose of tracking is to ensure that the position of the object is accurately known when the same species in the images are synthesized into a video with interval frames;the other is the saliency network,which is the basis of the most likely position of the object obtained from the tracking ***,the saliency network is used to complete the object *** verify the effectiveness of the network and consider the size of the data set,a lightweight CNN is used to identify the *** show that the accuracy of task recognition is improved by 1.89% by using the segmentation method proposed in this paper.

关键词： segmentation recognition tracking network saliency network

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Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Knowledge-aware Graph Transformer for Pedestrian Trajectory ...

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International Conference on Intelligent Transportation

作者： Yu Liu Yuexin Zhang Kunming Li Yongliang Qiao Stewart Worrall You-Fu Li He Kong Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China School of Automation Guangdong Polytechnic Normal University Guangzhou China Australian Centre for Field Robotics The University of Sydney NSW Australia Australian Institute for Machine Learning The University of Adelaide SA Australia Department of Mechanical Engineering City University of Hong Kong Hong Kong SAR China Shenzhen Key Laboratory of Control Theory and Intelligent Systems SUSTech Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation Rehabilitation Robotics in Universities SUSTech Shenzhen China

Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the uncertain nature of human motions in different environments. For training, recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians, among others. This can limit the prediction performance across various scenarios since the discrepancies between training datasets have not been properly incorporated. To overcome this limitation, this paper proposes a graph transformer structure to improve prediction performance, capturing the differences between the various sites and scenarios contained in the datasets. In particular, a self-attention mechanism and a domain adaption module have been designed to improve the generalization ability of the model. Moreover, an additional metric considering cross-dataset sequences is introduced for training and performance evaluation purposes. The proposed framework is validated and compared against existing methods using popular public datasets, i.e., ETH and UCY. Experimental results demonstrate the improved performance of our proposed scheme.

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EXPOSURE-BASED MULTI-AGENT INSPECTION OF A TUMBLING TARGET USING DEEP REINFORCEMENT learning

arXiv

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arXiv 2023年

作者： Aurand, Joshua Cutlip, Steven Lei, Henry Lang, Kendra Phillips, Sean Machine Learning Engineer - Robotics Verus Research 6100 Uptown Blvd NE AlbuquerqueNM87110 United States Research Engineer - Controls Verus Research United States Software Controls Engineer Verus Research United States Technical Director - Space and Autonomy Verus Research United States Research Mechanical Engineer Air Force Research Laboratory Space Vehicles Directorate Kirtland AFB NM United States

As space becomes more congested, on-orbit inspection is an increasingly relevant activity whether to observe a defunct satellite for planning repairs or to de-orbit it. However, the task of on-orbit inspection itself is challenging, typically requiring the careful coordination of multiple observer satellites. This is complicated by a highly nonlinear environment where the target may be unknown or moving unpredictably without time for continuous command and control from the ground. There is a need for autonomous, robust, decentralized solutions to the inspection task. To achieve this, we consider a hierarchical, learned approach for the decentralized planning of multi-agent inspection of a tumbling target. Our solution consists of two components: a viewpoint or high-level planner trained using deep reinforcement learning and a navigation planner handling point-to-point navigation between pre-specified viewpoints. We present a novel problem formulation and methodology that is suitable not only to reinforcement learning-derived robust policies, but extendable to unknown target geometries and higher fidelity information theoretic objectives received directly from sensor inputs. Operating under limited information, our trained multi-agent high-level policies successfully contextualize information within the global hierarchical environment and are correspondingly able to inspect over 90% of non-convex tumbling targets, even in the absence of additional agent attitude control. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Effective Tensor Completion via Element-wise Weighted Low-rank Tensor Train with Overlapping Ket Augmentation

arXiv

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arXiv 2021年

作者： Zhang, Yang Wang, Yao Han, Zhi Chen, Xi'ai Tang, Yandong The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Center for Intelligent Decision-Making and Machine Learning School of Mangement Xi'an Jiaotong University Xi'An710049 China

In recent years, there have been an increasing number of applications of tensor completion based on the tensor train (TT) format because of its efficiency and effectiveness in dealing with higher-order tensor data. However, existing tensor completion methods using TT decomposition have two obvious drawbacks. One is that they only consider mode weights according to the degree of mode balance, even though some elements are recovered better in an unbalanced mode. The other is that serious blocking artifacts appear when the missing element rate is relatively large. To remedy such two issues, in this work, we propose a novel tensor completion approach via the element-wise weighted technique. Accordingly, a novel formulation for tensor completion and an effective optimization algorithm, called as tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed. In addition, we specifically consider the recovery quality of edge elements from adjacent blocks. Different from traditional reshaping and ket augmentation, we utilize a new tensor augmentation technique called overlapping ket augmentation, which can further avoid blocking artifacts. We then conduct extensive performance evaluations on synthetic data and several real image data sets. Our experimental results demonstrate that the proposed algorithm TWMac-TT outperforms several other competing tensor completion methods. The code is available at https://***/yzcv/TWMac-TT-OKA. Copyright © 2021, The Authors. All rights reserved.

关键词： Tensors

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Chaotic Change of Extracellular Matrix Molecules Concentration in the Presence of Periodically Varying Neuronal Firing Rate 20th

Chaotic Change of Extracellular Matrix Molecules Concentrati...

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20th International Conference on Mathematical Modeling and Supercomputer Technologies, MMST 2020

作者： Rozhnova, Maiya A. Bandenkov, Daniil V. Kazantsev, Victor B. Pankratova, Evgeniya V. Department of Applied Mathematics Institute of Information Technologies Mathemaics and Mechanics Lobachevsky State University of Nizhni Novgorod Nizhny Novgorod Russia Neurotechnology Department Lobachevsky State University of Nizhni Novgorod Nizhny Novgorod Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center of Neurotechnologies and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9783030787585

Transmission and processing of information in the brain are highly complicated processes that are defined by a lot of non-trivial interconnections of structural elements of neural networks. To shed light on peculiarities of such communication, a lot of mathematical models were introduced and computer simulations were carried out. In this work, a model describing the impact of neural activity on changes in brain extracellular matrix (ECM) molecules concentration was considered. It was assumed that the rate of neural activity is periodically changed. For this case, various regular and chaotic modes in dynamics of ECM-molecules concentration were observed. The role of the amplitude and frequency of the periodically varying neuronal firing rate in transitions between various dynamical modes in the ECM-model were examined. Bifurcational mechanisms for chaotic oscillations appearance were demonstrated. © 2021, Springer Nature Switzerland AG.

关键词： Brain

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Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation

arXiv

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arXiv 2022年

作者： Goel, Yash Vaskevicius, Narunas Palmieri, Luigi Chebrolu, Nived Stachniss, Cyrill The laboratory for Photogrammetry and Robotics University of Bonn Germany Robert Bosch GmbH Germany Robert Bosch GmbH Corporate Research Stuttgart Germany The Dynamic Robot Systems Group University of Oxford United Kingdom The University of Bonn Germany The Department of Engineering Science the University of Oxford United Kingdom The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

We investigate the task of object goal navigation in unknown environments where the target is specified by a semantic label (e.g. find a couch). Such a navigation task is especially challenging as it requires understanding of semantic context in diverse settings. Most of the prior work tackles this problem under the assumption of a discrete action policy whereas we present an approach with continuous control which brings it closer to real world applications. We propose a deep neural network architecture and loss function to predict dense cost maps that implicitly contain semantic context and guide the robot towards the semantic goal. We also present a novel way of fusing mid-level visual representations in our architecture to provide additional semantic cues for cost map prediction. The estimated cost maps are then used by a sampling-based model predictive controller (MPC) for generating continuous robot actions. The preliminary experiments suggest that the cost maps generated by our network are suitable for the MPC and can guide the agent to the semantic goal more efficiently than a baseline approach. The results also indicate the importance of mid-level representations for navigation by improving the success rate by 7 percentage points. Copyright © 2022, The Authors. All rights reserved.

关键词： Navigation

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Bistable Dynamics of the Brain Extracellular Matrix in the Presence of Periodically Varying Neuronal Activity 5

Bistable Dynamics of the Brain Extracellular Matrix in the P...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Rozhnova, Maiya A. Kazantsev, Victor B. Pankratova, Evgeniya V. Department of Applied Mathematics Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhni Novgorod Nizhny Novgorod Russia Neurotechnology Department Lobachevsky State University Nizhny Novgorod Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Neuroscience Research Institute Samara State Medical University Samara Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

In this work, the role of the amplitude and frequency of the periodically varying neuronal firing rate in emergence of complicated dynamical modes in recently proposed mathematical model describing the change of extracellular matrix molecules concentration, was examined. Impact of the revealed co-existence of regular and chaotic modes in the extracellular matrix activity on the whole neuronal network functioning is discussed. © 2021 IEEE

关键词： Brain

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