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arXiv 2020年

作者： Zhou, Panpan Chen, Liyang Sochnikov, Ilya Wu, Tsz Chun Foster, Matthew S. Bollinger, Anthony T. He, Xi Božović, Ivan Natelson, Douglas Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics University of Connecticut StorrsCT06269 United States Brookhaven National Laboratory UptonNY11973-5000 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Atomically precise epitaxial structures are unique systems for tunneling spectroscopy that minimize extrinsic effects of disorder. We present a systematic tunneling spectroscopy study, over a broad doping, temperature, and bias range, in epitaxial c-axis La2−xSrxCuO4/La2CuO4/La2−xSrxCuO4 heterostructures. The behavior of these superconductor/insulator/superconductor (SIS) devices is unusual. Down to 20 mK there is complete suppression of c-axis Josephson critical current with a barrier of only 2 nm of La2CuO4, and the zero-bias conductance remains at 20-30% of the normal-state conductance, implying a substantial population of in-gap states. Tunneling spectra show greatly suppressed coherence peaks. As the temperature is raised, the superconducting gap fills in rather than closing at Tc. For all doping levels, the spectra show an inelastic tunneling feature at ∼ 80 meV, suppressed as T exceeds Tc. These nominally simple epitaxial cuprate junctions deviate markedly from expectations based on the standard Bardeen-Cooper-Schrieffer (BCS) theory. Copyright © 2020, The Authors. All rights reserved.

关键词： Copper compounds

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PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones

arXiv

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arXiv 2020年

作者： Pacheco, Andre G.C. Lima, Gustavo R. Salomão, Amanda S. Krohling, Breno A. Biral, Igor P. de Angelo, Gabriel G. Alves, Fábio C.R. Esgario, José G.M. Simora, Alana C. Castro, Pedro B.C. Rodrigues, Felipe B. Frasson, Patricia H.L. Krohling, Renato A. Knidel, Helder Santos, Maria C.S. do Espírito Santo, Rachel B. Macedo, Telma L.S.G. Canuto, Tania R.P. de Barros, Luíz F.S. Graduate Program in Computer Science Federal University of Espirito Santo Vitoria Brazil Faculty of Medicine Federal University of Espirito Santo Vitoria Brazil Federal University of Espirito Santo Vitoria Brazil Nature Inspired Computing Laboratory Federal University of Espirito Santo Vitoria Brazil Department of Specialized Medicine Federal University of Espirito Santo Vitoria Brazil Production Engineering Department Federal University of Espirito Santo Vitoria Brazil Secretary of Health of the Espírito Santo state Governor of Espírito Santo state Vitória Brazil Federal University of Espirito Santo Vitoria Brazil

Over the past few years, different computer-aided diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to design them. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 22 features. The dataset consists of 1,373 patients, 1,641 skin lesions, and 2,298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to aid future research and the development of new tools to assist clinicians to detect skin cancer. Copyright © 2020, The Authors. All rights reserved.

关键词： Dermatology

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Iterative Motion Compensation reconstruction ultra-short TE(iMoCo UTE) for high resolution free breathing pulmonary MRI

arXiv

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arXiv 2020年

作者： Zhu, Xucheng Chan, Marilynn Lustig, Michael Johnson, Kevin Larson, Peder E.Z. UCSF/UC Berkeley Graduate Program in Bioengineering University of California San Francisco San FranciscoCA United States Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCA United States Department of Pediatrics Division of Pediatric Pulmonology University of California San Francisco San FranciscoCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Department of Medical Physics University of Wisconsin Madison MadisonWI United States Department of Radiology University of Wisconsin Madison MadisonWI United States

Purpose: To develop a high scanning efficiency, motion corrected imaging strategy for free-breathing pulmonary MRI by combining a motion compensation reconstruction with a UTE acquisition, called iMoCo UTE. Methods: An optimized golden angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, then motion resolved data was reconstructed by XD Golden-angle RAdial Sparse Parallel reconstruction (XD-GRASP). One state from the motion resolved images was selected as a reference, and motion fields from the other states to the reference were derived via non-rigid registration. Finally, all motion resolved data and motion fields were reconstructed by using an iterative motion compensation reconstruction with a total generalized variation sparse constraint. Results: The iMoCo UTE strategy was evaluated in volunteers and non-sedated pediatric patient(4-6 y/o) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures, and higher apparent SNR and CNR, compared to using other motion correction strategies, such as soft-gating, motion resolved reconstruction, and non-rigid motion compensation(MoCo). iMoCo UTE also showed promising results in an infant study. Conclusions: The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency, SNR, and reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free breathing lung MRI scans, especially in very challenging application situations, such as pediatric MRI studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Bridge Sub Structure Defect Inspection Assistance by using Deep Learning

Bridge Sub Structure Defect Inspection Assistance by using D...

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IEEE International Conference on Awareness Science and Technology

作者： Pravee Kruachottikul Kittikul Kovitanggoon Nagul Cooharojananone Donnaphat Trakulwaranont Gridsada Phanomchoeng Kanokwan Atchariyachanvanich Thira Chavarnakul Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok Thailand Department of Mathematics and Computer Science Chulalongkorn University Bangkok Thailand Department of Mechanical Engineering Chulalongkorn University Bangkok Thailand Department of Commerce Chulalongkorn University Bangkok Thailand Faculty of Information Technology King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand

Road transportation is the most popular transportation in Thailand, which the top two highest traffic are the region-to-region highways;and then inter-city highways. Therefore, the regular maintenance is required to maintain the good condition due to road safety. The most significant process of bridge inspection procedures is sub structure inspection, which requires visual inspection as an initial step. This process is used to quick determine the damage severity i.e. appearance and crack that may cause damage to the structure strength. The current process requires that the experienced maintenance engineer to be on the field in order to visual inspect and estimate whether the maintenance is required. Yet, due to the limitation of number of expert engineers to be on the field, the photo verification is introduced to assist them so that they are no need on every inspection site. However, using human to verify has no standard and uncontrollable. They need to have experience and good knowledge. As well as it is highly depended on individual decisionmaking skill. Thus, in this paper, the deep learning technique will be presented to assist the expert for quality inspection process of bridge sub structure images. That is using image enhancement and then image splitting and overlapping for image pre-processing. After that applying CNNs for object classification. As a result, the total accuracy is 89% based on 3926 dataset.

关键词： Bridges Inspection Machine learning Maintenance engineering Convolution Training Concrete

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Hypermedia Driven Application programming Interface for Learning Object Management

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Procedia computer Science 2018年 135卷 120-127页

作者： Herru Darmadi Suryadiputra Liawatimena Bahtiar Saleh Abbas Agung Trisetyarso Computer Science Department School of Computer Science Bina Nusantara University Jl. Kebun Jeruk Raya No 27 Kemanggisan Jakarta 11480 Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jl. Kebun Jeruk Raya No 27 Kemanggisan Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jl. Kebun Jeruk Raya No 27 Kemanggisan Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor Computer Science Bina Nusantara University Jl. Kebun Jeruk Raya No 27 Kemanggisan Jakarta 11480 Indonesia

The research purpose is to create a hypermedia driven API for learning object management. The research focuses on conception, design, implementation and evaluation of the learning object management hypermedia API. The hypermedia API was designed based on learning object models, hypermedia concepts, and related studies. Learning object model can be represented and manipulated as a hypermedia resource. It contains page resources with text and links to multimedia elements. The hypermedia API was designed using UML and represented in HTML5 because of its rich affordance. The implementation and evaluation phase were conducted by testing the API using three different client’s software to prove the concept of hypermedia as the engine of application state for managing learning objects. The results show that the hypermedia API for learning object management is accessible, consistent, and discoverable by heterogonous client applications.

关键词： hypermedia API HTML5 learning object content management

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Erratum: “Two-dimensional porous graphitic carbon nitride C6N7 monolayer: First-principles calculations” [Appl. Phys. Lett. 119, 142102 (2021)]

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Applied Physics Letters 2022年第18期120卷

作者： A. Bafekry M. Faraji M. M. Fadlallah I. Abdolhosseini Sarsari H. R. Jappor S. Fazeli M. Ghergherehchi 1Department of Radiation Application Shahid Beheshti University Tehran Iran 2Micro and Nanotechnology Graduate Program TOBB University of Economics and Technology Sogutozu Caddesi No 43 Sogutozu 06560 Ankara Turkey 3Department of Physics Faculty of Science Benha University 13518 Benha Egypt 4Department of Physics Isfahan University of Technology Isfahan 84156-83111 Iran 5Department of Physics College of Education for Pure Sciences University of Babylon Hilla Iraq 6Department of Material Science and Engineering Sharif University of Technology PO Box 11155-9466 Tehran Iran 7Department of Electrical and Computer Engineering Sungkyunkwan University 16419 Suwon South Korea

The original article1 contains misprints in the values of the elastic parameters. The values of C11 = 258.6 GPa, C22 = 290.8 GPa, and C12 = 70.73 GPa and C13 =

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Identifying irregularity electricity usage of customer behaviors using logistic regression and linear discriminant analysis 3

Identifying irregularity electricity usage of customer behav...

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3rd International Conference on Science in Information Technology, ICSITech 2017

作者： Lawi, Armin Wungo, Supriyadi La Manjang, Salama Department of Computer Science Post-graduate Program of Electrical Engineering Dept. Electrical Engineering Universitas Hasanuddin Makassar Indonesia

ISBN: (纸本)9781509058662

This study aims to implement a machine learning technique in identifying the irregularities of customer behavior on the use of prepaid electricity pulses. The methods used are Linear Discriminant Analysis and Logistic Regression. The performance of the classification system will be evaluated using the 10-fold cross-validation technique. Validation results are measured using accuracy, precision and recall values. In this research shows that the use of machine learning technique has a good performance in classification of electrical consumption behavior. Experimental results with the different amount of data testing indicate that Logistic Regression method has high accuracy, precision, and recall value when compared with Linear Discriminant Analysis that is 100%. This is due to Logistic Regression method can predict irregularities accurately because the addition of the amount of data does not affect the performance of the method. © 2017 IEEE.

关键词： Regression analysis

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An approach to improve online sequential extreme learning machines using restricted Boltzmann machines

An approach to improve online sequential extreme learning ma...

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International Joint Conference on Neural Networks

作者： Andre G. C. Pacheco Renato A. Krohling Graduate program in computer science PPGI Federal University of Espirito Santo UFES Av. Fernando Ferrari 514 Vitoria Brazil Graduate program in computer science PPGI and Production engineering department Federal University of Espirito Santo UFES Av. Fernando Ferrari 514 Vitoria Brazil

The extreme learning machine (ELM) is an analytical algorithm developed to train single-hidden layer feedforward neural network (SLFN). Although the ELM provides a good generalization and a very fast training phase, two shortcomings can be pointed in this approach: it assigns its input weights randomly and it is not able to handle sequential learning. Thereby, the online sequential extreme learning machine (OS-ELM) was proposed in order to tackle the lack of sequential learning on ELMs. Nonetheless, the OS-ELM still assigns the input weights randomly. Thus, in this work, we proposed an approach based on a restricted Boltzmann machine (RBM) to improve the OS-ELM by determining its input weights throughout the training phase. We applied our method to several well-known benchmarks and the results show that the proposed approach is able to improve the OS-ELM for most them.

关键词： Training Training data Neurons Standards Feedforward neural networks Arrays

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Exciton Localization of High-Quality ZnO/Mg_xZn_1-xO Multiple Quantum Wells on Si (111) with a Y₂O₃ Buffer Layer (vol 1, pg 3829, 2018)

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ACS APPLIED NANO MATERIALS 2018年第10期1卷 5958-5958页

作者： Liu, Wei-Rein Huang, Wei-Lun Wu, Yung-Chi Lai, Liang-Hsun Hsu, Chia-Hung Hsieh, Wen-Feng Chiang, Tsung-Hung Wan, H. W. Hong, M. Kao, J. National Synchrotron Radiation Research Center Hsinchu 30076 Taiwan Program for Science and Technology of Accelerator Light Source National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Chiao Tung University Hsinchu 30010 Taiwan Department of Materials Science & Engineering National Tsing Hua University Hsinchu 30013 Taiwan Graduate Institute of Applied Physics and Department of Physics National Taiwan University Taipei 10617 Taiwan Department of Physics National Tsing Hua University Hsinchu 30013 Taiwan

We report the structural and optical properties of ten-period ZnO/MgxZn1–xO multiple quantum wells (MQWs) prepared on the most widely used semiconductor material, Si. The introduction of a nanometer thick high-k Y2O3transition layer between Si (111) substrate and a ZnO buffer layer significantly improves the structural perfection of the MQWs grown on top of it. The high structural quality of the ZnO/MgxZn1–xO MQWs is evidenced by the appearance of pronounced high order satellite peaks in X-ray crystal truncation rods; high resolution cross-sectional transmission electron microscopy images also confirmed the regularly arranged well and barrier layers. When the well width is less than ∼2.7 nm, the quantum-confined Stark effect in MQWs can be negligible. Not only the increasing exciton-binding energy but also reducing exciton–phonon coupling determined in temperature-dependent photoluminescence spectra indicate quantum-size effect. Our results demonstrate that ZnO/MgxZn1–xO MQWs integrated on Si have great potential in UV optoelectronic device applications.

关键词： Excitons Phonons Oxides Binding energy Layers

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