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IEEE International Symposium on Information Theory

作者： Nikita Polyanskii Ilya Vorobyev Institute for Communications Engineering Technical University of Munich Munich Germany Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9781728164328

ISBN: (纸本)9781728164335

We study the duplication with transposition distance between strings of length n over a q-ary alphabet and their roots. In other words, we investigate the number of duplication operations of the form x = (abcd) →y = (abcbd), where x and y are strings and a, b, c and d are their substrings, needed to get a q-ary string of length n starting from the set of strings without duplications. For exact duplication, we prove that the maximal distance between a string of length at most n and its root has the asymptotic order n/logn. For approximate duplication, where a β-fraction of symbols may be duplicated incorrectly, we show that the maximal distance has a sharp transition from the order n/logn to logn at β = (q - 1)/q. The motivation for this problem comes from genomics, where such duplications represent a special kind of mutation and the distance between a given biological sequence and its root is the smallest number of transposition mutations required to generate the sequence.

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Vec2SPARQL: Integrating SPARQL queries and knowledge graph embeddings 11

Vec2SPARQL: Integrating SPARQL queries and knowledge graph e...

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11th International Conference Semantic Web Applications and Tools for Life sciences, SWAT4LS 2018

作者： Kulmanov, Maxat Kafkas, Senay Karwath, Andreas Malic, Alexander Gkoutos, Georgios V Dumontier, Michel Hoehndorf, Robert Computer Electrical and Mathematical Science and Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Centre for Computational Biology University of Birmingham Birmingham United Kingdom Institute of Data Science Maastricht University Maastricht Netherlands

Recent developments in machine learning have led to a rise of large number of methods for extracting features from structured data. The features are represented as vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://***/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://***/. © 2018 CEUR Workshop Proceedings. All rights reserved.

关键词： Vector spaces

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Optimization-based approach to the calibration of mesoscale mechanical models for carbon nanotube systems

arXiv

引用

arXiv 2019年

作者： Petrov, Vitaly Ostanin, Igor Zhilyaev, Petr Center for Design Manufacturing and Materials Skolkovo Institute of Science and Technology Moscow Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

A new mesoscale mechanical model, describing elastic interactions in carbon nanotubes (CNT) and other nanofilaments, is proposed. Functional form of the developed model is based on enhanced vector model (EVM) that describes basic types of bond deformations: tension, torsion, bending and shear. Calibration of bond stiffnesses is performed by adjusting EVM parameters to reproduce both CNT's deformation energies and shape observed in a full-atomistic simulation. The parameters obtained are compared with the ones obtained from Euler-Bernoulli beam theory considerations. It is found that after certain critical length of a tested CNT specimen its stiffness parameters become length-independent and can be used in mesoscale simulations of CNTs of arbitrary length. Copyright © 2019, The Authors. All rights reserved.

关键词： Carbon nanotubes

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An Information-Theoretic Framework for Optimal Experimental Design in Magnetic Nanoparticle Hyperthermia

SSRN

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SSRN 2025年

作者： Nandyala, Mahesh Lanham, Andrew Jha, Prashant K. Wu, Chengyue Hazle, John D. Yankeelov, Thomas E. Stafford, R. Jason El-Gendy, Ahmed A. Fuentes, David Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Department of Imaging Physics The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Applied Research Laboratories The University of Texas at Austin AustinTX78758 United States Department of Mechanical Engineering South Dakota School of Mines and Technology Rapid CitySD57701 United States Department of Breast Imaging The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Department of Biostatistics The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States The Institute for Data Science in Oncology The University of Texas M.D. Anderson Cancer Center HoustonTX77030 United States Department of Biomedical Engineering The University of Texas at Austin AustinTX78712 United States Department of Diagnostic Medicine The University of Texas at Austin AustinTX78712 United States Department of Oncology The University of Texas at Austin AustinTX78712 United States Livestrong Cancer Institutes The University of Texas at Austin AustinTX78712 United States Department of Physics University of Texas at El Paso El PasoTX79968 United States

Magnetic nanoparticle hyperthermia is an emerging cancer therapy that utilizes magnetic nanoparticles subjected to alternating magnetic fields to generate localized heating and selectively target tumor tissues. Despite its potential, clinical implementation faces significant challenges due to uncertainties in the thermophysical properties, nanoparticle distribution, and magnetic particle parameters, which can compromise the precision and efficacy of the treatment. This article introduces an information-theoretic framework for optimal experimental design in magnetic nanoparticle hyperthermia to address these challenges. By accounting for uncertainties in key parameters, such as tissue thermal conductivity, blood perfusion rate, and nanoparticle anisotropy constant, the framework maximizes mutual information between the observed data and model parameters, enhancing the accuracy of parameter estimation. A surrogate 1D model is employed to reduce computational complexity, allowing the identification of optimal magnetic field amplitudes across diverse initial conditions and scenarios, including single and multiple uncertain parameters. The results highlight the robustness of the optimization approach, demonstrating consistent convergence to a stable solution that is expected to enable precise temperature measurement and effective parameter recovery. This study underscores the potential for mutual information-based optimization to advance the planning of magnetic nanoparticle hyperthermia treatment and provides a foundation for future experimental and clinical applications. © 2025, The Authors. All rights reserved.

关键词： Hyperthermia therapy

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Stochastic gradient algorithms from ode splitting perspective

arXiv

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arXiv 2020年

作者： Merkulov, Daniil Oseledets, Ivan Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 bld. 1 Moscow121205 Russia

We present a different view on stochastic optimization, which goes back to the splitting schemes for approximate solutions of ODE. In this work, we provide a connection between stochastic gradient descent approach and first-order splitting scheme for ODE. We consider the special case of splitting, which is inspired by machine learning applications and derive a new upper bound on the global splitting error for it. We present, that the Kaczmarz method is the limit case of the splitting scheme for the unit batch SGD for linear least squares problem. We support our findings with systematic empirical studies, which demonstrates, that a more accurate solution of local problems leads to the stepsize robustness and provides better convergence in time and iterations on the softmax regression problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Least squares approximations

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Average Age of Information of Irregular Repetition Slotted ALOHA

Average Age of Information of Irregular Repetition Slotted A...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Andrea Munari Alexey Frolov Institute of Communications and Navigation of the German Aerospace Center (DLR) Wessling Germany Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Flanking traditional metrics such as throughput and reliability, age of information (AoI) is emerging as a fundamental tool to capture the performance of IoT systems. In this context, we focus on a setup in which a large number of nodes attempt delivery of time-stamped updates to a common destination over a shared channel, and investigate the ability of different grant-free access strategies to maintain fresh information at the receiver. Specifically, we derive for the first time an exact closed-form expression of the average AoI achieved by irregular repetition slotted ALOHA (IRSA), and compare its performance to that of a slotted ALOHA approach. Our analysis reveals the potential of modern random access schemes, and pinpoints some fundamental trade-offs, providing useful hints for proper system design.

关键词： Peer-to-peer computing Protocols Receivers Throughput Information age Tools Internet of Things

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ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

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Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation

arXiv

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arXiv 2020年

作者： Schutski, Roman Khakhulin, Taras Oseledets, Ivan Kolmakov, Dmitry Center for Computational and Data-Intensive Science and Engineering Skoltech Skolkovo Innovation Center Moscow Region121205 Russia Central Research Institute Huawei Technologies

Tensor networks are the main building blocks in a wide variety of computational sciences, ranging from many-body theory and quantum computing to probability and machine learning. Here we propose a parallel algorithm for the contraction of tensor networks using probabilistic graphical models. Our approach is based on the heuristic solution of the μ -treewidth deletion problem in graph theory. We apply the resulting algorithm to the simulation of random quantum circuits and discuss the extensions for general tensor network contractions. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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High-resolution dynamic imaging of chromatin DNA communication using Oligo-LiveFISH

引用

Cell 2025年

作者： Zhu, Yanyu Balaji, Ashwin Han, Mengting Andronov, Leonid Roy, Anish R. Wei, Zheng Chen, Crystal Miles, Leanne Cai, Sa Gu, Zhengxi Tse, Ariana Yu, Betty Chentzu Uenaka, Takeshi Lin, Xueqiu Spakowitz, Andrew J. Moerner, W.E. Qi, Lei S. Department of Bioengineering Stanford University Stanford 94305 CA United States Department of Chemistry Stanford University Stanford 94305 CA United States Biophysics PhD Program Stanford University Stanford 94305 CA United States Computational Biology Program Public Health Sciences Division and Translational Data Science IRC Fred Hutchinson Cancer Center Seattle 98109 WA United States Department of Chemical Engineering Stanford University Stanford 94305 CA United States Department of Materials Science and Engineering Stanford University Stanford 94305 CA United States Institute for Stem Cell Biology & Regenerative Medicine and Department of Pathology Stanford University School of Medicine Stanford 94305 CA United States Sarafan ChEM-H Stanford University Stanford 94305 CA United States Chan Zuckerberg Biohub San Francisco 94080 CA United States

Three-dimensional (3D) genome dynamics are crucial for cellular functions and disease. However, real-time, live-cell DNA visualization remains challenging, as existing methods are often confined to repetitive regions, suffer from low resolution, or require complex genome engineering. Here, we present Oligo-LiveFISH, a high-resolution, reagent-based platform for dynamically tracking non-repetitive genomic loci in diverse cell types, including primary cells. Oligo-LiveFISH utilizes fluorescent guide RNA (gRNA) oligo pools generated by computational design, in vitro transcription, and chemical labeling, delivered as ribonucleoproteins. Utilizing machine learning, we characterized the impact of gRNA design and chromatin features on imaging efficiency. Multi-color Oligo-LiveFISH achieved 20-nm spatial resolution and 50-ms temporal resolution in 3D, capturing real-time enhancer and promoter dynamics. Our measurements and dynamic modeling revealed two distinct modes of chromatin communication, and active transcription slows enhancer-promoter dynamics at endogenous genes like FOS. Oligo-LiveFISH offers a versatile platform for studying 3D genome dynamics and their links to cellular processes and disease. © 2025 Elsevier Inc.

关键词： CRISPR imaging DNA communication dynamic tracking enhancer-promoter interaction high-resolution imaging live DNA imaging non-repetitive genome imaging Oligo-LiveFISH polymer modeling primary cell

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Automated Crowd Parameter Estimation and Crowd Movement Analysis in Kumbh Mela 6th

Automated Crowd Parameter Estimation and Crowd Movement Anal...

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6th International Conference of Transportation Research Group of India, CTRG 2021

作者： Choubey, Nipun Verma, Ashish Chakraborty, Anirban Department of Computational and Data Sciences Indian Institute of Science Bangalore India Robert Bosch Center for Cyber-Physical Systems Indian Institute of Science Bangalore India Department of Civil Engineering Indian Institute of Science Bangalore India

ISBN: (纸本)9789811942037

Understanding crowd behavior is essential in mass religious gatherings for crowd managers. Surveillance devices such as CCTV provide data in real time in the form of raw video, while the crowd manager estimates the crowd state from video based on their experience. In this study, we propose a methodology to automate the crowd parameter estimation process using an object detection model and tracking algorithm, which will assist crowd managers in estimating the state of the crowd. There are two key contributions to this study. First, the study proposes a methodology to automate crowd parameter estimation from video in a mass religious gathering. Second, the existing state-of-the-art object detection model has been improved to adapt to the challenging situation of mass religious gatherings with high density, high diversity crowd videos. CCTV videos from Kumbh Mela 2016 are used for this study. © 2023, Transportation Research Group of India.

关键词： Computer vision

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