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Janus faces of dipolar sources in directional near-field coupling with an oriented misalignment

Physical Review A 2024年第3期109卷 033505-033505页

作者： Chenxu Bian Yuhan Zhong Xuhuinan Chen Tony Low Hongsheng Chen Baile Zhang Xiao Lin State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China and International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China and Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing 312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences and Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore 637371 Singapore

Directional near-field coupling can enable many applications, such as directional routing, on-chip information processing, and chiral quantum optics. This directional near-field coupling is generally face dependent, since the induced near-field radiation pattern is dependent on which face (e.g., the upper or lower face) of the dipolar source is facing toward the outcoupler. In other words, the dipolar sources in the directional near-field coupling intrinsically have two faces (e.g., Janus faces), whose corresponding near-field coupling strength and coupling directionality might be distinct. Here, we outline a general physical framework to control these Janus faces in the directional near-field coupling and find that they can have the feature of an oriented misalignment. To be specific, the upper and lower radiation patterns of excited surface waves can have the same shape but are misaligned with a twist angle. For example, the twist angle can be any acute angle if the source is composed of a Huygens dipole and an electric dipole, and it can be any obtuse angle if the source is constructed by a circular electric dipole and a magnetic dipole.

关键词： Classical optics Light-matter interaction Metamaterials Near-field optics Photonics Plasmonics

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Method for Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma

Method for Preoperative Prediction of Microvascular Invasion...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Zhao Tian Bai Tongjia Chu Feng Wei Fa Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Hepatobiliary and Pancreas Department General Surgery Center First Hospital of Jilin University Changchun China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics.

关键词： Deep learning Pathology Correlation Biological system modeling Surgery Predictive models Data mining

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The Future of Multimedia: Micro Facial Recognition in Advanced Systems

The Future of Multimedia: Micro Facial Recognition in Advanc...

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International Conference for Convergence of Technology (I2CT)

作者： Thakkalapally Preethi Saila Ram Choudalla Sudeepthi Govathoti K Rajasri Karrar Shareef Mohsen Dudi Bhanu Prakash Sai Kumar K Department of CSE-(CYS DS) and AI&DS VNR Vignana Jyothi Institute of Engineering and Technology Aditya College Of Engineering And Technology (A) Surampalem Andhra Pradesh India Dept of CSE GITAM School of Technology GITAM Deemed-to-be-University Hyderabad Telangana Department of Electrical and Electronics Engineering MLR Institute of Technology Hyderabad Telangana India Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Department of ECE CVR College of Engineering Hyderabad Computer Science Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Paper attendance systems are still widely used at many universities, consuming significant amounts of valuable classroom time. Furthermore, the system is likely to fail due to the possibility of human mistake and the rising chance of records being misplaced or lost altogether. The authors overcame this issue by automatically posting attendance information using a facial recognition software. This strategy also helps the school take a look at the student's financial situation. Attendance records and a list of slackers might be generated by the system. They can notify students and guardians via email of attendance and financial obligations. Using KNN deep learning methodology, photos taken by a CC camera or cell phone mounted at the door are used to identify students as they enter the classroom. This study incorporates ResNet and KNN-based Deep-learning technologies to extract useful information from low-quality photos. They might also use a video taken using a cell phone to keep track of attendance. The suggested method improves upon the state-of-the-art to the extent that it achieves an accuracy of 97.34 percent, sensitivity of 98.34 percent, recall of 97.23 percent, and an F1 score of 98.23 percent.

关键词： Deep learning Sensitivity Webcams Face recognition Cellular phones Surveillance Multimedia systems

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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

Spectroscopic detection of pigments in tissues: correlation ...

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2022 International Conference Laser Optics, ICLO 2022

作者： Oliveira, L.M. Goncalves, T.M. Botelho, A.R. Martins, I.S. Silva, H.F. Carneiro, I. Carvalho, S. Henrique, R. Tuchin, V.V. Porto Portugal Center of Innovation in Engineering and Industrial Technology Isep Porto Portugal Department of Electrical and Computer Engineering Porto University-School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Porto Portugal Viana do Castelo Portugal Dept. of Pathol. and Molecular Immunology Porto University-Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Frc-Saratov Scientific Centre-of the Russian Academy of Sciences Saratov Russia

ISBN: (纸本)9781665466646

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans. © 2022 IEEE.

关键词： Tissue

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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

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Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

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Distributed de novo assembler for large-scale long-read datasets

Distributed de novo assembler for large-scale long-read data...

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IEEE International Conference on Big Data

作者： Sayan Goswami Kisung Lee Seung-Jong Park Louisiana State University Shreveport School of Electrical Engineering & Computer Science Louisiana State University School of Electrical Engineering & Computer Science Center for Computation & Technology Louisiana State University

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Third-generation DNA sequencing technologies such as single-molecule real-time sequencing (SMRT) and nanopore sequencing have the potential to fill the gaps in the existing genome databases since the raw sequences produced by these machines are much longer than those of previous generations and therefore result in more contiguous assemblies. However, these long reads have a high error rate, which makes the assembly process computationally challenging. Moreover, since existing long-read assemblers are designed to run on a single machine, they either take days to complete or run out of memory on even moderate-sized datasets. In this paper, we present a distributed long-read assembler that can assemble large-scale noisy sequence datasets on thousands of cores, resulting in orders of magnitude faster assembly times. By effectively using the map-reduce computation model with a distributed hash-map, both built using a high-performance active messaging middleware, we can assemble a PacBio human genome dataset with 139 billion base-pairs (about 130 GB) in about 33 minutes (using 2,560 cores) compared to more than 38 hours (using 28 cores) with the current state-of-the-art assembler.

关键词： Sequential analysis Genomics Big Data Real-time systems Noise measurement Bioinformatics Middleware

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Measuring and Comparing QoE of Hybrid VR Applications under Increased Network Load

Measuring and Comparing QoE of Hybrid VR Applications under ...

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Immersive Learning Research Network (iLRN), Conference of the

作者： Ioannis Doumanis Daphne Economou Lemonia Argyriou S School of Physical Sciences and Computing University of Central Lanchashire Preston UK School of Computer Science and Engineering University of Westminster London UK Research Associate at National Center for Scientific Research “Demokritos” Athens GR

The elements of presence and interaction have been connected with high-quality learning in online learning environments. This can be achieved by offering learning environments and material that can engage the learner throughout the learning experience, allowing them to connect with the content, find relevance, construct meaning and critical thinking. Hybrid VR applications offer great potential for engaging learners in environments that accurately capture the world, allowing them to make personal connections and find relevance. Coupled with a branching narrative, computer-generated imagery (CGI) and interactivity can lead to highly engaging learning experiences. Hybrid VR applications, though, are bandwidth-demanding that can impact the learning experience. This paper presents a study comparing the Quality of Experience (QoE) of a hybrid VR application streamed over the standard internet and POINT network under increased network load. The paper presents the project motivations, a pilot study and its output. It closes with conclusions about the effect of the QoE and presence in learning and future directions.

关键词： Streaming media Quality of experience Standards

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Berry Curvature and Bulk-Boundary Correspondence from Transport Measurement for Photonic Chern Bands

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Physical Review Letters 2023年第13期131卷 133601-133601页

作者： Chao Chen Run-Ze Liu Jizhou Wu Zu-En Su Xing Ding Jian Qin Lin Wang Wei-Wei Zhang Yu He Xi-Lin Wang Chao-Yang Lu Li Li Barry C. Sanders Xiong-Jun Liu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China National Laboratory of Solid State Microstructures School of Physics Nanjing University Nanjing 210093 China Department of Physics Southern University of Science and Technology Shenzhen 518055 China The Physics Department and the Solid State Institute Technion–Israel Institute of Technology Haifa 3200003 Israel Department of Physics University of Konstanz D-78457 Konstanz Germany School of Computer Science Northwestern Polytechnical University Xi’an 710129 China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Institute for Quantum Science and Technology University of Calgary Alberta T2N 1N4 Canada International Center for Quantum Materials School of Physics Peking University Beijing 100871 China CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing 100190 China International Quantum Academy Shenzhen 518048 China

Berry curvature is a fundamental element to characterize topological quantum physics, while a full measurement of Berry curvature in momentum space was not reported for topological states. Here we achieve two-dimensional Berry curvature reconstruction in a photonic quantum anomalous Hall system via Hall transport measurement of a momentum-resolved wave packet. Integrating measured Berry curvature over the two-dimensional Brillouin zone, we obtain Chern numbers corresponding to −1 and 0. Further, we identify bulk-boundary correspondence by measuring topology-linked chiral edge states at the boundary. The full topological characterization of photonic Chern bands from Berry curvature, Chern number, and edge transport measurements enables our photonic system to serve as a versatile platform for further in-depth study of novel topological physics.

关键词： Quantum simulation Quantum walks Topological effects in photonic systems Topological phase transition Topological phases of matter

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Differential-Critic GAN: Generating What You Want by a Cue of Preferences

arXiv

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arXiv 2021年

作者： Yao, Yinghua Pan, Yuangang Tsang, Ivor W. Yao, Xin Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Australian Artificial Intelligence Institute University of Technology Sydney Australia A*STAR Center for Frontier AI Research Singapore. Australian Artificial Intelligence Institute University of Technology Sydney Australia Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Computer Science University of Birmingham United Kingdom

This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired property. DiCGAN generates desired data that meets the user’s expectations and can assist in designing biological products with desired properties. Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution. However, the selection of the desired data relies on global knowledge and supervision over the entire dataset. DiCGAN introduces a differential critic that learns from pairwise preferences, which are local knowledge and can be defined on a part of training data. The critic is built by defining an additional ranking loss over the Wasserstein GAN’s critic. It endows the difference of critic values between each pair of samples with the user preference and guides the generation of the desired data instead of the whole data. For a more efficient solution to ensure data quality, we further reformulate DiCGAN as a constrained optimization problem, based on which we theoretically prove the convergence of our DiCGAN. Extensive experiments on a diverse set of datasets with various applications demonstrate that our DiCGAN achieves state-of-the-art performance in learning the user-desired data distributions, especially in the cases of insufficient desired data and limited supervision. The code is available in https://***/EvaFlower/Differential-Critic-GAN. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Nonreciprocity of Binary Magnetophotonic Crystal with the Voigt Configuration

Nonreciprocity of Binary Magnetophotonic Crystal with the Vo...

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Ukrainian Microwave Week (UkrMW), IEEE

作者： A. A. Shmat'ko E. N. Odarenko V. N. Mizernik N. G. Shevchenko School of Radiophysics Biomedical Electronics and Computer Systems V.N. Karazin Kharkiv National University Kharkiv Ukraine School of Electronics and Biomedical Engineering Kharkiv National University of Radio Electronics Kharkiv Ukraine Scientific Physical-Technologic Center of MES and NAS of Ukraine Kharkiv Ukraine Institute of Education and Science in Mechanical Engineering and Transport National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (纸本)9798350331530

Finite one-dimensional magnetophotonic crystal containing magnetized plasma layers is considered in Voigt configuration. Combined theoretical method based on the Floquet-Bloch waves method and transfer matrix one is used for obtained analytical expressions for dispersion relations, transmission and reflection coefficients. Pronounced nonreciprocal properties of the magnetophotonic crystal are shown for oblique incidence of wave on the structure. Transmission coefficients are different for opposite signs of incidence angle. Nonreciprocity is observed within the transmission band of surface gyrotropic waves within subbands between Fabry-Perot resonances.

关键词： Surface waves Magnetic separation Magnetic resonance Magnetic devices Crystals Spatial filters Reflection coefficient

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