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Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), International Conference on

作者： Kapil Joshi Rajiv Kumar Anil Kumar Jagdeep Reshi Aditi Sharma Ankur Dumka Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Data Science Research Group School of Computing DIT University Dehradun School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Department of CSE Women Institute of Technology Dehradun Department of CSE Graphic Era Deemed to be University Dehradun

XAMPP is an acronym that stands for Cross-Platform, Apache, MySQL, PHP, and Perl, with the Ps standing for PHP and Perl, respectively. This article investigates the various helpful assets and procedures which are utilized in site creation. In a discussion, chiefly dependent on a nearby host named Xampp stage, we likewise address the procedure that follows. Next, we break down different web application creation frameworks. Furthermore,, We're discussing life cycle model and web application engineering creation. Frequently remembered for this article is the result of various research articles for buyers to consider the issues they that experience. This paper discusses the innovation utilized in this turn of events, PHP and with screen captures clarified its highlights with Xampp subsequently. It is normal that it would have significant criticism context for the strategy.

关键词： Technological innovation Systematics Terminology Social networking (online) XML Programming Web servers

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High-Order Randomized Compiler for Hamiltonian Simulation

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PRX Quantum 2024年第2期5卷 020330-020330页

作者： Kouhei Nakaji Mohsen Bagherimehrab Alán Aspuru-Guzik Chemical Physics Theory Group Department of Chemistry University of Toronto Toronto Ontario Canada M5S 3H6 Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama Kanagawa 223-8522 Japan Department of Computer Science University of Toronto Toronto Ontario Canada M5S 2E4 Vector Institute for Artificial Intelligence Toronto Ontario Canada M5G 1M1 Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Ontario Canada M5S 3E5 Department of Materials Science & Engineering University of Toronto Toronto Ontario Canada M5S 3E4 Canadian Institute for Advanced Research (CIFAR) Toronto Ontario Canada M5S 1M1

Hamiltonian simulation is known to be one of the fundamental building blocks of a variety of quantum algorithms such as its most immediate application, that of simulating many-body systems to extract their physical properties. In this work, we present qSWIFT, a high-order randomized algorithm for Hamiltonian simulation. In qSWIFT, the required number of gates for a given precision is independent of the number of terms in the Hamiltonian, while the systematic error is exponentially reduced with regard to the order parameter. In this respect, our qSWIFT is a higher-order counterpart of the previously proposed quantum stochastic drift protocol (qDRIFT), the number of gates in which scales linearly with the inverse of the precision required. We construct the qSWIFT channel and establish a rigorous bound for the systematic error quantified by the diamond norm. qSWIFT provides an algorithm to estimate given physical quantities by using a system with one ancilla qubit, which is as simple as other product-formula-based approaches such as regular Trotter-Suzuki decompositions and qDRIFT. Our numerical experiment reveals that the required number of gates in qSWIFT is significantly reduced compared to qDRIFT. In particular, the advantage is significant for problems where high precision is required; e.g., to achieve a systematic relative propagation error of 10−6, the required number of gates in third-order qSWIFT is 1000 times smaller than that of qDRIFT.

关键词： Quantum computation Quantum simulation

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CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification

CARZero: Cross-Attention Alignment for Radiology Zero-Shot C...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haoran Lai Qingsong Yao Zihang Jiang Rongsheng Wang Zhiyang He Xiaodong Tao S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui P.R.China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu P.R.China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Medical Business Department iFlytek Co.Ltd Hefei China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The advancement of Zero-Shot Learning in the medi-cal domain has been driven forward by using pretrained models on large-scale image-text pairs, focusing on image-text alignment. However, existing methods primarily rely on cosine similarity for alignment, which may not fully capture the complex relationship between medical images and reports. To address this gap, we introduce a novel approach called Cross-Attention Alignment for Radiology Zero-Shot Classification (CARZero). Our approach innovatively leverages cross-attention mechanisms to process image and report features, creating a Similarity Representation that more accurately reflects the intricate relationships in medical semantics. This representation is then linearly projected to form an image-text similarity matrix for cross-modality alignment. Additionally, recognizing the pivotal role of prompt selection in zero-shot learning, CARZero in-corporates a Large Language Model-based prompt alignment strategy. This strategy standardizes diverse diagnostic expressions into a unified format for both training and inference phases, overcoming the challenges of manual prompt design. Our approach is simple yet effective, demonstrating state-of-the-art performance in zero-shot classification on five official chest radiograph diagnostic test sets, including remarkable results on datasets with long-tail distributions of rare diseases. This achievement is attributed to our new image-text alignment strategy, which effectively addresses the complex relationship between medical images and reports. Code and models are available at https://***/laihaoran/CARZero.

关键词： Training Zero-shot learning Semantics Focusing Manuals Radiology Pattern recognition

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Simultaneous Q-Space Sampling Optimization and Reconstruction for Fast and High-Fidelity Diffusion Magnetic Resonance Imaging

Simultaneous Q-Space Sampling Optimization and Reconstructio...

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IEEE International Symposium on Biomedical Imaging

作者： Jing Yang Jian Cheng Cheng Li Wenxin Fan Juan Zou Ruoyou Wu Qiegen Liu Hairong Zheng Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou China Department of Electronic Information Engineering Nanchang University Nanchang China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the in vivo human brain. However, to effectively capture the intricate characteristics of water diffusion at various directions and scales, it is important to employ comprehensive q-space sampling. Unfortunately, this requirement leads to long scan times, limiting the clinical applicability of dMRI. To address this challenge, we propose SSOR, a Simultaneous q-Space sampling Optimization and Reconstruction framework. We jointly optimize a subset of q-space samples using a continuous representation of spherical harmonic functions and a reconstruction network. Additionally, we integrate the unique properties of diffusion magnetic resonance imaging (dMRI) in both the q-space and image domains by applying l1-norm and total-variation *** experiments conducted on HCP data demonstrate that SSOR has promising strengths both quantitatively and qualitatively and exhibits robustness to noise.

关键词： Water In vivo Limiting Magnetic resonance imaging Harmonic analysis Robustness Image reconstruction

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Using Machine Learning to Predict the Users Ratings on TripAdvisor Based on Their Reviews

Using Machine Learning to Predict the Users Ratings on TripA...

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International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2021

作者： Carneiro, João Meira, Jorge Novais, Paulo Marreiros, Goreti GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering Polytechnic of Porto Porto4200-072 Portugal CITIC – Centro de Investigación en Tecnologías de la Información y las Comunicaciones University of A Coruña A Coruña15071 Spain ALGORITMI Centre University of Minho Guimarães4800-058 Portugal

ISBN: (纸本)9783030857097

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we intend to facilitate the acquiring information process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning algorithms (Logistic Regression, Random Forest, Decision Tree, K-Nearest Neighbors and Recurrent Neural Networks) and Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. The experiments carried out showed that the developed models can predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. © 2021, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

arXiv

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arXiv 2024年

作者： Haag, David Kumar, Devender Gruber, Sebastian Hofer, Dominik Sareban, Mahdi Treff, Gunnar Niebauer, Josef Bull, Christopher Schmidt, Albrecht Smeddinck, Jan David Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Department of Psychology Paris-Lodron-University of Salzburg Salzburg Austria Digital Health Information Systems Center for Health & Bioresources AIT Austrian Institute of Technology GmbH Graz Austria University of Southern Denmark Odense Denmark Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria Human Motion Analytics Salzburg Research Forschungsgesellschaft Salzburg Austria LMU Munich Munich Germany University Institute for Sports Medicine Prevention and Rehabilitation Salzburg Austria Institute of Molecular Sports and Rehabilitation Medicine Paracelsus Medical University Salzburg Austria Open Lab School of Computing Newcastle University Newcastle upon Tyne United Kingdom

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual’s current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing. © 2024, CC BY.

关键词： Electronic health record

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Solvent Selection for Anthrocyanin Dye Extraction from Kigelia Africana and Hibiscus Sabdariffa for Dye Sensitized Solar Cells

SSRN

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SSRN 2024年

作者： Kumar, T. Satish Shalini, S. Roy, Anurag Prasanna, S. Balasundaraprabhu, R. Sundaram, Senthilarasu Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Physics PSG Polytechnic College Tamilnadu Coimbatore India Solar Energy Research Group Environment and Sustainability Institute University of Exeter Penryn Campus CornwallTR10 9FE United Kingdom Department of Physics PSG College of Technology Tamil Nadu Coimbatore India School of Computing Engineering and Digital Technologies Teesside University Tees Valley MiddlesbroughTS1 3BX United Kingdom

The utilization of natural pigments in dye-sensitized solar cells (DSSCs) has garnered significant attention due to their eco-friendly and sustainable characteristics. Natural pigments, often derived from plant sources, offer an alternative to synthetic dyes in enhancing light absorption for solar energy conversion. The main flavonoid pigment in the petals of Kigelia Africana and the calyx of Hibiscus Sabdariffa is anthocyanin, responsible for the vibrant red, maroon, and purple hues in flowers. This pigment can modulate incident light on flowers, prompting its selection for detailed investigation. TiO2 nanostructures were synthesized using a one-step hydrothermal method, revealing the formation of nanorods and a single-phase rutile structure through FESEM and XRD analyses, respectively. The study aimed to assess the impact of various solvents on the extraction of natural dyes, which were subsequently sensitized on TiO2 photoanodes for DSSC applications. Four solvents-water, water with HCl, ethanol, and citric acid-were employed to extract natural dyes from Kigelia Africana's petals and Hibiscus Sabdariffa's calyx. Notably, dyes extracted with citric acid demonstrated promising results. The conversion efficiency of DSSCs fabricated with Kigelia Africana dye and Hibiscus Sabdariffa dye, extracted using citric acid as the solvent, was found to be 0.87% and 0.92%, respectively. The implications of these findings are discussed. © 2024, The Authors. All rights reserved.

关键词： Dye-sensitized solar cells

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Uncertainty Quantification of Signal Integrity Analysis for Neuromorphic Chips

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2022年 1卷 160-169页

作者： Hanzhi Ma Da Li Tuomin Tao Xingjian Shangguan En-Xiao Liu Jose Schutt-Aine Andreas C. Cangellaris Er-Ping Li College of Information Science and Electronic Engineering ZJU-UIUC Institute Zhejiang University Hangzhou China Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang University Hangzhou China Zhejiang-Singapore Innovation and AI Joint Research Lab China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA A*STAR Institute of High Performance Computing Singapore

A dimensionality reduction based neural network framework is introduced for uncertainty quantification of time-domain response based on system uncertain design parameters for neuromorphic chips. The proposed method firstly makes use of the singular value decomposition (SVD) method to find the basis functions and corresponding coefficients of time-domain response, of which coefficients are used as a lower dimensional target outputs in neural network model compared with time sampling points prediction. This newly proposed method then develops an integrated neural network structure to simultaneously find the mean and variance of target coefficients with a combined definition of loss function, which can be utilized together with basis functions to construct the prediction interval of time-domain response. A memrisor-based crossbar array is applied in this work to verify the performance of the proposed method with the comparison of Monte Carlo method.

关键词： Biological neural networks Uncertainty Memristors Time-domain analysis Integrated circuit modeling Neuromorphics Signal integrity Neural networks Neuromorphics

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STAR-RIS Assisted MISO-NOMA Networks: A Simultaneous Signal Enhancement and Interference Mitigation Design

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IEEE Transactions on Communications 2024年

作者： Li, Jie Song, Zhengyu Hou, Tianwei Huang, Chongwen Li, Anna Zhou, Gui Liu, Yuanwei China Mobile Group Design Institute Company Ltd China Mobile Research and Development Center for Network Planning and Optimization Beijing100080 China Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Lancaster University School of Computing and Communications Lancaster United Kingdom Katholieke Universiteit Leuven KU Leuven Leuven Belgium Institute for Digital Communications Erlangen91054 Germany The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong

Simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) technique has recently received considerable attention due to its omni-directional radiation capability. In this paper, motivated by the interference-mitigation-based (IMB) and signal-enhancement-based (SEB) designs, we introduce an innovative STAR-RIS assisted simultaneous-signal-enhancement-and-interference-mitigation (SSEIM) design in non-orthogonal multiple access (NOMA) multiple-input single-output cellular communication networks. Our objective is to maximize the system spectral efficiency (SE) by jointly optimizing the reflection and transmission phase shifts at the STAR-RIS, the precoding matrix of BSs, and the power allocation factors of NOMA users. We propose a low-complexity simultaneous enhancement and mitigation algorithm. Furthermore, by exploiting the manifold optimization technique, we introduce the Riemannian conjugate gradient algorithm to solve the non-convex subproblems with unit modulus constraint. Our analysis reveals that the proposed SSEIM design exceeds the traditional RIS-aided SEB and IMB designs. © 1972-2012 IEEE.

关键词： Mobile telecommunication systems

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VarBen: Generating in Silico Reference Data Sets for Clinical Next-Generation Sequencing Bioinformatics Pipeline Evaluation

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Journal of Molecular Diagnostics 2021年第3期23卷 285-299页

作者： Li, Ziyang Fang, Shuangsang Zhang, Rui Yu, Lijia Zhang, Jiawei Bu, Dechao Sun, Liang Zhao, Yi Li, Jinming National Center for Clinical Laboratories Beijing Hospital National Center of Gerontology Beijing China Graduate School Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Medical Sciences Beijing China Beijing Engineering Research Center of Laboratory Medicine Beijing Hospital Beijing China

Next-generation sequencing is increasingly being adopted as a valuable method for the detection of somatic variants in clinical oncology. However, it is still challenging to reach a satisfactory level of robustness and standardization in clinical practice when using the currently available bioinformatics pipelines to detect variants from raw sequencing data. Moreover, appropriate reference data sets are lacking for clinical bioinformatics pipeline development, validation, and proficiency testing. Herein, we developed the Variant Benchmark tool (VarBen), an open-source software for variant simulation to generate customized reference data sets by directly editing the original sequencing reads. VarBen can introduce a variety of variants, including single-nucleotide variants, small insertions and deletions, and large structural variants, into targeted, exome, or whole-genome sequencing data, and can handle sequencing data from both the Illumina and Ion Torrent sequencing platforms. To demonstrate the feasibility and robustness of VarBen, we performed variant simulation on different sequencing data sets and compared the simulated variants with real-world data. The validation study showed that the simulated data are highly comparable to real-world data and that VarBen is a reliable tool for variant simulation. In addition, our collaborative study of somatic variant calling in 20 laboratories emphasizes the need for laboratories to evaluate their bioinformatics pipelines with customized reference data sets. VarBen may help users develop and validate their bioinformatics pipelines using locally generated sequencing data. © 2021 Association for Molecular Pathology and American Society for Investigative Pathology

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