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Thermoresponsive mobility of liquid crystals and reactive mesogens during photopolymerization-induced phase separation

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Physical Review E 2022年第4期106卷 044704-044704页

作者： Hiroshi Kakiuchida Akihiko Matsuyama Eiichi Kobayashi Akifumi Ogiwara Innovative Functional Materials Research Institute National Institute of Advanced Industrial Science and Technology Nagoya 463-8560 Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka 820-8502 Japan Kyushu Synchrotron Light Research Center Tosu 841-0005 Japan Department of Electronic Engineering Kobe City College of Technology Kobe 651-2194 Japan

Molecular interactions between liquid crystals (LCs) and reactive mesogens (RMs) at temperatures across the phase transition regions were comprehensively studied during photopolymerization-induced phase separation (PPIPS) beginning with raw mixtures until the formation of polymer network liquid crystals (PNLCs). Then, the molecules were found to be nonuniformly more and less mobile in response to temperature as PPIPS progressed. Optical birefringence and infrared absorption were carefully measured throughout PPIPS, using 4-cyano-4′-hexylbiphenyl (6CB) and 1,4bis-[4-(3-acryloyloxypropyloxy) benzoyloxy]-2-methylbenzene (RM257) as typical LCs and RMs. Microscopic views of thermoresponsive changes in the molecular orientation order of both LCs and RMs were obtained: LCs and RMs in raw mixtures interacted with one another but uniformly transformed their molecular orientation. Such interactions continuously change to become nonuniform with progress in PPIPS. At the incipient stages of PPIPS, RMs, which are polymerized but not completely networked, inhibit LCs from changing their molecular orientation and vice versa. As PPIPS progresses, some LCs become more mobile and some less mobile owing to RM constraints. The domain configuration of the submicrometer phase separation affects the thermoresponsive mobility of LCs and RMs, that is, LCs become more mobile in LC-richer areas. The quantitative knowledge here provides comprehensive insight that LCs and RMs are mutually constrained and that such interactive behavior varies nonuniformly as PPIPS progresses.

关键词： Birefringence Liquid crystal phase transitions Nematic phase transition Order parameters Phase separation Polymerization Stimuli-responsive materials Liquid crystalline polymers Liquid crystals Nematic liquid crystals Thermotropic liquid crystals Fourier transform infrared spectroscopy Optical spectroscopy Polarized optical microscopy Scanning electron microscopy

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Towards affective walkability for healthy ageing in the future of the cities? 5

Towards affective walkability for healthy ageing in the futu...

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5th Italian Workshop on Artificial Intelligence for Ambient Assisted Living, AI*AAL.it 2019

作者： Bandini, Stefania Gasparini, Francesca Department of Computer Science Systems and Communications University of Milano Bicocca Italy RCAST - Research Center for Advanced Science and Technology University of Tokyo Japan

Social inclusion of elderly pedestrians in urban contexts by enhancing healthy mobility means increase the level of walkability, where the perception of safe walking and road crossing is a crucial factor. The measurement of stress during walking and dynamic collision avoidance through the acquisition and the analysis of physiological signals during experimental activity requires the design of proper experimental settings and protocols, in order to assess innovative approaches towards an affective walkability, and open novel investigation fields involving affective computing, pedestrian dynamics and artificial intelligence. The main aim of the paper is to illustrate some preliminary studies conducted within an experimental activity to test the validity of the approach. Copyright © 2020 for this paper by its authors.

关键词： Physiology

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Retraction Note: Differences between CeO2-doped sodium borate glass and Cu2O3-doped sodium borate glass in terms of structural defects concentration and plasmon frequency

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Optical and Quantum Electronics 2025年第4期57卷 1-2页

作者： Gomaa, Hosam M. Saudi, H. A. Zahran, H. Y. Yahia, I. S. Pharaohs-Higher Institute for Computer Information Systems and Management Giza Egypt Department of Physics Faculty of Science Al-Azhar University (Girls’ Branch) Nasr City Egypt Laboratory of Nano-Smart Materials for Science and Technology (LNSMST) Department of Physics Faculty of Science King Khalid University Abha Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Abha Saudi Arabia

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A Consistency-Based Loss for Deep Odometry Through Uncertainty Propagation

A Consistency-Based Loss for Deep Odometry Through Uncertain...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hamed Damirchi Roohollah Khorrambakht Hamid D. Taghirad Behzad Moshiri Advanced Robotics and Automated Systems Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering New York University New York USA School of ECE University College of Engineering University of Tehran Tehran Iran

Conventionally, deep odometry networks use objective functions that only penalize short-term deviations from the true path. Since such an objective does not impose any constraints on the long-term deviations from the path, a second consistency-based loss term may be added to lower long-term drift. However, maintaining a balance between the two loss terms is challenging and often treated as a design hyperparameter. To mitigate this balancing issue, we propose to use the uncertainty over both odometry and the long-term transformations in a maximum likelihood setting and allow the network to tune the weighting between the two loss terms. To this end, we derive the odometry uncertainty alongside the pose outputs using the network itself and to derive the covariance matrix over the integrated transformation, we propose to propagate the odometry uncertainty through each iteration. This formulation provides an adaptive and statistically consistent method to weigh the incremental and integrated loss terms against each other, noting the increase in uncertainty as more steps are integrated over. We show that our approach to consistency-based losses allows the network to surpass the accuracy of the state-of-the-art visual odometry approaches. Then, the efficacy of the derived uncertainty as weighting medium is visualized and the performance benefits of uncertainty quantification are shown in a pose-graph based localization scenario.

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A Segmentation Algorithm for Cerebral Hematoma Based on Spatial and Multi-scale Positional Attention Mechanism and Unet

A Segmentation Algorithm for Cerebral Hematoma Based on Spat...

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2021 International Conference on Networking, Communications and Information technology, NetCIT 2021

作者： Deng, Hongyu Guo, Taipeng Gong, Linbo Wang, Wentao Liu, Yaohui Xiangnan University School of Computer and Artificial Intelligence Chenzhou42300 China Chenzhou Stroke Artificial Intelligence Technology Research and Development Center Hunan42300 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan42300 China

ISBN: (纸本)9781665400701

For segmenting the target lesions accurately in CT images of cerebral hematoma, this paper proposes a spatial and multi-scale neural network segmentation model (MSHA-Unet). The model fuses the semantic information of the top and bottom layers of images in the same level by multi-kernel convolution, and fuses the attention mechanism in multi-scale feature images by jumping connection, so as to accurately locate and segment cerebral hematoma in medical CT images. To verify the effectiveness of the model, the experiment was carried out on 3716 CT images of 81 cases of cerebral hemorrhage, and the model was compared with six neural network models. The experimental results were measured by various evaluation standards, and showed that the model was superior to other network models. © 2021 IEEE.

关键词： Semantics

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Distributed Active Disturbance Rejection Control of Multiple Mobile Robots for Online Workload Partition

Distributed Active Disturbance Rejection Control of Multiple...

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Chinese Automation Congress (CAC)

作者： Xin Li Chao Zhai Hehong Zhang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan Campus School of Automation Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method.

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A Trustless Federated Framework for Decentralized and Confidential Deep Learning

A Trustless Federated Framework for Decentralized and Confid...

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Global Emerging technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Chao Li Qiuyu Shen Cheng Xiang Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Nowadays, deep learning models can be trained on large amounts of web data on power hungry servers and be deployment-ready for specific real-world applications. With a state-of-the-art model architecture and a large publicly available dataset for pre-training, convolutional neural network models can be further fine-tuned via transfer learning for a related task. Nonetheless, the training process required of privacy-sensitive applications needs to protect data confidentiality and simultaneously boost performance using limited training data. For such data-deprived and privacy-centric learning, we introduce a trustless federated learning framework that seamlessly integrates deep learning models from different edge nodes using a blockchain-based architecture. Our framework performs federated learning without the need of a central server by leveraging a smart contract blockchain platform with a distributed file system for model storage. Users can locally train on their data while routinely benefiting from an enhanced model obtained by merging the models from all users in a decentralized fashion. Most importantly, this framework is free of the potential single point of failure in centralized federated learning. Besides, our framework has in-built incentive mechanisms to prevent model corruption and temper bad actors. We tested our framework on various computer vision datasets. The experimental results show that the merged model accuracy is on-par compared to a centralized federated training setup. To the best of our knowledge, this work represents the first systematic attempt at building a blockchain-based federated deep learning framework for computer vision. The code is publicly made available at: https://***/s-elo/DNN-Blockchain.

关键词： Deep learning Training Data privacy Federated learning Computational modeling Smart contracts Data models

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC systems

arXiv

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arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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