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QCaMP: A 4-Week Summer Camp Introducing High School Students to Quantum Information science and Technology

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arXiv 2025年

作者： Ivory, Megan Balewski, Jan Bettale, Alisa Brewer, Jaden Boren, Rachel Camps, Daan Hackett, Lisa Juarez, Martin Kononov, Alina Lee, Kan-Heng Long, Maryanne Mounce, Andy Mniszewski, Susan Naik, Ravi K. Pakin, Scott Sanchez, Ernesto Sandia National Laboratories AlbuquerqueNM United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Office of Government and Community Relations Lawrence Berkeley National Laboratory BerkeleyCA United States Quantum Photonics Lab Northern Arizona University FlagstaffAZ United States College of Health Education and Social Transformation New Mexico State University Las CrucesNM United States Bourns College of Engineering University of California Riverside RiversideCA United States Quantum Algorithms and Applications Collaboratory Sandia National Laboratories AlbuquerqueNM United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States Center for Integrated Nanotechnology Sandia National Laboratories AlbuquerqueNM United States Los Alamos National Laboratory Los AlamosNM United States Applied Computer Science Los Alamos National Laboratory Los AlamosNM United States

The 2024 Quantum Computing, Math and Physics Camp (QCaMP) for Students was a 4-week long summer camp aimed at introducing high school students to quantum concepts and careers, including applications spanning quantum computing, sensing, and communication. The program ran for 7 hours/day, Monday–Friday, July 1–26, and included hands-on modules and activities, professional development, and project-based learning. Here we provide details on the camp curriculum and outcomes based on pre and post knowledge and attitudes assessments. © 2025, CC BY.

关键词： Qubits

A dynamical system model for predicting gene expression from the epigenome

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arXiv 2020年

作者： Brunner, James D. Kim, Jacob Downing, Timothy Mjolsness, Eric Kober, Kord M. Center for Individualized Medicine Microbiome Program Mayo Clinic RochesterMN55901 United States Department of Biological Sciences Columbia University New YorkNY10027 United States Henry Samueli School of Engineering University of California Irvine IrvineCA92697 United States Department of Computer Science and Mathematics University of California Irvine IrvineCA92697 United States Department of Physiological Nursing Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA94143 United States

Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigenetic processes (e.g., DNA methylation). Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data. In contrast, dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN) which can also serve as a mechanistic hypothesis. Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN. We provide an evaluation of the model using real patient data and a GRN created from robust reference sources. Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at https://***/kordk/stoch_epi_lib. Copyright © 2020, The Authors. All rights reserved.

关键词： Gene expression

Roadmap on Nonlocality in Photonic Materials and Metamaterials

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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

Geometry based data generation 18

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Geometry based data generation

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Ofir Lindenbaum Jay S. Stanley, III Guy Wolf Smita Krishnaswamy Applied Mathematics Program Yale University New Haven CT Computational Biology & Bioinformatics Program Yale University New Haven CT Departments of Genetics & Computer Science Yale University New Haven CT

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.

关键词：

Data quality measures and data cleansing for research information systems

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arXiv 2019年

作者： Azeroual, Otmane Saake, Gunter Abuosba, Mohammad Berlin10117 Germany Otto-Von-Guericke University Magdeburg Department of Computer Science Institute for Technical and Business Information Systems Database Research Group P.O. Box 4120 Magdeburg39106 Germany University of Applied Sciences Htw Berlin Study Program Computational Science and Engineering Wilhelminenhofstrasse 75a Berlin12459 Germany

The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased. In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the research information. Copyright © 2019, The Authors. All rights reserved.

关键词： Standardization

Nonlocality in photonic materials and metamaterials: roadmap

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Optical Materials Express 2025年第7期15卷 1544-1709页

作者： Francesco Monticone N Asger Mortensen Antonio I Fernández-Domínguez Yu Luo Xuezhi Zheng Christos Tserkezis Jacob B Khurgin Tigran V Shahbazyan André J Chaves Nuno M R Peres Gino Wegner Kurt Busch Huatian Hu Fabio Della Sala Pu Zhang Cristian Ciracì Javier Aizpurua Antton Babaze Andrei G Borisov Xue-Wen Chen Thomas Christensen Wei Yan Yi Yang Ulrich Hohenester Lorenz Huber Martijn Wubs Simone De Liberato P A D Gonçalves F Javier García de Abajo Ortwin Hess Illya Tarasenko Joel D Cox Line Jelver Eduardo J C Dias Miguel Sánchez Sánchez Dionisios Margetis Guillermo Gómez-Santos Igor M Vasilevskiy Tobias Stauber Sergei Tretyakov Constantin Simovski Samaneh Pakniyat J Sebastián Gómez-Díaz Igor V Bondarev Svend-Age Biehs Alexra Boltasseva Vladimir M Shalaev Alexey V Krasavin Anatoly V Zayats Andrea Alù Jung-Hwan Song Mark L Brongersma Uriel Levy Olivia Y Long Cheng Guo Shanhui Fan Sergey I Bozhevolnyi Adam Overvig Filipa R Prudêncio Mário G Silveirinha S Ali Hassani Gangaraj Christos Argyropoulos Paloma A Huidobro Emanuele Galiffi Fan Yang John B Pendry David A B Miller School of Electrical and Computer Engineering Cornell University Ithaca New York 14853 USA POLIMA—Center for Polariton-driven Light-Matter Interactions University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid E-28049 Madrid Spain Condensed Matter Physics Center (IFIMAC) Universidad Autónoma de Madrid E-28049 Madrid Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China WaveCoRE Division Department of Electrical Engineering KU Leuven Leuven B-3001 Belgium Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland 21218 USA Department of Physics Jackson State University Jackson Mississippi 39217 USA Department of Physics Aeronautics Institute of Technology 12228-900 So Jos dos Campos SP Brazil Centro de Física (CF-UM-UP) and Departamento de Física Universidade do Minho P-4710-057 Braga Portugal International Iberian Nanotechnology Laboratory (INL) Av Mestre José Veiga 4715-330 Braga Portugal Max-Born-Institut 12489 Berlin Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik 12489 Berlin Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 73010 Arnesano Italy Institute for Microelectronics and Microsystems (CNR-IMM) Via Monteroni Campus Unisalento 73100 Lecce Italy School of Physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Donostia International Physics Center DIPC 20018 Donostia-San Sebastián Spain Ikerbasque Basque Foundation fo

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 adopted in optical material modeling. 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 metasurfaces, in particular, nonlocality engineering has emerged as 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 and metamaterials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward—toward new scientific discoveries and technological *** by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License . Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

关键词： Chemical elements Localized surface plasmon resonance Material properties Near field scanning optical microscopy Optical materials Thin films

3D Tortuosity: a Morphological Characterization of the Central Sulcus to Differentiate Patients with Alzheimer's Disease and Controls

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3D Tortuosity: a Morphological Characterization of the Centr...

IEEE Symposium on Nuclear science (NSS/MIC)

作者： Maria Julieta Mateos Jorge Márquez Ernesto Bribiesca Universidad Nacional Autónoma de México Graduate Program in Computer Science and Engineering Ciudad Universitaria Coyoacán CDMX México Instituto de Ciencias Aplicadas y Tecnología Universidad Nacional Autónoma de México Circuito Exterior S/N Ciudad Universitaria Mexico City Universidad Nacional Autónoma de México Institute of Research in Applied Mathematics and Systems (IIMAS) Circuito Escolar 3000 Ciudad Universitaria Coyoacán CDMX

Magnetic Resonance Images of the brain provide detailed anatomical information that allows morphological analysis of the different brain structures. The analysis of the cortical folding patterns variation is inspiring great interest, as this relates to cognitive function. The sulci are a depression in the cerebral cortex and represent ≈ 70% of the cortical surface. The sulcal width, depth and cortical thickness are the most frequent morphological descriptors applied to the sulci. In this work, the 3D tortuosity is proposed as new descriptor to capture information about the twist and turns of the sulci. 1 The 3D tortuosity of the central sulcus of both hemispheres was measured using the Minimal Interval Resonance Imaging in the Alzheimer's Disease (MIRIAD) database, for a set of high-resolution MRI of 66 subjects: 43 patients with Alzheimer Disease (AD) and 23 control subjects. As it is known AD causes significant gray matter loss which cause morphological changes in the cortical structure. It is expected for the tortuosity to capture these changes and serve as a biomarker to differentiate between populations. The result of the Wilcoxon tests show that the tortuosity values of the central sulci are significantly larger for the AD patients (p < 0.05) for the left hemisphere, which is consistent with the hypothesis. The evaluation of the proposed tortuosity measurement of the Central Sulcus as a potential biomarker was performed and the results indicate its effectiveness to extract additional anatomical information and discriminate between AD patients and Controls.

关键词： Brain Three-dimensional displays Shape Shape measurement Sociology Surface morphology Skeleton

Modeling temporal networks with bursty activity patterns of nodes and links

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Physical Review Research 2020年第2期2卷 023073-023073页

作者： Takayuki Hiraoka Naoki Masuda Aming Li Hang-Hyun Jo Department of Computer Science Aalto University Espoo 00076 Finland Asia Pacific Center for Theoretical Physics Pohang 37673 Republic of Korea Department of Mathematics University at Buffalo State University of New York Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New York 14260-5030 USA Department of Zoology University of Oxford Oxford OX1 3PS United Kingdom Department of Biochemistry University of Oxford Oxford OX1 3QU United Kingdom Department of Physics The Catholic University of Korea Bucheon 14662 Republic of Korea

The concept of temporal networks provides a framework to understand how the interaction between system components changes over time. In empirical communication data, we often detect non-Poissonian, so-called bursty behavior in the activity of nodes as well as in the interaction between nodes. However, such reconciliation between node burstiness and link burstiness cannot be explained if the interaction processes on different links are independent of each other. This is because the activity of a node is the superposition of the interaction processes on the links incident to the node, and the superposition of independent bursty point processes is not bursty in general. Here we introduce a temporal network model based on bursty node activation, and we show that it leads to heavy-tailed interevent time distributions for both node dynamics and link dynamics. Our analysis indicates that activation processes intrinsic to nodes give rise to dynamical correlations across links. Our framework offers a way to model competition and correlation between links, which is key to understanding dynamical processes in various systems.

关键词： Collective behavior in networks Dynamics of networks Scaling laws of complex systems Social systems Communication networks Multiple time scale dynamics Networks & random structures Social networks Evolving network models Network Models Non-Markovian processes Time series analysis

Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks

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Modeling Global Dynamics from Local Snapshots with Deep Gene...

International Conference on Sampling Theory and Applications (SampTA)

作者： Scott Gigante David van Dijk Kevin R. Moon Alexander Strzalkowski Guy Wolf Smita Krishnaswamy Computational Biology and Bioinformatics Program Yale University New Haven CT USA Department of Genetics Yale University New Haven CT USA Department of Mathematics and Statistics Utah State University Logan UT USA Department of Computer Science Princeton University Princeton NJ USA Department of Mathematics and Statistics Université de Montréal Montéal QC Canada

ISBN: (数字)9781728137414

ISBN: (纸本)9781728137421

Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describe analytically, "snapshot" measurements that sample the output of the system are often available. In order to model the dynamics of such systems given snapshot data, or local transitions, we present a deep neural network framework we call Dynamics Modeling Network or DyMoN. DyMoN is a neural network framework trained as a deep generative Markov model whose next state is a probability distribution based on the current state. DyMoN is trained using samples of current and next-state pairs, and thus does not require longitudinal measurements. We show the advantage of DyMoN over shallow models such as Kalman filters and hidden Markov models, and other deep models such as recurrent neural networks in its ability to embody the dynamics (which can be studied via perturbation of the neural network) and generate longitudinal hypothetical trajectories. We perform three case studies in which we apply DyMoN to different types of biological systems and extract features of the dynamics in each case by examining the learned model.

关键词： Hidden Markov models Trajectory Neural networks Biological system modeling Data models Markov processes