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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Guozheng Feng Jindong Xu Baode Fan Tianyu Zhao Meng Zhu Xiao Sun Jin Zhou Shiyuan Han School of Computer and Control Engineering Yantai University Yantai China

ISBN: (纸本)9781728105529

The heterogeneity of objects within the class and the ambiguity of objects between the classes in remote sensing images cause the uncertainty in ground objects classification. Fuzzy set theory could express the fuzziness effectively, while interval-value data model can reflect the uncertainty of the data. Therefore, combining the interval-value data model and fuzzy c-means algorithm, a preferential adaptive interval-value fuzzy c-means (PA-IVFCM) algorithm is proposed in this paper. The overall interval width of the category is adjusted by normalizing mean square error in the class, the interval modeling of the data is selected by using the preferential factor dynamically, thereby increasing the intra-class compactness and the boundary separability. The experimental results show that PA-IVFCM method can be effectively applied in the SPOT5 remote sensing data classification, and the overall classification accuracy and Kappa coefficients are greatly improved compared with the existing popular fuzzy classification methods.

关键词： Classification algorithms Remote sensing Heuristic algorithms Mathematical model Clustering algorithms Data models Uncertainty

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Underwater Sensor Network Clock Synchronization Algorithm Based on Markov Chain Group Consistency

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Ruan Jian Xue Bao/Journal of Software 2019年 30卷 27-39页

作者： Wei, Lian-Suo Su, Yang Li, Hua Wu, Di School of Computer and Control Engineering Qiqihar University Qiqihar161006 China School of Computing Science and Technology Harbin Engineering University Harbin150001 China

Most of the existing UWSNs clock synchronization algorithms use nodes to exchange data frequently, but neglect the synchronization information received by neighbor nodes within the scope of node-based communication, so there are some problems such as high energy consumption and low synchronization efficiency in synchronization communication, which affect the accuracy of network clock synchronization. Therefore, based on the analysis of the influence of UWSNs multi-objective optimization network topology evolution process on clock synchronization, this paper uses the theory of group consistency and Markov chain to establish the synchronization cycle regulation mechanism and clock synchronization model without too much increase of the average transmission radius and communication energy consumption. Then, using linear regression fitting method, the inter-cluster synchronization and intra-cluster synchronization are established. Finally, the performance of UWSNs clock synchronization algorithm is verified by simulation. © Copyright 2019, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Synchronization

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Event-triggered control for consensus of multi-agent systems with nonlinear output and directed topologies

arXiv

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Wu, Junfeng Johansson, Karl H. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States College of Control Science and Engineering Zhejiang University Hangzhou310027 China

We propose a distributed event-triggered control law to solve the consensus problem for multi-agent systems with nonlinear output. Under the condition that the underlying digraph is strongly connected, we propose some sufficient conditions related to the nonlinear output function and initial states to guarantee that the event-triggered controller realizes consensus. Then the results are extended to the case where the underlying directed graph contains a directed spanning tree. These theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

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A Hybrid Fireworks Explosion Algorithm 9th

A Hybrid Fireworks Explosion Algorithm

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9th International Symposium on Intelligence Computation and Applications, ISICA 2017

作者： Wang, Liping Chen, Renwen Xie, Chengwang State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics 29 Yudao Street NanjingJiangsu210016 China School of Information and Computer Engineering Pingxiang University Pingxiang337000 China School of Computer and Information Engineering Guangxi Teachers Education University Nanning530023 China

ISBN: (纸本)9789811316470

In order to improve the abilities of global exploration and local exploitation for the fireworks explosion algorithm (FA), the opposition-based learning method is introduced to generate the opposition-based population and to expand the exploration range of the FA. In addition, a computing method of adaptive adjustment of explosion radius is proposed based on the fitness differences of the individuals in the population. These above strategies are integrated to form an adaptive fireworks explosion optimization algorithm with opposition-based learning. The presented FA is experimented with other four swarm intelligence optimization algorithms, and the results show that our improved FA clearly outperforms the others in the performance of convergence and accuracy. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Explosions

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Multi-sensor state estimation over lossy channels using coded measurements

arXiv

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arXiv 2019年

作者： Sui, Tianju Marelli, Damian Sun, Ximing Fu, Minyue School of Control Science and Engineering Dalian University of Technology Dalian China School of Automation Guangdong University of Technology Guangzhou China French Argentine International Center for Information and Systems Sciences National Scientific and Technical Research Council Argentina School of Electrical Engineering and Computer Science University of Newcastle NSW2308 Australia

This paper focuses on a networked state estimation problem for a spatially large linear system with a distributed array of sensors, each of which offers partial state measurements, and the transmission is lossy. We propose a measurement coding scheme with two goals. Firstly, it permits adjusting the communication requirements by controlling the dimension of the vector transmitted by each sensor to the central estimator. Secondly, for a given communication requirement, the scheme is optimal, within the family of linear causal coders, in the sense that the weakest channel condition is required to guarantee the stability of the estimator. For this coding scheme, we derive the minimum mean-square error (MMSE) state estimator, and state a necessary and sufficient condition with a trivial gap, for its stability. We also derive a sufficient but easily verifiable stability condition, and quantify the advantage offered by the proposed coding scheme. Finally, simulations results are presented to confirm our claims. Copyright © 2019, The Authors. All rights reserved.

关键词： Mean square error

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GZMK^high CD8⁺ T effector memory cells are associated with CD15^high neutrophil abundance in non-metastatic colorectal tumors and predict poor clinical outcome (vol 13, 6752, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 6752-6752页

作者： Tiberti, Silvia Catozzi, Carlotta Croci, Ottavio Ballerini, Mattia Cagnina, Danilo Soriani, Chiara Scirgolea, Caterina Gong, Zheng He, Jiatai Macandog, Angeli D. Nabinejad, Amir Lauson, Carina B. Nava Quinte, Arianna Bertalot, Giovanni Petz, Wanda L. Ravenda, Simona P. Licursi, Valerio Paci, Paola Rasponi, Marco Rotta, Luca Fazio, Nicola Ren, Guangwen Fumagalli-Romario, Uberto Schaefer, Martin H. Campaner, Stefano Lugli, Enrico Nezi, Luigi Manzo, Teresa Department of Experimental Oncology European Institute of Oncology- IRCCS Milano Italy Center for Genomic Science of IIT CGS@SEMM (Istituto Italiano di Tecnologia at European School of Molecular Medicine) Fondazione Istituto Italiano di Tecnologia (IIT) Milano Italy Laboratory of Translational Immunology IRCCS- Humanitas Research Hospital- Milan Italy The Jackson Laboratory Bar Harbor ME USA Department of Molecular & Biomedical Sciences University of Maine Orono ME 04469 USA Department of Pathology APSS Trento Hospital & Health Care Trento Italy Digestive Surgery Division European Institute of Oncology-IRCCS Milan Italy Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors European Institute of Oncology-IRCCS Milan Italy Institute of Molecular Biology and Pathology (IBPM) National Research Council (CNR) of Italy Rome Italy Department of Computer Control and Management Engineering Sapienza - University of Rome Rome Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy

CD8+ T cells are a major prognostic determinant in solid tumors, including colorectal cancer (CRC). However, understanding how the interplay between different immune cells impacts on clinical outcome is still in its infancy. Here, we describe that the interaction of tumor infiltrating neutrophils expressing high levels of CD15 with CD8+ T effector memory cells (T EM) correlates with tumor progression. Mechanistically, stromal cell-derived factor-1 (CXCL12/SDF-1) promotes the retention of neutrophils within tumors, increasing the crosstalk with CD8+ T cells. As a consequence of the contact-mediated interaction with neutrophils, CD8+ T cells are skewed to produce high levels of GZMK, which in turn decreases E-cadherin on the intestinal epithelium and favors tumor progression. Overall, our results highlight the emergence of GZMKhigh CD8+ TEM in non-metastatic CRC tumors as a hallmark driven by the interaction with neutrophils, which could implement current patient stratification and be targeted by novel therapeutics. The tumor immune microenvironment is an important prognostic determinant in colorectal cancer (CRC). Here the authors show that tumor infiltrating neutrophils expressing high levels of CD15 interact with CD8+ T effector memory skewing them to produce GZMK, associated with tumor progression in CRC patients.

关键词： Tumour immunology Colorectal cancer

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What is that in your hand? Recognizing grasped objects via forearm electromyography sensing

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–24页

作者： Fan, Junjun Fan, Xiangmin Tian, Feng Li, Yang Liu, Zitao Sun, Wei Wang, Hongan State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Google Research and Machine Intelligence Mountain ViewCA United States TAL AI Lab Beijing China

Knowing the object in hand can offer essential contextual information revealing a user's fine-grained activities. In this paper, we investigate the feasibility, accuracy, and robustness of recognizing the uninstrumented object in a user's hand by sensing and decoding her forearm muscular activities via off-the-shelf electromyography (EMG) sensors. We present results from three studies to advance our fundamental understanding of the opportunities that EMG brings in object interaction recognition. In the first study, we investigated the influence of physical properties of objects such as shape, size, and weight on EMG signals. We also conducted a thorough exploration of the feature spaces and sensor positions which can provide a solid base to rely on for future designers and practitioners for such interactive technique. In the second study, we assessed the feasibility and accuracy of inferring the types of grasped objects via using forearm muscular activity as a cue. Our results indicate that the types of objects can be recognized with up to 94.2% accuracy by employing user-dependent training. In the third study, we investigated the robustness of this approach in a realistic office setting where users were allowed to interact with objects as they would naturally. Our approach achieved up to 82.5% accuracy in discriminating 15 types of objects, even when training and testing phrases were purposefully performed on different days to incorporate changes in EMG patterns over time. Overall, this work contributes a set of fundamental findings and guidelines on using EMG technologies for object-based activity tracking. © 2018 Association for Computing Machinery.

关键词： Object detection

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Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

Learning Structured Twin-Incoherent Twin-Projective Latent D...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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Patient-Clinician Brain Response During Clinical Encounter and Pain Treatment

Patient-Clinician Brain Response During Clinical Encounter a...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： A. Anzolin K. Isenburg A. Grahl J. Toppi M. Yücel D.M. Ellingsen J. Gerber A. Ciaramidaro L. Astolfi T.J. Kaptchuk V. Napadow Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital and Harvard Medical School Boston MA USA Dept. of Computer Control and Management Engineering "Sapienza" University of Rome Italy Boston University Neurophotonics Center Department of Education and Human Sciences University of Modena and Reggio Emilia Modena Italy Program in Placebo Studies Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA Department of Anesthesiology Brigham and Women’s Hospital

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

The patient-clinician relationship is known to significantly affect the pain experience, as empathy, mutual trust and therapeutic alliance can significantly modulate pain perception and influence clinical therapy outcomes. The aim of the present study was to use an EEG hyperscanning setup to identify brain and behavioral mechanisms supporting the patient-clinician relationship while this clinical dyad is engaged in a therapeutic interaction. Our previous study applied fMRI hyperscanning to investigate whether brain concordance is linked with analgesia experienced by a patient while undergoing treatment by the clinician. In this current hyperscanning project we investigated similar outcomes for the patient-clinician dyad exploiting the high temporal resolution of EEG and the possibility to acquire the signals while patients and clinicians were present in the same room and engaged in a face-to-face interaction under an experimentally-controlled therapeutic context. Advanced source localization methods allowed for integration of spatial and spectral information in order to assess brain correlates of therapeutic alliance and pain perception in different clinical interaction contexts. Preliminary results showed that both behavioral and brain responses across the patient-clinician dyad were significantly affected by the interaction *** Relevance- The context of a clinical intervention can significantly impact the treatment of chronic pain. Effective therapeutic alliance, based on empathy, mutual trust, and warmth can improve treatment adherence and clinical outcomes. A deeper scientific understanding of the brain and behavioral mechanisms underlying an optimal patient-clinician interaction may lead to improved quality of clinical care and physician training, as well as better understanding of the social aspects of the biopsychosocial model mediating analgesia in chronic pain patients.

关键词： Pain Electroencephalography Biomedical imaging Hospitals Spatial resolution

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Spatio-temporal fusion for Macro- and Micro-expression Spotting in Long Video Sequences

Spatio-temporal fusion for Macro- and Micro-expression Spott...

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International Conference on Automatic Face and Gesture Recognition

作者： Li-Wei Zhang Jingting Li Su-Jing Wang Xian-Hua Duan Wen-Jing Yan Hai-Yong Xie Shu-Cheng Huang Key Laboratory of Behavior Sciences Institute of Psychology Chinese Academy of Sciences Beijing China School of Computer Jiangsu University of Science and Technology Zhenjiang Jiangsu China University of the Chinese Academy of Sciences Beijing China Institute of Psychology and Behavior Sciences Wenzhou University Wenzhou Zhejiang China School of Cyber Science University of Science and Technology of China HeFei Anhui China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC) Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

In this paper, we aim to construct a spotting framework automatically. It is still a great challenge to spot micro-expression(ME) intervals accurately due to short duration, low intensity, and shaking. Under the uncontrolled condition, the transformation is caused by head shaking. In order to remove the global movement caused by head shaking, we propose a simple yet effective method to disentangle local movement vector from the global optical flow field by the estimation of mean optical flow in the nose region. After preprocessing, we extract the completed specific pattern(SP) of ME in each region of interest(ROI). The pattern consists of two sub-patterns: magnitude and angle. However, influenced by frame rate and different intensities of micro- and macro- expressions, we propose to use a multi-scale filter to improve the ability to spot both micro- and macro-expressions. The spotting result performed on CAS(ME) 2 and SAMM shows that our proposed method outperforms the basline method.

关键词： Optical imaging Feature extraction Nose Optical filters Faces Databases Face recognition

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