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IEEE International Symposium on Information Theory

作者： Jun-Kai You Hao-Chung Cheng Yen-Huan Li Department of Computer Science and Information Engineering National Taiwan University Department of Mathematics National Taiwan University Department of Electrical Engineering and Graduate Institute of Communication Engineering National Taiwan University Hon Hai (Foxconn) Quantum Computing Research Center Center for Quantum Science and Engineering National Taiwan University

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

Quantum information quantities play a substantial role in characterizing operational quantities in various quantum information-theoretic problems. We consider the numerical computation of four quantum information quantities: Petz-Augustin information, sandwiched Augustin information, conditional sandwiched Rényi entropy, and sandwiched Rényi information. To compute these quantities requires minimizing some order-α quantum Rényi divergences over the set of quantum states. Whereas the optimization problems are obviously convex, they violate standard bounded gradient/Hessian conditions in the literature, so existing convex optimization methods and their convergence guarantees do not directly apply. In this paper, we propose a new class of convex optimization methods called mirror descent with the Polyak step size. We prove their convergence under a weak condition, showing that they provably converge for minimizing quantum Rényi divergences. Numerical experiment results show that entropic mirror descent with the Polyak step size converges fast in minimizing quantum Rényi divergences.

关键词： Quantum computing Quantum state Convex functions Entropy Mirrors Standards Optimization

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning Program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Parkinson's Disease Detection Based on Vocal Biomarkers and Machine Learning Approach

Parkinson's Disease Detection Based on Vocal Biomarkers and ...

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Telecommunications Conference (ITC-Egypt), International

作者： Ayat Alrosan M. Abdel-Aty Mohamed Hafez Shawkat Alkhazaleh Mohanad A.Deif Rania ELGohary School of Computing Skyline University College Sharjah United Arab Emirates Deanship of Research and Graduate Studies Ahlia University Kingdom of Bahrain Faculty of Engineering FEQS INTI-IU-University Nilai Malaysia Department of Mathematics Faculty of Science and Information Technology Jadara University Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Parkinson's disease (PD) must be identified early to provide prompt treatment and better patient outcomes. This study looks into using machine learning methods in conjunction with vocal biomarkers to identify Parkinson's disease early. Vocal data was analyzed using four techniques: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest, and Logistic Regression algorithms. The third strategy, which used the Synthetic Minority Over-sampling Technique (SMOTE) for data balance, performed exceptionally well and stood out among the others. Utilizing SMOTE for dataset balancing, the Random Forest algorithm produced an exceptional 98.3 % accuracy. The study highlights the effectiveness of voice biomarkers, examines the various approaches used, and draws comparisons between them.

关键词： Support vector machines Machine learning algorithms Parkinson's disease Computational modeling Biomarkers Predictive models Nearest neighbor methods

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Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs

arXiv

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arXiv 2024年

作者： Chen, Junhao Li, Xiang Ye, Xiaojun Li, Chao Fan, Zhaoxin Zhao, Hao Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China School of Software and Microelectronics Peking University China College of Computer Science Zhejiang University China College of Computer Science and Technology Harbin Engineering University China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence Beihang University China

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don’t fully unleash human creativity. We often imagine 3D content made from multimodal inputs, such as what it would look like if my pet bunny were eating a doughnut on the table. In this paper, we explore a novel 3D AIGC approach: generating 3D content from IDEAs. An IDEA is a multimodal input composed of text, image, and 3D models. To our knowledge, this challenging and exciting 3D AIGC setting has not been studied before. We propose the new framework Idea23D, which combines three agents based on large multimodal models (LMMs) and existing algorithmic tools. These three LMM-based agents are tasked with prompt generation, model selection, and feedback reflection. They collaborate and critique each other in a fully automated loop, without human intervention. The framework then generates a text prompt to create 3D models that align closely with the input IDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods. To comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the Eval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation tasks. This dataset evaluates the alignment between generated 3D content and input IDEAs. Our user study and quantitative results show that Idea23D significantly improves the success rate and accuracy of 3D generation, with excellent compatibility across various LMM, Text-to-Image, and Image-to-3D models. Code and dataset are available at https://***/. © 2024, CC BY-NC-SA.

关键词： 3D modeling

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Dynamic Insights into Lower Urinary Tract Function: Exploring Intraindividual and Interindividual Variability through Personalized Mathematical Modeling

Dynamic Insights into Lower Urinary Tract Function: Explorin...

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

作者： Mohamed Zaid Caleb Hendrick Elie Alhajjar John Yin Zachary C. Danziger Giovanna Guidoboni Graduate School of Biomedical Science and Engineering University of Maine Orono ME USA Maine College of Engineering and Computing Orono ME USA Engineering and Applied Sciences Department RAND Arlington VA USA Chemical and Biological Engineering Wisconsin Institute for Discovery University of Wisconsin–Madison Madison Wisconsin USA Department of Rehabilitation Medicine Division of Physical Therapy Emory University Georgia USA W.H. Coulter Department of Biomedical Engineering Emory University Georgia USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study proposes a novel approach to obtain personalized estimates of Lower Urinary Tract Function leveraging on the simulated and experimentally acquired bladder pressure. Our findings reveal the model’s adaptability across intraindividual cycles and consistency within specific cohorts, shedding light on the importance of variability in the micturition process. The study emphasizes the significance of intraindividual and interindividual variability, paving the way for improved diagnostics and therapeutic strategies in the field of lower urinary function.

关键词： Neurological diseases Biomechanics Biological system modeling Bladder Rats Biomarkers Mathematical models Data models Complexity theory engineering in medicine and biology

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Hybrid Machine Learning Approach for Enhanced Vulnerability Detection in Cloud Environments Using NIST and MITRE Frameworks

Hybrid Machine Learning Approach for Enhanced Vulnerability ...

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Advanced Research in computing (ICARC), International Conference on

作者： N W Chanaka Lasantha M W P Maduranga Ruvan Abeysekara Valmik Tilwari Nilotpal Chakraborty Debashree Sharma Faculty of Graduate Studies IIC University of Technology Phnom Penh Cambodia Department of Electrical and Electronic Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Faculty of Computing BCAS Campus Colombo Sri Lanka Department. of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science Engineering Indian Institute of Information Technology Guwahati India Department of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Cloud security is challenged by constant adaptive cyber threats and traditional detection methods lack real time adaptability. In this paper, we propose a new hybrid ML approach stitching data from National Institute of Standards and Technology (NIST) and MITRE Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) databases together. In contrast to existing methods, this framework combines, in unique and unprecedented fashion, ensemble ML models and Realtime data processing for increased detection accuracy and reduced false positives. This approach addresses specific challenges in the cloud environments and by leveraging standardised frameworks, it offers actionable audiences for efficient and effective vulnerability management process. The framework is validated by the experimental results which prove its robustness to evolution of the cybersecurity demands.

关键词： Adaptation models Accuracy Cloud computing security Scalability NIST Transformers Data models Threat assessment Robustness Anomaly detection

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Explicit error bounds with commutator scaling for time-dependent product and multi-product formulas

arXiv

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arXiv 2024年

作者： Mizuta, Kaoru Ikeda, Tatsuhiko N. Fujii, Keisuke Department of Applied Physics Graduate School of Engineering The University of Tokyo Hongo 7-3-1 Bunkyo Tokyo113-8656 Japan Photon Science Center Graduate School of Engineering The University of Tokyo Hongo 7-3-1 Bunkyo Tokyo113-8656 Japan Hirosawa 2-1 Wako Saitama351-0198 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University 560-0043 Japan Fujitsu Quantum Computing Joint Research Division at QIQB Osaka University 1-2 Machikaneyama Toyonaka560-0043 Japan

Product formula (PF), which approximates the time evolution under a many-body Hamiltonian by the product of local time evolution operators, is one of the central approaches for simulating quantum dynamics by quantum computers. It has been of great interest whether PFs have a bound of the error from the exact time evolution, which is expressed by commutators among local terms (called commutator scaling), since it brings the substantial suppression of the computational cost in terms of the system size. Although a series of recent studies have revealed the presence and the explicit formulas of the PF error bounds for time-independent systems, those for time-dependent Hamiltonians remain to be a difficult problem except for low-order PFs. In this paper, we derive an explicit error bound of generic PFs for smooth time-dependent Hamiltonians H(t) = PΓγ=1 Hγ(t), which is expressed by commutators among the terms {Hγ(t)}Γγ=1 and their time derivatives. This error bound can also host the substantial suppression in the system size for generic local Hamiltonians with finite-, short-, and long-ranged interactions, thereby giving a much better estimate of gate counts. Our derivation employs the so-called Floquet theory;Embedding generic smooth time-dependent Hamiltonians into time-periodic ones, we map the time-dependent PF error to the time-independent one defined on an infinite-dimensional space. This approach allows to obtain the error bounds not only for the ordinary time-dependent PF but also for its various family, such as the one for non-unitary dynamics. In particular, we also clarify the explicit error bound of a time-dependent multi-product formula, with which we can achieve much smaller error by a linear combination of time-dependent PFs. Our results will shed light on applications of near-term and future quantum computers, ranging from quantum simulation of nonequilibrium materials to faster algorithms exploiting time-dependent Hamiltonians such as adiabatic state pre

关键词： Hamiltonians

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Characteristics Fluctuation of Sub-3-nm Bulk FinFET Devices Induced by Random Interface Traps

Characteristics Fluctuation of Sub-3-nm Bulk FinFET Devices ...

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IEEE Conference on Nanotechnology

作者： Sekhar Reddy Kola Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering Institute of Communications Engineering Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu Taiwan

Three-dimensional (3D) bulk fin-typed field effect transistors (FinFETs) have emerged as key devices that can scale down the technology node beyond 22-nm. However, the scaled devices have created new sources of fluctuation inherent in 3D geometry. The interface trap is one such fluctuation that is caused by the trapping and de-trapping of charge carriers and has an adverse effect on device characteristics and variability. In this work, we study impacts of random interface traps (RITs) on electrical characteristic of bulk FinFETs by using a 3D quantum-mechanically corrected device simulation. RIT position effects on short channel effects (SCEs) are examined with physical governed influence to show the major fluctuations. More than 50% reductions of the RITs-induced characteristic fluctuation of the germanium (Ge) devices are observed, compared with Si devices. The Ge ones can reduce SCE variations and exhibit high immunity to RITs.

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Simple and Tighter Derivation of Achievability for Classical Communication Over Quantum Channels

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PRX Quantum 2023年第4期4卷 040330-040330页

作者： Hao-Chung Cheng (鄭皓中) Department of Electrical Engineering and Graduate Institute of Communication Engineering National Taiwan University Taiwan Republic of China Department of Mathematics National Taiwan University Taiwan Republic of China Center for Quantum Science and Engineering National Taiwan University Taiwan Republic of China Physics Division National Center for Theoretical Sciences Taiwan Republic of China Hon Hai (Foxconn) Quantum Computing Center Taiwan Republic of China

Achievability in information theory refers to demonstrating a coding strategy that accomplishes a prescribed performance benchmark for the underlying task. In quantum information theory, the crafted Hayashi-Nagaoka operator inequality is an essential technique in proving a wealth of one-shot achievability bounds since it effectively resembles a union bound in various problems. In this work, we show that the so-called pretty-good measurement naturally plays a role as the union bound as well. A judicious application of it considerably simplifies the derivation of one-shot achievability for classical-quantum channel coding via an elegant three-line proof. The proposed analysis enjoys the following favorable features. (i) The established one-shot bound admits a closed-form expression as in the celebrated Holevo-Helstrom theorem. Namely, the average error probability of sending M messages through a classical-quantum channel is upper bounded by the minimum error of distinguishing the joint channel input-output state against (M−1) decoupled product states. (ii) Our bound directly yields asymptotic achievability results in the large deviation, small deviation, and moderate deviation regimes in a unified manner. (iii) The coefficients incurred in applying the Hayashi-Nagaoka operator inequality or the quantum union bound are no longer needed. Hence, the derived one-shot bound sharpens existing results relying on the Hayashi-Nagaoka operator inequality. In particular, we obtain the tightest achievable ε-one-shot capacity for classical communication over quantum channels heretofore, improving the third-order coding rate in the asymptotic scenario. (iv) Our result holds for infinite-dimensional Hilbert space. (v) The proposed method applies to deriving one-shot achievability for classical data compression with quantum side information, entanglement-assisted classical communication over quantum channels, and various quantum network information-processing protocols.

关键词： Quantum communication Quantum communication, protocols & technology Quantum information processing Quantum information theory

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All-Microwave Manipulation of Superconducting Qubits with a Fixed-Frequency Transmon Coupler

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Physical Review Letters 2023年第26期130卷 260601-260601页

作者： Shotaro Shirai Yuta Okubo Kohei Matsuura Alto Osada Yasunobu Nakamura Atsushi Noguchi Komaba Institute for Science (KIS) The University of Tokyo Meguro-ku Tokyo 153-8902 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan PRESTO Japan Science and Technology Agency Kawaguchi-shi Saitama 332-0012 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351–0198 Japan Inamori Research Institute for Science (InaRIS) Kyoto-shi Kyoto 600-8411 Japan

All-microwave control of fixed-frequency superconducting quantum computing circuits is advantageous for minimizing the noise channels and wiring costs. Here we introduce a swap interaction between two data transmons assisted by the third-order nonlinearity of a coupler transmon under a microwave drive. We model the interaction analytically and numerically and use it to implement an all-microwave controlled-Z gate. The gate based on the coupler-assisted swap transition maintains high drive efficiency and small residual interaction over a wide range of detuning between the data transmons.

关键词： Quantum control Quantum engineering Quantum information with solid state qubits Superconducting qubits

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