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BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery

Computers in Biology and Medicine 2023年 167卷 107602-107602页

作者： Plotka, Szymon Grzeszczyk, Michal K. Brawura-Biskupski-Samaha, Robert Gutaj, Pawel Lipa, Michal Trzciński, Tomasz Išgum, Ivana Sánchez, Clara I. Sitek, Arkadiusz Sano Centre for Computational Medicine Cracow Poland Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center Amsterdam Netherlands Second Department of Obstetrics and Gynecology The Medical Centre of Postgraduate Education Warsaw Poland Department of Reproduction Poznan University of Medical Sciences Poznan Poznan Poland First Department of Obstetrics and Gynecology Medical University of Warsaw Warsaw Poland Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Radiology and Nuclear Medicine Amsterdam University Medical Center location University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise. © 2023 The Author(s)

关键词： Forecasting

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Majorana polarization in non-Hermitian topological superconductors

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Physical Review B 2021年第13期103卷 134507-134507页

作者： Zhen-Hua Wang Fuming Xu Lin Li Dong-Hui Xu Wei-Qiang Chen Bin Wang Shenzhen Key Laboratory of Advanced Thin Films and Applications College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China College of Physics and Electronic Engineering and Center for Computational Sciences Sichuan Normal University Chengdu 610068 China Department of Physics Hubei University Wuhan 430062 China Department of Physics and Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices Southern University of Science and Technology Shenzhen 518055 China Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

Topological invariants play an important role in characterizing topological phases. However, the topological invariants of Hermitian systems usually fail to characterize non-Hermitian topological systems due to non-Hermiticity. In this work, we generalize the Majorana polarization, which is initially defined to describe Hermitian topological superconductors, as a topological edge invariant to characterize the non-Hermitian topological superconductors. The definition of the generalized Majorana polarization depends upon two inequivalent particle-hole symmetries in non-Hermitian systems. The spinless Kitaev chain model and topological superconductor model on the honeycomb lattice are considered to examine the reliability and validity of the generalized Majorana polarization. We find that the phase transitions obtained by using Majorana polarization are consistent with the commonly used complex-energy point gap descriptions, which indicates the Majorana polarization is a reliable topological invariant to characterize the topological phase transition in non-Hermitian topological superconductors. In addition, the non-Hermitian skin effect on Majorana bound states in non-Hermitian topological superconductors is also discussed.

关键词： Majorana bound states Skin effect Topological phase transition Honeycomb lattice Kitaev model Tight-binding model

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Neural Brain: A Neuroscience-inspired Framework for Embodied Agents

arXiv

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arXiv 2025年

作者： Liu, Jian Shi, Xiongtao Nguyen, Thai Duy Zhang, Haitian Zhang, Tianxiang Sun, Wei Li, Yanjie Vasilakos, Athanasios V. Iacca, Giovanni Khan, Arshad Ali Kumar, Arvind Cho, Jae Won Mian, Ajmal Xie, Lihua Cambria, Erik Wang, Lin School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Robotics Hunan University China School of Intelligence Science and Engineering Harbin Institute of Technology Shenzhen China Department of Information and Communication Technology University of Agder Norway Department of Information Engineering and Computer Science University of Trento Italy Elm Company London United Kingdom Division of Computational Science and Technology KTH Royal Institute of Technology Sweden School of Artificial Intelligence and Data Science Sejong University Korea Republic of Department of Computer Science the University of Western Australia Australia College of Computing and Data Science Nanyang Technological University Singapore

The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios. Our project page is at Neural-Brain-for-Embodied-Agents. Copyright © 2025, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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***: A Comprehensive Genomic Visualisation Database for Malaysian Species

***: A Comprehensive Genomic Visualisation Database for Mala...

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computing (ICOCO), IEEE International Conference on

作者： Vei Siang Chan Farhan Bin Mohamed Faezah Mohd Salleh Andres Iglesias Alina Binti Wagiran Ali Bin Selamat School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Media and Game Innovation Center of Excellence Universiti Teknologi Malaysia Johor Bahru Malaysia Dept. of Biosciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru Malaysia Dept. of Applied Mathematics and Computational Sciences University of Cantabria Santander Spain Malaysia-Japan International Institute of Technology University of Technology Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781665489973

Malaysia is one of the most megadiverse regions in the world. Therefore, it is important to record these rich biodiversity data to safeguard and improve national conservation efforts. However, the lack of nationwide genomic sequencing hampers a more sophisticated species identification, data management, and analytics activities. Thus, this project aims to design and implement ***, a comprehensive one-stop online DNA reference database with integrated DNA visualisation and digital references to allow the users to search, retrieve and analyse the collected DNA sequencing data interactively. This paper documented the system architecture and implementation methods to create ***. The results of *** can display taxonomy information of biodiversity in the web’s user interfaces and visualise the complex set of DNA data to allow new insights. Lastly, the authors identified several future works to improve *** including improve user interaction, add visualisation methods to support more bioinformatics tasks, and conduct formal user evaluation.

关键词： Sequential analysis Annotations Taxonomy Data visualization DNA Genomics Systems architecture

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Time-Division Energy Beamforming for Multiuser Wireless Power Transfer with Non-Linear Energy Harvesting

arXiv

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arXiv 2020年

作者： Ma, Ganggang Xu, Jie Liu, Ya-Feng Vedady Moghadam, Mohammad R. Shenzhen China Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China TransferFi Pte Ltd Singapore

Energy beamforming has emerged as a promising technique for enhancing the energy transfer efficiency of wireless power transfer (WPT). However, the performance of conventional energy beamforming may seriously degrade due to the nonlinear radio frequency (RF) to direct current (DC) conversion at energy receivers (ERs). To tackle this issue, this letter proposes a new time-division energy beamforming, in which different energy beamforming matrices (of high ranks in general) are time shared to exploit the "convex-concave" shape of the RF-DC power relation at ERs. By considering a particular time duration for WPT, we maximize the minimum harvested DC energy among all ERs, by jointly optimizing the energy beamforming matrices and the corresponding time allocation. In order to solve the non-convex min-DC-energy maximization problem, we propose an efficient solution by using the techniques of alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed time-division energy beamforming design indeed outperforms the conventional multi-beam and time-division-multiple-access (TDMA)-based energy transmissions. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy harvesting

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Artificial Intelligence for science in Quantum, Atomistic, and Continuum Systems

arXiv

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arXiv 2023年

作者： Zhang, Xuan Wang, Limei Helwig, Jacob Luo, Youzhi Fu, Cong Xie, Yaochen Liu, Meng Lin, Yuchao Xu, Zhao Yan, Keqiang Adams, Keir Weiler, Maurice Li, Xiner Fu, Tianfan Wang, Yucheng Strasser, Alex Yu, Haiyang Xie, YuQing Fu, Xiang Xu, Shenglong Liu, Yi Du, Yuanqi Saxton, Alexandra Ling, Hongyi Lawrence, Hannah Stärk, Hannes Gui, Shurui Edwards, Carl Gao, Nicholas Ladera, Adriana Wu, Tailin Hofgard, Elyssa F. Tehrani, Aria Mansouri Wang, Rui Daigavane, Ameya Bohde, Montgomery Kurtin, Jerry Huang, Qian Phung, Tuong Xu, Minkai Joshi, Chaitanya K. Mathis, Simon V. Azizzadenesheli, Kamyar Fang, Ada Aspuru-Guzik, Alán Bekkers, Erik Bronstein, Michael Zitnik, Marinka Anandkumar, Anima Ermon, Stefano Liò, Pietro Yu, Rose Günnemann, Stephan Leskovec, Jure Ji, Heng Sun, Jimeng Barzilay, Regina Jaakkola, Tommi Coley, Connor W. Qian, Xiaoning Qian, Xiaofeng Smidt, Tess Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Chemical Engineering Massachusetts Institute of Technology Cambridge United Kingdom AMLab University of Amsterdam Amsterdam Netherlands Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United Kingdom Department of Materials Science & Engineering Texas A&M University College StationTX United States Department of Physics & Astronomy Texas A&M University College StationTX United States Department of Applied Mathematics & Statistics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States Department of Computer Science Cornell University IthacaNY United States Department of Computer Science Technical University of Munich München Germany Department of Computer Science Stanford University StanfordCA United States Department of Computer Science & Engineering University of California San Diego La Jolla CA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Nvidia Santa ClaraCA United States Department of Chemistry and Chemical Biology Harvard University Cambridge United Kingdom Department of Chemistry University of Toronto Toronto Canada Department of Computer Science University of Toronto Toronto Canada Department of Computer Science University of Oxford Oxford United Kingdom Department of Biomedical Informatics Harvard University BostonMA United States Department of Computing & Mathematical Sciences California Institute of Technology PasadenaCA United States Computational Science Initiative Brookhaven National Laboratory UptonNY United States

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a wide range of spatial and temporal scales, giving rise to a new area of research known as AI for science (AI4science). Being an emerging research paradigm, AI4science is unique in that it is an enormous and highly interdisciplinary area. Thus, a unified and technical treatment of this field is needed yet challenging. This work aims to provide a technically thorough account of a subarea of AI4science;namely, AI for quantum, atomistic, and continuum systems. These areas aim at understanding the physical world from the subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales and form an important subarea of AI4science. A unique advantage of focusing on these areas is that they largely share a common set of challenges, thereby allowing a unified and foundational treatment. A key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods. We provide an in-depth yet intuitive account of techniques to achieve equivariance to symmetry transformations. We also discuss other common technical challenges, including explainability, out-of-distribution generalization, knowledge transfer with foundation and large language models, and uncertainty quantification. To facilitate learning and education, we provide categorized lists of resources that we found to be useful. We strive to be thorough and unified and hope this initial effort may trigger more community interests and efforts to further advance AI4science. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

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Discrete Vector Calculus and Helmholtz Hodge Decomposition for Classical Finite Difference Summation by Parts Operators

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Communications on Applied Mathematics and Computation 2020年第4期2卷 581-611页

作者： Hendrik Ranocha Katharina Ostaszewski Philip Heinisch Institute Computational Mathematics TU BraunschweigUniversitatsplatz 238106 BrunswickGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Institut für Angewandte Numerische Wissenschaft e.V.(IANW) Bienroder Straae 338110 BrunswickGermany Institut für Geophysik und Extraterrestrische Physik TU BraunschweigMendelssohnstraBe 338106 BrunswickGermany

In this article,discrete variants of several results from vector calculus are studied for clas-sical finite difference summation by parts operators in two and three space *** is shown that existence theorems for scalar/vector potentials of irrotational/solenoidal vector fields cannot hold discretely because of grid oscillations,which are characterised *** results in a non-vanishing remainder associated with grid oscillations in the discrete Helmholtz Hodge ***,iterative numerical methods based on an interpretation of the Helmholtz Hodge decomposition via orthogonal projections are pro-posed and applied *** numerical experiments,the discrete remainder vanishes and the potentials converge with the same order of accuracy as usual in other first-order partial differential *** by the successful application of the Helmholtz Hodge decomposition in theoretical plasma physics,applications to the discrete analysis of magnetohydrodynamic(MHD) wave modes are presented and discussed.

关键词： Summation by parts Vector calculus Helmholtz Hodge decomposition Mimetic properties Wave mode analysis

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DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network

APL Machine Learning

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APL Machine Learning 2023年第2期1卷

作者： Yu-Chuan Hsu Markus J. Buehler Laboratory for Atomistic and Molecular Mechanics (LAMM) Department of Civil and Environmental Engineering Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge Massachusetts 02139 USA Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave. Cambridge Massachusetts 02139 USA

The dynamics of material failure is a critical phenomenon relevant to a range of scientific and engineering fields, from healthcare to structural materials. We propose a specially designed deep neural network, DyFraNet, which can predict dynamic fracture behaviors by identifying a complete history of fracture propagation—from the onset of cracking, as a crack grows through the material, modeled as a series of frames evolving over time and dependent on each other. Furthermore, the model can not only forecast future fracture processes but also backcast to elucidate past fracture histories. In this scenario, once provided with the outcome of a fracture event, the model will reveal past events that led to this state and can also predict future evolutions of the failure process. By comparing the predicted results with atomistic-level simulations and theory, we show that DyFraNet can capture dynamic fracture mechanics by accurately predicting how cracks develop over time, including measures such as the crack speed, as well as when cracks become unstable. We use Gradient-weighted Class Activation Mapping, Grad-CAM, to interpret how DyFraNet perceives the relationship between geometric conditions and fracture dynamics, and we find that DyFraNet pays special attention to the areas around crack tips that have a critical influence in the early stage of fracture propagation. In later stages, the model pays increased attention to the existing or newly formed damaged regions in the material. The proposed approach offers the potential to accelerate the exploration of dynamical processes in material design against failure and can be adapted for all kinds of dynamical problems.

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