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Mechanically reprogrammable Pancharatnam-Berry metasurface for microwaves

Advanced Photonics 2022年第1期4卷 69-79页

作者： Quan Xu Xiaoqiang Su Xueqian Zhang Lijuan Dong Lifeng Liu Yunlong Shi Qiu Wang Ming Kang Andrea Alù Shuang Zhang Jiaguang Han Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics EngineeringKey Laboratory of Optoelectronic Information Technology(Ministry of Education of China)TianjinChina Shanxi Datong University Institute of Solid State Physics and College of Physics and Electronic ScienceShanxi Province Key Laboratory of Microstructure Electromagnetic Functional MaterialsDatongChina Wuhan University of Technology School of Information EngineeringWuhanChina Tianjin Normal University College of Physics and Materials ScienceTianjinChina City University of New York Advanced Science Research CenterPhotonics InitiativeNew YorkUnited States City University of New York Graduate CenterPhysics ProgramNew YorkUnited States University of Hong Kong Faculty of ScienceDepartment of PhysicsHong KongChina University of Hong Kong Department of Electrical and Electronic EngineeringHong KongChina Guilin University of Electronic Technology Guangxi Key Laboratory of Optoelectronic Information ProcessingSchool of Optoelectronic EngineeringGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Metasurfaces have enabled the realization of several optical functionalities over an ultrathin platform,fostering the exciting field of flat *** metasurfaces are achieved by arranging a layout of static meta-atoms to imprint a desired operation on the impinging wavefront,but their functionality cannot be *** and programmability of metasurfaces are the next important step to broaden their impact,adding customized on-demand functionality in which each meta-atom can be individually *** demonstrate a mechanical metasurface platform with controllable rotation at the meta-atom level,which can implement continuous Pancharatnam–Berry phase control of circularly polarized *** the proof-of-concept experiments,we demonstrate metalensing,focused vortex beam generation,and holographic imaging in the same metasurface template,exhibiting versatility and superior *** dynamic control of electromagnetic waves using a single,low-cost metasurface paves an avenue towards practical applications,driving the field of reprogrammable intelligent metasurfaces for a variety of applications.

关键词： reprogrammable metasurfaces Pancharatnam-Berry phase mechanical metasurfaces microwaves

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Supervised Contrastive Learning Enhances MHC-II Peptide Binding Affinity Prediction

SSRN

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SSRN 2024年

作者： Shen, Long-Chen Liu, Yan Liu, Zi Zhang, Yumeng Wang, Zhikang Guo, Yuming Rossjohn, Jamie Song, Jiangning Yu, Dong-Jun School of Computer Science and Engineering Nanjing University of Science and Technology 200 Xiaolingwei Nanjing210094 China Department of Computer Science Yangzhou University Yangzhou225100 China School of Information Enginnering Jingdezhen Ceramic University Jingdezhen333403 China Monash Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVIC3800 Australia Monash Data Futures Institute Monash University MelbourneVIC3800 Australia School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200240 China Department of Epidemiology and Preventive Medicine Xiangya Hospital China Infection and Immunity Program Netherlands

Accurate prediction of major histocompatibility complex (MHC)-peptide binding affinity can improve our understanding of cellular immune responses and guide personalized immunotherapies. Nevertheless, the existing deep learning-based approaches for predicting MHC-II peptide interactions fall short of satisfactory performance and offer restricted model interpretability. In this study, we propose a novel deep neural network, termed ConBoTNet, to address the above issues by introducing the designed supervised contrastive learning and bottleneck transformer extractors. Specifically, the supervised contrastive learning pre-training enhances the model’s representative and generalizable capabilities on MHC-II peptides by pulling positive pairs closer and pushing negative pairs further in the feature space, while the bottleneck transformer module focuses on MHC-II peptide interactions to precisely identify binding cores and anchor positions in an unsupervised manner. Extensive experiments on benchmark datasets under 5-fold cross-validation, leave-one-molecule-out validation, independent testing, and binding core prediction settings highlighted the superiority of our proposed ConBoTNet over current state-of-the-art methods. Data distribution analysis in the latent feature space demonstrated that supervised contrastive learning can aggregate MHC-II-peptide samples with similar affinity labels and learn common features of similar affinity. Additionally, we interpreted the trained neural network by associating the attention weights with peptides and innovatively find both well-established and potential peptide motifs. This work not only introduces an innovative tool for accurately predicting MHC-II peptide affinity, but also provides new insights into a new paradigm for modeling essential biological interactions, advancing data-driven discovery in biomedicine. © 2024, The Authors. All rights reserved.

关键词： Peptides

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Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

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Scientific Reports 2023年第1期13卷 1-10页

作者： Kuo, Tzu-Yun Wang, Jen-Hung Huang, Yung-Wen Sung, Ting-Yi Chen, Ching-Tai Department of Biochemical Science and Technology National Taiwan University Taipei 10617 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Statistical Science Academia Sinica Taipei 11529 Taiwan Institute of Information Science Academia Sinica Taipei 11529 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Bioinformatics and Biomedical Engineering Asia University Taichung 41354 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms;its executable files, user manual, and sample data sets are freely available at https://***/comics/Software_*** . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline. © 2023, Springer Nature Limited.

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Improving the accuracy of k-nearest neighbor (k-NN) using Synthetic Minority Oversampling Technique (SMOTE) and Gain Ratio (GR) for imbalanced class data

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AIP Conference Proceedings 2023年第1期2714卷

作者： Adli A. Nababan Sutarman Muhammad Zarlis Erna B. Nababan Doctoral Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Universitas Sumatera Utara Medan Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia

k-Nearest Neighbor (k-NN) has fairly good accuracy results on data with almost the same class (balanced), whereas for data whose class distribution is not balanced, the accuracy value of k-NN tends to be low, moreover k-NN does not differentiate each feature data, meaning that each feature has the same weight in determining the new data class so that before classifying the feature selection process it is necessary to select the features that are most relevant to the data class. In this research, to overcome this problem, we will propose a framework using the Synthetic Minority Oversampling Technique (SMOTE) method to solve the class imbalance problem and the Gain Ratio (GR) to perform feature selection, so that get a new dataset with a balanced class distribution and features relevant to class data. The datasets used in this research such as E-Coli, Glass, and New Thyroid. For objective results, the 10-fold-cross validation method will be used as an evaluation method with k values 1 to 10. The results of the research prove that SMOTE and GR can increase the accuracy of the k-NN method, where the highest increase occurred in the Glass dataset by a difference increase of 18.43%. The lowest increase in accuracy occurred in the new thyroid dataset with an increase of 8.73%. The increase in accuracy occurred in all datasets used with an average increase of 12.87

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An intelligent applied Fuzzy Logic to prediction the Parts per Million (PPM) as hydroponic nutrition on the based Internet of Things (IoT) 4

An intelligent applied Fuzzy Logic to prediction the Parts p...

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4th International Conference on Informatics and Computing, ICIC 2019

作者： Musa, Purnawarman Sugeru, Herik Mufza, Haya Fadhila Faculty of Computer Science and Information Technology Universitas Gunadarma Indonesia Faculty of Industrial Technology Study Program of Agrotechnology Universitas Gunadarma Indonesia

ISBN: (纸本)9781728122076

Hydroponic plants require regular monitoring of plants that grow well and can even cause plants to die. Because too late in providing nutrition or getting too little or too little food or routine monitoring in the hydroponic system. The innovation proposed in the agricultural sector that we suggest is the process of monitoring hydroponic plants in real-time. This study aims to predict the excess or lack of nutrient content based on the results of routine monitoring and has the analytical ability to determine the prediction of the value of Parts per Million (PPM) to find the nutritional value of plant media. Measurement of nutritional value using Electrical Conductivity (EC) and the potential of Hydrogen (pH) in hydroponic plant media. In this research, using the NFT hydroponic system as the application of fuzzy logic with the Mamdani method to determine the predictions of PPM values in providing nutrients at plants to adjusting the nutritional at each plant (e.g., for water spinach plants are need 1050 until 1400 PPM). The implemented is method Internet of Things (IoT) to remote sensing on measure using a smartphone as the representation of the measurement results on the hydroponic system. © 2019 IEEE.

关键词： Hydroponics

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Multimodal deep learning framework for image popularity prediction on social media

arXiv

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arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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GAN Generated Portraits Detection Using Modified VGG-16 and EfficientNet

GAN Generated Portraits Detection Using Modified VGG-16 and ...

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National Foundation for Science and technology Development Conference on information and computer Science (NICS)

作者： Kha-Luan Pham Khanh-Mai Dang Loi-Phat Tang Thanh-Nhan Nguyen Advanced Program in Computer Science Faculty of Information Technology Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9780738105536

ISBN: (纸本)9781665405188

Generative Adversarial Networks can generate deceptive portraits of people who do not exist. The misuse of this technology leads to severe security issues such as fake identities and credentials. Since 2017, many works on Deep Learning have focused on detecting GAN synthesized images to prevent the threat against credibility in media. In this work, the authors propose a lightweight VGG-like model to detect state-of-the-art StyleGAN generated portraits. The authors also adopt EfficientNet-B0 to train a classifier on the same StyleGAN architecture. The VGG-like model and EfficientNet-based model achieve 98.9% and 100%, respectively, on the StyleGAN dataset published by Nvidia in 2019. Both models show the potential in generalizing to other GAN architectures and synthetic technologies.

关键词： Computational modeling Transfer learning computer architecture Network architecture Generative adversarial networks Security Task analysis

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Vietnamese Speech Synthesis with End-to-End Model and Text Normalization 7

Vietnamese Speech Synthesis with End-to-End Model and Text N...

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7th NAFOSTED Conference on information and computer Science, NICS 2020

作者： Nhan, Do Tri Tri, Nguyen Minh Nam, Cao Xuan Advance Program in Computer Science University of Science Ho Chi Minh City Viet Nam Faculty of Information Technology University of Science Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9780738105536

Speech synthesis systems are now getting smarter and more natural thanks to the power of deep neural networks. However, each language has a different phonological and contextual characteristics, we have conducted experiments, statistics, and applied Vietnamese phonetics to improve speech synthesis systems based on Tacotron2 neural networks. Our methods achieve the accuracy of 97% in text normalization task, and the synthesized speeches with a MOS score of 3.97, asymptotic to 4.43 of the voices that are directly recorded. We also provide a library for standardizing Vietnamese text called Vinorm and a package that converts text into a phonetic format called Viphoneme, which is used as an input for end-to-end neural networks, make the synthesis process faster, more intelligent and natural than using character inputs. © 2020 IEEE.

关键词： Speech synthesis

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SRGAN with Total Variation Loss in Face Super-Resolution

SRGAN with Total Variation Loss in Face Super-Resolution

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National Foundation for Science and technology Development Conference on information and computer Science (NICS)

作者： Hai Nguyen-Truong Khoa N. A. Nguyen San Cao Advanced Program in Computer Science Faculty of Information Technology Vietnam National University Ho Chi Minh city Vietnam

ISBN: (数字)9780738105536

ISBN: (纸本)9781665405188

Facial image super-resolution is a crucial preprocessing for facial image analysis, face recognition, and image-based 3D face reconstruction. Convolutional neural networks were earlier used to produce high-resolution images that train quicker and shown excellent performance by learning mapping relation using pairs of low-resolution and high-resolution images. However, in some cases, they are incapable of recovering finer details and often generate blurry images. In this paper, we evaluate a method of applying Generative adversarial networks in generating realistic super-resolution images from low-resolution ones by using three typical losses for super-resolution: Content Loss, Adversarial Loss, Perceptual Loss, and proposed to use Total Variation Loss. We try different pre-trained famous Convolutional neural networks models (VGG19, FaceNet, and EfficientNet) in Perceptual Loss to have a general view with different backbones. Our network gains 32.67 of Peak signal-to-noise ratio (PSNR) and 0.89 of Structural similarity index (SSIM) in 100 random samples from the Flickr-Faces-HQ dataset.

关键词： Training Visualization PSNR Face recognition Superresolution Feature extraction Convolutional neural networks

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Testing Real-Time Applications on Windows 10 IOT Using the Nyquist Theory 1

Testing Real-Time Applications on Windows 10 IOT Using the N...

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1st International Conference on computer Science and Applied Mathematic, ICCSAM 2018

作者： Syahputa, Zulfahmi Zarlis, M. Nasution, Beny B. Informatics Engineering Study Program Magister Faculty of Computer Science Information Technology Universitas Sumatera Utara Medan Indonesia

Raspberry Pi is a mini-computer that is provided to carry out activities quickly and precisely, but Raspberry Pi was created to not be able to do the real-time system with the support of Windows 10 IoT operating system, so the real-time system can be done on Raspberry Pi. The real-time applied in the application needs to be tested with the Nyquist theory. The purpose of this study was to get real-time system measurements available on Windows 10 IoT. This test is done using the Nyquist theory by calculating the results of measurements on mp3 streaming performed on Windows 10 IoT. © Published under licence by IOP Publishing Ltd.

关键词： Real time systems

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