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Symmetry-protected topological phase transitions and robust chiral order on a tunable zigzag lattice

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Physical Review B 2020年第16期101卷 165131-165131页

作者： Qibin Zheng Xing Li Haiyuan Zou Institute of Biomedical Engineering University of Shanghai for Science and Technology Shanghai 200093 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China

Symmetry fractionalization, generating a large amount of symmetry-protected topological phases, provides scenarios for continuous phase transitions different from spontaneous symmetry breaking. However, it is hard to detect these symmetry-protected topological phase transitions experimentally. Motivated by the recent development of highly tunable ultracold polar molecules, we show that the setup in a zigzag optical lattice of this system provides the perfect platform to realize symmetry-protected topological phase transitions. By using infinite time-evolving block decimation, we obtain the phase diagram in a large parameter region and find another scheme to realize the long-sought vector chiral phase, which is robust from quantum fluctuations. We discuss the existence of the chiral phase by an effective field analysis.

关键词： Dipolar gases Topological phase transition Approximation methods for many-body systems Tensor network methods XY model

The design of dynamic probabilistic caching with time-varying content popularity

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arXiv 2020年

作者： Gao, Jie Zhang, Shan Zhao, Lian Shen, Xuemin Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada School of Computer Science and Engineering Beihang Univesity Beijing100191 China

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design. Copyright © 2020, The Authors. All rights reserved.

关键词： Probability distributions

Multi-window SRS imaging using a rapid widely tunable fiber laser

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arXiv 2021年

作者： Ni, Hongli Lin, Peng Zhu, Yifan Zhang, Meng Tan, Yuying Zhan, Yuewei Cheng, Ji-Xin Department of Electrical and Computer Engineering Boston University 8 St. Mary's St. BostonMA02215 United States Department of Chemistry Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Biomedical Engineering Boston University 44 Cummington Mall MA02215 United States

Spectroscopic stimulated Raman scattering (SRS) imaging has become a useful tool finding a broad range of applications. Yet, wider adoption is hindered by the bulky and environmentally-sensitive solid-state optical parametric oscillator (OPO) in current SRS microscope. Moreover, chemically-informative multi-window SRS imaging across C-H, C-D and fingerprint Raman regions is challenging due to the slow wavelength tuning speed of the solid-state OPO. In this work, we present a multi-window SRS imaging system based on a compact and robust fiber laser with rapid and widely tuning capability. To address the relative intensity noise intrinsic to fiber laser, we implemented auto-balanced detection which enhances the signal-to-noise ratio of stimulated Raman loss imaging by 23 times. We demonstrate high-quality SRS metabolic imaging of fungi, cancer cells, and Caenorhabditis elegans across the C-H, C-D and fingerprint Raman windows. Our results showcase the potential of the compact multi-window SRS system for a broad range of applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Fiber lasers

GT U-Net: A U-net like group transformer network for tooth root segmentation

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arXiv 2021年

作者： Li, Yunxiang Wang, Shuai Wang, Jun Zeng, Guodong Liu, Wenjun Zhang, Qianni Jin, Qun Wang, Yaqi Microelectronics CAD Center Hangzhou Dianzi University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan College of Media Engineering Communication University of Zhejiang Hangzhou China

To achieve an accurate assessment of root canal therapy, a fundamental step is to perform tooth root segmentation on oral X-ray images, in that the position of tooth root boundary is significant anatomy information in root canal therapy evaluation. However, the fuzzy boundary makes the tooth root segmentation very challenging. In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation. The proposed network retains the essential structure of U-Net but each of the encoders and decoders is replaced by a group Transformer, which significantly reduces the computational cost of traditional Transformer architectures by using the grouping structure and the bottleneck structure. In addition, the proposed GT U-Net is composed of a hybrid structure of convolution and Transformer, which makes it independent of pre-training weights. For optimization, we also propose a shape-sensitive Fourier Descriptor (FD) loss function to make use of shape prior knowledge. Experimental results show that our proposed network achieves the state-of-the-art performance on our collected tooth root segmentation dataset and the public retina dataset DRIVE. Code has been released at https://***/Kent0n-Li/GTU-Net. © 2021, CC BY.

关键词： Image segmentation

Deformation-Compensated Learning for Image Reconstruction without Ground Truth

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arXiv 2021年

作者： Gan, Weijie Sun, Yu Eldeniz, Cihat Liu, Jiaming An, Hongyu Kamilov, Ulugbek S. The Department of Computer Science & Engineering Washington University in St. Louis St. LouisMO63130 United States The Mallinckrodt Institute of Radiology Washington University in St. Louis St. LouisMO63130 United States The Department of Electrical & System Engineering Washington University in St. Louis St. LouisMO63130 United States The Mallinckrodt Institute of Radiology Department of Neurology Department of Biomedical Engineering Saint LouisMO63130 United States The Division of Biology and Biomedical Sciences Washington University in St. Louis St. LouisMO63130 United States The Department of Computer Science & Engineering and Electrical & Systems Engineering Washington University in St. Louis St. LouisMO63130 United States

Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has shown the potential of using multiple noisy measurements of the same object as an alternative to having a ground-truth. However, existing N2N-based methods are not suitable for learning from the measurements of an object undergoing nonrigid deformation. This paper addresses this issue by proposing the deformation-compensated learning (DeCoLearn) method for training deep reconstruction networks by compensating for object deformations. A key component of DeCoLearn is a deep registration module, which is jointly trained with the deep reconstruction network without any ground-truth supervision. We validate DeCoLearn on both simulated and experimentally collected magnetic resonance imaging (MRI) data and show that it significantly improves imaging quality. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Highly Elastic,Bioresorbable Polymeric Materials for Stretchable,Transient Electronic Systems

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Nano-Micro Letters 2024年第6期16卷 1-13页

作者： Jeong‑Woong Shin Dong‑Je Kim Tae‑Min Jang Won Bae Han Joong Hoon Lee Gwan‑Jin Ko Seung Min Yang Kaveti Rajaram Sungkeun Han Heeseok Kang Jun Hyeon Lim Chan‑Hwi Eom Amay J.Bandodkar Hanul Min Suk‑Won Hwang KU‑KIST Graduate School of Converging Science and Technology Korea University145 Anam‑roSeongbuk‑guSeoul 02841Republic of Korea SK Hynix 2091Gyeongchung‑daeroBubal‑eupIcheon‑siGyeonggi‑do 17336Republic of Korea Hanwha Systems Co. Ltd.188Pangyoyeok‑roBundang‑guSeongnam‑siGyeonggi‑do 13524Republic of Korea Department of Electrical and Computer Engineering North Carolina State UniversityRaleighNC 27606USA Center for Advanced Self‑Powered Systems of Integrated Sensors and Technologies(ASSIST) North Carolina State UniversityRaleighNC 27606USA Department of Integrative Energy Engineering Korea University145 Anam‑roSeongbuk‑guSeoul 02841Republic of Korea Biomaterials Research Center Korea Institute of Science and Technology(KIST)5 Hwarang‑ro 14‑gilSeongbuk‑guSeoul 02792Republic of Korea Semiconductor R&D Center Samsung Electronics Co.Ltd.Hwaseong‑siGyeonggi‑do 18448Republic of Korea Center for Advanced Biomolecular Recognition Biomedical Research DivisionKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea

Substrates or encapsulants in soft and stretchable formats are key components for transient,bioresorbable electronic systems;however,elastomeric polymers with desired mechanical and biochemical properties are very limited compared to nontransient ***,we introduce a bioresorbable elastomer,poly(glycolide-co-ε-caprolactone)(PGCL),that contains excellent material properties including high elongation-at-break(<1300%),resilience and toughness,and tunable dissolution *** of PGCLs as polymer matrices,in combination with conducing polymers,yields stretchable,conductive composites for degradable interconnects,sensors,and actuators,which can reliably function under external *** of device components with wireless modules demonstrates elastic,transient electronic suture system with on-demand drug delivery for rapid recovery of postsurgical wounds in soft,time-dynamic tissues.

关键词： Biodegradable elastomer Conductive polymer composites biomedical device Transient electronics

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

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arXiv 2021年

作者： Khozeimeh, Fahime Sharifrazi, Danial Izadi, Navid Hoseini Joloudari, Javad Hassannataj Shoeibi, Afshin Alizadehsani, Roohallah Gorriz, Juan M. Hussain, Sadiq Sani, Zahra Alizadeh Moosaei, Hossein Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Deakin University Geelong Australia Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Department of Signal Theory Networking and Communications Universidad de Granada Department of Psychiatry University of Cambridge Cambridge United Kingdom Dibrugarh University Assam 786004 India Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Mathematics Faculty of Science University of Bojnord Iran Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University GeelongVIC3220 Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

Multi-feature semi-supervised learning for COVID-19 diagnosis from chest X-ray images

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arXiv 2021年

作者： Qi, Xiao Nosher, John L. Foran, David J. Hacihaliloglu, Ilker Department of Electrical and Computer Engineering Rutgers University PiscatawayNJ United States Department Radiology Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Rutgers Cancer Institute of New Jersey New BrunswickNJ United States Department Biomedical Engineering Rutgers University PiscatawayNJ United States

Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is preferred over CT. However, the interpretation of CXR images, compared to CT, is more challenging due to low image resolution and COVID-19 image features being similar to regular pneumonia. computer-aided diagnosis via deep learning has been investigated to help mitigate these problems and help clinicians during the decision-making process. The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain. To provide a solution to this, in this work, we propose a semi-supervised learning (SSL) approach using minimal data for training. We integrate local-phase CXR image features into a multi-feature convolutional neural network architecture where the training of SSL method is obtained with a teacher/student paradigm. Quantitative evaluation is performed on 8,851 normal (healthy), 6,045 pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48% unlabeled data for training, 5.53% for validation, our method achieves 93.61% mean accuracy on a large-scale (70.93%) test data. We provide comparison results against fully supervised and SSL methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

Person Identification from Ear Images Using Convolutional Neural Networks 9

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Person Identification from Ear Images Using Convolutional Ne...

9th IEEE International Conference on Control System, Computing and engineering, ICCSCE 2019

作者： Petaitiemthong, Natchapon Chuenpet, Potsawat Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Faculty of Engineering Graduate School Chiang Mai University Computer Engineering Department Chiang Mai Thailand Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand Electrical Engineering Department Faculty of Engineering Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9781728135007

Nowadays, biometric identification is utilized in several applications especially in security system. One of the recently popular biometric identifications is person identification from ear because each person has a unique ear and it does not change overtime. In addition, we believe that not only side view ear image is useful in identifying a person, but a front view ear image is also useful. Hence, in this paper, we develop two convolutional neural networks (CNNs) schemes to recognize front view and side view human ear. From the blind test data set results, we found that the system based on front view images provides 84% correct. Meanwhile, the side view image-based system yields 80% correct classification on the same data set. © 2019 IEEE.

关键词： Biometrics