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arXiv 2024年

作者： Zhou, Shuang Xu, Zidu Zhang, Mian Xu, Chunpu Guo, Yawen Zhan, Zaifu Ding, Sirui Wang, Jiashuo Xu, Kaishuai Fang, Yi Xia, Liqiao Yeung, Jeremy Zha, Daochen Lin, Mingquan Zhang, Rui Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States School of Nursing Columbia University New YorkNY United States Erik Jonsson School of Engineering and Computer Science University of Texas at Dallas RichardsonTX United States Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Informatics University of California Irvine IrvineCA United States Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States Shanghai China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Department of Computer Science Rice University HoustonTX United States

Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the growing attention in this field, many critical research questions remain under-explored. For instance, what diseases and LLM techniques have been investigated for diagnostic tasks? How can suitable LLM techniques and evaluation methods be selected for clinical decision-making? To answer these questions, we performed a comprehensive analysis of LLM-based methods for disease diagnosis. This scoping review examined the types of diseases, associated organ systems, relevant clinical data, LLM techniques, and evaluation methods reported in existing studies. Furthermore, we offered guidelines for data preprocessing and the selection of appropriate LLM techniques and evaluation strategies for diagnostic tasks. We also assessed the limitations of current research and delineated the challenges and future directions in this research field. In summary, our review outlined a blueprint for LLM-based disease diagnosis, helping to streamline and guide future research endeavors. Copyright © 2024, The Authors. All rights reserved.

关键词： Diseases

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VILMA: A ZERO-SHOT BENCHMARK FOR LINGUISTIC AND TEMPORAL GROUNDING IN VIDEO-LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Kesen, Ilker Pedrotti, Andrea Dogan, Mustafa Cafagna, Michele Acikgoz, Emre Can Parcalabescu, Letitia Calixto, Iacer Frank, Anette Gatt, Albert Erdem, Aykut Erdem, Erkut Koç University KUIS AI Center 2 Koç University Department of Computer Engineering Turkey University of Pisa Department of Computer Science Italy Institute of Information Science and Technologies "Alessandro Faedo" Italy Hacettepe University Department of Computer Engineering 6 Aselsan Research Turkey University of Malta Institute of Linguistics and Language Technology Malta Heidelberg University Department of Computational Linguistics Germany Amsterdam UMC University of Amsterdam Department of Medical Informatics Netherlands Amsterdam Public Health Methodology & Mental Health Amsterdam Netherlands Utrecht University Department of Information and Computing Sciences Netherlands

With the ever-increasing popularity of pretrained Video-Language Models (VidLMs), there is a pressing need to develop robust evaluation methodologies that delve deeper into their visio-linguistic capabilities. To address this challenge, we present VILMA1 (Video Language Model Assessment), a task-agnostic benchmark that places the assessment of fine-grained capabilities of these models on a firm footing. Task-based evaluations, while valuable, fail to capture the complexities and specific temporal aspects of moving images that VidLMs need to process. Through carefully curated counterfactuals, VILMA offers a controlled evaluation suite that sheds light on the true potential of these models, as well as their performance gaps compared to human-level understanding. VILMA also includes proficiency tests, which assess basic capabilities deemed essential to solving the main counterfactual tests. We show that current VidLMs’ grounding abilities are no better than those of vision-language models which use static images. This is especially striking once the performance on proficiency tests is factored in. Our benchmark serves as a catalyst for future research on VidLMs, helping to highlight areas that still need to be explored. © 2023, CC BY.

关键词： computational linguistics

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Accurately Simulating the Time Evolution of an Ising Model with Echo Verified Clifford Data Regression on a Superconducting Quantum Computer

arXiv

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arXiv 2024年

作者： Weaving, Tim Ralli, Alexis Love, Peter J. Succi, Sauro Coveney, Peter V. Centre for Computational Science Department of Chemistry University College London WC1H 0AJ United Kingdom Department of Physics and Astronomy Tufts University MedfordMA02155 United States Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Center for Life Nano-Neuro Science @ La Sapienza Italian Institute of Technology Roma00161 Italy Department of Mechanical Engineering University College London WC1E 7JE United Kingdom Department of Physics Harvard University CambridgeMA02138 United States Advanced Research Computing Centre University College London WC1H 0AJ United Kingdom Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands

We present an error mitigation strategy composed of Echo Verification (EV) and Clifford Data Regression (CDR), the combination of which allows one to learn the effect of the quantum noise channel to extract error mitigated estimates for the expectation value of Pauli observables. We analyse the behaviour of the method under the depolarizing channel and derive an estimator for the depolarization rate in terms of the ancilla purity and postselection probability. We also highlight the sensitivity of this probability to noise, a potential bottleneck for the technique. We subsequently consider a more general noise channel consisting of arbitrary Pauli errors, which reveals a linear relationship between the error rates and the estimation of expectation values, suggesting the learnability of noise in EV by regression techniques. Finally, we present a practical demonstration of Echo Verified Clifford Data Regression (EVCDR) on a superconducting quantum computer and observe accurate results for the time evolution of an Ising model over spin-lattices consisting of up to 35 sites and circuit depths up to 173 entangling layers. © 2024, CC BY.

关键词： Qubits

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Temporal networks provide a unifying understanding of the evolution of cooperation

arXiv

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arXiv 2023年

作者： Li, Aming Meng, Yao Zhou, Lei Masuda, Naoki Wang, Long Center for Systems and Control College of Engineering Peking University Beijing100871 China Center for Multi-Agent Research Institute for Artificial Intelligence Peking University Beijing100871 China School of Automation Beijing Institute of Technology Beijing100081 China Department of Mathematics State University of New York at Buffalo BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260 United States

Understanding the evolution of cooperation in structured populations represented by networks is a problem of long research interest, and a most fundamental and widespread property of social networks related to cooperation phenomena is that the node’s degree (i.e., number of edges connected to the node) is heterogeneously distributed. Previous results indicate that static heterogeneous (i.e., degree-heterogeneous) networks promote cooperation in stationarity compared to static regular (i.e., degree-homogeneous) networks if equilibrium dynamics starting from many cooperators and defectors is employed. However, the above conclusion reverses if we employ non-equilibrium stochastic processes to measure the fixation probability for cooperation, i.e., the probability that a single cooperator successfully invades a population. Here we resolve this conundrum by analyzing the fixation of cooperation on temporal (i.e., time-varying) networks. We theoretically prove and numerically confirm that on both synthetic and empirical networks, contrary to the case of static networks, temporal heterogeneous networks can promote cooperation more than temporal regular networks in terms of the fixation probability of cooperation. Given that the same conclusion is known for the equilibrium fraction of cooperators on temporal networks, the present results provide a unified understanding of the effect of temporal degree heterogeneity on promoting cooperation across two main analytical frameworks, i.e., equilibrium and non-equilibrium ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Application of online science practicum by using Microsoft Teams and Learning Management System (LMS) during the Covid 19 pandemic 10

Application of online science practicum by using Microsoft T...

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10th National Physics Seminar, SNF 2021

作者： Pertiwi, W.A. Sanjaya, L.A. Nadiyah Hasirah Haramen, M. Budi, A.S. Zarvianti, E. Puspa, R.W.D. Sianipar, L.K. Qoiriyah, W. Sitompul, H. Islamic Education Management Study Program Faculty Islamic of Education and Teacher Training Muhammad Azim Institute Jambi Indonesia Department of Physics Faculty of Mathematics and Natural Science Universitas Negeri Jakarta Indonesia Sharia Economics Study Program Faculty of Sharia and Law Muhammad Azim Islamic Institute Jambi Indonesia Physics Education Study Program STKIP Muhammadiyah Sungai Penuh Indonesia Kartika VIII-1 Senior High School Jakarta Indonesia Department of Physics Education Universitas Pelita Harapan Tangerang15811 Indonesia Informatics Engineering Study Program Faculty of Engineering Sheikh Yusuf Islamic University Tangerang Indonesia

The research objective of this is to see how to apply science practicum online using Microsoft Teams and Learning Management System (LMS). The form of this research is classroom action research which aims to apply the online science practicum during the Covid 19 pandemic. Therefore, it is carried out using two cycles in the application of online science practicum. The subjects of this study were students of the seventh semester in the Natural science practicum course. Through observation and evaluation, it is shown that in the cycle I the percentage of practicum implementation which includes aspects of preparation, process and data and practicum results is 87.5%, in cycle II it has increased to 92.5%. In the assessment aspect of the practicum report, the average practicum assessment obtained in the first cycle was 3.09 from the highest score of 4 and in the second cycle was 3.7, while the aspect of student knowledge about the use of the Microsoft team which was taken using google form was 93%. In the aspect of student knowledge about the use of Learning Management System (LMS) by 75% and aspects of student understanding in the implementation of online science practicum using Microsoft Teams by 75%. © 2021 institute of Physics Publishing. All rights reserved.

关键词： Students

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On the robustness of EEG tensor completion methods

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science China(Technological sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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Benchmarking noisy intermediate scale quantum error mitigation strategies for ground state preparation of the HCl molecule

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Physical Review Research 2023年第4期5卷 043054-043054页

作者： Tim Weaving Alexis Ralli William M. Kirby Peter J. Love Sauro Succi Peter V. Coveney Centre for Computational Science Department of Chemistry University College London WC1H 0AJ London United Kingdom Department of Physics and Astronomy Tufts University Medford Massachusetts 02155 USA Computational Science Initiative Brookhaven National Laboratory Upton New York 11973 USA Center for Life Nano-Neuro Science @ La Sapienza Italian Institute of Technology 00161 Roma Italy Department of Mechanical Engineering University College London WC1E 7JE London United Kingdom Department of Physics Harvard University Cambridge Massachusetts 02138 USA Advanced Research Computing Centre University College London WC1H 0AJ London United Kingdom Informatics Institute University of Amsterdam Amsterdam 1098 XH Netherlands

Due to numerous limitations including restrictive qubit topologies, short coherence times, and prohibitively high noise floors, few quantum chemistry experiments performed on existing noisy intermediate-scale quantum hardware have achieved the high bar of chemical precision, namely energy errors to within 1.6 mHa of full configuration interaction. To have any hope of doing so, we must layer contemporary resource reduction techniques with best-in-class error mitigation methods; in particular, we combine the techniques of qubit tapering and the contextual subspace variational quantum eigensolver with several error mitigation strategies comprised of measurement-error mitigation, symmetry verification, zero-noise extrapolation, and dual-state purification. We benchmark these strategies across a suite of eight 27-qubit IBM Falcon series quantum processors, taking preparation of the HCl molecule's ground state as our testbed.

关键词： Measurement-based quantum computing Quantum algorithms Quantum algorithms for chemical calculations Quantum benchmarking Quantum circuits Quantum computation Quantum gates Quantum protocols Quantum simulation Quantum software Quantum chemistry methods Variational approach

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Artificial intelligence accelerates efficient mining of functional peptides

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Life Medicine 2023年第2期2卷 2页

作者： Xudong Zhang Cesar de la Fuente-Nunez Jun Wang CAS Key Laboratory of Pathogenic Microbiology and Immunology Institute of Microbiology Chinese Academy of Sciences Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA 19104 USA Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia PA 19104 USA Penn Institute for Computational Science University of Pennsylvania Philadelphia PA 19104 USA

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Multi-level phase-change memory with ultralow power consumption and resistance drift

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science Bulletin 2021年第21期66卷 2217-2224,M0004页

作者： Bin Liu Kaiqi Li Wanliang Liu Jian Zhou Liangcai Wu Zhitang Song Stephen R.Elliott Zhimei Sun School of Materials Science and Engineering Beihang UniversityBeijing 100191China Center for Integrated Computational Materials Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijing 100191China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China College of Science Donghua UniversityShanghai 201620China Department of Chemistry University of CambridgeCambridge CB21EWUK Physical and Theoretical Chemistry Laboratory University of OxfordOxford OX13QZUK

By controlling the amorphous-to-crystalline relative volume,chalcogenide phase-change memory materials can provide multi-level data storage(MLS),which offers great potential for high-density storageclass memory and neuro-inspired ***,this type of MLS system suffers from high power consumption and a severe time-dependent resistance increase(‘‘drift")in the amorphous phase,which limits the number of attainable storage ***,we report a new type of MLS system in yttriumdoped antimony telluride,utilizing reversible multi-level phase transitions between three states,i.e.,amorphous,metastable cubic and stable hexagonal crystalline phases,with ultralow power consumption(0.6–4.3 p J)and ultralow resistance drift for the lower two states(power-law exponent<0.007).The metastable cubic phase is stabilized by yttrium,while the evident reversible cubic-to-hexagonal transition is attributed to the sequential and directional migration of Sb ***,the decreased heat dissipation of the material and the increase in crystallinity contribute to the overall high *** study opens a new way to achieve advanced multi-level phase-change memory without the need for complicated manufacturing procedures or iterative programming operations.

关键词： Phase-change memory Multi-level storage Antimony telluride Yttrium doping Power consumption Resistance drift

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