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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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List homomorphism problems for signed trees

arXiv

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arXiv 2020年

作者： Bok, Jan Brewster, Richard Feder, Tomás Hell, Pavol Jedličková, Nikola Computer Science Institute Faculty of Mathematics and Physics Charles University Czech Republic Department of Mathematics and Statistics Thompson Rivers University Canada 268 Waverley St. Palo Alto United States School of Computing Science Simon Fraser University Canada Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Czech Republic

We consider homomorphisms of signed graphs from a computational perspective. In particular, we study the list homomorphism problem seeking a homomorphism of an input signed graph (G, σ), equipped with lists L(v) ⊆ V (H), v ∈ V (G), of allowed images, to a fixed target signed graph (H, π). The complexity of the similar homomorphism problem without lists (corresponding to all lists being L(v) = V (H)) has been previously classified by Brewster and Siggers, but the list version remains open and appears difficult. We illustrate this difficulty by classifying the complexity of the problem when H is a tree (with possible loops). The tools we develop will be useful for classifications of other classes of signed graphs, and in a future companion paper we will illustrate this by using them to classify the complexity for certain irreflexive signed graphs. The structure of the signed trees in the polynomial cases is interesting, suggesting that the class of general signed graphs for which the problems are polynomial may have nice structure, analogous to the so-called bi-arc graphs (which characterized the polynomial cases of list homomorphisms to unsigned graphs).MSC Codes 05C60, 05C22, 05C85 Copyright © 2020, The Authors. All rights reserved.

关键词： Polynomials

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Toward the end-to-end optimization of particle physics instruments with differentiable programming

引用

Reviews in Physics 2023年 10卷

作者： Dorigo, Tommaso Giammanco, Andrea Vischia, Pietro Aehle, Max Bawaj, Mateusz Boldyrev, Alexey de Castro Manzano, Pablo Derkach, Denis Donini, Julien Edelen, Auralee Fanzago, Federica Gauger, Nicolas R. Glaser, Christian Baydin, Atılım G. Heinrich, Lukas Keidel, Ralf Kieseler, Jan Krause, Claudius Lagrange, Maxime Lamparth, Max Layer, Lukas Maier, Gernot Nardi, Federico Pettersen, Helge E.S. Ramos, Alberto Ratnikov, Fedor Röhrich, Dieter de Austri, Roberto Ruiz del Árbol, Pablo Martínez Ruiz Savchenko, Oleg Simpson, Nathan Strong, Giles C. Taliercio, Angela Tosi, Mia Ustyuzhanin, Andrey Zaraket, Haitham MODE Collaboration Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy Université catholique de Louvain Belgium Chair for Scientific Computing Technische Universität Kaiserslautern Germany Università di Perugia and INFN Sezione di Perugia Italy HSE University Russia Université Clermont Auvergne Laboratoire de Physique de Clermont CNRS/IN2P3 France SLAC National Accelerator Laboratory United States Department of Physics and Astronomy Uppsala University Sweden Department of Computer Science University of Oxford United Kingdom Physik-Department Technische Universität München Germany Center for Technology and Transfer University of Applied Sciences Worms Germany CERN Switzerland NHETC Department of Physics and Astronomy Rutgers University United States Università di Napoli "Federico II" Italy Germany Università degli Studi di Padova Italy Department of Oncology and Medical Physics Haukeland University Hospital Norway Instituto de Física Corpuscular UV-CSIC Spain Department of Physics and Technology University of Bergen Norway Instituto de Física de Cantabria UC-CSIC Spain Lund University Sweden Multi-Disciplinary Physics Laboratory Optics and Fiber Optics Group Faculty of Sciences Lebanese University Lebanon the World Constructor University Bremen gGmbH Campus Ring 1 Bremen 28759 Germany

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this white paper, we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications. © 2023 The Author(s)

关键词： Astrophysics

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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A sparse semismooth Newton based proximal majorization-minimization algorithm for nonconvex square-root-loss regression problem

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 9253-9290页

作者： Peipei Tang Chengjing Wang Defeng Sun Kim-Chuan Toh School of Computer and Computing Science Zhejiang University City College Hangzhou China School of Mathematics and National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu China Department of Applied Mathematics The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Mathematics and Institute of Operations Research and Analytics National University of Singapore Singapore

In this paper, we consider high-dimensional nonconvex square-root-loss regression problems and introduce a proximal majorization-minimization (PMM) algorithm for solving these problems. Our key idea for making the proposed PMM to be efficient is to develop a sparse semismooth Newton method to solve the corresponding subproblems. By using the Kurdyka-Łojasiewicz property exhibited in the underlining problems, we prove that the PMM algorithm converges to a d-stationary point. We also analyze the oracle property of the initial subproblem used in our algorithm. Extensive numerical experiments are presented to demonstrate the high efficiency of the proposed PMM algorithm.

关键词： nonconvex square-root regression problems proximal majorization-minimization semismooth Newton method

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computing with bricks and mortar: Classification of waveforms with a doped concrete blocks

arXiv

引用

arXiv 2020年

作者： Przyczyna, Dawid Suchecki, Maciej Adamatzky, Andrew Szacilowski, Konrad AGH University of Science and Technology Academic Centre for Materials and Nanotechnology al. Mickiewicza 30 Kraków30-059 Poland AGH University of Science and Technology Faculty of Physics and Applied Computer Science al. Mickiewicza 30 Kraków30-059 Poland University of the West of England Unconventional Computing Lab Department of Computer Science and Creative Technologies BristolBS16 1QY United Kingdom

We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for "reservoir of states" necessary for simple tuning of the readout layer. In that perspective, buildings constructed from computing concrete could function as a highly parallel information processor for smart architecture. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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An initialization strategy for addressing barren plateaus in parametrized quantum circuits

arXiv

引用

arXiv 2019年

作者： Grant, Edward Ostaszewski, Mateusz Wossnig, Leonard Benedetti, Marcello Rahko Limited Department of Computer Science University College London Institute of Theoretical and Applied Informatics Polish Academy of Sciences Cambridge Quantum Computing Limited Department of Computer Science University College London

Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of qubits. In this technical note we theoretically motivate and empirically validate an initialization strategy which can resolve the barren plateau problem for practical applications. The technique involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity. This initialization limits the effective depth of the circuits used to calculate the first parameter update so that they cannot be stuck in a barren plateau at the start of training. In turn, this makes some of the most compact ansätze usable in practice, which was not possible before even for rather basic problems. We show empirically that variational quantum eigensolvers and quantum neural networks initialized using this strategy can be trained using a gradient based method. Copyright © 2019, The Authors. All rights reserved.

关键词： Timing circuits

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BDoTs: Blockchain-based Evaluation Scheme for Online Teaching under COVID-19 Environment

BDoTs: Blockchain-based Evaluation Scheme for Online Teachin...

引用

International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Arpit Shukla Nirav Patel Sudeep Tanwar Balqies Sadoun Mohammad S. Obaidat Nirma University India Department of Computer Science and Engineering Nirma University India College of Engineering Sharjah Universiiy UAE College of Engineering Al-Balqa' Applied Universiiy Jordan College of Computing and Informatics University of Sharjah UAE King Abdullah II School of IT University of Jordan Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728165448

ISBN: (纸本)9781728165455

Online teaching has become mandatory across the globe during the COVID-19 pandemic situations. Hence, there is a need to uplift the online teaching technology for data privacy preservation and transparency in the system. Various online teaching schemes have been proposed by various authors, but they lack in handling decentralised governance, transparency, trust and communication issues. Blockchain (BC) Technology has emerged to provide decentralised solution in solving the real-time problems. Motivated by these facts, in this paper, we propose a BC-based decentralised online teaching scheme known as BDoTs. The security and data privacy issues in BDoTs are resolved by developing smart contracts (SCs) over BC. Moreover, the data storage cost issues are handled by the Inter Planetary File System (IPFS) protocol for Off-Chain data storage. Moreover, we present a real-time BC simulation and deployment of SC in Truffle suite. Results show that the proposed scheme performs better in comparison to the state-of-the-art schemes in terms of scalability, data storage cost, and packet loss.

关键词： Education Peer-to-peer computing Internet Distributed databases COVID-19 Security Scalability

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Fast estimation of outcome probabilities for quantum circuits

arXiv

引用

arXiv 2021年

作者： Pashayan, Hakop Reardon-Smith, Oliver Korzekwa, Kamil Bartlett, Stephen D. Institute for Quantum Computing Department of Combinatorics and Optimization University of Waterloo ONN2L 3G1 Canada Perimeter Institute for Theoretical Physics WaterlooONN2L 2Y5 Canada Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Centre for Engineered Quantum Systems School of Physics The University of Sydney SydneyNSW2006 Australia

We present two classical algorithms for the simulation of universal quantum circuits on n qubits constructed from c instances of Clifford gates and t arbitrary-angle Z-rotation gates such as T gates. Our algorithms complement each other by performing best in different parameter regimes. The Estimate algorithm produces an additive precision estimate of the Born rule probability of a chosen measurement outcome with the only source of run-time inefficiency being a linear dependence on the stabilizer extent (which scales like ≈ 1.17t for T gates). Our algorithm is state-of-the-art for this task: as an example, in approximately 13 hours (on a standard desktop computer), we estimated the Born rule probability to within an additive error of 0.03, for a 50-qubit, 60 non-Clifford gate quantum circuit with more than 2000 Clifford gates. Our second algorithm, Compute, calculates the probability of a chosen measurement outcome to machine precision with run-time O (2t−rt) where r is an efficiently computable, circuit-specific quantity. With high probability, r is very close to min {t, n − w} for random circuits with many Clifford gates, where w is the number of measured qubits. Compute can be effective in surprisingly challenging parameter regimes, e.g., we can randomly sample Clifford+T circuits with n = 55, w = 5, c = 105 and t = 80 T gates, and then compute the Born rule probability with a run-time consistently less than 10 minutes using a single core of a standard desktop computer. We provide a C+Python implementation of our algorithms and benchmark them using random circuits, the hidden shift algorithm and the quantum approximate optimization algorithm (QAOA). Copyright © 2021, The Authors. All rights reserved.

关键词： Probability

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DPb-MOPSO: A Novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization Algorithm

TechRxiv

引用

TechRxiv 2020年

作者： Aboud, Ahlem Rokbani, Nizar Fdhila, Raja Qahtani, Abdulrahman M. Almutiry, Omar Dhahri, Habib Hussain, Amir Alimi, Adel M. University of Sousse ISITCom Sousse4011 Tunisia BP 1173 Sfax3038 Tunisia High Institute of Applied Science and Technology of Sousse University of Sousse Tunisia Department of Computer Science College of Computers and Information Technology Taif University P.O.Box. 11099 Taif21944 Saudi Arabia College of Applied Computer Science King Saud University Riyadh Saudi Arabia Edinburgh Napier University School of Computing EdinburghEH10 5DT United Kingdom Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Particle swarm optimization system based on the distributed architecture over multiple sub-swarms has shown its efficiency for static optimization and has not been studied to solve dynamic multi-objective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected in the objective values, the Pareto ranking operator is used to enable a multiple sub-swarm’ subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman's analysis of variance while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD metrics. © 2020, CC BY.

关键词： Particle swarm optimization (PSO)

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