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IEEE Global Communications Conference

作者： Leonardo R. Rodrigues Marcelo Pasin Omir C. Alves Charles C. Miers Mauricio A. Pillon Pascal Felber Guilherme P. Koslovski Graduate Program in Applied Computing Santa Catarina State University Joinville Brazil University of Neuchâtel (UniNE) Institut d'informatique Switzerland

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Network management on multi-tenant container-based data center has critical impact on performance. Tenants encapsulate applications in containers abstracting away details on hosting infrastructures, and entrust data center management framework with the provisioning of network Quality-of-Service requirements. In this paper, we propose a network-aware multi-criteria container scheduler to jointly process containers and network requirements. We introduce a new Mixed Integer Linear programming formulation for network-aware scheduling encompassing both tenants and providers metrics. We describe two GPU-accelerated modules to address the complexity barrier of the problem and efficiently process scheduling requests. Our experiments show that our scheduling approach accounting for both network and containers outperforms traditional algorithms used by containers orchestrators.

关键词： Containers Servers Bandwidth Quality of service Resource management Scheduling Processor scheduling

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Glucose metabolism changes in cerebellar tonsils as an early predictor of cognitive decline

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Alzheimer's & Dementia 2022年第S4期17卷

作者： Caroline Machado Dartora Michel Koole Ana Maria Marques da Silva Medical Image Computing Laboratory School of Technology PUCRS Porto Alegre Brazil Graduate Program in Biomedical Gerontology School of Medicine PUCRS Porto Alegre Brazil KU Leuven and University Hospital Leuven Leuven Belgium Brain Institute of Rio Grande do Sul (BraIns) PUCRS Porto Alegre Brazil

Background Mild cognitive impairment (MCI) is associated with an increased risk of dementia, but imaging biomarkers are not clearly related to the likelihood of developing cognitive decline. This preliminary study aims to investigate the brain glucose metabolic PET imaging patterns in subjects that are cognitively unimpaired older adults (CUI) and those who convert to MCI within two years. Method CUI subjects with 18 F-FDG PET and MR images were selected from the ADNI study. Two groups were separated in our research: (i) 36 stable CUI subjects during a minimum of 5 years; (ii) 12 subjects initially cognitively unimpaired (preCONV) that convert to MCI within two years (posCONV). Static 18 F-FDG PET images were co-registered with the correspondent MR image and normalized to MNI space with an 8 mm Gaussian filter. A normalized whole-brain (WB) mask was determined. Each PET was normalized by its WB mean value. Image analysis was performed using SPM 12, with proportional scaling and age-corrected. Groups have no difference in age (79.5±5)y and years of education (15.8±3)y. Brain glucose hyper- and hypometabolism were evaluated between groups (CUI versus preCONV, CUI versus posCONV) and longitudinally (preCONV versus posCONV). Result The cluster analysis (p(unc)≤0.001) of CUI subjects when compared to preCONV shows brain glucose hypometabolism in the cerebellar tonsil and putamen regions, and infero-medial temporoparietal lobe, and hypermetabolism in the left parietal and right temporal and frontal lobes. CUI shows hypometabolism compared to posCONV in regions of the left and right cerebellar tonsil, culmen, frontal lobe, Brodmann Area (BA) 6 and midbrain, and hypermetabolism in the parietal lobe, including the precuneus and BA39. No differences are found between preCONV and *** hypometabolism in cerebellar tonsil in CUI compared to preCONV and posCONV survived correction for multiple comparisons (cluster p(FWE)≤0.05), showing a possible involvement in the cognit

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Quantum computing hardware for HEP algorithms and sensing

arXiv

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arXiv 2022年

作者： Alam, M. Sohaib Belomestnykh, Sergey Bornman, Nicholas Cancelo, Gustavo Chao, Yu-Chiu Checchin, Mattia Dinh, Vinh San Grassellino, Anna Gustafson, Erik J. Harnik, Roni McRae, Corey Rae Harrington Huang, Ziwen Kapoor, Keshav Kim, Taeyoon Kowalkowski, James B. Kramer, Matthew J. Krasnikova, Yulia Kumar, Prem Kurkcuoglu, Doga Murat Lamm, Henry Lyon, Adam L. Milathianaki, Despina Murthy, Akshay Mutus, Josh Nekrashevich, Ivan Oh, JinSu Özgüler, A. Barış Perdue, Gabriel Nathan Reagor, Matthew Romanenko, Alexander Sauls, James A. Stefanazzi, Leandro Tubman, Norm M. Venturelli, Davide Wang, Changqing You, Xinyuan van Zanten, David M.T. Zhou, Lin Zhu, Shaojiang Zorzetti, Silvia NASA Ames Research Center Moffett FieldCA94035 United States Mountain ViewCA94043 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Scientific Computing Division Fermi National Accelerator Laboratory BataviaIL60510 United States Northwestern-Fermilab Center for Applied Physics and Superconducting Technologies Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Theory Division Fermi National Accelerator Laboratory BataviaIL60510 United States Department of Physics Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO80305 United States National Institute of Standards and Technology Boulder CO80305 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Ames Laboratory U.S. Department of Energy AmesIA50011 United States Center for Photonic Communication and Computing ECE Department Northwestern University EvanstonIL60208 United States Rigetti Computing Inc. BerkeleyCA94710 United States NASA Ames Research Center Moffett FieldCA94035 United States

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware. Copyright © 2022, The Authors. All rights reserved.

关键词： High energy physics

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Artificial neural network-based method to classify sedimentary rocks 12

Artificial neural network-based method to classify sedimenta...

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12th International Conference on Sensing Technology, ICST 2018

作者： Figueiredo, Rodrigo Marques Veronez, Mauricio Roberto Wohnrath, Francisco Manoel Da Silva, Marcio Rosa Gonzaga, Luiz Kupssinsku, Lucas Silveira Bordin, Fabiane Brum, Diego Cazarin, Caroline Lessio Graduate Program in Geology Unisinos University Saõ Leopoldo Brazil Advanced Visualization and Geoinformatics Laboratory Unisinos University Saõ Leopoldo Brazil Graduate Program in Electrical Engineering Unisinos University Saõ Leopoldo Brazil Graduate Program in Applied Computing Unisinos University Saõ Leopoldo Brazil Rio de Janeiro Brazil

ISBN: (纸本)9781538651476

In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of specialists working under rigid criteria also increases. This paper proposes a novel methodology to classify grain size using unique wavelength reflectance data and artificial neural networks. The results indicate that the proposed method can be reliably used in the field. © 2018 IEEE.

关键词： Neural networks

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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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Intelligent E-commerce for Special Needs 7

Intelligent E-commerce for Special Needs

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7th International Conference on Cyber and IT Service Management, CITSM 2019

作者： Warnars, Diana Teresia Spits Putra, Leonardo Primadala Logiansa Warnars, Harco Leslie Hendric Spits Utomo, Wiranto Herry School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Department of Magister Science of Information Technology Faculty of Computing President University Bekasi17550 Indonesia

ISBN: (纸本)9781728129099

Special need is a person who need assistance for their disabilities which include medical, physical, mental or psychological such as person with autism, down syndrome, dyslexia, Attention Deficit Hyperactivity Disorder (ADHD), multiple sclerosis, schizophrenia, cystic fibrosis, cerebral palsy, muscular dystrophy, blindness, deafness, epilepsy, chronic asthma and so on. In this proposed research idea about intelligent E-commerce for special needs, we limited to four types of difficulties such as hearing difficulty, vision difficulty, language difficulty, and learning difficulty. In this paper, we interest in those special needs persons who can use e-commerce and how to model a system that can help them to joy and easy to join with e-commerce. For some, this intelligent e-commerce is not only for shopping only but will become part of their healing for special needs in order to engage with daily human activities and increase their daily confidence in life. This paper also a preliminary investigation that can be used for Ph.D. or Doctoral research degree who is interested in building e-commerce framework for people with a special need or disability. © 2019 IEEE.

关键词： Electronic commerce

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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： Computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase

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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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Analysis of Negative Capacitance MOSFETs Characteristic with Spacer

Analysis of Negative Capacitance MOSFETs Characteristic with...

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International Symposium on VLSI Technology, Systems and Applications

作者： Yu-Chun Lee Sekhar Reddy Kola Chieh-Yang Chen Min-Hui Chuang Yiming Li Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering EECS International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu Taiwan

ISBN: (数字)9781728142326

ISBN: (纸本)9781728142333

In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable I on /I off ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS.

关键词： Capacitance Iron Semiconductor device modeling MOSFET Integrated circuit modeling Mathematical model Ferroelectric materials

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A Tutorial on Trusted and Untrusted Non-3GPP Accesses in 5G Systems - First Steps Toward a Unified Communications Infrastructure

arXiv

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arXiv 2021年

作者： Lemes, Mario Teixeira Alberti, Antonio Marcos Both, Cristiano Bonato de Oliveira, Antonio Carlos Cardoso, Kleber Vieira Instituto Federal de Educação Ciência e Tecnologia de Goiás Formosa73813-816 Brazil Instituto de Informática Universidade Federal de Goiás Goiânia74690-900 Brazil Instituto Nacional de Telecomunicações Santa Rita do Sapucaí37540-000 Brazil Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos São Leopoldo93022-750 Brazil Fraunhofer AICOS Porto4200-135 Portugal

fifth-generation (5G) systems are designed to enable convergent access-agnostic service availability. This means that 5G services will be available over 5G New Radio air interface and also through other non-Third Generation Partnership Project (3GPP) access networks, e.g., IEEE 802.11 (Wi-Fi). 3GPP has recently published the Release 16 that includes trusted non-3GPP access network concept and wireless wireline convergence. The main goal of this tutorial is to present an overview of access to 5G core via non-3GPP access networks specified by 3GPP until Release 16 (i.e., untrusted, trusted, and wireline access). The tutorial describes convergence aspects of a 5G system and these non-3GPP access networks, such as the authentication and authorization procedures and the data session establishment from the point of view of the protocol stack and exchanged messages among the network functions. In order to illustrate several concepts and part of 3GPP specification, we present a basic but fully operational implementation of untrusted non-3GPP access using WLAN. We perform experiments illustrating how a Wi-Fi user is authorized in a 5G core, establishing user plane connectivity to a data network. Moreover, we evaluate the performance of this access in terms of time consumed, the number of messages, and protocol overhead to established data sessions. © 2021, CC BY.

关键词： Wi-Fi

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