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TechRxiv 2023年

作者： Petrenko, Sasha Hier, Daniel B. Obafemi-Ajayi, Tayo Bone, Mary Timpson, Erik J. Speight, Michael Marsh, William E. Wunsch, Donald C. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Department of Neurology and Rehabilitation University of Illinois at Chicago ChicagoIL United States University of Southeastern Norway Kongsberg Norway Engineering Program Missouri State University SpringfieldMO United States Honeywell Federal Manufacturing & Technologies Kansas CityMO United States

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multicategorical symbolic data. This article proposes Symbolic Tree Adaptive Resonance Theory (START), with additional supervised, Dual-Vigilance (DV-START), and Distributed Dual-Vigilance (DDV-START) formulations, for the clustering of multicategorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot-Marie-Tooth disease using genomic, phenotypic, and proteomic data. © 2023, CC BY.

关键词： Ontology

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Cyber-physical infrastructures for advancing pyrolysis conversion process: A case study of biochar production

Cyber-physical infrastructures for advancing pyrolysis conve...

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ASME 2020 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2020

作者： Hersh, Benjamin Mohajeri, Amir Mirkouei, Amin Xian, Min Department of Mechanical Engineering University of Idaho ID United States Industrial Technology Program University of Idaho ID United States Industrial Technology Program Department of Mechanical Engineering University of Idaho ID United States Department of Computer Science University of Idaho ID United States

ISBN: (纸本)9780791883983

Biomass-based products (bioproducts) have been introduced as a means to address food-energy-water nexus challenges. However, the existing approaches have not been integrated to convert biomass into market-competitive bioproducts (e.g., biochar and bio-oil). Pretreatment and conversion processes represent the most substantial portion of the total bioproduct cost. This study proposes a portable conversion process for high-quality biochar and bio-oil production, using mixed reactors and advanced cyber-physical infrastructures to promote cross-cutting technological and commercialization opportunities. The proposed portable process converts biomass to bioproducts near the collection sites, which can address collection, staging, and logistics challenges. The fundamental novelty of this study lies in utilizing cyber-physical technologies for advancing pre-/postconversion processes and crossing the boundaries to meet the market needs. A case study for biochar production from various biomass feedstocks is used to demonstrate and verify the application of the conversion process and the cyber-based advances. The results indicate that the integrated cyber-physical conversion pathway can increase process yield and quality. It is also found that biomass properties and conversion process configurations play a crucial role in determining the process yield and biochar quality in terms of the physical-chemical structure. Copyright © 2020 ASME.

关键词： Biomass

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A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

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Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

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SkinSource: A Data-Driven Toolbox for Predicting Touch-Elicited Vibrations in the Upper Limb

SkinSource: A Data-Driven Toolbox for Predicting Touch-Elici...

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International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (HAPTICS)

作者： Neeli Tummala Gregory Reardon Simone Fani Dustin Goetz Matteo Bianchi Yon Visell the Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara CA USA the Media Arts and Technology Program University of California Santa Barbara Santa Barbara CA USA the School of Biological and Health Systems Engineering Arizona State University Tempe AZ USA the Soft Robotics for Human Cooperation and Rehabilitation Lab Instituto Italiano di Tecnologia Geneva GE Italy the Department of Mechanical Engineering University of California Santa Barbara Santa Barbara CA USA the Department of Information Engineering University of Pisa PI Italy the Department of Biological Engineering University of California Santa Barbara Santa Barbara CA USA

Vibrations transmitted throughout the hand and arm during touch contact play a central role in haptic science and engineering but are challenging to model or experimentally characterize. Here, we present SkinSource, a data-driven toolbox for predicting skin vibrations across the upper limb in response to user-specified input forces. The toolbox leverages impulse response measurements that encode the physics of vibration transmission across the hands and arms of four participants and provides software tools for analyzing the predicted skin responses. We show that the SkinSource predictions closely match experimental measurements and confirm the underlying assumption of linear vibration transmission in the skin. We also demonstrate through several usage examples how SkinSource can act as a versatile computational platform for haptic research applications, such as characterizing vibrotactile transmission in the skin, engineering haptic interfaces, and investigating touch perception.

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Van der Waals device integration beyond the limits of van der Waals forces using adhesive matrix transfer

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Nature Electronics 2024年第1期7卷 17-28页

作者： Satterthwaite, Peter F. Zhu, Weikun Jastrzebska-Perfect, Patricia Tang, Melbourne Spector, Sarah O. Gao, Hongze Kitadai, Hikari Lu, Ang-Yu Tan, Qishuo Tang, Shin-Yi Chueh, Yu-Lun Kuo, Chia-Nung Lue, Chin Shan Kong, Jing Ling, Xi Niroui, Farnaz Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States Department of Chemistry Boston University BostonMA United States Department of Materials Science and Engineering National Tsing Hua University Hsinchu Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei Taiwan College of Semiconductor Research National Tsing Hua University Hsinchu Taiwan Department of Physics National Cheng Kung University Tainan Taiwan Taiwan Consortium of Emergent Crystalline Materials National Science and Technology Council Taipei Taiwan Program on Key Materials Academy of Innovative Semiconductor and Sustainable Manufacturing National Cheng Kung University Tainan Taiwan Division of Materials Science and Engineering Boston University BostonMA United States The Photonics Center Boston University BostonMA United States

Pristine van der Waals (vdW) heterostructures formed between two-dimensional (2D) and other materials can be used to create optical and electronic devices. However, the weak vdW forces alone do not allow direct physical stacking of arbitrary layers. As a result, vdW heterostructure fabrication typically requires solvents, sacrificial layers or high temperatures, which can introduce damage and contaminants. Here, we show that adhesive matrix transfer can eliminate these limitations and can provide vdW integration beyond the limits of vdW forces. In the approach, a hybrid high- and low-adhesion surface is used to decouple the forces driving the transfer from the vdW forces defining the heterostructure of interest. We show that the technique can be used to achieve direct vdW integration of diverse 2D materials (MoS2, WSe2, PtS2 and GaS) with dielectrics (SiO2 and Al2O3), which is conventionally forbidden but critical for active devices and scalable, aligned heterostructure formation. The approach also allows single-step 2D material-to-device integration, which we illustrate by fabricating arrays of monolayer MoS2 transistors. As any exposure to solvents or polymers is avoided, the interfaces and surfaces remain pristine. Thus, intrinsic 2D material properties can be probed without the influence of processing steps. The materials can be further engineered through surface treatments and used to fabricate unconventional device form factors. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Van der Waals forces

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LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization

LaPE: Layer-adaptive Position Embedding for Vision Transform...

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International Conference on computer Vision (ICCV)

作者： Runyi Yu Zhennan Wang Yinhuai Wang Kehan Li Chang Liu Haoyi Duan Xiangyang Ji Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China School of Computer Science and Technology Zhejiang University Zhejiang China

Position information is critical for Vision Transformers (VTs) due to the permutation-invariance of self-attention operations. A typical way to introduce position information is adding the absolute Position Embedding (PE) to patch embedding before entering VTs. However, this approach operates the same Layer Normalization (LN) to token embedding and PE, and delivers the same PE to each layer. This results in restricted and monotonic PE across layers, as the shared LN affine parameters are not dedicated to PE, and the PE cannot be adjusted on a per-layer basis. To overcome these limitations, we propose using two independent LNs for token embeddings and PE in each layer, and progressively delivering PE across layers. By implementing this approach, VTs will receive layer-adaptive and hierarchical PE. We name our method as Layer-adaptive Position Embedding, abbreviated as LaPE, which is simple, effective, and robust. Extensive experiments on image classification, object detection, and semantic segmentation demonstrate that LaPE significantly outperforms the default PE method. For example, LaPE improves +1.06% for CCT on CIFAR100, +1.57% for DeiT-Ti on ImageNet-1K, +0.7 box AP and +0.5 mask AP for ViT-Adapter-Ti on COCO, and +1.37 mIoU for tiny Segmenter on ADE20K. This is remarkable considering LaPE only increases negligible parameters, memory, and computational cost.

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First Demonstration of HZO-LNOI Integrated Ferroelectric Electro-Optic Modulator and Memory to Enable Reconfigurable Photonic Systems

First Demonstration of HZO-LNOI Integrated Ferroelectric Ele...

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International Electron Devices Meeting (IEDM)

作者： Zefeng Xu Chun-Kuei Chen Hong-Lin Lin Yuan Gao Wei Ke Baochang Xu Pavel Dmitriev Carlan Arbiz Evgeny Zamburg Steven Touzard Xinlun Cai James Lee Suresh Venkatesan Aaron Danner Aaron Voon-Yew Thean Department of Electrical and Computer Engineering National University of Singapore (NUS) Integrative Sciences and Engineering Program NUS Graduate School Singapore Next-Generation Hybrid μ-Electronics Center (SHINE) Singapore School of Electronics and Information Technology Sun Yat-Sen University China 510275 Singapore Centre for Quantum Technologies NUS POET Technologies

We have successfully demonstrated, for the first time, an innovative back-end-of-line (BEOL) compatible electro-optic modulator and memory (EOMM) based on Lithium Niobate on Insulator (LNOI) micro-ring resonator (MRR) integrated with Ferroelectric Hafnium Zirconate Hf 0.5 Zr 0.5 O (HZO) non-volatile analog memory. High non-volatile memory and modulation performances are both achieved in a single compact device, exhibiting high extinction ratio of 13.3 dB, excellent efficiency of 66pm/V, stable nine-state switching, record-high endurance exceeding 10 9 cycles. This is accomplished by utilizing Pockels effect in LNOI, induced by electric-field effect from remnant HZO ferroelectric polarization. We studied the system implementation of reconfigurable chiplet-interposer photonic interconnect, enabled by the EOMM and EOMM with hybrid thermal-optical modulation. Our model shows a potential 70% energy efficiency improvement over conventional electrical interposer interconnect. We have also tested the integration of the EOMM with POET technologies’ 400G Tx/Rx optical interposer chip and studied a limited scale demonstration of the EOMM device.

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Development of Educational Game for Indonesian History Lesson Focusing on Indonesia Pre-Literacy Relics 'Prehistoria' for Grade X High School Student

Development of Educational Game for Indonesian History Lesso...

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2020 International Conference on computer engineering, Network, and Intelligent Multimedia, CENIM 2020

作者： Nugroho, Supeno Mardi Susiki Rahman, Muhammad Daffa Abiyyu Hariadi, Mochamad Arief, Muhammad Faculty of Intelligent Electrical and Informatics Technology Sepuluh Nopember Institute of Technology Computer Engineering Department Surabaya Indonesia Tokyo University of Technology Media Science Program Graduate School of Bionics Computer and Media Sciences Tokyo Japan

ISBN: (纸本)9781728182834

The development of game technology has opened up new possibilities in learning methods through educational games. However, in Indonesia, the application of this concept is lacking. One lesson that can benefit from educational games is Indonesian History in class X, with a focus on history in the pre-literacy period. This study aims to create a puzzle-Adventure type of digital game that can help educate students about the pre-literacy period, developed with Unity, and uses WebGL as a target device while referring to Indonesia's revised 2013 national curriculum. The result of this research is the creation of a digital game that can be played on average of 254.36 seconds, with potential for educational use. A survey result shows that many of the ancient objects and some of the ages are well-recognized. However, the survey also shows many of the gameplay aspects, such as UI, gameplay features, and adventure elements, need improvements. © 2020 IEEE.

关键词： Surveys

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Assessing disruptive potential of retail central bank digital currency and influence of design considerations: An open innovation approach in Thailand

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Journal of Open Innovation: technology, Market, and Complexity 2025年第1期11卷

作者： Wathahong, Thanwa Ratanabanchuen, Roongkiat Israsena Na Ayudhya, Praima Tientanopajai, Kitt Technology Management and Innopreneurship Program Graduate School Chulalongkorn University Phayathai Road Bangkok 10330 Thailand Department of Banking and Finance Chulalongkorn Business School Chulalongkorn University Phayathai Road Bangkok 10330 Thailand Department of Industrial Design Faculty of Architecture Chulalongkorn University Phayathai Road Bangkok 10330 Thailand Department of Computer Engineering Faculty of Engineering Khon Kaen University Mittraphap Road Khon Kaen 40002 Thailand

Monetary authorities globally have studied central bank digital currency (CBDC) for wholesale and retail applications since the 1990s. Besides countering private cryptocurrencies' influence, central banks have also explored other benefits of CBDC, such as improving their financial oversight, enhancing digital payment systems, and reducing transaction fees and friction. For several years, the Bank of Thailand has also invested in research and development of CBDC, including retail use cases. However, an optimized design for an official launch of a retail CBDC in Thailand has yet to be determined, resembling fragmented progress observed among other central banks. This research has leveraged disruptive innovation theory to provide a fresh perspective on retail CBDC as potentially disruptive financial innovations. Additionally, it has proposed an open innovation-based methodology to determine industry preferences concerning twelve design considerations for retail CBDC and their impact on nine indicators of disruptive innovation. Data has been collected and analyzed from 98 respondents outside the Bank of Thailand, all possessing relevant expertise or experience in electronic payments. Crosstab and logistic regression analyses have indicated that retail CBDC in Thailand could embark on a disruptive innovation trajectory by adhering to defined design choices. Significantly, entering a new detached market has been identified as the most promising entry point, accompanied by explicit recommendations on nine out of twelve design considerations. © 2025 The Authors

关键词： CBDC Central bank digital Currency Disruptive innovation Open Innovation Retail

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