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arXiv 2022年

作者： Kepner, Jeremy Cho, Kenjiro Claffy, K.C. Gadepally, Vijay McGuire, Sarah Milechin, Lauren Arcand, William Bestor, David Bergeron, William Byun, Chansup Hubbell, Matthew Houle, Michael Jones, Michael Prout, Andrew Reuther, Albert Rosa, Antonio Samsi, Siddharth Yee, Charles Michaleas, Peter MIT Lincoln Laboratory United States Research Laboratory Internet Initiative Japan Inc. Japan UCSD Center for Applied Internet Data Analysis United States MIT Dept. of Earth Atmospheric and Planetary Sciences United States

Theinternet derstanding is of transforming internet traffic. our Our society, team necessitating collects and curates a quantitative the largest unpublicly available internet traffic data sets. An analysis of 50 billion packets using 10,000 processors in the MIT SuperCloud reveals a new phenomenon: the importance of otherwise unseen leaf nodes and isolated links in internet traffic. Our analysis further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The measured model parameters distinguish different network streams, and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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The Impact of the General data Protection Regulation on internet Interconnection

SSRN

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SSRN 2021年

作者： Zhuo, Ran Huffaker, Bradley Claffy, K.C. Greenstein, Shane Department of Economics Harvard University Center for Applied Internet Data Analysis University of California San Diego Harvard Business School Harvard University

The internet comprises thousands of independently operated networks, interconnected using bi-laterally negotiated data exchange agreements. The European Union (EU)'s General data Protection Regulation (GDPR) imposes strict restrictions on handling of personal data of European Economic Area (EEA) residents. A close examination of the text of the law suggests significant cost to application firms. Available empirical evidence confirms reduction in data usage in the EEA relative to other markets. We investigate whether this decline in derived demand for data exchange impacts EEA networks' decisions to interconnect relative to those of non-EEA OECD networks. Our data consists of a large sample of interconnection agreements between networks globally in 2015–2019. All evidence estimates zero effects: the number of observed agreements, the inferred agreement types, and the number of observed IP-address-level inter-connection points per agreement. We also find economically small effects of the GDPR on the entry and the observed number of customers of networks. We conclude there is no visible short run effects of the GDPR on these measures at the internet layer. © 2021, The Authors. All rights reserved.

关键词： Electronic data interchange

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Nested spectral analysis of brain functional networks in depression based on inner composition alignment

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AIP Advances 2025年第5期15卷

作者： Ding, Jiahao Xie, Xiaoyu Fu, Bincheng Hua, Jianchun Wang, Jun Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province School of Chemistry and Life Sciences Nanjing University of Posts and Telecommunications Nanjing210023 China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210023 China School of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province School of Applied Technology Nanjing University of Posts and Telecommunications Nanjing210023 China

To address the limitations of traditional phase-locked value methods in electroencephalography (EEG) functional connectivity analysis, such as loss of frequency-domain information and insufficient capture of nonlinear dynamics, this study proposes a time-domain permutation-based inner composition alignment network nested-spectral analysis framework for analyzing depression-related brain functional networks. The results demonstrate that, regardless of depressive status, EEG networks in the theta and alpha bands predominantly operate in an integrated state globally. However, with increased depressive symptoms, the segregated state becomes more dominant. This shift leads to higher state transition frequencies in the alpha band for depression patients, while depressive conditions are also associated with reduced dynamic switching in the beta and gamma bands. We identified a "low-frequency integration decline and high-frequency segregation dysregulation" dual-modal imbalance phenomenon in the brain functional networks of depression patients, providing a novel paradigm to support the clinical diagnosis of depression. © 2025 Author(s).

关键词： Frequency response

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Hypersparse Neural Network analysis of Large-Scale internet Traffic

Hypersparse Neural Network Analysis of Large-Scale Internet ...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Jeremy Kepner Kenjiro Cho KC Claffy Vijay Gadepally Peter Michaleas Lauren Milechin MIT Lincoln Laboratory Supercomputing Center Research Laboratory Internet Initiative Japan Inc. UCSD Center for Applied Internet Data Analysis MIT Dept. of Earth Atmospheric & Planetary Sciences

The internet is transforming our society, necessitating a quantitative understanding of internet traffic. Our team collects and curates the largest publicly available internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of “video” streams of this traffic using 10,000 processors in the MIT SuperCloud reveals a new phenomena: the importance of otherwise unseen leaf nodes and isolated links in internet traffic. Our neural network approach further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The inferred model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies. The hypersparse neural network pipeline is highly adaptable and different network statistics and training models can be incorporated with simple changes to the image filter functions.

关键词： internet Neural networks Streaming media Adaptation models Network topology Computational modeling data models

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Hypersparse neural network analysis of large-scale internet traffic

arXiv

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arXiv 2019年

作者： Kepner, Jeremy Cho, Kenjiro Claffy, K.C. Gadepally, Vijay Michaleas, Peter Milechin, Lauren MIT Lincoln Laboratory Supercomputing Center Research Laboratory Internet Initiative Japan Inc. UCSD Center for Applied Internet Data Analysis MIT Dept. of Earth Atmospheric & Planetary Sciences

The internet is transforming our society, necessitating a quantitative understanding of internet traffic. Our team collects and curates the largest publicly available internet traffic data containing 50 billion packets. Utilizing a novel hypersparse neural network analysis of "video" streams of this traffic using 10,000 processors in the MIT SuperCloud reveals a new phenomena: the importance of otherwise unseen leaf nodes and isolated links in internet traffic. Our neural network approach further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents. The inferred model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies. The hypersparse neural network pipeline is highly adaptable and different network statistics and training models can be incorporated with simple changes to the image filter functions. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Robust feature selection method of radiomics for grading glioma 1

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2nd International Conference on Healthcare Science and Engineering, Healthcare 2018

作者： Wu, Yaping Liu, Bo Zhao, Guohua Yang, Han Chen, Yuanqin Lv, Qiujie Wang, Meiyun Lin, Yusong School of Electronic and Information Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China School of Software and Applied Technology Zhengzhou University ZhengzhouHenan450001 China Cooperative Innovation Center for Internet Healthcare Zhengzhou University ZhengzhouHenan450001 China Department of Radiology Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital ZhengzhouHenan450001 China Henan Provincial Clinical Big Data Analysis and Service Engineering Research Center Zhengzhou University People’s Hospital and Henan Provincial People’s Hospital ZhengzhouHenan450001 China

ISBN: (数字)9789811368370

ISBN: (纸本)9789811368363

The accuracy of glioma segmentation is significantly affected by the radiomics-based prediction model for grading glioma. This study proposed a robust feature selection method that can select stable and insensitive features to the segmentation of the region of interest (ROI). The method consists of three main steps. First, stable features are selected from 360 radiomics features based on the Pearson correlation coefficient. Then, an unsupervised K-means algorithm is introduced to remove redundant features from those selected in the first step and obtain sets of K group candidate features. Finally, by using these K group feature sets to train four prediction models, the final feature set and final prediction models that have the best prediction performance are selected. Experiments were conducted on 156 glioma samples from Henan Provincial People’s Hospital between 2012 and 2016, and 11 robust features were selected. The results demonstrated excellent accuracy, sensitivity, specificity, and AUC (0.88, 0.94, 0.88, and 0.85, respectively). Compare with the performance without feature selection, a 5% increase in accuracy, sensitivity, and AUC and 13% increase in specificity were observed. The proposed feature selection method can reduce the training time by 94.04%. © Springer Nature Singapore Pte Ltd 2019.

关键词： Grading

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Guest Editorial Measuring and Troubleshooting the internet: Algorithms, Tools and Applications

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IEEE Journal on Selected Areas in Communications 2016年第6期34卷 1805-1806页

作者： Maurizio Dusi Alessandro Finamore Kimberly Claffy Nevil Brownlee Darryl Veitch Copan Diagnostics Inc. Murrieta CA USA Telefonica Research Distrito Telefonica Madrid Spain Center for Applied Internet Data Analysis San Diego Supercomputer Center La Jolla CA USA Department of Computer Science The University of Auckland Auckland New Zealand School of Computing and Communications Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia

The ubiquity of internet access and the wide variety of internet-enabled devices and applications have made the internet a principal pillar of the Information Society. Decentralized and diverse, the internet is resilient and universal. However, its distributed nature leads to operational brittleness and difficulty in identifying and tracking the root causes of performance and availability issues.

关键词： Special issues and sections internet Algorithm design and analysis Performance analysis Quality of service

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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