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Ethical Aspects of computational Modelling in science, Decision Support and Communication 15th

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Ethical Aspects of Computational Modelling in Science, Decis...

15th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021

作者： Füchslin, Rudolf Marcel Ambühl, Jacques Faggian, Alessia Fellermann, Harold M. Flumini, Dandolo Geller, Armando Hanczyc, Martin M. Klinkert, Andreas Krütli, Pius Matuttis, Hans-Georg Ott, Thomas Scheidegger, Stephan Schmid, Gary Bruno Smieszek, Timo Schneider, Johannes J. Steiner, Albert Weyland, Mathias S. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Zurich Technikumstrasse 9 Winterthur8400 Switzerland European Centre for Living Technology Ca’ Bottacin Dorsoduro 3911 Calle Crosera Venice30123 Italy World Bank Group Geneva Switzerland University of Trento Trento38123 Italy School of Computing Newcastle University Science Square 1 Newcastle Upon TyneNE4 5TG United Kingdom GmbH Fraumünsterstrasse 11 Zurich8001 Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8400 Switzerland Transdisciplinarity Lab ETH Zurich Zurich8092 Switzerland Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Tokyo182-8585 Japan Institute of Computational Life Sciences Zurich University of Applied Sciences Schloss 1 Wädenswil8820 Switzerland Swiss NeuroCreations GmbH Hambergersteig 25 Zurich8008 Switzerland Muesmattweg 70 Allschwil4123 Switzerland

ISBN: (纸本)9783031239281

The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential;however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a "narrative." We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

Electronically phase separated nano-network in antiferromagnetic insulating LaMnO3/PrMnO3/CaMnO3 tricolor superlattice

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arXiv 2022年

作者： Li, Qiang Miao, Tian Zhang, Huimin Lin, Weiyan He, Wenhao Zhong, Yang Xiang, Lifen Deng, Lina Ye, Biying Shi, Qian Zhu, Yinyan Guo, Hangwen Wang, Wenbin Zheng, Changlin Yin, Lifeng Zhou, Xiaodong Xiang, Hongjun Shen, Jian State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Department of Physics Fudan University Shanghai200433 China School of Materials Science and Engineering Xi’an Jiaotong University Shanxi Xi’an710049 China Institute of Computational Physical Sciences Fudan University Shanghai200433 China Shanghai Qi Zhi Institute Shanghai200232 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China

Strongly correlated materials often exhibit an electronic phase separation (EPS) phenomena whose domain pattern is random in nature. The ability to control the spatial arrangement of the electronic phases at microscopic scales is highly desirable for tailoring their macroscopic properties and/or designing novel electronic devices. Here we report the formation of EPS nanoscale network in a mono-atomically stacked LaMnO3/CaMnO3/PrMnO3 superlattice grown on SrTiO3 (STO) (001) substrate, which is known to have an antiferromagnetic (AFM) insulating ground state. The EPS nano-network is a consequence of an internal strain relaxation triggered by the structural domain formation of the underlying STO substrate at low temperatures. The same nanoscale network pattern can be reproduced upon temperature cycling allowing us to employ different local imaging techniques to directly compare the magnetic and transport state of a single EPS domain. Our results confirm the one-to-one correspondence between ferromagnetic (AFM) to metallic (insulating) state in manganite. It also represents a significant step in a paradigm shift from passively characterizing EPS in strongly correlated systems to actively engaging in its manipulation. © 2022, CC BY-NC-SA.

关键词： Substrates

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

Deep language models for interpretative and predictive materials science

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APL Machine Learning

APL Machine Learning 2023年第1期1卷

作者： Yiwen Hu Markus J. Buehler 1Laboratory for Atomistic and Molecular Mechanics (LAMM) Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge Massachusetts 02139 USA 2Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge Massachusetts 02139 USA

Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cannot be applied. While conventional feedforward neural networks are typically limited to performing tasks related to static patterns in data, recursive models can both work iteratively based on a changing input and discover complex dynamical relationships in the data. Deep language models can model flexible modalities of data and are capable of learning rich dynamical behaviors as they operate on discrete or continuous symbols that define the states of a physical system, yielding great potential toward end-to-end predictions. Similar to how words form a sentence, materials can be considered as a self-assembly of physically interacted building blocks, where the emerging functions of materials are analogous to the meaning of sentences. While discovering the fundamental relationships between building blocks and function emergence can be challenging, language models, such as recurrent neural networks and long-short term memory networks, and, in particular, attention models, such as the transformer architecture, can solve many such complex problems. Application areas of such models include protein folding, molecular property prediction, prediction of material failure of complex nonlinear architected materials, and also generative strategies for materials discovery. We outline challenges and opportunities, especially focusing on extending the deep-rooted kinship of humans with symbolism toward generalizable artificial intelligence (AI) systems using neuro-symbolic AI, and outline how tools such as ChatGPT and DALL·E can drive materials discovery.

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Efficient charge-preserving excited state preparation with variational quantum algorithms

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arXiv 2024年

作者： Chandani, Zohim Ikeda, Kazuki Kang, Zhong-Bo Kharzeev, Dmitri E. McCaskey, Alexander Palermo, Andrea Ramakrishnan, C.R. Rao, Pooja Sundaram, Ranjani G. Yu, Kwangmin NVIDIA Quantum Algorithm Engineering London United Kingdom Department of Physics University of Massachusetts Boston BostonMA02125 United States Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University Stony BrookNY11794-3800 United States Co-design Center for Quantum Advantage Department of Physics and Astronomy Stony Brook University Stony BrookNY11794-3800 United States Department of Physics and Astronomy University of California Los AngelesCA90095 United States Mani L. Bhaumik Institute for Theoretical Physics University of California Los AngelesCA90095 United States Center for Quantum Science and Engineering University of California Los AngelesCA90095 United States Energy and Photon Sciences Directorate Condensed Matter and Materials Science Division Brookhaven National Laboratory UptonNY11973-5000 United States NVIDIA Quantum Computing Architecture Santa ClaraCA United States Department of Computer Science Stony Brook University Stony BrookNY11794-3800 United States NVIDIA Quantum Algorithm Engineering Santa ClaraCA United States Computational Science Initiative Brookhaven National Laboratory UptonNY11973-5000 United States

Determining the spectrum and wave functions of excited states of a system is crucial in quantum physics and chemistry. Low-depth quantum algorithms, such as the Variational Quantum Eigensolver (VQE) and its variants, can be used to determine the ground-state energy. However, current approaches to computing excited states require numerous controlled unitaries, making the application of the original Variational Quantum Deflation (VQD) algorithm to problems in chemistry or physics suboptimal. In this study, we introduce a charge-preserving VQD (CPVQD) algorithm, designed to incorporate symmetry and the corresponding conserved charge into the VQD framework. This results in dimension reduction, significantly enhancing the efficiency of excitedstate computations. We present benchmark results with GPU-accelerated simulations using systems up to 24 qubits, showcasing applications in high-energy physics, nuclear physics, and quantum chemistry. This work is performed on NERSC's Perlmutter system using NVIDIA's open-source platform for accelerated quantum supercomputing - CUDA-Q. © 2024, CC BY.

关键词： Wave functions

Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach

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arXiv 2023年

作者： Spitzer, Hannah Ripart, Mathilde Fawaz, Abdulah Williams, Logan Z.J. Project, Meld Robinson, Emma Iglesias, Juan Eugenio Adler, Sophie Wagstyl, Konrad Institute of Computational Biology Helmholtz Center Munich Munich85764 Germany University Hospital LMU Munich Munich Germany Department of Developmental Neuroscience UCL Great Ormond Street Institute for Child Health LondonWC1N 1EH United Kingdom Department of Biomedical Engineering School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Martinos Center for Biomedical Imaging MGH & Harvard Medical School United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Boston02139 United States Centre for Medical Image Computing University College London LondonWC1V 6LJ United Kingdom Wellcome Centre for Human Neuroimaging University College London LondonWC1N 3AR United Kingdom

Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, which can be cured by surgery. These lesions are extremely subtle and often missed even by expert neuroradiologists. "Ground truth" manual lesion masks are therefore expensive, limited and have large inter-rater variability. Existing FCD detection methods are limited by high numbers of false positive predictions, primarily due to vertex- or patch-based approaches that lack whole-brain context. Here, we propose to approach the problem as semantic segmentation using graph convolutional networks (GCN), which allows our model to learn spatial relationships between brain regions. To address the specific challenges of FCD identification, our proposed model includes an auxiliary loss to predict distance from the lesion to reduce false positives and a weak supervision classification loss to facilitate learning from uncertain lesion masks. On a multi-centre dataset of 1015 participants with surface-based features and manual lesion masks from structural MRI data, the proposed GCN achieved an AUC of 0.74, a significant improvement against a previously used vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison to 49% when using the MLP. This improvement in specificity is vital for clinical integration of lesion-detection tools into the radiological workflow, through increasing clinical confidence in the use of AI radiological adjuncts and reducing the number of areas requiring expert review. © 2023, CC BY.

关键词： Brain

Dynamic Kosterlitz-Thouless theory for two-dimensional ultracold atomic gases

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Physical Review A 2020年第4期102卷 043311-043311页

作者： Zhigang Wu Shizhong Zhang Hui Zhai Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 Guangdong China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 Guangdong China Department of Physics and HKU-UCAS Joint Institute for Theoretical and Computational Physics at Hong Kong University of Hong Kong Hong Kong China Institute for Advanced Study Tsinghua University Beijing 100084 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China

In this paper we develop a theory for the first and second sounds in a two-dimensional atomic gas across the superfluid transition based on the dynamic Koterlitz-Thouless theory. We employ a set of modified two-fluid hydrodynamic equations which incorporate the dynamics of the quantized vortices, rather than the conventional ones for a three-dimensional superfluid. As far as the sound dispersion equation is concerned, the modification is essentially equivalent to replacing the static superfluid density with a frequency-dependent one, renormalized by the frequency-dependent “dielectric constant” of the vortices. This theory has two direct consequences. First, because the renormalized superfluid density at finite frequencies does not display discontinuity across the superfluid transition, in contrast to the static superfluid density, the sound velocities vary smoothly across the transition. Second, the theory includes dissipation due to free vortices and thus naturally describes the sound-to-diffusion crossover for the second sound in the normal phase. With only one fitting parameter, our theory gives a perfect agreement with the experimental measurements of sound velocities across the transition, as well as the quality factor in the vicinity of the transition. The predictions from this theory can be further verified by future experiments.

关键词： BKT transition Bose-Einstein condensates Cold atoms & matter waves Superfluidity Vortices in superfluids

Federated simulation for medical imaging 23rd

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Federated simulation for medical imaging

23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Li, Daiqing Kar, Amlan Ravikumar, Nishant Frangi, Alejandro F. Fidler, Sanja NVIDIA Toronto Canada Vector Institute Toronto Canada Department of Computer Science University of Toronto Toronto Canada CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds Leeds United Kingdom LICAMM Leeds Institute of Cardiovascular and Metabolic Medicine School of Medicine University of Leeds Leeds United Kingdom Department of Cardiovascular Sciences and Department of Electrical Engineering ESAT/PSI KU Leuven Leuven Belgium

ISBN: (纸本)9783030597092

Labelling data is expensive and time consuming especially for domains such as medical imaging that contain volumetric imaging data and require expert knowledge. Exploiting a larger pool of labeled data available across multiple centers, such as in federated learning, has also seen limited success since current deep learning approaches do not generalize well to images acquired with scanners from different manufacturers. We aim to address these problems in a common, learning-based image simulation framework which we refer to as Federated Simulation. We introduce a physics-driven generative approach that consists of two learnable neural modules: 1) a module that synthesizes 3D cardiac shapes along with their materials, and 2) a CT simulator that renders these into realistic 3D CT Volumes, with annotations. Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers. We show that our data synthesis framework improves the downstream segmentation performance on several datasets. © Springer Nature Switzerland AG 2020.

关键词： Medical imaging

DPA-2: a large atomic model as a multi-task learner

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning