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Weak- and hyperfine-interaction-induced 1s2s ~1S_0→ 1s^2 ~1S_0 E1 transition rates of He-like ions

Chinese Physics B 2015年第4期24卷 117-123页

作者： Laima Radziūt Erikas Gaidamauskas Gediminas Gaigalas 李冀光董晨钟 Per Jnsson Vilnius University Institute of Theoretical Physics and Astronomy Data Center for High Energy Density Physics Institute of Applied Physics and Computational Mathematics Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province College of Physics and Electronic EngineeringNorthwest Normal University Materials Science and Applied Mathematics Malm University

Weak- and hyperfine-interaction-induced 1 s2s 1S0→ 1S2 1 S0 E 1 transition rates for the isoelectronic sequence of Helike ions have been calculated using the multi-configuration Dirac-Hartree-Fock （MCDHF） and relativistic configuration interaction methods. The results should be helpful for the future experimental investigations of parity non-conservation effects.

关键词： parity non-conservation effect hyperfine interaction MCDHF method

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MatterChat: A Multi-Modal LLM for Material science

arXiv

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arXiv 2025年

作者： Tang, Yingheng Xu, Wenbin Cao, Jie Ma, Jianzhu Gao, Weilu Farrell, Steve Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Institute for AI Industry Research Tsinghua University Beijing China Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human–AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. © 2025, CC BY.

关键词： Materials properties

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Realizing Body-Machine Interface for Quadrotor Control Through Kalman Filters and Recurrent Neural Network

Realizing Body-Machine Interface for Quadrotor Control Throu...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Alexander Menshchikov Daniil Lopatkin Evgeny Tsykunov Dzmitry Tsetserukou Andrey Somov Center for Computational and Data-Intensive Science and Engineering (CDISE) Skolkovo Institute of Science and Technology Moscow Russia Space Center Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Unmanned Aerial Vehicles (UAV) have been recently applied in several various civilian applications. Based on this, there is a growing need for intuitive UAV control interfaces. In this work, we report on the Body-Machine Interface (BMI), helping a human operator to control a quadrotor through the gesture commands. We perform the human motion capture through wearable sensors and Kalman filter to reduce the noise. For the gesture command recognition, we designed the Recurrent Neural Network recognizing gestures within 65 ms. For the quadrotor orientation estimation, we designed the Extended Kalman Filter (EKF). We assess the proposed BMI via the simulations and experiments: the standard deviation of the trajectories varies for up to 10 cm.

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Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning 53

Kidney Tumor Recognition from Abdominal CT Images using Tran...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Wasi, Sefatul Alam, Saadia Binte Rahman, Rashedur Amin, M Ashraful Kobashi, Syoji Independent University Center for Computational & Data Sciences Department of Computer Science and Engineering Dhaka Bangladesh1299 Bangladesh University of Hyogo Graduate School of Engineering 2167 Shosha Hyogo Himeji Japan

ISBN: (纸本)9781665464161

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Recent Advancements of Computer Vision in Healthcare: A Systematic Review

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IEIE Transactions on Smart Processing and Computing 2024年第6期13卷 563-571页

作者： Islam, Mahmudul Nayan, Nasim Mahmud Islam, Ashraful Sikder, Sankar Rashel, Masud Rana Alam, Md. Zahangir Department of Computer Science and Engineering Independent University Bangladesh Dhaka1229 Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka1229 Bangladesh Department of Mathematics University of Louisiana LafayetteLA70503 United States Department of Mechatronics Engineering University of Évora Évora Portugal

The advancements in Computer Vision (CV) techniques have demonstrated significant promise in healthcare applications. The adoption of CV within the healthcare sector has drawn considerable attention. The aim of this study is to systematically review and synthesize the recent advancements and applications of CV techniques in various domains of healthcare. 125 papers were selected initially, and after gradual filtering, 20 papers were selected for the final study. In this study, we have identified five medical domains such as disease detection, drug discovery, surgical procedures, human identity decoding, and remote patient monitoring where CV applications are being successfully implemented. Among these domains, the use of CV in surgical assistance is notable. It capitalizes on the precision and efficiency offered by CV. Deep Learning (DL) models show adaptability and accuracy in medical imaging. The combination of computer vision and sensors enhances real-time surgical skills assessment. This study revealed that CV applications can be utilized for predictive analytics and personalized patient treatments. Standardized performance metrics, ethics, and data governance are crucial for responsible computer vision deployment in healthcare. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Deep learning

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Duplication with transposition distance to the root for q-ary strings

Duplication with transposition distance to the root for q-ar...

引用

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

引用

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

引用

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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