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arXiv 2025年

作者： Sagawa, Yuki Hance, Jonte R. Hofmann, Holger F. Ono, Takafumi Program in Advanced Materials Science Faculty of Engineering and Design Kagawa University 2217-20 Hayashi-cho Kagawa Takamatsu761-0396 Japan School of Computing Newcastle University 1 Science Square Newcastle upon TyneNE4 5TG United Kingdom Quantum Engineering Technology Laboratories Department of Electrical and Electronic Engineering University of Bristol Woodland Road BristolBS8 1US United Kingdom Department of Quantum Matter Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama 1-3-1 Higashi Hiroshima739-8530 Japan

Quantum contextuality, where measurement outcomes depend on the measurement context, implies a failure of classical realism in quantum systems. As recently shown, the transition between measurement contexts can be mapped onto the path that a quantum particle takes through an interferometer. Here, we investigate the relation between contextuality and the counterfactual gain observed in the output ports of such an interferometer when one of the paths is blocked. It is shown that experimental evidence of contextuality can only be obtained when counterfactual gain is observed for a specific combination of blocked path and output port. Using a silicon photonic integrated circuit, we experimentally observe the counterfactual gain for a selection of input states and evaluate the associated evidence for contextuality. The results confirm that contextuality can only be observed in the presence of counterfactual gain. Copyright © 2025, The Authors. All rights reserved.

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Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation

arXiv

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arXiv 2025年

作者： Chen, Samuel Yen-Chi Liu, Chen-Yu Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Huang, Wei-Hao Wells Fargo New YorkNY United States Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Research Institute Taipei Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan Jij inc. Tokyo Japan

The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also known as quantum neural networks (QNNs), have shown promise both empirically and theoretically. However, their broader adoption is hindered by reliance on quantum hardware during inference. Hardware imperfections and limited access to quantum devices pose practical challenges. To address this, the Quantum-Train (QT) framework leverages the exponential scaling of quantum amplitudes to generate classical neural network parameters, enabling inference without quantum hardware and achieving significant parameter compression. Yet, designing effective quantum circuit architectures for such quantum-enhanced neural programmers remains non-trivial and often requires expertise in quantum information science. In this paper, we propose an automated solution using differentiable optimization. Our method jointly optimizes both conventional circuit parameters and architectural parameters in an end-to-end manner via automatic differentiation. We evaluate the proposed framework on classification, time-series prediction, and reinforcement learning tasks. Simulation results show that our method matches or outperforms manually designed QNN architectures. This work offers a scalable and automated pathway for designing QNNs that can generate classical neural network parameters across diverse applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation

JACC. Asia

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JACC. Asia 2025年

作者： Jung-Chi Hsu Yi-Hsien Hsieh Yen-Yun Yang Shu-Lin Chuang Che Lin Lian-Yu Lin Department of Internal Medicine National Taiwan University Hospital Jinshan Branch New Taipei City Taiwan Division of Cardiology Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan. Department of Medical Research National Taiwan University Hospital Taipei Taiwan. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan Smart Medicine and Health Informatics Program National Taiwan University Taipei Taiwan. Electronic address: chelin@ntu.edu.tw. Division of Cardiology Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan Smart Medicine and Health Informatics Program National Taiwan University Taipei Taiwan Cardiovascular Center National Taiwan University Hospital Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital Yunlin Branch Yunlin Taiwan. Electronic address: hspenos@***.

BACKGROUND:Atrial fibrillation (AF) is a major risk factor for transient ischemic attack (TIA)/ischemic stroke (IS). OBJECTIVES:Given the dynamic nature of IS risk, this study aimed to predict IS risk in AF patients using a high-dimensional time-series model. METHODS:We conducted a cohort study at the National Taiwan University Hospital from 2014 to 2019, including 7,710 AF patients, with external validation in 6,822 patients from the National Taiwan University Hospital Yunlin Branch. The Forecasting Strokes via Interpretable Independent Networks (ForeSIIN) model, based on gated recurrent units, was proposed. Kaplan-Meier analysis with log-rank test evaluated risk group differences. RESULTS:The annual TIA/IS incidence rate ranged from 181.96 (95% CI: 164.42-200.93) to 15.81 (95% CI: 12.38-20.18) per 1,000 person-years, with an overall incidence of 42.40 (95% CI: 39.60-45.39). The ForeSIIN model achieved the best prediction with an area under the receiver-operating characteristics curve of 0.764 (95% CI: 0.722-0.810), compared with the CHADS-VASc score (AUC: 0.650; 95% CI: 0.596-0.699) and other nonsequential models: extreme gradient boosting AUC: 0.722 (95% CI: 0.676-0.769), support vector machine AUC 0.691 (95% CI: 0.637-0.741), random forest AUC: 0.689 (95% CI: 0.637-0.742). External validation showed area under the receiver-operating characteristics curve of 0.646 (95% CI: 0.618-0.671) and area under the precision-recall curve of 0.222 (95% CI: 0.184-0.259). Feature impact analysis identified the top 5 factors: history of TIA/IS, estimated glomerular filtration rate, C-reactive protein, hematocrit, and plasma fasting glucose. Kaplan-Meier analysis showed significant risk differences between ForeSIIN groups (log-rank P < 0.001). CONCLUSIONS:The innovative ForeSIIN model demonstrated accurate stroke prediction in AF patients and enhanced the interpretation of dynamic risk factors over time.

关键词： atrial fibrillation explainable artificial intelligence gated recurrent unit recurrent neural network (RNN) stroke

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A Configurable Pruning Gaussian Image Filter for Energy-Efficient Edge Detection

A Configurable Pruning Gaussian Image Filter for Energy-Effi...

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IEEE International Conference on electronics, Circuits and Systems

作者： Leonardo Bandeira Soares Eduardo Antonio Cesar da Costa Sergio Bampi Federal Institute of Technology of Rio Grande do Sul (IFRS) Brazil Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil Graduate Program on Microelectronics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

This paper presents a new configurable pruning Gaussian image filter CMOS architecture to address energy efficiency requirements regarding edge detection applications. Low-energy consumption is key for Internet of Things (IoT) devices. Many emerging IoT applications rely on cameras to extract video or image features by running power-hungry computer vision algorithms. The Gaussian image filter is one of the most compute intensive tasks for pre-processing edge detection techniques which are widely adopted in the computer vision domain. Therefore, our proposed 2D Gaussian filter architecture enables: i) a low power and low area overhead runtime configuration scheme based on clock gating technique to prune the Gaussian filter (GF) window size, and ii) run-time capability to balance the tradeoff between edge detection quality and energy efficiency. Our proposed configurable architecture is synthesized and mapped onto 45 nm technology for an ASIC implementation. Results show that for 6 different run-time profiles our proposed configurable architecture provides power dissipation reduction of up to 64% with multiple levels of edge detection quality, which is assessed by considering the performance conformance metric.

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Approximate Sum of Absolute Transformed Differences Hardware Accelerator

Approximate Sum of Absolute Transformed Differences Hardware...

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IEEE International Conference on electronics, Circuits and Systems

作者： Matheus F. Stigger Victor H. S. Lima Leonardo B. Soares Claudio M. Diniz Sergio Bampi Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil Federal Institute of Rio Grande do Sul (IFRS) Rio Grande Brazil Graduate Program in Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

Motion Estimation and mode decision are the most compute-intensive tasks in recent video encoders. Sum of Absolute Transformed Differences (SATD) is a distortion metric used inside ME and mode decision to decide the best block to be used as prediction for each block to be encoded. To reduce the complexity of SATD calculation, this work proposes an approximate SATD hardware accelerator based on 1D 4 × 4 Hadamard Transform, in contrast with the precise SATD hardware accelerator that contains a 2D 4 × 4 Hadamard Transform. The proposed approximate SATD accelerator reduces power dissipation in 66.86% compared to the precise SATD accelerator while increasing BD-BR only by less than 0.748%, which is a better result than SAD accelerator that increases BD-BR in up to 0.830% compared to precise SATD.

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Exploiting Partial Distortion Elimination in the Sum of Absolute Differences for Energy-Efficient HEVC Integer Motion Estimation

Exploiting Partial Distortion Elimination in the Sum of Abso...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Brunno Abreu Gustavo Santana Mateus Grellert Guilherme Paim Leandro Rocha Eduardo A. C. da Costa Sergio Bampi Graduate Program in Microelectronics (PGMicro) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Graduate Program in Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (纸本)9781538674321

Integer Motion Estimation (IME) is an important tool that explores the temporal redundancies in video encoders. It stands out among the most time-consuming and memory intensive tasks in the encoding process. Most of the IME processing is spent on the computation of similarities between pixel blocks, usually using the Sum of Absolute Differences (SAD) as block selection criterion. Designing high-throughput and energy-efficient SAD units is an effective way of increasing the performance of IME architectures. This paper proposes an optimization of SAD units considering the state-of-the-art High Efficiency Video Coding (HEVC) standard, which avoids the computation of candidates that will surely not be selected in the IME search, by applying Partial Distortion Elimination (PDE). The method achieves, without any loss in coding performance, an average reduction of 16.41% and 11.64% in the energy consumption for 1080p and 2160p (4K) videos, respectively, when compared to an HEVC SAD implementation without PDE.

关键词： Encoding Software Motion estimation Computer architecture Video coding Hardware Software algorithms

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Deep generative endmember modeling: An application to unsupervised spectral unmixing

arXiv

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arXiv 2019年

作者： Borsoi, Ricardo Augusto Imbiriba, Tales Moreira Bermudez, José Carlos Department of Electrical Engineering Federal University of Santa Catarina DEE–UFSC SC Florianópolis Brazil Lagrange Laboratory Université Côte d’Azur Nice France DEE–UFSC SC Florianópolis Brazil ECE department Northeastern University BostonMA United States Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas UCPel Pelotas Brazil

Endmember (EM) spectral variability can greatly impact the performance of standard hyperspectral image analysis algorithms. Extended parametric models have been successfully applied to account for the EM spectral variability. However, these models still lack the compromise between flexibility and low-dimensional representation that is necessary to properly explore the fact that spectral variability is often confined to a low-dimensional manifold in real scenes. In this paper we propose to learn a spectral variability model directly from the observed data, instead of imposing it a priori. This is achieved through a deep generative EM model, which is estimated using a variational autoencoder (VAE). The encoder and decoder that compose the generative model are trained using pure pixel information extracted directly from the observed image, what allows for an unsupervised formulation. The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model. Simulations using both synthetic and real data indicate that the proposed strategy can outperform the competing state-of-the-art algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Exploiting addition schemes for the improvement of optimized radix-2 and radix-4 fft butterflies

Exploiting addition schemes for the improvement of optimized...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Renato H. Neuenfeld Mateus B. Fonseca Eduardo A. C. da Costa Jean P. Oses Graduate Program on Electronic Engineering and Computing Catholic University of Pelotas (UCPel) Pelotas Brazil Graduate Program on Helth and Behavior Catholic University of Pelotas (UCPel) Pelotas Brazil

ISBN: (纸本)9781509058600

In FFT computation, the butterflies play a central role, since they allow the calculation of complex terms. Therefore, the optimization of the butterfly can contribute for the power reduction in FFT architectures. In this paper we exploit different addition schemes in order to improve the efficiency of 16 bit-width radix-2 and radix-4 FFT butterflies. Combinations of simultaneous addition of three and seven operands are inserted in the structures of the butterflies in order to produce power-efficient structures. The used additions schemes include Carry Save Adder (CSA), and adder compressors. The radix-2 and radix-4 butterflies were implemented in hardware description language and synthesized to 45nm Nangate Open Cell Library using Cadence RTL Compiler. The main results show that both radix-2 and radix-4 butterflies, with CSA, are more efficient when compared with the same structures with other adder circuits.

关键词： Adders Compressors Computer architecture Delays Logic gates Libraries Hardware

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Low power sum of absolute differences architecture using novel hybrid adder

Low power sum of absolute differences architecture using nov...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Rafael Ferreira Bianca Silveira Mateus Beck Fonseca Cláudio M. Diniz Eduardo A. C. da Costa Graduate Program on Electronic Engineering and Computing-Catholic University of Pelotas (UCPel) Pelotas Brazil Electronic Engineering-Center for Engineering (CEng) Federal University of Pelotas (UFPel) Pelotas Brazil

ISBN: (纸本)9781509058600

Sum of Absolute Differences (SAD) is an intensive time-consuming operation of state-of-art video encoders. It is used as a block matching metric inside Motion Estimation (ME) and also on mode decision in Intra Prediction. SAD hardware architectures employ an adder tree to accumulate the coefficients from absolute difference between two video blocks. Due to the simplicity, the SAD metric is the better choice when the video encoders project space is focused on power efficiency. In order to reduce the SAD power dissipation, this work proposes a new hybrid encoded adder operator. The power-efficient hybrid encoding representation groups m bits and uses gray encoding to potentially reduce the switching activity, both internally and the inputs of the arithmetic operators. The SAD architecture, using the proposed hybrid adder operator, was synthesized to 45 nm standard cell technology and compared in terms of power dissipation using real video sequences. Results show that 7.58% of total power is saved (on average) when compared with SAD architecture using the macro-function adder from the synthesis tool. Compared to SAD architecture using state-of-the-art hybrid adder, our architecture saves 12.97% on average power dissipation.

关键词： Adders Computer architecture Encoding Hybrid power systems Hardware Tools Logic gates

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