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Frequency-dependent dielectric constant prediction of polymers using machine learning

npj Computational Materials 2020年第1期6卷 1147-1155页

作者： Lihua Chen Chiho Kim Rohit Batra Jordan P.Lightstone Chao Wu Zongze Li Ajinkya A.Deshmukh Yifei Wang Huan D.Tran Priya Vashishta Gregory A.Sotzing Yang Cao Rampi Ramprasad School of Materials Science and Engineering Georgia Institute of Technology771 Ferst Drive NWAtlantaGA 30332USA Electrical and Computer Engineering University of Connecticut371 Fairfield WayStorrsCT 06269USA Polymer Program Institute of Material ScienceUniversity of Connecticut97 North Eagleville RoadStorrsCT 06269USA Collaboratory for Advanced Computing and Simulations University of Southern CaliforniaLos AngelesCA 90089-0242USA

The dielectric constant(ϵ)is a critical parameter utilized in the design of polymeric dielectrics for energy storage capacitors,microelectronic devices,and high-voltage ***,agile discovery of polymer dielectrics with desirableϵremains a challenge,especially for high-energy,high-temperature *** aid accelerated polymer dielectrics discovery,we have developed a machine-learning(ML)-based model to instantly and accurately predict the frequency-dependentϵof polymers with the frequency range spanning 15 orders of *** model is trained using a dataset of 1210 experimentally measuredϵvalues at different frequencies,an advanced polymer fingerprinting scheme and the Gaussian process regression algorithm.

关键词： dielectric constant polymer

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Online Recursive ICA Algorithm Used for Motor Imagery EEG Signal

Online Recursive ICA Algorithm Used for Motor Imagery EEG Si...

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

作者： Xueyi Lin Lu Wang Tomoaki Ohtsuki Graduate School of Science and Technology Keio University Yokohama Japan Department of Information and Computer Science Keio University Yokohama Japan

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.

关键词： Electroencephalography Electrooculography Approximation algorithms Electrodes Blind source separation Computational complexity

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Discrete Polar Decoder using Information Bottleneck Method

Discrete Polar Decoder using Information Bottleneck Method

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IEEE International Conference on Communications (ICC)

作者： Akira Yamada Tomoaki Ohtsuki Graduate School of Science and Technology Keio University Yokohama Japan Department of Information and Computer Science Keio University Yokohama Japan

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Polar codes are attracting much attention and being used for control channels of the 5th generation of mobile communication system (5G). As a feature, it is easier to implement encoder and decoder than Turbo codes and LDPC (Low Density Parity Check) codes. One of the decoding methods of polar codes is BP (Belief Propagation) decoding, which can decode in parallel, so that decoding can be performed at high speed. However, due to hardware limitation, calculations on the decoder get very complicated. This issue can be solved by using the information bottleneck method. This method compresses an observation variable to a quantized one while attempting to preserve the mutual information shared with a relevant random variable. In the conventional research, the information bottleneck method is applied to the BP decoding of the LDPC codes. In this paper, the information bottleneck method is used for the BP decoding of polar codes. The BP decoding of polar codes is distinct from that of LDPC codes. It has several types of the messages, and each time a message is updated, the decoding becomes more complex. By using the information bottleneck method, the decoder can compress the channel outputs and the messages of BP into unsigned integers while preventing degradation of the error correcting performance. Thus, we can reduce the complexity of calculation in the decoding process and easily implement the decoder. This paper also investigates the minimum bit width for quantization with negligible degradation and the suboptimal E b /N 0 for designing the lookup tables. These lookup tables are used for updating the messages. The simulation results show that the error correcting capability of the discrete polar decoders of the proposed method is negligibly degraded compared to the BP decoding without compression.

关键词： Parity check codes Maximum likelihood decoding Quantization (signal) Complexity theory Hardware 5G mobile communication

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Effects of Mobile Health Care App "Asmile" on Physical Activity of 80,689 Users in Osaka Prefecture, Japan: Longitudinal Observational Study

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Journal of Medical Internet Research 2025年 27卷 e65943页

作者： Oyama, Asuka Taguchi, Kenshiro Seto, Hiroe Kanaya, Reiko Kotoku, Jun'ichi Yamakawa, Miyae Toki, Hiroshi Yamamoto, Ryohei Health and Counseling Center The University of Osaka Toyonaka Japan Graduate Degree Program of Health Data Science Teikyo University Tokyo Japan Graduate School of Human Sciences The University of Osaka Suita Japan Division of Health Sciences Graduate School of Medicine The University of Osaka Suita Japan Graduate School of Medical Care and Technology Teikyo University Tokyo Japan Department of Nephrology Graduate School of Medicine The University of Osaka Suita Japan Laboratory of Behavioral Health Promotion Department of Health Promotion Graduate School of Medicine The University of Osaka Suita Japan Institute for Sports and Global Health The University of Osaka Suita Japan

Background: Lifestyle-related diseases can be controlled by improving individuals’ lifestyles;however, improving and maintaining a healthy lifestyle is difficult. Mobile health (mHealth) applications have recently attracted attention as tools for maintaining and improving health, and their use may also increase physical activity. Objective: This study aimed to verify the effect of registration in Asmile, an mHealth application provided by the Osaka Prefectural Government, on step counts using a Causal Impact approach based on the step count data recorded in the Asmile application. Methods: This observational study included Osaka residents aged 20-79 years, newly registered to Asmile, between the fiscal years 2020 and 2023. Of these, 80,689 participants with step count records for 4 weeks before and after the day they registered to Asmile were included in the analysis. We used daily step counts that were automatically transferred from a standard smartphone health care app into Asmile. We used a Causal Impact model to estimate the increase in step count after registration to Asmile. Results: Of the 80,689 participants analyzed, 38.5% (31,082/80,689) were men, and the mean age was 51.6 (SD 13.2) years. The mean step count before registration was 5923 (SD 4860) steps per day, with the highest proportion of new users registered in spring (38,389/80,689, 47.6%) and in fiscal year 2020 (34,491/80,689, 42.7%). The analysis revealed that the effect of Asmile registration on step counts was 360 steps (95% CI 331-389) per day and 10,041 steps (95% CI 9632-10,450) over 4 weeks. Stratified analysis showed that the impact of increased step count was more pronounced in younger groups and groups with fewer step counts before registration. Conversely, the effect of registration on step count was relatively minor in the groups registered in summer or winter. Conclusions: This study demonstrates increased physical activity among users registered with the Asmile app. These findings sug

关键词： app apps Causal Impact Causal Inference exercise healthy lifestyle Japan lifestyle-related disease mHealth mobile health mobile phone observational study Osaka physical activity smartphone step counts

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Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification 16th

Deep Credible Metric Learning for Unsupervised Domain Adapta...

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16th European Conference on computer Vision, ECCV 2020

作者： Chen, Guangyi Lu, Yuhao Lu, Jiwen Zhou, Jie Department of Automation Tsinghua University Beijing China State Key Lab of Intelligent Technologies and Systems Beijing China Beijing National Research Center for Information Science and Technology Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030585976

The trained person re-identification systems fundamentally need to be deployed on different target environments. Learning the cross-domain model has great potential for the scalability of real-world applications. In this paper, we propose a deep credible metric learning (DCML) method for unsupervised domain adaptation person re-identification. Unlike existing methods that directly finetune the model in the target domain with pseudo labels generated by the source pre-trained model, our DCML method adaptively mines credible samples for training to avoid the misleading from noise labels. Specifically, we design two credibility metrics for sample mining including the k-Nearest Neighbor similarity for density evaluation and the prototype similarity for centrality evaluation. As the increasing of the pseudo label credibility, we progressively adjust the sampling strategy in the training process. In addition, we propose an instance margin spreading loss to further increase instance-wise discrimination. Experimental results demonstrate that our DCML method explores credible and valuable training data and improves the performance of unsupervised domain adaptation. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Image Aesthetics Prediction Using Multiple Patches Preserving the Original Aspect Ratio of Contents

arXiv

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arXiv 2020年

作者： Wang, Lijie Wang, Xueting Yamasaki, Toshihiko Department of Computer Science Graduate School of Information Science and Technology University of Tokyo Tokyo113-8656 Japan Department of Information and Communication Engineering Graduate School of Information Science and Technology University of Tokyo Tokyo113-8656 Japan

The spread of social networking services has created an increasing demand for selecting, editing, and generating impressive images. This trend increases the importance of evaluating image aesthetics as a complementary function of automatic image processing. We propose a multi-patch method, named MPA-Net (Multi-Patch Aggregation Network), to predict image aesthetics scores by maintaining the original aspect ratios of contents in the images. Through an experiment involving the large-scale AVA dataset, which contains 250,000 images, we show that the effectiveness of the equal-interval multi-patch selection approach for aesthetics score prediction is significant compared to the single-patch prediction and random patch selection approaches. For this dataset, MPA-Net outperforms the neural image assessment algorithm, which was regarded as a baseline method. In particular, MPA-Net yields a 0.073 (11.5%) higher linear correlation coefficient (LCC) of aesthetics scores and a 0.088 (14.4%) higher Spearman’s rank correlation coefficient (SRCC). MPA-Net also reduces the mean square error (MSE) by 0.0115 (4.18%) and achieves results for the LCC and SRCC that are comparable to those of the state-of-the-art continuous aesthetics score prediction methods. Most notably, MPA-Net yields a significant lower MSE especially for images with aspect ratios far from 1.0, indicating that MPA-Net is useful for a wide range of image aspect ratios. MPA-Net uses only images and does not require external information during the training nor prediction stages. Therefore, MPA-Net has great potential for applications aside from aesthetics score prediction such as other human subjectivity prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Aspect ratio

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A System of Clothing Boundary Recognition Using Machine Learning For Life Support Robots

A System of Clothing Boundary Recognition Using Machine Lear...

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International Conference on Intelligent Informatics and Biomedical sciences (ICIIBMS)

作者： Hanqing Zhao Hidetaka Nambo Artificial Intelligence Laboratory Graduate School of Natural Science and Technology Electrical Engineering and Computer Science Kanazawa University Kanazawa Japan

ISBN: (数字)9781728180229

ISBN: (纸本)9781728180236

Machine learning and image processing are widely used in various fields, such as, robot vision, object recognition, and automated driving technology. This paper is, the use of cameras to acquire image information, and machine learning methods to analyze and identify the edge boundaries of clothes in the Difficulty in putting on and taking off clothes in the elderly and some patients, especially when going to the toilet. Then, clothing boundary recognition and assistive robots can ameliorate these problems by distinguishing where the edges of the pants are so that they can Determine the position of the waistband of the trousers, and perform dressing and undressing assistance work. The ultimate goal of this research is to be able to apply it to the system control of the clothing donning and doffing device.

关键词： Performance evaluation Clothing Senior citizens Robot vision systems Machine learning Object recognition Robots

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Identifying potential cheaters by tracking their behaviors through mouse activities

Identifying potential cheaters by tracking their behaviors t...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Hamidullah Sokout Fajar Purnama Ahmad Nawid Mustafazada Tsuyoshi Usagawa Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Computer Science Kabul Polytechnic University Kabul Afghanistan

ISBN: (数字)9781728169422

ISBN: (纸本)9781728169439

Academic cheating is a significantly common occurrence at the university level in developing countries particularly, in Afghanistan. In online education practices, it could be a difficult task for a better process of secret reconstruction and identifying/ detecting the potential cheaters. Due to a huge number of students and the rapid increase of online education and penetration of the internet (the diversity of electronic devices used by learners in online activities), a big gap exists across creating an honest culture and teacher practices in the classroom. As such, raising the way of early prediction of potential cheaters through the mouse-tracking technique should be an urgent priority. In this paper, the authors examine the developed mouse tracking application along with the developed Moodle plugin in a blended course mid-term (20%) examination for the purpose of detecting and identifying the potential cheaters. The proposed model correctly predicted 94% of students committing illicit actions during the online mid-term examination, which can be possible to early intervene and prevent illegal actions. The study outcome can be used to analyze the learners' mouse tracking behaviors that lead to a better process of secret reconstruction and transparent space.

关键词： Electric potential Sensitivity Visual analytics Education Predictive models Mice Task analysis

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Detecting Hemileia vastatrix using Vision AI as Supporting to Food Security for Smallholder Coffee Commodities

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IOP Conference Series: Earth and Environmental science 2022年第1期998卷

作者： Teddy Suparyanto Erick Firmansyah Tjeng Wawan Cenggoro Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The loss of food crops during the COVID-19 pandemic threatens food security in Indonesia as one of the world's top coffee producers. This affects the insecurity of coffee commodities which is influenced by several factors other than the pandemic such as pests, plant diseases, and extreme weather. Plant diseases, such as leaf rust, are a significant factor in the insecurity problem in coffee commodities. The fungus Hemileia vastatrix B et Br causes leaf rust disease, which is a pest that frequently damages coffee plants. This disease not only interferes with plant growth but also causes a decrease in coffee quality and quantity. This initial research aims to carry out early prevention of these diseases as supporting to food security in smallholder coffee commodities. An AI-based visual detection application is the result of this research. We collected 100 images of coffee leaves from various coffee plants. The image is reshaped to 256 x 256 pixels and randomly trimmed to 224 x 224 pixels to fulfill the size requirements of a standard Deep Learning technique. Each image was classified into two classes by a plant pest and disease specialist. The dataset was divided into training, validation, and testing series with a ratio of 60:20:20 for training procedures. The Convolutional Neural Network (CNN) research method used a variation of the ResNet CNN model with 18 layers. The best model validation was 59%.

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