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arXiv 2019年

作者： Pieper, Konstantin Walter, Daniel Oak Ridge National Laboratory Computer Science and Mathematics Division One Bethel Valley Road P.O. Box 2008 MS-6211 Oak RidgeTN37831 United States Johann Radon Institute for Computational and Applied Mathematics Ã-AW AltenbergerstraÃ e 69 Linz4040 Austria

A class of generalized conditional gradient algorithms for the solution of optimization problem in spaces of Radon measures is presented. The method iteratively inserts additional Dirac-delta functions and optimizes the corresponding coefficients. Under general assumptions, a sub-linear $\mathcal{O}(1/k)$ rate in the objective functional is obtained, which is sharp in most cases. To improve efficiency, one can fully resolve the finite-dimensional subproblems occurring in each iteration of the method. We provide an analysis for the resulting procedure: Under a structural assumption on the optimal solution, a linear $\mathcal{O}(\zeta^k)$ convergence rate is obtained *** Codes 46E27, 65J22, 65K05, 90C25, 49M05 Copyright © 2019, The Authors. All rights reserved.

关键词： Iterative methods

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A methodology for gamifying of the educational process

A methodology for gamifying of the educational process

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2018 IEEE Global Engineering Education Conference - Emerging Trends and Challenges of Engineering Education, EDUCON 2018

作者： Mozgaleva, Polina Zamyatina, Oxana Mozgaleva, Alena Da Costa Brito Cabral, Pedro Division for Information Technology Tomsk Polytechnic University Tomsk Russia Institute of Applied Mathematics and Computer Science Tomsk State University Tomsk Russia Nova Information Management School Universidade Nova de Lisboa Lisboa Portugal

ISBN: (纸本)9781538629574

In this research study, the authors designed, proposed, tested, and partially re-searched a methodology for the gamification of academic disciplines through the example of the Elite Engineering Education (EEE) program in Tomsk Polytechnic University (TPU). Gamification implies augmenting the process of training in academic disciplines by introducing game elements that motivate students to gain new knowledge, develop their competencies, and, in the end, improve a university graduate's knowledge and skill levels. © 2018 IEEE.

关键词： Students

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Regional assessment of general education quality in Tomsk region

Regional assessment of general education quality in Tomsk re...

引用

2018 IEEE Global Engineering Education Conference - Emerging Trends and Challenges of Engineering Education, EDUCON 2018

ISBN: (纸本)9781538629574

From 1998 Tomsk region holds the regional monitoring research with the goal of receiving actual information on the condition and dynamics of the quality of education, and also the liquidation of lacks in the knowledge of school learners (hereinafter learners) of educational institutions of the system of general education in Tomsk region. The article provides information on the stages of organization and provision of the regional monitoring;the model of the analysis and interpretation of the received data is demonstrated for further application of the monitoring research results by educational organizations of Tomsk region. © 2018 IEEE.

关键词： Quality control

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Roadmap on Nonlocality in Photonic Materials and Metamaterials

arXiv

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arXiv 2025年

作者： Monticone, Francesco Mortensen, N. Asger Fernández-Domínguez, Antonio I. Luo, Yu Tserkezis, Christos Khurgin, Jacob B. Shahbazyan, Tigran V. Chaves, André J. Peres, Nuno M.R. Wegner, Gino Busch, Kurt Hu, Huatian della Sala, Fabio Zhang, Pu Ciracì, Cristian Aizpurua, Javier Babaze, Antton Borisov, Andrei G. Chen, Xue-Wen Christensen, Thomas Yan, Wei Yang, Yi Hohenester, Ulrich Huber, Lorenz Wubs, Martijn de Liberato, Simone Gonçalves, P.A.D. de Abajo, F. Javier García Hess, Ortwin Tarasenko, Illya Cox, Joel D. Jelver, Line Dias, Eduardo J.C. Sánchez, Miguel Sánchez Margetis, Dionisios Gómez-Santos, Guillermo Stauber, Tobias Tretyakov, Sergei Simovski, Constantin Pakniyat, Samaneh Gómez-Díaz, J. Sebastián Bondarev, Igor V. Biehs, Svend-Age Boltasseva, Alexandra Shalaev, Vladimir M. Krasavin, Alexey V. Zayats, Anatoly V. Alù, Andrea Song, Jung-Hwan Brongersma, Mark L. Levy, Uriel Long, Olivia Y. Guo, Cheng Fan, Shanhui Bozhevolnyi, Sergey I. Overvig, Adam Prudêncio, Filipa R. Silveirinha, Mário G. Gangaraj, S. Ali Hassani Argyropoulos, Christos Huidobro, Paloma A. Galiffi, Emanuele Yang, Fan Pendry, John B. Miller, David A.B. School of Electrical and Computer Engineering Cornell University IthacaNY14853 United States POLIMA Center for Polariton-driven Light–Matter Interactions University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 Odense MDK-5230 Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid MadridE-28049 Spain Universidad Autónoma de Madrid MadridE-28049 Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Physics Jackson State University JacksonMS39217 United States Department of Physics Aeronautics Institute of Technology SP So Jos dos Campos12228-900 Brazil Departamento de Física Universidade do Minho BragaP-4710-057 Portugal Av Mestre José Veiga Braga4715-330 Portugal Max-Born-Institut Berlin12489 Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik Berlin12489 Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 Arnesano73010 Italy Via Monteroni Campus Unisalento Lecce73100 Italy School of physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan430074 China Donostia International Physics Center DIPC Donostia-San Sebastián20018 Spain Ikerbasque Basque Foundation for Science Bilbao48009 Spain Department of Electricity and Electronics University of the Basque Country Leioa48940 Spain Department of Applied Physics University of the Basque Country Donostia20018 Spain Université Paris-Saclay CNRS Institut des Scienc

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally made in optical material modeling. On one end, the growing interest in plasmonic, polaritonic and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces–can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In the case of metasurfaces, in particular, nonlocality engineering has become a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward–toward new scientific discoveries and technological advancements. Copyright © 2025, The Authors. All rights reserved.

关键词： Nanocrystals

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Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

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International Journal of Medical Informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management of Medical Sciences Kyushu University Fukuoka Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies of Medical Sciences Kyushu University Fukuoka Japan

Background: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. Methods: A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3–6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. Results: The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79–0.92) and 0.91 (95% CI: 0.84–0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Conclusion: Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical dec

关键词： Logistic regression

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Development of Quantum Interconnects (QuICs) for Next-Generation Information Technologies

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PRX Quantum 2021年第1期2卷 017002-017002页

作者： David Awschalom Karl K. Berggren Hannes Bernien Sunil Bhave Lincoln D. Carr Paul Davids Sophia E. Economou Dirk Englund Andrei Faraon Martin Fejer Saikat Guha Martin V. Gustafsson Evelyn Hu Liang Jiang Jungsang Kim Boris Korzh Prem Kumar Paul G. Kwiat Marko Lončar Mikhail D. Lukin David A.B. Miller Christopher Monroe Sae Woo Nam Prineha Narang Jason S. Orcutt Michael G. Raymer Amir H. Safavi-Naeini Maria Spiropulu Kartik Srinivasan Shuo Sun Jelena Vučković Edo Waks Ronald Walsworth Andrew M. Weiner Zheshen Zhang Pritzker School of Molecular Engineering University of Chicago Chicago Illinois 60637 USA Department of Electrical Engineering and Computer Science MIT Cambridge Massachusetts 02139 USA School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics Colorado School of Mines 1500 Illinois Street Golden Colorado 80401 USA Photonic & Phononic Microsystems Sandia National Laboratory Albuquerque New Mexico 87185 USA Department of Physics Virginia Tech Blacksburg Virginia 24061 USA T .J. Watson Laboratory of Applied Physics California Institute of Technology Pasadena California 91125 USA Kavli Nanoscience Institute California Institute of Technology Pasadena California 91125 USA E.L. Ginzton Laboratory Stanford University Stanford California 94305 USA College of Optical Sciences The University of Arizona Tucson Arizona 85721 USA Department of Electrical and Computer Engineering The University of Arizona Tucson Arizona 85721 USA Department of Applied Mathematics The University of Arizona Tucson Arizona 85721 USA Raytheon BBN Technologies Cambridge Massachusetts 02138 USA John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA Harvard Quantum Initiative (HQI) Harvard University Cambridge Massachusetts 02138 USA Department of Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA IonQ Inc. College Park Maryland 20740 USA Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA Department of Electrical and Computer Engineering Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Physics University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA IQUIST University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Physics Harvard Universi

Just as “classical” information technology rests on a foundation built of interconnected information-processing systems, quantum information technology (QIT) must do the same. A critical component of such systems is the “interconnect,” a device or process that allows transfer of information between disparate physical media, for example, semiconductor electronics, individual atoms, light pulses in optical fiber, or microwave fields. While interconnects have been well engineered for decades in the realm of classical information technology, quantum interconnects (QuICs) present special challenges, as they must allow the transfer of fragile quantum states between different physical parts or degrees of freedom of the system. The diversity of QIT platforms (superconducting, atomic, solid-state color center, optical, etc.) that will form a “quantum internet” poses additional challenges. As quantum systems scale to larger size, the quantum interconnect bottleneck is imminent, and is emerging as a grand challenge for QIT. For these reasons, it is the position of the community represented by participants of the NSF workshop on “Quantum Interconnects” that accelerating QuIC research is crucial for sustained development of a national quantum science and technology program. Given the diversity of QIT platforms, materials used, applications, and infrastructure required, a convergent research program including partnership between academia, industry, and national laboratories is required.

关键词： Cold atoms & matter waves Nonlinear optics Optics & lasers Photonics Quantum communication Quantum computation Quantum error correction Quantum information architectures & platforms Quantum information processing Quantum metrology Quantum optics Atomic systems Optical materials & elements Quantum dots

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The First 18 Months of JGR: MLC

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第2期2卷

作者： Enrico Camporeale Raffaele Marino John Rundle Doris Folini Yangkang Chen Donald D. Lucas Xiaofeng Li Thomas E. Berger Geoffrey C. Fox Yixin Wen Chaopeng Shen Renata Wentzcovitch School of Physical and Chemical Sciences Queen Mary University of London London UK Space Weather Technology Research and Education Center University of Colorado Boulder CO USA Laboratoire de Mécanique des Fluides et d’Acoustique CNRS - École Centrale de Lyon Écully France Department of Physics and Astronomy Department of Earth and Planetary Sciences University of California Davis CA USA Department of Environmental System Science Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland Bureau of Economic Geology Jackson School of Geosciences The University of Texas at Austin Austin TX USA Atmospheric Earth and Energy Division Lawrence Livermore National Laboratory Livermore CA USA Institute of Oceanology Chinese Academy of Sciences Qingdao P. R. China Biocomplexity Institute and Computer Science Department University of Virginia Charlottesville VA USA Department of Geography University of Florida Gainesville FL USA Civil and Environmental Engineering The Pennsylvania State University University Park PA USA Department of Applied Physics and Applied Mathematics Department of Earth and Environmental Sciences Columbia University New York NY USA

As editors of Journal of Geophysical Research: Machine Learning and Computation , we are delighted to celebrate the successes of our first 18 months. The journal's editors reflect on the first year and a half since its founding They express concern for the restriction or removal of publicly funded data sources Our effort focuses on ensuring that software shared in FAIR repositories is not only available, but also functional and citable

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Geometry based data generation 32

Geometry based data generation

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32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Lindenbaum, Ofir Wolf, Guy Stanley, Jay S. Krishnaswamy, Smita Applied Mathematics Program Yale University New HavenCT06511 United States Computational Biology and Bioinformatics Program Yale University New HavenCT06510 United States Departments of Genetics and Computer Science Yale University New HavenCT06510 United States

We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis. © 2018 Curran Associates Inc..All rights reserved.

关键词： Genes

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Manifold learning-based methods for analyzing single-cell RNA-sequencing data

引用

Current Opinion in Systems Biology 2018年 7卷 36-46页

作者： Moon, Kevin R. Stanley, Jay S. Burkhardt, Daniel van Dijk, David Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New Haven CT United States Applied Mathematics Program Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States

Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data. © 2017 Published by Elsevier Ltd.

关键词： Data mining Gene regulatory networks Imputation Manifold learning Pseudotime trends Single cell RNA sequencing

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Frequency-Resolved Optical Gating Recovery via Smoothing Gradient

arXiv

引用

arXiv 2019年

作者： Pinilla, Samuel Bendory, Tamir Eldar, Yonina C. Arguello, Henry Department of Electrical Engineering Universidad Industrial de Santander BucaramangaSantander680002 Colombia Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Weizmann Institute of Science Rehovot7610001 Israel Department of Computer Science Universidad Industrial de Santander BucaramangaSantander680002 Colombia

Frequency-resolved optical gating (FROG) is a popular technique for complete characterization of ultrashort laser pulses. The acquired data in FROG, called FROG trace, is the Fourier magnitude of the product of the unknown pulse with a time-shifted version of itself, for several different shifts. To estimate the pulse from the FROG trace, we propose an algorithm that minimizes a smoothed non-convex least-squares objective function. The method consists of two steps. First, we approximate the pulse by an iterative spectral algorithm. Then, the attained initialization is refined based upon a sequence of block stochastic gradient iterations. The algorithm is theoretically simple, numerically scalable, and easy-to-implement. Empirically, our approach outperforms the state-of-the-art when the FROG trace is incomplete, that is, when only few shifts are recorded. Simulations also suggest that the proposed algorithm exhibits similar computational cost compared to a state-of-the-art technique for both complete and incomplete data. In addition, we prove that in the vicinity of the true solution, the algorithm converges to a critical point. A Matlab implementation is publicly available at https://***/samuelpinilla/FROG. Copyright © 2019, The Authors. All rights reserved.

关键词： Ultrashort pulses

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