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arXiv 2018年

作者： Lipková, Jana Angelikopoulos, Panagiotis Wu, Stephen Alberts, Esther Wiestler, Benedikt Diehl, Christian Preibisch, Christine Pyka, Thomas Combs, Stephanie Hadjidoukas, Panagiotis van Leemput, Koen Koumoutsakos, Petros Lowengrub, John Menze, Bjoern Germany Dept. of Neuroradiolog Klinikum Rechts der Isar TUM Germany D.E. Shaw Research L.L.C United States Institute of Statistical Mathematics Tokyo Japan Dept. of Diagnostic and Interventional Neuroradiology & Neuroimaging Center & Clinic for Neurology Klinikum Rechts der Isar TUM Germany Dept. of Nuclear Medicine Klinikum Rechts der Isar TUM Germany Dept. of Radiation Oncology Klinikum Rechts der Isar TUM Institute of Innovative Radiotherapy Helmholtz Zentrum Munich & Deutsches Konsortium für Translationale Krebsforschung Germany IBM Research - Zurich Switzerland Harvard Medical School Boston United States Dept. of Applied Mathematics and Computer Science TU Denmark Denmark Computational Science and Engineering Lab ETH Zürich Switzerland Dept. of Mathematics Biomedical Engineering Chemical Engineering and Materials Science Center for Complex Biological Systems Chao Family Comprehensive Cancer Center UC Irvine United States Institute for Advanced Study TUM Germany

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in glioblastoma patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and thus is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized dose-escalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design. Copyright © 2018, The Authors. All rights reserved.

关键词： Radiotherapy

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

Automated Detection of Epileptic Spikes and Seizures Incorpo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanyang Dong Shurong Sheng Xiongfei Wang Jia-Hong Gao Yi Sun Wanli Yang Kuntao Xiao Pengfei Teng Guoming Luan Zhao Lv School of Artificial Intelligence Anhui University Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset.

关键词： Convolution Magnetic sensors Neural networks Magnetoencephalography Manuals Feature extraction Electroencephalography Encoding Recording Magnetic fields

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Peptide Programmed Hydrogels: Peptide Programmed Hydrogels as Safe Sanctuary Microenvironments for Cell Transplantation (Adv. Funct. Mater. 9/2020)

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advanced Functional Materials 2020年第9期30卷

作者： Yi Wang Xuefei He Kiara F. Bruggeman Bishakhdatta Gayen Antonio Tricoli Woei Ming Lee Richard J. Williams David R. Nisbet Laboratory of Advanced Biomaterials Research School of Electrical and Energy Engineering Australian National University Canberra ACT 2601 Australia Applied Optics Lab Research School of Electrical and Energy Engineering College of Engineering and Computer Science Australian National University Canberra ACT 2601 Australia Department of Mechanical Engineering University of Melbourne Parkville VIC 3010 Australia Nanotechnology Research Laboratory Research School of Electrical and Energy Engineering Australian National University Canberra ACT 2601 Australia Centre for Molecular and Medical Research School of Medicine Deakin University Warun Ponds VIC 3216 Australia

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New horizons in near-zero refractive index photonics and hyperbolic metamaterials

arXiv

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arXiv 2023年

作者： Lobet, Michaël Kinsey, Nathaniel Liberal, Iñigo Caglayan, Humeyra Huidobro, Paloma A. Galiffi, Emanuele Mejía-Salazar, Jorge Ricardo Palermo, Giovanna Jacob, Zubin Maccaferri, Nicolò Department of Physics Namur Institute of Structured Materials University of Namur Rue de Bruxelles 61 Namur5000 Belgium John A. Paulson School of Engineering and Applied Sciences Harvard University 9 Oxford Street CambridgeMA02138 United States Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA23284 United States Pamplona31006 Spain Faculty of Engineering and Natural Science Photonics Tampere University Tampere33720 Finland Universidad Autónoma de Madrid MadridE-28049 Spain Instituto de Telecomunicações Instituto Superior Técnico University of Lisbon Avenida Rovisco Pais 1 Lisboa1049-001 Portugal Photonics Initiative Advanced Science Research Center City University of New York New York10027 United States Santa Rita do Sapucaí37540-000 MG Brazil Department of Physics NLHT Lab. University of Calabria Rende87036 Italy Rende87036 Italy Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Department of Physics Umeå University Linnaeus väg 24 Umeå90187 Sweden Department of Physics and Materials Science University of Luxembourg 162a avenue de la Faïencerie LuxembourgL-1511 Luxembourg

The engineering of the spatial and temporal properties of both the electric permittivity and the refractive index of materials is at the core of photonics. When vanishing to zero, those two variables provide efficient knobs to control light-matter interactions. This perspective aims at providing an overview of the state of the art and the challenges in emerging research areas where the use of near-zero refractive index and hyperbolic metamaterials is pivotal, in particular light and thermal emission, nonlinear optics, sensing applications and time-varying photonics. © 2023, CC BY.

关键词： Refractive index

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THz near-field imaging of extreme subwavelength metal structures

arXiv

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arXiv 2021年

作者： Chen, Xinzhong Liu, Xiao Guo, Xiangdong Chen, Shu Hu, Hai Nikulina, Elizaveta Yao, Ziheng Betchel, Hans Martin, Michael C. Carr, G. Lawrence Dai, Qing Zhuang, Songlin Hu, Qing Zhu, Yiming Hillenbrand, Rainer Liu, Mengkun You, Guanjun Department of Physics and Astronomy Stony Brook University Stony BrookNY11794 United States Shanghai Key Lab of Modern Optical Systems Terahertz Technology Innovation Research Institute Engineering Research Center of Optical Instrument and System Ministry of Education University of Shanghai for Science and Technology Shanghai200093 China Division of Nanophotonics CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology Beijing100190 China CIC nanoGUNE Donostia-San Sebastián20018 Spain CIC nanoGUNE UPV/EHU Donostia-San Sebastián20018 Spain IKERBARSQUE Basque Foundation of Science Bilbao48013 Spain Advanced Light Source Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Synchrotron Light Source II Brookhaven National Laboratory UptonNY11973 United States Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States

Modern scattering-type scanning near-field optical microscopy (s-SNOM) has become an indispensable tool in material research. However, as the s-SNOM technique marches into the far-infrared (IR) and terahertz (THz) regimes, emerging experiments sometimes produce puzzling results. For example, "anomalies" in the near-field optical contrast have been widely reported. In this letter, we systematically investigate a series of extreme subwavelength metallic nanostructures via s-SNOM near-field imaging in the GHz to THz frequency range. We find that the near-field material contrast is greatly impacted by the lateral size of the nanostructure, while the spatial resolution is practically independent of it. The contrast is also strongly affected by the connectivity of the metallic structures to a larger metallic "ground plane". The observed effect can be largely explained by a quasi-electrostatic analysis. We also compare the THz s-SNOM results to those of the mid-IR regime, where the size-dependence becomes significant only for smaller structures. Our results reveal that the quantitative analysis of the near-field optical material contrasts in the long-wavelength regime requires a careful assessment of the size and configuration of metallic (optically conductive) structures. Copyright © 2021, The Authors. All rights reserved.

关键词： Metals

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NTIRE 2020 challenge on perceptual extreme super-resolution: Methods and results

arXiv

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arXiv 2020年

作者： Zhang, Kai Gu, Shuhang Timofte, Radu Shang, Taizhang Dai, Qiuju Zhu, Shengchen Yang, Tong Guo, Yandong Jo, Younghyun Yang, Sejong Kim, Seon Joo Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Liu, Jing Yoon, Kwangjin Jeon, Taegyun Akita, Kazutoshi Ooba, Takeru Ukita, Norimichi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu He, Dongliang Wu, Wenhao Ding, Yukang Li, Chao Li, Fu Wen, Shilei Li, Jianwei Yang, Fuzhi Yang, Huan Fu, Jianlong Kim, Byung-Hoon Baek, JaeHyun Ye, Jong Chul Fan, Yuchen Huang, Thomas S. Lee, Junyeop Lee, Bokyeung Min, Jungki Kim, Gwantae Lee, Kanghyu Park, Jaihyun Mykhailych, Mykola Zhong, Haoyu Shi, Yukai Yang, Xiaojun Yang, Zhijing Lin, Liang Zhao, Tongtong Peng, Jinjia Wang, Huibing Jin, Zhi Wu, Jiahao Chen, Yifu Shang, Chenming Zhang, Huanrong Min, Jeongki Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Computer Vision Lab ETH Zurich Switzerland OPPO Research Yonsei University Korea Republic of Facebook United States Xidian University China China SI Analytics Co. Ltd. 441 Expo-ro Yuseong-gu Daejeon34051 Korea Republic of Japan Co. Ltd China Baidu Inc China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Peking University China State Key Laboratory of Digital Publishing Technology Founder Group Shanghai Jiao Tong University China Microsoft Research Beijing China Korea Republic of Amazon Web Services University of Illinois at Urbana-Champaign United States Korea University Korea Republic of *** Ltd Guangdong University of Technology China Sun Yat-sen University China Dalian Maritime University China School of Intelligent Systems Engineering Sun Yat-sen University China College of Engineering Trivandrum India

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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End-to-end AI Framework for Interpretable Prediction of Molecular and Crystal Properties

arXiv

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arXiv 2022年

作者： Park, Hyun Zhu, Ruijie Huerta, E.A. Chaudhuri, Santanu Tajkhorshid, Emad Cooper, Donny Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in Gitlab, and ready-to-use Jupyter notebooks in Google Colab. Copyright © 2022, The Authors. All rights reserved.

关键词： Organometallics

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NTIRE 2021 Challenge on Image Deblurring

arXiv

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arXiv 2021年

作者： Nah, Seungjun Son, Sanghyun Lee, Suyoung Timofte, Radu Lee, Kyoung Mu Chen, Liangyu Zhang, Jie Lu, Xin Chu, Xiaojie Chen, Chengpeng Xiong, Zhiwei Xu, Ruikang Xiao, Zeyu Huang, Jie Zhang, Yueyi Xi, Si Wei, Jia Bai, Haoran Cheng, Songsheng Wei, Hao Sun, Long Tang, Jinhui Pan, Jinshan Lee, Donghyeon Lee, Chulhee Kim, Taesung Wang, Xiaobing Zhang, Dafeng Pan, Zhihong Lin, Tianwei Wu, Wenhao He, Dongliang Li, Baopu Li, Boyun Xi, Teng Zhang, Gang Liu, Jingtuo Han, Junyu Ding, Errui Tao, Guangpin Chu, Wenqing Cao, Yun Luo, Donghao Tai, Ying Lu, Tong Wang, Chengjie Li, Jilin Huang, Feiyue Chen, Hanting Chen, Shuaijun Guo, Tianyu Wang, Yunhe Zamir, Syed Waqas Arora, Aditya Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zuo, Yushen Ou, Yimin Chai, Yuanjun Shi, Lei Liu, Shuai Lei, Lei Feng, Chaoyu Zeng, Kai Yao, Yuying Liu, Xinran Zhang, Zhizhou Huang, Huacheng Zhang, Yunchen Jiang, Mingchao Zou, Wenbin Miao, Si Kim, Yangwoo Sun, Yuejin Deng, Senyou Ren, Wenqi Cao, Xiaochun Wang, Tao Suin, Maitreya Rajagopalan, A.N. Duong, Vinh Van Nguyen, Thuc Huu Yim, Jonghoon Jeon, Byeungwoo Li, Ru Xie, Junwei Han, Jong-Wook Choi, Jun-Ho Kim, Jun-Hyuk Lee, Jong-Seok Zhang, Jiaxin Peng, Fan Svitov, David Pakulich, Dmitry Kim, Jaeyeob Jeong, Jechang Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland University of Science and Technology of China China Megvii China Fudan University China Peking University China Netease Games AI Lab Nanjing University of Science and Technology China Guilin University of Electronic Technology China Samsung Electronics Co. Ltd Sunmoon University Asan Korea Republic of Samsung Research China Beijing China Baidu Research United States Department of Computer Vision Technology Baidu Inc China Fudan University China Megvii China Nanjing University China Tencent Noah's Ark Lab Huawei Technologies Co. Ltd Inception Institute of Artificial Intelligence Tsinghua University Beijing China North China University of Technology China Xiaomi South China University of Technology China Lab of Image Science and Technology Southeast University China China Design Group Co. Ltd China JOYY AI GROUP Fujian Normal University China Shanghai Advanced Research Institute Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Huawei Noah's Ark Lab Indian Institute of Technology Madras India Department of ECE Sungkyunkwan University Korea Republic of Fuzhou University China Imperial Vision Co. Ltd School of Integrated Technology Yonsei University Korea Republic of Expasoft LLC Institute of Automation and Electrometry The SB RAS Image Communication & Signal Processing Laboratory Hanyang University Korea Republic of

Motion blur is a common photography artifact in dynamic environments that typically comes jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on Image Deblurring. In this challenge report, we describe the challenge specifics and the evaluation results from the 2 competition tracks with the proposed solutions. While both the tracks aim to recover a high-quality clean image from a blurry image, different artifacts are jointly involved. In track 1, the blurry images are in a low resolution while track 2 images are compressed in JPEG format. In each competition, there were 338 and 238 registered participants and in the final testing phase, 18 and 17 teams competed. The winning methods demonstrate the state-of-the-art performance on the image deblurring task with the jointly combined artifacts. © 2021, CC BY-NC-ND.

关键词： Image enhancement

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Atomic structure of the continuous random network of amorphous C[(C6H4)2]2, PAF-1

arXiv

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arXiv 2021年

作者： Cai, Guanqun Lin, He Zhao, Ziqiang Liu, Jiaxun Phillips, Anthony E. Headen, Thomas F. Youngs, Tristan G.A. Hai, Yang Tian, Haolai He, Chunyong Ke, Yubin Tao, Juzhou Ben, Teng Dove, Martin T. College of Computer Science Sichuan University Sichuan Chengdu610065 China Department of Physics School of Sciences Wuhan University of Technology 205 Luoshi Road Hubei Hong-shan district Wuhan430070 China School of Mechanical Engineering Dongguan University of Technology Guangdong Dongguan523830 China School of Physical and Chemical Sciences Queen Mary University of London Mile End Road LondonE1 4NS United Kingdom Shanghai Synchrotron Radiation Facility Shanghai Advanced Research Institute Chinese Academy of Sciences 99 Haike Road Shanghai201210 China Institute of Molecules Plus Tianjin University Tianjin300072 China ISIS Neutron and Muon Facility Rutherford Appleton Laboratory Harwell Campus Oxfordshire DidcotOX11 0QX United Kingdom Spallation Neutron Source Science Center Guangdong Dongguan523803 China Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Zhejiang Eng. Lab. for Green Syntheses and Applications of Fluorine-Containing Specialty Chemicals Institute of Advanced Fluorine-Containing Materials Zhejiang Normal University Jinhua321004 China Key Laboratory of the Ministry of Education for Advanced Catalysis Materials Institute of Physical Chemistry Zhejiang Normal University Jinhua321004 China

We demonstrate that the amorphous material PAF-1, C[(C6H4)2]2forms a continuous random network in which tetrahedral carbon sites are connected by 4,4'-biphenyl linkers. Experimental neutron total scattering measurements on deuterated, hydrogenous, and null-scattering samples agree with molecular dynamics simulations based on this model. From the MD model, we are able for the first time to interrogate the atomistic structure. The small-angle scattering is consistent with Porod scattering from particle surfaces, of the form Q-4, where Q is the scattering vector. There We measure a distinct peak in the scattering at Q = 0:45 Å-1, corresponding to the first sharp diffraction peak in amorphous silica, which indicates the structural analogy between these two amorphous tetrahedral networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Molecular dynamics

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Flatband-Induced Itinerant Ferromagnetism in RbCo2Se2

arXiv

引用

arXiv 2021年

作者： Huang, Jianwei Wang, Zhicai Pang, Hongsheng Wu, Han Cao, Huibo Mo, Sung-Kwan Rustagi, Avinash Kemper, A.F. Wang, Meng Yi, Ming Birgeneau, R.J. Department of Physics and Astronomy Rice University HoustonTX77005 United States School of the Gifted Young University of Science and Technology of China Hefei Anhui230026 China Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Light Source Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Physics North Carolina State University RaleighNC27695 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States School of Physics Sun Yat-Sen University Guangzhou Guangdong510275 China Department of Physics University of California BerkeleyCA94720 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

ACo2Se2 (A=K,Rb,Cs) is a homologue of the iron-based superconductor, AFe2Se2. From a comprehensive study of RbCo2Se2 via measurements of magnetization, transport, neutron diffraction, angle-resolved photoemission spectroscopy, and first-principle calculations, we identify a ferromagnetic order accompanied by an orbital-dependent spin-splitting of the electronic dispersions. Furthermore, we identify the ordered moment to be dominated by a dx2−y2 flatband near the Fermi level, which exhibits the largest spin splitting across the ferromagnetic transition, suggesting an itinerant origin of the ferromagnetism. In the broader context of the iron-based superconductors, we find this dx2−y2 flatband to be a common feature in the band structures of both iron-chalcogenides and iron-pnictides, accessible via heavy electron doping. Copyright © 2021, The Authors. All rights reserved.

关键词： Selenium compounds

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