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A Neuroimaging ML-Based Framework for Anosmia Grading in Covid-19

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A Neuroimaging ML-Based Framework for Anosmia Grading in Cov...

IEEE International Symposium on Biomedical Imaging

作者： Hossam Magdy Balaha Mayada Elgendy Ahmed Alksas Mohamed Shehata Norah Saleh Alghamdi Fatma Taher Mohammed Ghazal Mahitab Ghoneim Eslam Hamed Fatma Sherif Ahmed Elgarayhi Mohammed Sallah Mohamed Abdelbadie Salem Elsharawy Kamal Harpal Sandhu Ayman El-Baz Department of Bioengineering J.B. Speed School of Engineering University of Louisville Louisville USA Applied Theoretical Physics Research Group Physics Department Faculty of Science Mansoura University Egypt Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia The College of Technological Innovation Zayed University Dubai UAE Electrical Computer and Biomedical Engineering Depatrment Abu Dhabi University UAE Department of Radiology Faculty of Medicine Mansoura University Egypt Otolaryngology Department Faculty of Mediine Mansoura University Egypt Higher Institute of Engineering and Technology Egypt

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

SARS-CoV-2 is the cause of the acute respiratory illness COVID-19. In order to identify whether a COVID-19 patient has normal, moderate, or severe anosmia, this study suggests a computer-assisted diagnostic system. It utilizes fluid-attenuated inversion recovery Magnetic Resonance Imaging and Diffusion Tensor Imaging to extract the olfactory nerve’s appearance, morphology, and diffusivity markers. The suggested CAD system consists of the following stages: (1) extract appearance markers, morphology markers, and diffusivity markers, (2) apply markers fusion, and (3) select the most promising classifier as the basis for the decision and the relevant performance indicators. The current work is unique in that it uses an ensemble of the trained, tuned, and improved ML classifiers to identify OB anosmia using majority voting. From the fusion tests using different folds, stacking boosted the weight accuracy, weighted precision, weighted specificity, weighted sensitivity, and weighted F1-score to 85.30%, 80%, 89.50%, 78.90%, and 78.30% respectively. Regarding the various performance criteria, the LGBM outperforms other ML classifiers.

关键词： COVID-19 Neuroimaging Sensitivity Shape Stacking Olfactory Morphology

Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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Engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

Giant long-range dipole-dipole interactions in a plasmonic lattice

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Giant long-range dipole-dipole interactions in a plasmonic l...

Frontiers in Optics + Laser science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Boddeti, Ashwin K. Guan, Jun Sentz, Tyler Juarez, Xitali Newman, Ward Cortes, Cristian Odom, Teri W. Jacob, Zubin School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Intel Corporation BeavertonOR97007 United States Center for Nanoscale Materials Argonne National Laboratory LemontIL60439 United States Graduate Program in Applied Physics Department of Materials Science and Engineering Department of Chemistry Northwestern University EvanstonIL60208 United States

We observe long-range dipole-dipole interactions in a plasmonic lattice. Fluorescence lifetime measurements show density-dependent non-exponential decay dynamics over 800nm mean nearest-neighbour separation distances ... 详细信息

ISBN: (纸本)9781557528209

关键词： Plasmonics

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

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arXiv 2023年

作者： Baker, Megan M. New, Alexander Aguilar-Simon, Mario Al-Halah, Ziad Arnold, Sébastien M.R. Ben-Iwhiwhu, Ese Brna, Andrew P. Brooks, Ethan Brown, Ryan C. Daniels, Zachary Daram, Anurag Delattre, Fabien Dellana, Ryan Eaton, Eric Fu, Haotian Grauman, Kristen Hostetler, Jesse Iqbal, Shariq Kent, Cassandra Ketz, Nicholas Kolouri, Soheil Konidaris, George Kudithipudi, Dhireesha Learned-Miller, Erik Lee, Seungwon Littman, Michael L. Madireddy, Sandeep Mendez, Jorge A. Nguyen, Eric Q. Piatko, Christine Pilly, Praveen K. Raghavan, Aswin Rahman, Abrar Ramakrishnan, Santhosh Kumar Ratzlaff, Neale Soltoggio, Andrea Stone, Peter Sur, Indranil Tang, Zhipeng Tiwari, Saket Vedder, Kyle Wang, Felix Xu, Zifan Yanguas-Gil, Angel Yedidsion, Harel Yu, Shangqun Vallabha, Gautam K. Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd. LaurelMD20723 United States Teledyne Scientific Company Intelligent Systems Laboratory 19 T.W. Alexander Drive NC27709 United States Department of Computer Science University of Texas at Austin AustinTX United States Department of Computer Science University of Southern California Los AngelesCA United States Department of Computer Science Loughborough University Loughborough United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States SRI International 201 Washington Rd PrincetonNJ United States University of Texas at San Antonio San AntonioTX United States Department of Computer Science University of Massachusetts Amherst AmherstMA United States Sandia National Laboratories AlbuquerqueNM United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Computer Science Brown University ProvidenceRI United States Information and Systems Sciences Laboratory HRL Laboratories 3011 Malibu Canyon Road MalibuCA90265 United States Department of Computer Science Vanderbilt University NashvilleTN United States Argonne National Laboratory 9700 S Cass Ave LemontIL United States

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Low Temperature Spin Seebeck Effect in Non-Magnetic Vanadium Dioxide

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arXiv 2023年

作者： Luo, Renjie Legvold, Tanner J. Chen, Liyang Navarro, Henry Basaran, Ali C. Hong, Deshun Liu, Changjiang Bhattacharya, Anand Schuller, Ivan K. Natelson, Douglas Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Rice Quantum Institute Rice University HoustonTX77005 United States Department of Physics Center for Advanced Nanoscience University of California La Jolla San DiegoCA92093 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Electrical and Computer Engineering Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in VO2 films that grows as the temperature decreases below 50 K. The field and temperature dependent SSE voltage is qualitatively consistent with a general model of paramagnetic SSE response and inconsistent with triplet spin transport. Quantitative estimates find a spin Seebeck coefficient comparable in magnitude to that observed in strongly magnetic materials. The microscopic nature of the magnetic excitations in VO2 requires further examination. Copyright © 2023, The Authors. All rights reserved.

关键词： Paramagnetism

Complexity-Constrained Quantum Thermodynamics

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PRX Quantum 2025年第1期6卷 010346-010346页

作者： Anthony Munson Naga Bhavya Teja Kothakonda Jonas Haferkamp Nicole Yunger Halpern Jens Eisert Philippe Faist Joint Center for Quantum Information and Computer Science NIST and University of Maryland College Park Maryland 20742 USA Department of Physics University of Maryland College Park Maryland 20742 USA Grup d’Informació Quàntica Departament de Física Universitat Autònoma de Barcelona 08193 Bellaterra (Barcelona) Spain Dahlem Center for Complex Quantum Systems Freie Universität Berlin 14195 Berlin Germany School of Engineering and Applied Sciences Harvard University Massachusetts 02134 USA Institute for Physical Science and Technology University of Maryland College Park Maryland 20742 USA

Quantum complexity measures the difficulty of realizing a quantum process, such as preparing a state or implementing a unitary. We present an approach to quantifying the thermodynamic resources required to implement a process if the process’s complexity is restricted. We focus on the prototypical task of information erasure, or Landauer erasure, wherein an n-qubit memory is reset to the all-zero state. We show that the minimum thermodynamic work required to reset an arbitrary state in our model, via a complexity-constrained process, is quantified by the state’s complexity entropy. The complexity entropy therefore quantifies a trade-off between the work cost and complexity cost of resetting a state. If the qubits have a nontrivial (but product) Hamiltonian, the optimal work cost is determined by the complexity relative entropy. The complexity entropy quantifies the amount of randomness a system appears to have to a computationally limited observer. Similarly, the complexity relative entropy quantifies such an observer’s ability to distinguish two states. We prove elementary properties of the complexity (relative) entropy. In a random circuit—a simple model for quantum chaotic dynamics—the complexity entropy transitions from zero to its maximal value around the time corresponding to the observer’s computational-power limit. Also, we identify information-theoretic applications of the complexity entropy. The complexity entropy quantifies the resources required for data compression if the compression algorithm must use a restricted number of gates. We further introduce a complexity conditional entropy, which arises naturally in a complexity-constrained variant of information-theoretic decoupling. Assuming that this entropy obeys a conjectured chain rule, we show that the entropy bounds the number of qubits that one can decouple from a reference system, as judged by a computationally bounded referee. Overall, our framework extends the resource-theoretic approach to thermo

关键词： Qubits

On ordered Ramsey numbers of tripartite 3-uniform hypergraphs

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arXiv 2022年

作者： Balko, Martin Vizer, Máté Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Czech Republic Alfréd Rényi Institute of Mathematics ELKH Budapest Hungary Department of Computer Science and Information Theory Budapest University of Technology and Economic Hungary

For an integer k ≥ 2, an ordered k-uniform hypergraph H = (H, N(k) on N vertices contains a blue copy of H or a red copy of G. The ordered Ramsey numbers are quite extensively studied for ordered graphs, but little is known about ordered hypergraphs of higher uniformity. We provide some of the first nontrivial estimates on ordered Ramsey numbers of ordered 3-uniform hypergraphs. In particular, we prove that for all d, n ∈ and for every ordered 3-uniform hypergraph H on n vertices with maximum degree d and with interval chromatic number 3 there is an Ε = Ε(d) > 0 such that (Equation presented). In fact, we prove this upper bound for the number (Equation presented), where G is an ordered 3-uniform hypergraph with n vertices and maximum degree d and K3(3)(n) is the ordered complete tripartite hypergraph with consecutive color classes of size n. We show that this bound is not far from the truth by proving (Equation presented) for some fixed ordered 3-uniform hypergraph H. © 2022, CC BY.

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Recent progress of integrated circuits and optoelectronic chips

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science China(information sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit photonic neural computing semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic

Increasing the mobility and power-electronics figure of merit of AlGaN with atomically thin AlN/GaN digital-alloy superlattices

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arXiv 2022年

作者： Pant, Nick Lee, Woncheol Sanders, Nocona Kioupakis, Emmanouil Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Alloy scattering in random AlGaN alloys drastically reduces the electron mobility and therefore the power-electronics figure of merit. As a result, Al compositions greater than 75% are required to obtain even a two-fold increase of the Baliga figure of merit compared to GaN. However, beyond approximately 80% Al composition, donors in AlGaN undergo the DX transition which makes impurity doping increasingly more difficult. Moreover, the contact resistance increases exponentially with increasing Al content, and integration with dielectrics becomes difficult due to the upward shift of the conduction band. Atomically thin superlattices of AlN and GaN, also known as digital alloys, are known to grow experimentally under appropriate growth conditions. These chemically ordered nanostructures could offer significantly enhanced figure of merit compared to their random-alloy counterparts due to the absence of alloy scattering, as well as better integration with contact metals and dielectrics. In this work, we investigate the electronic structure and phonon-limited electron mobility of atomically thin AlN/GaN digital-alloy superlattices using first-principles calculations based on density-functional and many-body perturbation theory. The band gap of the atomically thin superlattices reaches 4.8 eV, and the in-plane (out-of-plane) mobility is 369 (452) cm2 V-1 s-1. Using the modified Baliga figure of merit that accounts for the dopant ionization energy, we demonstrate that atomically thin AlN/GaN superlattices with a monolayer sublattice periodicity have the highest modified Baliga figure of merit among several technologically relevant ultra-wide band-gap materials, including random AlGaN, β-Ga2O3, cBN, and diamond. Copyright © 2022, The Authors. All rights reserved.

关键词： Aluminum gallium nitride