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arXiv 2022年

作者： Vargas–Hernández, Rodrigo A. Jorner, Kjell Pollice, Robert Aspuru–Guzik, Alán Chemical Physics Theory Group Department of Chemistry University of Toronto TorontoONM5S 3H6 Canada Vector Institute for Artificial Intelligence 661 University Ave. Suite 710 TorontoONM5G 1M1 Canada Department of Computer Science University of Toronto 40 St. George St ONM5S 2E4 Canada Department of Chemistry and Chemical Engineering Chalmers University of Technology Kemigården 4 GothenburgSE-41258 Sweden Department of Chemical Engineering & Applied Chemistry University of Toronto 200 College St. ONM5S 3E5 Canada Department of Materials Science & Engineering University of Toronto 184 College St. ONM5S 3E4 Canada 661 University Ave. TorontoONM5G 1M1 Canada

Semi-empirical quantum chemistry has recently seen a renaissance with applications in high-throughput virtual screening and machine learning. The simplest semi-empirical model still in widespread use in chemistry is Hückel’s π-electron molecular orbital theory. In this work, we implemented a Hückel program using differentiable programming with the JAX framework, based on limited modifications of a pre-existing NumPy version. The auto-differentiable Hückel code enabled efficient gradient-based optimization of model parameters tuned for excitation energies and molecular polarizabilities, respectively, based on as few as 100 data points from density functional theory simulations. In particular, the facile computation of the polarizability, a second-order derivative, via auto-differentiation shows the potential of differentiable programming to bypass the need for numeric differentiation or derivation of analytical expressions. Finally, we employ gradient-based optimization of atom identity for inverse design of organic electronic materials with targeted orbital energy gaps and polarizabilities. Optimized structures are obtained after as little as 15 iterations, using standard gradient-based optimization algorithms. © 2022, CC BY.

关键词： Molecular orbitals

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Roadmap on Deep Learning for Microscopy

arXiv

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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Self-stabilizing snapshot objects for asynchronous fail-prone network systems (preliminary report)

arXiv

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arXiv 2019年

作者： Georgiou, Chryssis Lundström, Oskar Schiller, Elad M. Department of Computer Science University of Cyprus Nicosia Cyprus Department of Engineering and Computer Science Chalmers University of Technology GothenburgSE-412 96 Sweden

A snapshot object simulates the behavior of an array of single-writer/multi-reader shared registers that can be read atomically. Delporte-Gallet et al. proposed two fault-tolerant algorithms for snapshot objects in asynchronous crash-prone messagepassing systems. Their first algorithm is non-blocking;it allows snapshot operations to terminate once all write operations had ceased. It uses O(n) messages of O(n*μ) bits, where n is the number of nodes and μ is the number of bits it takes to represent the object. Their second algorithm allows snapshot operations to always terminate independently of write operations. It incurs O(n2) messages. The fault model of Delporte-Gallet et al. considers both node failures (crashes). We aim at the design of even more robust snapshot objects. We do so through the lenses of self-stabilization|a very strong notion of fault-tolerance. In addition to Delporte-Gallet et al.'s fault model, a self-stabilizing algorithm can recover after the occurrence of transient faults;these faults represent arbitrary violations of the assumptions according to which the system was designed to operate (as long as the code stays intact). In particular, in this work, we propose self-stabilizing variations of Delporte-Gallet et al.'s non-blocking algorithm and always-terminating algorithm. Our algorithms have similar communication costs to the ones by Delporte-Gallet et al. and O(1) recovery time (in terms of asynchronous cycles) from transient faults. The main differences are that our proposal considers repeated gossiping of O(μ) bits messages and deals with bounded space (which is a prerequisite for self-stabilization). Lastly, we explain how to extend the proposed solutions to reconfigurable ones. Copyright © 2019, The Authors. All rights reserved.

关键词： Fault tolerance

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The constructive Kan-Quillen model structure: Two new proofs

arXiv

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arXiv 2019年

作者： Gambino, Nicola Sattler, Christian Szumilo, Karol School of Mathematics University of Leeds United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Sweden

We present two new proofs of Simon Henry's result that the category of simplicial sets admits a constructive counterpart of the classical Kan-Quillen model structure. Our proofs are entirely self-contained and avoid complex combinatorial arguments on anodyne extensions. We also give new constructive proofs of the left and right properness of the model structure. © 2019, CC BY.

关键词： Algebra

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A quantum instruction set implemented on a superconducting quantum processor

arXiv

引用

arXiv 2020年

作者： Kjaergaard, M. Schwartz, M.E. Greene, A. Samach, G.O. Bengtsson, A. O'Keeffe, M. McNally, C.M. Braumüller, J. Kim, D.K. Krantz, P. Marvian, M. Melville, A. Niedzielski, B.M. Sung, Y. Winik, R. Yoder, J. Rosenberg, D. Obenland, K. Lloyd, S. Orlando, T.P. Marvian, I. Gustavsson, S. Oliver, W.D. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States Microtechnology and Nanoscience Chalmers University of Technology GöteborgSE-412 96 Sweden Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Departments of Physics & Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Microtechnology and Nanoscience Chalmers University of Technology

A quantum algorithm consists of a sequence of operations and measurements applied to a quantum processor. To date, the instruction set which defines this sequence has been provided by a classical computer and passed via control hardware to the quantum processor. Here, we demonstrate the first experimental realization of a quantum instruction set, in which a fixed sequence of classically-defined gates perform an operation that is fully determined only by a quantum input to the fixed sequence. Specifically, we implement the density matrix exponentiation algorithm, which consumes N copies of the instruction state ρ to approximate the operation e−iρθ (θ an arbitrary angle). Our implementation relies on a 99.7% fidelity controlled-phase gate between two superconducting transmon qubits. We achieve an average algorithmic fidelity ≈ 0.9, independent of the setting of ρ, to circuit depth nearly 90. This new paradigm for quantum instructions has applications to resource-efficient protocols for validating entanglement spectra, principal component analysis of large quantum states, and universal quantum emulation. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum entanglement

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RL Perceptron: Generalization Dynamics of Policy Learning in High Dimensions

引用

Physical Review X 2025年第2期15卷 021051-021051页

作者： Nishil Patel Sebastian Lee Stefano Sarao Mannelli Sebastian Goldt Andrew Saxe Gatsby Computational Neuroscience Unit University College London Gower Street London WC1E 6BT United Kingdom Imperial College London Exhibition Road South Kensington London SW7 2AZ United Kingdom Data Science and AI Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg Sweden School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa International School of Advanced Studies (SISSA) Via Bonomea 265 34136 Trieste Italy Sainsbury Wellcome Centre University College London 25 Howland Street London W1T 4JG United Kingdom

Reinforcement learning (RL) algorithms have transformed many domains of machine learning. To tackle real-world problems, RL often relies on neural networks to learn policies directly from pixels or other high-dimensional sensory input. By contrast, many theories of RL have focused on discrete state spaces or worst-case analysis, and fundamental questions remain about the dynamics of policy learning in high-dimensional settings. Here, we propose a solvable high-dimensional RL model that can capture a variety of learning protocols, and we derive its typical policy learning dynamics as a set of closed-form ordinary differential equations. We obtain optimal schedules for the learning rates and task difficulty—analogous to annealing schemes and curricula during training in RL—and show that the model exhibits rich behavior, including delayed learning under sparse rewards, a variety of learning regimes depending on reward baselines, and a speed-accuracy trade-off driven by reward stringency. Experiments on variants of the Procgen game “Bossfight” and Arcade Learning Environment game “Pong” also show such a speed-accuracy trade-off in practice. Together, these results take a step toward closing the gap between theory and practice in high-dimensional RL.

关键词： Differential equations

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Business as Unusual: A Model for Continuous Real-Time Business Insights Based on Low Level Metrics

Business as Unusual: A Model for Continuous Real-Time Busine...

引用

Euromicro Conference on Software engineering and Advanced Applications (SEAA)

作者： Iris Figalist Christoph Elsner Jan Bosch Helena Holmström Olsson Siemens Corporate Technology Munich Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg Sweden Malmö University

ISBN: (纸本)9781728134222

A wide variety of tools to monitor and track software systems, such as websites or smartphone applications, during runtime already exists. However, their aggregated results are often not sufficient to answer questions on a product management level since these questions address several levels of complexity and abstractions, and tend to be formulated on a rather high level, for instance concerning the efficiency of their website structure for their users. A straightforward mapping between low level metrics and high level insights is typically not possible. This causes a gap that makes it challenging to continuously provide quantitative high-level insights in real-time. In order to address this challenge, we conducted a study within three distinct platforms and products, and propose a model based on our results. After defining a case for each of the independent platforms and products, we implemented a process to measure high level insights using low level metrics for each of these cases. Next, we compared the procedures and steps that were taken in each of the cases and derived a model that describes a generic approach how to utilize and process data in order to gain higher level insights. Our model structures the steps from data to knowledge over different levels of complexity and abstraction, namely operational, tactical, and strategic. Thereby, the knowledge acquired in each phase serves as input in the next phase which increases the measurable level of complexity with each iteration. Since the steps in our model are specifically arranged as a pipeline, it enables practitioners to automate a continuous and quantitative measurement of high level insights in real-time.

关键词： Navigation Measurement Real-time systems Business Ecosystems Software Complexity theory

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It takes three to tango: Requirement, outcome/data, and AI driven development 1

It takes three to tango: Requirement, outcome/data, and AI d...

引用

1st International Workshop on Software-Intensive Business: Start-Ups, Ecosystems and Platforms, SiBW 2018

作者： Bosch, Jan Olsson, Helena H. Crnkovic, Ivica Chalmers University of Technology Department of Computer Science and Engineering Göteborg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

Today’s software-intensive organizations are experiencing a paradigm-shift with regards to how to develop software systems. With the increasing availability and access to data and with artificial intelligence (AI) and technologies such as machine learning and deep learning emerging, the traditional requirement driven approach to software development is becoming complemented with other approaches. In addition to having development teams executing on requirements specified by product management, the development of software systems is progressing towards a data driven practice where teams receive an outcome to realize and where design decisions are taken based on continuous collection and analysis of data. On top of this, and due to artificial intelligence components being introduced to more and more software systems, learning algorithms, automatically generated models and data is replacing code and the development process is no longer only a manual effort but instead a combination of human and automated processes. In this paper, and based on multi-case study research in embedded systems and online companies, we see that companies use different approaches to software development but that they often take a requirement driven approach even if they would benefit from one of the other two. Also, we see that picking the wrong approach results in a number of problems such as e.g. inefficiency and waste of development efforts. To help address these problems, we develop a holistic development framework and we provide guidelines on how to improve effectiveness in development. The contribution of this paper is two-fold. First, we identify that there are three distinct approaches to software development;(1) Requirement driven development, (2) Outcome/data driven development and (3) AI driven development and we outline the typical problems that companies experience when using the wrong approach for the wrong purpose. Second, we provide a holistic framework with guidelines for when

关键词： Software design

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Correction to: Introduction: special issue—critical robotics research

引用

AI & SOCIETY 2021年第3期39卷 1525-1525页

作者： Serholt, Sofia Ljungblad, Sara Ní Bhroin, Niamh Department of Applied Information Technology University of Gothenburg Gothenburg Sweden Department of Computer Science and Engineering University of Gothenburg Chalmers University of Technology Gothenburg Sweden Department of Media and Communication University of Oslo Oslo Norway

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Industry-Scale Orchestrated Federated Learning for Drug Discovery 37

Industry-Scale Orchestrated Federated Learning for Drug Disc...

引用

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Oldenhof, Martijn Ács, Gergely Pejó, Balázs Schuffenhauer, Ansgar Holway, Nicholas Sturm, Noé Dieckmann, Arne Fortmeier, Oliver Boniface, Eric Mayer, Clément Gohier, Arnaud Schmidtke, Peter Niwayama, Ritsuya Kopecky, Dieter Mervin, Lewis Rathi, Prakash Chandra Friedrich, Lukas Formanek, András Antal, Peter Rahaman, Jordon Zalewski, Adam Heyndrickx, Wouter Oluoch, Ezron Stößel, Manuel Vančo, Michal Endico, David Gelus, Fabien de Boisfossé, Thaïs Darbier, Adrien Nicollet, Ashley Blottière, Matthieu Telenczuk, Maria Nguyen, Van Tien Martinez, Thibaud Boillet, Camille Moutet, Kelvin Picosson, Alexandre Gasser, Aurélien Djafar, Inal Simon, Antoine Arany, Ádám Simm, Jaak Moreau, Yves Engkvist, Ola Ceulemans, Hugo Marini, Camille Galtier, Mathieu KU Leuven ESAT-STADIUS Kasteelpark Arenberg 10 Heverlee3001 Belgium BME-HIT CrySyS Lab Budapest Hungary BME-MIT Budapest Hungary Novartis Institutes for BioMedical Research Novartis Campus BaselCH-4002 Switzerland Bayer AG Leverkusen51373 Germany Substra Foundation - Labelia Labs Nantes France Discngine 79 Avenue Ledru Rollin Paris75012 France Institut de Recherches Servier 125 chemin de ronde Croissy-sur-Seine78290 France Boehringer Ingelheim RCV GmbH & Co KG Vienna Austria Molecular AI Discovery Sciences R&D AstraZeneca Cambridge United Kingdom R&D IT AstraZeneca Cambridge United Kingdom Molecular AI Discovery Sciences R&D AstraZeneca Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Merck KGaA Global Research & Development Frankfurter Strasse 250 Darmstadt64293 Germany GmbH Staffelseestraße 2 Munich81477 Germany Pillar Biosciences Inc. 9 Strathmore Rd NatickMA01760 United States Janssen Pharmaceutica NV Turnhoutseweg 30 Beerse2340 Belgium Kubermatic Hamburg Germany Owkin Paris France

ISBN: (纸本)9781577358800

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Industrial research

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