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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Fumiyoshi Kuwashima Mona Jarrahi Semih Cakmakyapan Osamu Morikawa Takuya Shirao Kazuyuki Iwao Kazuyoshi Kurihara Hideaki Kitahara Takashi Furuya Kenji Wada Makoto Nakajima And Masahiko Tani Department of Electrical and Electronic Engineering Fukui University of Technology Fukui Japan Electrical and Computer Engineering Department University of California Los Angeles Los Angeles CA USA Chair of Liberal Arts Japan Coast Guard Academy Hiroshima Japan School of Education University. of Fukui Fukui Japan Research Center for Development of Far-Infrared Region University of Fukui Fukui Japan Department of Physics and Electronics Osaka Prefecture University Osaka Japan Institute of Laser engineering Osaka Univ. Osaka Japan

Stability of optical beats in a chaotically oscillating laser is compared to that of a free-running continuous-wave laser using a highly efficient plasmonic photomixer. Using a chaotically oscillating laser diode, stable optical beats are observed over an operation current range of 60-90 mA. And stability of optical beats near the laser threshold level in a chaotically oscillating laser is also evaluated. The high stability of chaotically oscillating lasers makes these lasers promising candidates for optical pump sources in terahertz time-domain spectroscopy systems.

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Best arm identification for cascading bandits in the fixed confidence setting 37

Best arm identification for cascading bandits in the fixed c...

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37th International Conference on Machine Learning, ICML 2020

作者： Zhong, Zixin Cheung, Wang Chi Tan, Vincent Y. F. Department of Mathematics National University of Singapore Singapore Singapore Department of Industrial Systems and Management National University of Singapore Singapore Singapore Institute of Operations Research and Analytics National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9781713821120

We design and analyze CASCADEBAI, an algorithm for finding the best set of K items, also called an arm, within the framework of cascading bandits. An upper bound on the time complexity of CASCADEBAI is derived by overcoming a crucial analytical challenge, namely, that of probabilistically estimating the amount of available feedback at each step. To do so, we define a new class of random variables (r.v. s) which we term as left-sided sub-Gaussian r.v. s;this class is a relaxed version of the sub-Gaussian r.v. s. This enables the application of a sufficiently tight Bernstein-Type concentration inequality. We show, through the derivation of a lower bound on the time complexity, that the performance of CASCADEBAI is optimal in some practical regimes. Finally, extensive numerical simulations corroborate the efficacy of CASCADEBAI as well as the tightness of our upper bound on its time complexity. © 2020 by the Authors All rights reserved.

关键词： Machine learning

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinkai Lv Yuyong Hu Quanshui Fu Zhiwang Zhang Yuqiang Hu Lin Lv Guoqing Yang Jinpeng Li Yi Zhao Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Suining Central Hospital Suining China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP.

关键词： Convolutional codes Degradation Image segmentation Fuses Convolution Image edge detection Source coding

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Network Effects on Dual Machine Learning Models Predicting Smart Home Sensor Measurements

Network Effects on Dual Machine Learning Models Predicting S...

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IEEE Instrumentation and Measurement Technology Conference

作者： Saif Almhairat Bruce Wallace Julien Lariviè re-Chartier Ali El-Haraki Rafik Goubran Frank Knoefel Department of Systems and Computer Engineering (SCE) Carleton University Ottawa Canada Department of SCE Bruy&#x00E8 re Research Institute AGE-WELL SAM3 Innovation Hub Carleton University Ottawa Canada re Research Institute Carleton University Ottawa Canada Managing Consultant Cyber Security Strategy TELUS Communications Ottawa Canada Bruy&#x00E8 Elisabeth Bruy&#x00E8 re Hospital Department of SCE AGE-WELL SAM3 Innovation Hub Carleton University Ottawa Canada

Supportive smart home systems for older adults are growing in popularity. However, as an example of sensor networks, such systems can generate large amounts of small information packets that are repetitive and contain no new information. This has led to research projects focused on data reduction through dual machine learning models that reduce the network traffic between a home and cloud processing. This report focuses on the sensor-derived measurements that need to be efficiently communicated to the cloud. The report highlights the effects of network latency (or clock drift), jitter, and packet loss on overall performance for dual machine learning, as this has received minimal consideration previously. The current work presents a dual Autoregression machine learning model focused on predicting future sensor measurements of a smart home system. The work emulates a smart home system environment that includes cloud processing and a simulated imperfect network to reflect a real-life implementation. The results show how network effects such as the introduction of latency, jitter, or packet loss have caused as much as 50% of the cloud recording data samples being incorrect, compared to a perfect replication of data when network effects did not exist.

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Energy systems Test Case Discovery Enabled by Test Case Profile and Repository

Energy Systems Test Case Discovery Enabled by Test Case Prof...

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Open Source Modelling and Simulation of Energy systems (OSMSES)

作者： Petra Raussi Jirapa Kamsamrong Alexandros Paspatis Kai Heussen Tesfaye Amare Zerihun Edmund Widl Filip Pröstl Andrén Jawad H. Kazmi Thomas I. Strasser Felipe Castro Luigi Pellegrino Carbon Neutral Solutions VTT Techn. Research Centre of Finland Espoo Finland Energy Division OFFIS e.V. Oldenburg Germany School of Electrical and Computer Eng. National Technical University of Athens Athens Greece Wind and Energy Systems Technical University of Denmark Roskilde Denmark Energy Systems SINTEF Energy AS Trondheim Norway Center for Energy AIT Austrian Institute of Technology Vienna Austria Engineering Services SMA Solar Technology AG Kassel Germany Materials and Generation Technology Ricerca sul Sistema Energetico S.p.A. Milan Italy

Smart energy systems comprise multiple domains like power, thermal, control, information, and communication technology, which increases the complexity of research and development studies. This expansion also requires larger and ever so complex experimental pilot environments driving the demand for geographically distributed multi-research infrastructure tests. The Holistic Test Description approach supports the design of multi-domain and multi-research infrastructure tests by orga-nizing the test cases into comprehensive segments, ensuring all relevant items for testing are covered. These test cases eventually form a pool, which to understand holistically would require studying and reading all the descriptions. This work proposes therefore the concept of Test Case Profiles to improve test case discovery and the structured creation of them. Test Case Profiles add further structure to the indexing in test case repositories. Along with the proposed indexing method, four different use cases are introduced to motivate additional applications of the proposed concept.

关键词： Organizations Drives Benchmark testing Complexity theory Information and communication technology research and development Indexing

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

arXiv

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arXiv 2022年

作者： Lv, Jinkai Hu, Yuyong Fu, Quanshui Zhang, Zhiwang Hu, Yuqiang Lv, Lin Yang, Guoqing Li, Jinpeng Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Suining Central Hospital Suining China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multi-scale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our in-house dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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How Can Emerging Economies Meet development and Climate Goals in the Transport-Energy System? Modelling Co-Developed Scenarios in Kenya Using a Socio-Technical Approach

SSRN

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SSRN 2023年

作者： Dixon, James Pierard, Elena C. Mwanzia, Patrick Onjala, Joseph Giki, Paschal Ondanje, Warren Oduor, Joshua Mwangi, Francis Brand, Christian Mitullah, Winnie Courtright, Thomas Muhia, Paul Maranga, Ignatius Bundi, Thomas Balongo, Samuel Li, Tang Oyuke, Abel Sivakumar, Aruna Dalkmann, Holger Hirmer, Stephanie A. Department of Civil & Environmental Engineering University of Strathclyde Glasgow United Kingdom Transport Studies Unit University of Oxford Oxford United Kingdom Smith School of Enterprise and the Environment University of Oxford Oxford United Kingdom Strathmore Energy Research Centre Strathmore University Nairobi Kenya Department for Economics and Development Studies University of Nairobi Nairobi Kenya Nairobi Kenya Centre for Environmental Science Hasselt University Hasselt Belgium Institute for Development Studies University of Nairobi Nairobi Kenya Urban Systems Lab Centre for Transport Studies Imperial College London London United Kingdom Sustain2030 Berlin Germany Energy and Power Group University of Oxford Oxford United Kingdom UK Energy Research Centre Oxford United Kingdom

Transport-energy transitions pose complex challenges that have been extensively studied in high-income countries in response to national mandates for climate action. Low- and middle-income countries, however, have low but rapidly growing motorisation rates and face very different challenges in adopting new technologies to foster economic development and ensure equitable, clean access to transportation. Here, we present a set of narrative scenarios for the future of the Kenyan transport-energy system co-developed through engagement with 41 local experts and decision-makers. Through the co-development of a Kenyan transport-energy systems model, we present a decision-support tool to assist policymakers at regional, national and international levels in building policy, project and finance pipelines to support the realisation of climate-compatible development objectives in the transport-energy sector. We quantify the impacts of those scenarios on transport-energy system outcomes including vehicle stock, energy consumption, emissions, fiscal implications from changing fuel tax revenues and grid implications from e-mobility uptake. It was found that Kenya can vastly expand transport services to a growing population in a manner compatible with improving equitable access to mobility and limiting future emissions to within its nationally determined contribution (NDC) to the Paris Agreement. This transition will require strong policy in public transport provision, targeted support for electrification of road vehicles and significant investment in supporting infrastructure. Access to affordable finance, to government, businesses and individuals, is crucial to success in the transition. Kenyan policymakers can leverage international climate finance, which must be made available by the global community. © 2023, The Authors. All rights reserved.

关键词： Climate change

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Exact solution to two-body financial dealer model: revisited from the viewpoint of kinetic theory

arXiv

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arXiv 2022年

作者： Kanazawa, Kiyoshi Takayasu, Hideki Takayasu, Misako Faculty of Engineering Information and Systems University of Tsukuba Tennodai Ibaraki Tsukuba305-8573 Japan JST PRESTO 4-1-8 Honcho Saitama Kawaguchi332-0012 Japan Sony Computer Science Laboratories 3-14-13 Higashi-Gotanda Shinagawa-ku Tokyo141-0022 Japan Institute of Innovative Research Tokyo Institute of Technology 4259 Nagatsuta-cho Midori-ku Yokohama226-8502 Japan

The two-body stochastic dealer model is revisited to provide an exact solution to the average order-book profile using the kinetic approach. The dealer model is a microscopic financial model where individual traders make decisions on limit-order prices stochastically and then reach agreements on transactions. In the literature, this model was solved for several cases: an exact solution for two-body traders N = 2 and a mean-field solution for many traders N 1. Remarkably, while kinetic theory plays a significant role in the mean-field analysis for N 1, its role is still elusive for the case of N = 2. In this paper, we revisit the two-body dealer model N = 2 to clarify the utility of the kinetic theory. We first derive the exact master-Liouville equations for the two-body dealer model by several methods. We next illustrate the physical picture of the master-Liouville equation from the viewpoint of the probability currents. The master-Liouville equations are then solved exactly to derive the order-book profile and the average transaction interval. Furthermore, we introduce a generalised two-body dealer model by incorporating interaction between traders via the market midprice and exactly solve the model within the kinetic framework. We finally confirm our exact solution by numerical simulations. This work provides a systematic mathematical basis for the econophysics model by developing better mathematical intuition. Copyright © 2022, The Authors. All rights reserved.

关键词： Random processes

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A Mobile Cloud-Based eHealth Scheme

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computers, Materials & Continua 2020年第4期63卷 31-39页

作者： Yihe Liu Aaqif Afzaal Abbasi Atefeh Aghaei Almas Abbasi Amir Mosavi Shahaboddin Shamshirband Mohammed A.A.Al-qaness Data Recovery Key Laboratory of Sichuan Province College of Computer Science and AINeijiang Normal UniversityNeijiang641100China Department of Software Engineering Foundation UniversityIslamabad44000Pakistan Department of Communication Faculty of Social SciencesUniversity of TehranTehran***Iran Department of Computer Science International Islamic UniversityIslamabad46000Pakistan Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Kalman Kando Faculty of Electrical Engineering Obuda UniversityBudapest1034Hungary School of the Built Environment Oxford Brookes UniversityOxfordOX30BPUK Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam School of Computer Science Wuhan UniversityWuhan430072China

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid ***,the field of health informatics is also considered as an extremely important *** work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing *** developed system has two front ends,the first dedicated for the user to perform the photographing of the trace *** the photographing is complete,mobile computing is used to extract the *** the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the *** this is done,the second interface,intended for the use of the physician,can download and view the trace from the *** data is securely held using a password-based authentication *** system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.

关键词： Cloud computing electrocardiograms health-care signal analysis signal processing

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Detection of effective genes in colon cancer: A machine learning approach

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Informatics in Medicine Unlocked 2021年 24卷

作者： Fahami, Mohammad Amin Roshanzamir, Mohamad Izadi, Navid Hoseini Keyvani, Vahideh Alizadehsani, Roohallah Department of Electrical and Computer Engineering Isfahan University of Technology 84156-83111 Isfahan Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Department of Biology Faculty of Science Shahid Chamran University of Ahvaz Ahvaz Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Australia

Nowadays, a variety of cancers have become common among humans which unfortunately are the cause of death for many of these people. Early detection and diagnosis of cancers can have a significant impact on the survival of patients and treatment cost reduction. Colon cancer is the third and the second main cause of women's and men's death worldwide among cancers. Hence, many researchers have been trying to provide new methods for early diagnosis of colon cancer. In this study, we apply statistical hypothesis tests such as t-test and Mann–Whitney–Wilcoxon and machine learning methods such as Neural Network, KNN and Decision Tree to detect the most effective genes in the vital status of colon cancer patients. We normalize the dataset using a new two-step method. In the first step, the genes within each sample (patient) are normalized to have zero mean and unit variance. In the second step, normalization is done for each gene across the whole dataset. Analyzing the results shows that this normalization method is more efficient than the others and improves the overall performance of the research. Afterwards, we apply unsupervised learning methods to find the meaningful structures in colon cancer gene expressions. In this regard, the dimensionality of the dataset is reduced by employing Principle Component Analysis (PCA). Next, we cluster the patients according to the PCA extracted features. We then check the labeling results of unsupervised learning methods using different supervised learning algorithms. Finally, we determine genes which have major impact on colon cancer mortality rate in each cluster. Our conducted study is the first which suggests that the colon cancer patients can be categorized into two clusters. In each cluster, 20 effective genes were extracted which can be important for early diagnosis of colon cancer. Many of these genes have been identified for the first time. © 2021 The Authors

关键词： Colon cancer Dimension reduction Machine learning Statistical hypothesis tests Unsupervised learning

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