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Quantum coarsening and collective dynamics on a programmable simulator

Nature 2025年第8049期638卷 86-92页

作者： Manovitz, Tom Li, Sophie H. Ebadi, Sepehr Samajdar, Rhine Geim, Alexandra A. Evered, Simon J. Bluvstein, Dolev Zhou, Hengyun Koyluoglu, Nazli Ugur Feldmeier, Johannes Dolgirev, Pavel E. Maskara, Nishad Kalinowski, Marcin Sachdev, Subir Huse, David A. Greiner, Markus Vuletić, Vladan Lukin, Mikhail D. Department of Physics Harvard University Cambridge MA United States Department of Physics Princeton University Princeton NJ United States Princeton Center for Theoretical Science Princeton University Princeton NJ United States QuEra Computing Inc. Boston MA United States Harvard Quantum Initiative Harvard University Cambridge MA United States Department of Physics and Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA United States Department of Physics Massachusetts Institute of Technology Cambridge MA United States

Understanding the collective quantum dynamics of non-equilibrium many-body systems is an outstanding challenge in quantum science. In particular, dynamics driven by quantum fluctuations are important for the formation of exotic quantum phases of matter¹, fundamental high-energy processes², quantum metrology^3,4 and quantum algorithms⁵. Here we use a programmable quantum simulator based on Rydberg atom arrays to experimentally study collective dynamics across a (2+1)-dimensional Ising quantum phase transition. After crossing the quantum critical point, we observe a gradual growth of correlations through coarsening of antiferromagnetically ordered domains⁶. By deterministically preparing and following the evolution of ordered domains, we show that the coarsening is driven by the curvature of domain boundaries, and find that the dynamics accelerate with proximity to the quantum critical point. We quantitatively explore these phenomena and further observe long-lived oscillations of the order parameter, corresponding to an amplitude (‘Higgs’) mode⁷. These observations offer a viewpoint into emergent collective dynamics in strongly correlated quantum systems and non-equilibrium quantum processes. © The Author(s) 2025.

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Long-term Integration Ability of the Submillimeter Wave Astronomy Satellite(SWAS)Spectral Line Receivers

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research in Astronomy and Astrophysics 2024年第6期24卷 315-322页

作者： Bing-Ru Wang Di Li Paul F.Goldsmith Jingwen Wu Chao-Wei Tsai Rudolf Schieder Gary J.Melnick National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311100China Jet Propulsion Laboratory California Institute of TechnologyPasadenaCA 91109USA Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China KOSMA I.Physik.InstitutUniversität zu KölnZülpicher Str.7750937 KölnGermany Center for Astrophysics|Harvard&Smithsonian 60 Garden StreetMS 66CambridgeMA 02138USA

The Submillimeter Wave Astronomy Satellite(SWAS)was the first space telescope capable of high spectral resolution observations of terahertz spectral *** have investigated the integration ability of its two receivers and spectrometer during five and a half years of on-orbit *** CI,O_(2),H_(2)O,and^(13)CO spectra taken toward all observed Galactic sources were *** present results are based on spectra with a total integration time of up to 2.72×10~4hr(■10~8s).The noise in the spectra is generally consistent with that expected from the radiometer equation,without any sign of approaching a noise *** noise performance reflects the extremely stable performance of the passively cooled front end as well as other relevant components in the SWAS instrument throughout its mission lifetime.

关键词： telescopes space vehicles:instruments instrumentation:miscellaneous ISM:lines and bands

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High Performance computing Framework for Variable Selection on Genome-wide Association Studies

High Performance Computing Framework for Variable Selection ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Wei, Dehui Xie, Qipeng Li, Xia Jiang, Linshan National University Health System Group Technology Office Singapore National University of Singapore School of Computing Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Hong Kong University of Science and Technology IoT Thrust Information Hub Guangzhou China Eth Zurich Department of Computer Science Switzerland National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response *** address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers. © 2024 IEEE.

关键词： Confounding factors genome-wide association study structured relatedness variable selection

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Hydrodynamic Performance of Euplectella aspergillum:Simulating Real Life Conditions in the Abyss

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Communications in Computational Physics 2023年第1期33卷 273-282页

作者： Giacomo Falcucci Giorgio Amati Pierluigi Fanelli Sauro Succi Maurizio Porfiri Department of Enterprise Engineering“Mario Lucertini” University of Rome“Tor Vergata”RomeItaly Department of Physics Harvard UniversityCambridgeMAUSA High Performance Computing Department CINECA Rome SectionRomeItaly DEIM School of EngineeringUniversity of TusciaViterboItaly Italian Institute of Technology Center for Life Nano-and Neuro-ScienceRomeItaly National Research Council of Italy–Institute for Applied Computing(IAC) RomeItaly Department of Biomedical Engineering Tandon School of EngineeringNew York UniversityNew YorkNYUSA Department of Mechanical and Aerospace Engineering Tandon School of EngineeringNew York UniversityNew YorkNYUSA Center for Urban Science and Progress Tandon School of EngineeringNew York UniversityNew YorkNYUSA

We detail some of the understudied aspects of the flow inside and around the Hexactinellid Sponge Euplectella *** leveraging the flexibility of the Lattice Boltzmann Method,High Performance computing simulations are performed to dissect the complex conditions corresponding to the actual environment at the bottom of the ocean,at depths between 100 and 1,000 *** large-scale simulations unveil potential clues on the evolutionary adaptations of these deep-sea sponges in response to the surrounding fluid flow,and they open the path to future investigations at the interface between physics,engineering and biology.

关键词： Lattice Boltzmann method fluid-structure interaction sponge hydrodynamics high performance computing complex boundary conditions

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Reinforcement learning for unified allocation and patrolling in signaling games with uncertainty 20

Reinforcement learning for unified allocation and patrolling...

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20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021

作者： Venugopal, Aravind Bondi, Elizabeth Kamarthi, Harshavardhan Dholakia, Keval Ravindran, Balaraman Tambe, Milind Robert Bosch Centre for Data Science and AI Indian Institute of Technology Madras India Center for Research on Computation and Society Harvard University United States College of Computing Georgia Institute of Technology United States

ISBN: (纸本)9781713832621

Green Security Games (GSGs) have been successfully used in the protection of valuable resources such as fisheries, forests, and wildlife. Real-world deployment involves both resource allocation and subsequent coordinated patrolling with communication in the presence real-time, uncertain information. Previous game models do not address both of these stages simultaneously. Furthermore, adopting existing solution strategies is difficult since they do not scale well for larger, more complex variants of the game models. We propose a novel GSG model to address these challenges. We also present a novel algorithm, CombSGPO, to compute a defender strategy for this game model. CombSGPO performs policy search over a multidimensional, discrete action space to compute an allocation strategy that is best suited to a best-response patrolling strategy for the defender, learnt by training a multi-agent Deep QNetwork. We show via experiments that CombSGPO converges to better strategies and is more scalable than comparable approaches. From a detailed analysis of the coordination and signaling behavior learnt by CombSGPO, we find that strategic signaling emerges in the final learnt strategy. © 2021 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Xie, Yongyi Lai, Kang Geng, Xiao Song, Yijun Jiang, Linshan National University of Singapore School of Computing Singapore National University Health System Group Technology Office Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Tongji University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine China Nanyang Technological University School of Mechanical & Aerospace Engineering Singapore Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Orthopaedic Department China Zhejiang University of Finance & Economics Dongfang College China National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nalively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential. © 2024 IEEE.

关键词： Confounding factors genome-wide association study mixed model variable selection

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Molecular Oxygen Abundance in Galactic Massive Star Formation Regions Based on SWAS Observations

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research in Astronomy and Astrophysics 2024年第9期24卷 83-98页

作者： Bing-Ru Wang Di Li Paul F.Goldsmith Jingwen Wu Chao-Wei Tsai Donghui Quan Xia Zhang Junzhi Wang Gary J.Melnick Jin-Zeng Li Gary A.Fuller Jinjin Xie National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Tsinghua University Beijing 100084China University of Chinese Academy of Sciences Beijing 100049China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311100China Jet Propulsion Laboratory California Institute of TechnologyPasadenaCA 91109USA Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China Center for Astrophysics Harvard&Smithsonian60 Garden StreetMS 66CambridgeMA 02138USA Jodrell Bank Center for Astrophysics Department of Physics&AstronomyUniversity of ManchesterOxford RoadManchesterM139PLUK I.Physikalisches Institut University of CologneZülpicher Str.7750937 KölnGermany

Molecular oxygen abundance is a key parameter in understanding the chemical network of the interstellar *** estimate the molecular oxygen column density and abundance for a sample of Galactic massive star formation regions based on observations from the Submillimiter Wave Astronomy Satellite(SWAS)*** obtained an averaged O_(2)spectrum based on this sample using the(SWAS)survey data(O_(2),487.249 GHz,N=3-1,J=3-2).No emission or absorption feature is seen around the supposed central velocity with a total integration time of t_(total)=8.67×10^(3)hr and an rms noise per channel of 1.45 m *** a kinetic temperature T_(kin)=30 K,we derive the 3σupper limit of the O_(2)column density to be 3.3×10^(15)cm^(-2),close to the lowest values reported in Galactic massive star formation regions in previous *** corresponding O_(2)abundance upper limit is6.7×10^(-8),lower than all previous results based on SWAS observations and is close to the lowest reported value in massive star formation *** a galactic scale,our statistical results confirm a generally low O_(2)abundance for Galactic massive star formation *** abundance is also lower than results reported in extragalactic sources.

关键词： ISM:molecules galaxies:abundances ISM:lines and bands Galaxy:abundances

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A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history (vol 29, pg 761, 2022)

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2022年第9期29卷 1657-1657页

作者： Maurits, Marc P. Korsunsky, Ilya Raychaudhuri, Soumya Murphy, Shawn N. Smoller, JordanW Weiss, Scott T. Petukhova, Lynn M. Weng, Chunhua Wei, Wei-Qi Huizinga, Thomas W. J. Reinders, Marcel J. T. Karlson, Elizabeth W. van den Akker, Erik B. Knevel, Rachel Department of Rheumatology Leiden University Medical Center Leiden The Netherlands. Leiden Computational Biology Center Leiden University Medical Center Leiden The Netherlands. Center for Data Sciences Brigham and Women's Hospital Harvard Medical School Boston MA USA. Research Information Science and Computing Mass General Brigham Boston MA USA. Center for Precision Psychiatry Department of Psychiatry Massachusetts General Hospital Boston MA USA. Psychiatric and Neurodevelopmental Genetics Unit Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Channing Division of Network Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. The Delft Bioinformatics Lab Delft University of Technology Delft The Netherlands. Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Harvard Medical School Boston MA USA. Section of Molecular Epidemiology Leiden University Medical Center Leiden The Netherlands.

OBJECTIVE: To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects. MATERIAL AND METHODS: We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features. RESULTS: We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles. DISCUSSION: Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data. CONCLUSION: We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

关键词： ICD PhenoGraph clustering eMERGE electronic health records electronic medical records

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Predictors of Treatment Outcome in Adolescent Depression

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Current Treatment Options in Psychiatry 2021年第1期8卷 18-28页

作者： Ang, Yuen-Siang Pizzagalli, Diego A. Department of Social and Cognitive Computing Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis C16-49 Singapore 138632 Singapore Department of Psychiatry Harvard Medical School Boston MA United States Center for Depression Anxiety and Stress Research McLean Hospital Belmont MA United States

Purpose of review: Major depressive disorder is a global public health concern that is common in adolescents. Targeting this illness at the early stages of development is critical and could lead to better long-term outcomes because the adolescent brain is highly plastic and, hence, neural systems are likely to be more malleable to interventions. Although a variety of treatments are available, there are currently no guidelines to inform clinicians which intervention might be most suitable for a given youth. Here, we discuss current knowledge of prognostic and prescriptive markers of treatment outcome in adolescent depression, highlight two major limitations of the extant literature, and suggest future directions for this important area of research. Recent findings: Despite significant effort, none of the potential demographic (gender, age, race), environmental (parental depression, family functioning), and clinical (severity of depression, comorbid diagnoses, suicidality, hopelessness) predictors have been robustly replicated to warrant implementation in clinical care. Studies on biomarkers that truly reflect pathophysiology are scarce and difficult to draw conclusions from. Summary: More efforts should be directed towards potential neurobiological predictors of treatment outcome. Moreover, rather than evaluating potential predictors in isolation, modern machine learning methods could be used to build models that combine information across a large array of features and predict treatment outcome for individual patients. These strategies hold promise for advancing personalized healthcare in adolescent depression, which remains a high clinical priority. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.

关键词： Adolescent depression Prescriptive marker Prognostic marker Treatment prediction

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High Performance computing Framework for Variable Selection on Genome-wide Association Studies

High Performance Computing Framework for Variable Selection ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Dehui Wei Qipeng Xie Xia Li Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University IoT Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science ETH Zurich Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

关键词： Accuracy High dimensional data Input variables High performance computing Instruments Noise Genomics Feature extraction Bioinformatics Optimization

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