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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Hyunsu Jeong Chiho Yoon Hyunseok Lim Jongjun Won Gongning Luo Mingwang Xu Kiduk Kim Namkug Kim Chulhong Kim Graduate School of Artificial Intelligence Pohang University of Science and Technology Pohang South Korea Department of Electrical Engineering Pohang University of Science and Technology Pohang South Korea Department of Medical Science Asan Medical Institute of Convergence Science and Technology Asan Medical Center College of Medicine University of Ulsan Seoul Republic of Korea Faculty of Computing Harbin Institute of Technology Harbin China Department of Convergence Medicine Asan Medical Center College of Medicine University of Ulsan Seoul Republic of Korea Department of Biomedical Engineering Asan Medical Institute of Convergence Science and Technology Asan Medical Center College of Medicine University of Ulsan Seoul Republic of Korea Department of Electrical Engineering Graduate School of Artificial Intelligence Convergence IT Engineering Mechanical Engineering Medical Science and Engineering and Medical Device Innovation Center Pohang University of Science and Technology Pohang South Korea

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

3D automated breast ultrasound (ABUS) data includes a huge number of slices with lesion-related artifacts and size variants, causing false positive cases. In addition, applying transformer-based models with self-attention considering long-range dependency to 3D ABUS data is challenging because of the small number of data. We propose 3D deep adaptively efficient attention model with a multi-task learning to segment the small number of 3D ABUS datasets. Our network consists of adaptive hybrid-transformer blocks for computationally efficient self-attention and multi-task learning to minimize false positive cases. Additionally, a novel attention gate is also implanted to considering long-range dependency between channel-attentive features. Our model performed 58.27%, 23.59 mm, and 0.054% on Dice, HD95, and FPR, respectively.

关键词： Adaptation models Solid modeling Three-dimensional displays Ultrasonic imaging Computational modeling Biological system modeling Breast Logic gates Transformers Multitasking

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Diversity and stylization of the contemporary user-generated visual arts in the complexity-entropy plane

arXiv

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arXiv 2024年

作者： Kim, Seunghwan Lee, Byunghwee Lee, Wonjae Graduate School of Culture Technology KAIST Daejeon34141 Korea Republic of Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute of Basic Science Sungkyunkwan University Suwon16419 Korea Republic of

The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Bēhance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time. © 2024, CC BY.

关键词： Deep neural networks

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Supervised Score-Based Modeling by Gradient Boosting 39

Supervised Score-Based Modeling by Gradient Boosting

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Changyuan Du, Hongyang Liu, Guangyuan Niyato, Dusit College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way # 08-01 Create Tower 138602 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong The Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore

ISBN: (纸本)157735897X

Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Via the ablation experiment in selected examples, we demonstrate the outstanding performances of the proposed techniques. Additionally, we compare our model with other probabilistic models, including Natural Gradient Boosting (NGboost), Classification and Regression Diffusion Models (CARD), Diffusion Boosted Trees (DBT), and non-probabilistic gradient boosting models. The experimental results show that our model outperforms existing models in both accuracy and inference time. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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Design and Development of an IoT-Based Plant Growth Monitoring System Using Esp-32 Cam

Design and Development of an IoT-Based Plant Growth Monitori...

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Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE International Conference on

作者： Rex Basuel Arnel Fajardo Novie Dave Perez Graduate School Technological Institute of the Philippines - Quezon City Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Cauayan City Philippines College of Engineering and Information Technology Caraga State University Cabadbaran City Cabadbaran City Philippines

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

This paper presents a study that combines the advances of technology specifically in the field of Internet-of-Things (IoT) and Image Processing. Plant growth monitoring is an essential task especially when growing a high value crop like okra. In this study, an IoT-enabled device like the ESP32-Cam was used to capture and record the growth development of a certain plant. ESP32-Cam is a microcontroller integrated with a camera and has the capability of doing IoT. The system was integrated with other modules such as Nitrogen-Phosphorus-Potassium (NPK), soil moisture sensor and soil temperature sensor. The NPK sensor was used to measure the amount of nutrient in the soil, while the soil moisture and temperature sensor detects the moisture content and temperature of the soil respectively. It is also integrated with a $D C$ pump that will be activated to water the plants once the soil moisture drops to its threshold value. Upon evaluation of the developed prototype, it is evident that it functions based on its intended design.

关键词： Temperature sensors Temperature measurement Soil measurements Image processing Soil moisture Prototypes Robot sensing systems Nutrients Internet of Things Monitoring

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Applying IoT and Deep Learning for ECG Data Analysis

Applying IoT and Deep Learning for ECG Data Analysis

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Artificial Intelligence and Internet of Things (GCAIoT), IEEE Global Conference

作者： Paul Lussier Chen-Hsiang Yu School of Computing and Data Science Wentworth Institute of Technology Boston MA U.S.A Multidisciplinary Graduate Engineering College of Engineering Northeastern University Boston MA U.S.A

ISBN: (纸本)9798350309850

The current standard for catching many heart conditions before symptoms arise is by getting a reading from an electrocardiogram (ECG). While revolutionary in the detection and understanding of heart problems, ECG machines are costly and time consuming such that many people can't easily use them. This behavior directly opposes the possibility of catching the issue before it becomes problematic. With the rise of the Internet of Things (IoT) and data analysis, we believe it is possible to design and create a small, affordable, and relatively unobtrusive device with the goal of collecting and classifying ECG signals. In this research, we propose a mobile health system that not only utilizes IoT technologies, but also applies deep learning methods to classify three different rhythm types, including Normal Sinus Rhythm, Congestive Heart Failure, and Atrial Fibrillation. During the study, we also found that IoT devices are prone to noises, especially in the neighborhood of 63Hz. We were also able to address some of them with Real Fast Fourier Transform and a high pass filter. In addition, we also created a host of functions to help us analyze and process the ECG data collected by the sensor. We compiled these methods and placed them into a python class that can be leveraged by others to help drive research forward. Compared with traditional machine learning methods, our results show that the deep learning approach provides an improvement in classifying three-class ECG signals. However, we also found some challenges for future work.

关键词： Deep learning Heart Data analysis Fast Fourier transforms Learning (artificial intelligence) Electrocardiography Rhythm

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AR-CNN: an attention ranking network for learning urban perception

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science China(Information sciences) 2022年第1期65卷 164-174页

作者： Zhetao LI Ziwen CHEN Wei-Shi ZHENG Sangyoon OH Kien NGUYEN Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Data and Computer Science Sun Yat-sen University Department of Computer and Information Engineering Ajou University Graduate School of Engineering Chiba University

An increasing number of deep learning methods is being applied to quantify the perception of urban environments, study the relationship between urban appearance and resident safety, and improve urban appearance. Most advanced methods extract image feature representations from street-level images through conventional visual computation algorithms or deep convolutional neural networks and then directly predict the results using features. Unfortunately, these methods take color and texture information together during processing. Color and texture are prime image features, and they affect human perception and judgment differently. We argue that color and texture should be operated differently; therefore, we formulate an end-to-end learning methodology to process input images according to color and texture information before inputting it into the neural network. The processed images and the original image constitute three input streams for the triad attention ranking convolutional neural network(AR-CNN) model proposed in this *** accordance with the aspects of color and texture, an improved attention mechanism in the convolution layer is proposed. Our objective is to obtain the scores of humans on urban appearance in accordance with the prediction results computed from pairwise comparisons generated by the AR-CNN model.

关键词： ranking network urban perception attribute learning attention network colour and texture

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Nonlinear transformation of complex amplitudes via quantum singular value transformation

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Physical Review Research 2024年第4期6卷 043227-043227页

作者： Naixu Guo Kosuke Mitarai Keisuke Fujii Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan Center for Quantum Information and Quantum Biology Osaka University Toyonaka 560-0043 Japan JST PRESTO Saitama 332-0012 Japan RIKEN Center for Quantum Computing Wako Saitama 351-0198 Japan

Due to the linearity of quantum operations, it is not straightforward to implement nonlinear transformations on a quantum computer, making some practical tasks like a neural network hard to achieve. In this paper, we define a task called nonlinear transformation of complex amplitudes and provide an algorithm to achieve this task. Specifically, we construct a block encoding of complex amplitudes from a state preparation unitary. This allows us to transform the complex amplitudes by using quantum singular value transformation. We evaluate the required overhead in terms of input dimension and precision, which reveals that the algorithm depends on the roughly square root of input dimension and achieves an exponential speedup on precision compared with previous work. We also discuss its possible applications to quantum machine learning, where complex amplitudes encoding classical or quantum data are processed by the proposed method. In this paper, we provide a promising way to introduce the highly complex nonlinearity of the quantum states, which is essentially missing in quantum mechanics.

关键词： Quantum algorithms Quantum algorithms & computation Quantum information processing

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Supplemental Information

arXiv

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arXiv 2025年

作者： Sunada, Satoshi Niiyama, Tomoaki Kanno, Kazutaka Nogami, Rin Röhm, André Awano, Takato Uchida, Atsushi Faculty of Mechanical Engineering Institute of Science and Engineering Kanazawa University Kakuma-machi Kanazawa Ishikawa920-1192 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama338-8570 Japan Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient neuromorphic information processing by harnessing the innate computational power of physical processes;however, training their weight parameters is computationally expensive. We propose a training approach for substantially reducing this training cost. Our training approach merges an optimal control method for continuous-time dynamical systems with a biologically plausible training method—direct feedback alignment. In addition to the reduction of training time, this approach achieves robust processing even under measurement errors and noise without requiring detailed system information. The effectiveness was numerically and experimentally verified in an optoelectronic delay system. Our approach significantly extends the range of physical systems practically usable as PNNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Resolvability of classical-quantum channels

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IEEE Transactions on Information Theory 2025年

作者： Hayashi, Masahito Cheng, Hao-Chung Gao, Li Chinese University of Hong Kong School of Data Science Shenzhen Longgang District Shenzhen518172 China Nagoya University Graduate School of Mathematics Nagoya464-8602 Japan National Taiwan University Graduate Institute of Communication Engineering Department of Mathematics Institute of Applied Mathematical Sciences Center for Quantum Science and Engineering epartment of Electrical Engineering Taipei10617 Taiwan National Center for Theoretical Sciences Physics Division Taipei10617 Taiwan Quantum Computing Center New Taipei City236 Taiwan Wuhan University School of Mathematics and Statistics Wuhan430072 China

Channel resolvability concerns the minimum resolution for approximating the channel output. We study the resolvability of classical-quantum channels in two settings, for the channel output generated from the worst input, and from the fixed independent and identically distributed (i.i.d.) input. The direct part of the worst-input setting is derived from sequential hypothesis testing as it involves non-i.i.d. inputs. The strong converse of the worst-input setting is obtained via the connection to identification codes. For the fixed-input setting, while the direct part follows from the known quantum soft covering result, we exploit the recent alternative quantum Sanov theorem to prove the strong converse. © 1963-2012 IEEE.

关键词： Quantum channel

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Axion Dark Matter Search around 4.55 μeV with Dine-Fischler-Srednicki-Zhitnitskii Sensitivity

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Physical Review Letters 2023年第7期130卷 071002-071002页

作者： Andrew K. Yi Saebyeok Ahn Çağlar Kutlu JinMyeong Kim Byeong Rok Ko Boris I. Ivanov HeeSu Byun Arjan F. van Loo SeongTae Park Junu Jeong Ohjoon Kwon Yasunobu Nakamura Sergey V. Uchaikin Jihoon Choi Soohyung Lee MyeongJae Lee Yun Chang Shin Jinsu Kim Doyu Lee Danho Ahn SungJae Bae Jiwon Lee Younggeun Kim Violeta Gkika Ki Woong Lee Seonjeong Oh Taehyeon Seong DongMin Kim Woohyun Chung Andrei Matlashov SungWoo Youn Yannis K. Semertzidis Department of Physics Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea Center for Axion and Precision Physics Research Institute for Basic Science Daejeon 34051 Republic of Korea RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan

We report an axion dark matter search at Dine-Fischler-Srednicki-Zhitnitskii sensitivity with the CAPP-12TB haloscope, assuming axions contribute 100% of the local dark matter density. The search excluded the axion-photon coupling gaγγ down to about 6.2×10−16 GeV−1 over the axion mass range between 4.51 and 4.59 μeV at a 90% confidence level. The achieved experimental sensitivity can also exclude Kim-Shifman-Vainshtein-Zakharov axion dark matter that makes up just 13% of the local dark matter density. The CAPP-12TB haloscope will continue the search over a wide range of axion masses.

关键词： Dark matter Particle dark matter Axions Dark matter detectors Microwave techniques

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