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IEEE International Symposium on Information Theory

作者： Hao-Chung Cheng Li Gao Department of Electrical Engineering Graduate Institute of Communication Engineering Department of Mathematics National Taiwan University Taipei Taiwan Center for Quantum Science and Engineering National Taiwan University Hon Hai (Foxconn) Quantum Computing Center New Taipei City Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan Department of Mathematics University of Houston Houston TX USA

Convex splitting is a powerful technique in quantum information theory that applies to proving the achievability of numerous information-processing protocols. In this paper, we establish a one-shot error exponent and a one-shot strong converse for convex splitting with trace distance as the error criterion. Our result exhibits the following features. Firstly, the derived error exponent is given in terms of a generalized sandwiched Rényi mutual information which is additive for product states. Hence, our bound directly applies to the n-fold product scenario for any blocklength. Secondly, the one-shot error bound is meaningful in the sense that the error exponent is all positive on the achievable rate region. This leads to new one-shot exponent results in various tasks in quantum information theory. Conversely, we prove a one-shot strong converse to demonstrate that the convex splitting error converges to one exponentially fast at rates outside the achievable rate region. We also establish an optimal one-shot characterization of the sample complexity for convex splitting, which yields matched second-order asymptotics, showing the tightness of our result. This then leads to a stronger one-shot analysis in quantum information *** technique is to introduce a unified functional analytic approach using Kosaki’s noncommutative weighted L p norm. Our results then follow from tight analysis based on such a norm *** full version of the manuscript can be found at [arXiv:2304.12055] [1].

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Augmented Reality-Assisted Pediatric Surgery: A Machine Learning-Based Approach for Enhancing Accuracy

Augmented Reality-Assisted Pediatric Surgery: A Machine Lear...

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Advances in Computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sumedha Bhardwaj Swapna Narla Md Nurul Absur Shuvo Dutta J B Hyzil S M Krishna Ganesh Department of School of Computing DIT University Dehradun Uttarakhand India Tek Yantra Inc California USA Computer Science CUNY Graduate Center New York New York USA Department of Physics Western Michigan University USA Department of Zoology Govt College for Women Affiliated to University of Kerala Thiruvananthapuram Department of Artificial Intelligence and Data Science New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Numerous advancements have been identified as potentially disruptive innovations in the surgical workplace as surgical workflow continues to improve in the digital age. The application of virtual reality (VR) and augmented reality (AR) within health care to enhance the use of data for medical reasons is unavoidable given their rapid expansion in availability, affordability, and accessibility. Using augmented reality may help paediatric orthopaedic and trauma surgeons overcome the difficult problem of comprehending the spatial linkages among the implants, anatomy, and their instruments. As needs be, the current examination proposes another AR of the surgical scene that deftly incorporates the various information sources given by a mobile C-arm and a Kinect RGB-depth sensor. This paper present a learning-based worldview to perceive the significant items or life structures in both Kinect and data of X-ray, and to produce an object unambiguous pixel-wise alpha guide that permits importance based combination of the X-ray pictures and the video inside a solitary view. We demonstrate very encouraging results in 10 simulated procedures with the goal of giving physicians a better grasp of the surgical environment and an enhanced perception.

关键词： Pediatrics Accuracy Navigation Instruments Surgery Medical services Information age Object recognition Augmented reality X-ray imaging

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Development of a Nursing Skill Training System Based on Manipulator Variable Admittance Control

Development of a Nursing Skill Training System Based on Mani...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Yuhao Zhou Ryota Takamido Masako Kanai-Pak Jukai Maeda Yasuko Kitajima Mitsuhiro Nakamura Noriaki Kuwahara Taiki Ogata Jun Ota Department of Precision Engineering Graduate School of Engineering The University of Tokyo Tokyo Japan Research Into Artifacts Center for Engineering The University of Tokyo Tokyo Japan Faculty of Nursing Kanto Gakuin University Yokohama Japan Faculty of Nursing Tokyo Ariake University of Medical and Health Sciences Tokyo Japan Department of Advanced Fibro-Science Kyoto Institute of Technology Kyoto Japan Department of Computer Science School of Computing Tokyo Institute of Technology Tokyo Japan

The use of robot-based skill training systems is an emerging topic in nursing education, as many innovative robotic systems have been developed to simulate real patients, offering a safe and self-directed platform for nursing students to learn and practice their skills. Among these training systems, several human patient simulators (HPS) have been proposed to simulate the patients performance during patient transfer; however, without an entire motion model and control strategy, most HPS show limited effectiveness in simulating actual patient behavior. Herein, this work presents a novel patient transfer training system that has the potential of improving the practical skills of nursing students. First, we propose a simplified force model for patient transfer motion to estimate the contact force in the absence of wearable sensors. We then reveal the correlation between the nurses force and patients motion during the transfer through the utilization of the variable admittance model. Finally, we demonstrate the feasibility of the proposed patient transfer training system by performing several experiments on a UR10e robot. To the best of our knowledge, this system is the first patient transfer skills training system that simulates force interaction between nurse and patient using a collaborative robot.

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Correction to: Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review (Archives of Computational Methods in engineering, (2024), 10.1007/s11831-024-10145-z)

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Archives of Computational Methods in engineering 2024年 1-1页

作者： Wei Ze Chew, Alvin He, Renfei Zhang, Limao National University of Singapore Singapore NUS School of Computing COM1 13 Computing Dr 117417 Singapore School of Civil and Environmental Engineering Nanyang Technological University Singapore 50 Nanyang Avenue Singapore639798 Singapore Interdisciplinary Graduate Programme Nanyang Technological University Singapore 1 Cleantech Loop Singapore639798 Singapore School of Civil and Hydraulic Engineering National Center of Technology Innovation for Digital Construction Huazhong University of Science and Technology 1037 Luoyu Road Hongshan District Hubei Wuhan430074 China

In this article the title was incorrectly given as ‘Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: Concept, State-of-the-Art, Challenges and Opportunities’ but should have been ‘Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review’. The original article has been corrected. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

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Adaptive Circuit Learning of Born Machine: Towards Realization of Amplitude Embedding and Data Loading

arXiv

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arXiv 2023年

作者： Li, Chun-Tse Cheng, Hao-Chung Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taipei106 Taiwan Department of Mathematics National Taiwan University Taipei106 Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan Physics Division National Center for Theoretical Sciences Taipei10617 Taiwan Hon Hai Quantum Computing Research Center Taipei Taiwan

With the progress in the quantum algorithm in recent years, much of the existing literature claims the exponential quantum advantage against their classical counterpart. However, many of these successes hinge on the assumption that arbitrary states can be efficiently prepared in quantum circuits. In reality, crafting a circuit to prepare a generic n-qubit quantum state demands an operation count on the order of O(2n), which is prohibitively demanding for the quantum algorithm to demonstrate its advantage against the classical one. To tackle this data-loading problem, numerous strategies have been put forward. Nonetheless, most of these approaches only consider a very simple and easy-to-implement circuit structure, which has been shown to suffer from serious optimization issues. In this study, we harness quantum circuits as Born machines to generate probability distributions. Drawing inspiration from methods used to investigate electronic structures in quantum chemistry and condensed matter physics, we present a novel algorithm"Adaptive Circuit Learning of Born Machine" (ACLBM) that dynamically expands the ansatz circuit. Our algorithm is tailored to selectively integrate two-qubit entangled gates that best capture the complex entanglement present within the target state. Empirical results underscore the proficiency of our approach in encoding real-world data through amplitude embedding, demonstrating not only compliance with but also enhancement over the performance benchmarks set by previous research. © 2023, CC BY.

关键词： Quantum chemistry

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Telemedicine and Smart Healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies

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International Journal of Communications, Network and System sciences 2023年第3期16卷 31-51页

作者： Taofik Ahmed Suleiman Abdulkareem Adinoyi Department of Medical Imaging and Computing University of Girona Girona Spain Graduate School of Science and Technology Medical Imaging and Applications (MAIA) University of Burgundy Le Creusot France Department of Electrical and Information Engineering MAIA University of Cassino and Southern Lazio Cassino Italy Research and Innovation Department academy from stc Saudi Telecommunication Company (stc) Riyadh Saudi Arabia

This paper discusses telemedicine and the employment of advanced mobile technologies in smart healthcare delivery. It covers the technological advances in connected smart healthcare, including the roles of artificial intelligence, machine learning, 5G and IoT platforms, and other enabling technologies. It also presents the challenges and potential risks that could arise from delivering connected smart healthcare services. Healthcare delivery is witnessing revolutions engineered by the developments in mobile connectivity and the plethora of platforms, applications, sensors, devices, and equipment that go along with it. Human society is evolving fast in response to these technological developments, which are also pushing the connectivity-providing sector to create and adopt new waves of network technologies. Consequently, new communications technologies have been introduced into the healthcare system and many novel applications have been developed to make it easier for sharing data in various forms and volumes within health-related services. These applications have also made it possible for telemedicine to be effectively adopted. This paper provides an overview of some of the recent developments within the space of mobile connectivity and telemedicine.

关键词： Telemedicine Smart Healthcare 5G Artificial Intelligence Machine Learning Internet-of-Medical-Things

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Does the full configuration interaction method based on quantum phase estimation with Trotter decomposition satisfy the size consistency condition?

arXiv

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arXiv 2024年

作者： Sugisaki, Kenji Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kanagawa Kawasaki212-0032 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan Centre for Quantum Engineering Research and Education TCG Centres for Research and Education in Science and Technology Sector V Salt Lake Kolkata700091 India

Electronic structure calculations of atoms and molecules are considered to be a promising application for quantum computers. Two key algorithms, the quantum phase estimation (QPE) and the variational quantum eigensolver (VQE), have been extensively studied. The condition that the energy of a dimer consisting of two monomers separated by a large distance should be equal to twice the energy of a monomer, known as size consistency, is essential in quantum chemical calculations. Recently, we reported that the size consistency condition can be violated by Trotterization in the unitary coupled cluster singles and doubles (UCCSD) ansatz in VQE when employing molecular orbitals delocalized to the dimer (K. Sugisaki et al., J. Comput. Chem., published online;DOI:10.1002/jcc.27438). It is well known that the full configuration interaction (full-CI) energy is invariant to arbitrary rotations of molecular orbitals, and therefore the QPE-based full-CI should theoretically satisfy the size consistency. However, Trotterization of the time evolution operator can break the size consistency conditions. In this work, we investigated whether size consistency can be maintained with Trotterization of the time evolution operator in QPE-based full-CI calculations. Our numerical simulations revealed that size consistency in QPE-based full-CI is not automatically violated by using molecular orbitals delocalized to the dimer, but employing an appropriate Trotter decomposition condition is crucial to maintain size consistency. We also report on the acceleration of QPE simulations through the sequential addition of ancillary qubits. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular orbitals

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HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids

arXiv

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arXiv 2024年

作者： Wisnu, Dyah A.M.G. Rini, Stefano Zezario, Ryandhimas E. Wang, Hsin-Min Tsao, Yu Taiwan International Graduate Program—Social Network and Human Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan College of Informatics National Chengchi University Taipei Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

This paper introduces HAAQI-Net, a non-intrusive deep learning-based music audio quality assessment model for hearing aid users. Unlike traditional methods like the Hearing Aid Audio Quality Index (HAAQI) that require intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. By utilizing a Bidirectional Long Short-Term Memory (BLSTM) architecture with attention mechanisms and features extracted from the pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. Experimental results demonstrate HAAQI-Net’s effectiveness, achieving a Linear Correlation Coefficient (LCC) of 0.9368, a Spearman’s Rank Correlation Coefficient (SRCC) of 0.9486, and a Mean Squared Error (MSE) of 0.0064 and inference time significantly reduces from 62.52 to 2.54 seconds. To address computational overhead, a knowledge distillation strategy was applied, reducing parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores like the Mean Opinion Score (MOS) through fine-tuning. This adaptation significantly improved prediction accuracy, validated through statistical analysis. Furthermore, the robustness of HAAQI-Net was evaluated under varying Sound Pressure Level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a scalable solution for music audio quality assessment in hearing aid applications, contributing to efficient and accurate models in audio signal processing and hearing aid technology. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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