Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction ($DA^3$) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only $25\%$ label proportions. It shows highly competitive performance even if compared with fully supervised learners with $100\%$ label proportions.Comment: This paper has been accepted for publication in Information Science

Scaling Genetic Algorithms to Large Distributed Datasets

Analysing large-scale data brings promises of new levels of scientific discovery and economic value. However, the fact that such volume of data is by its nature distributed and the need for new computational methods to be effective in the face of significant changes in data complexity and size has led to the need to develop large-scale data analytics. Genetic algorithms (GAs) have proven their flexibility in many application areas, and substantial research has been dedicated to improving their performance through parallelisation. In contrast with most previous efforts, we reject approaches based on the centralisation of data in the main memory of a single node or requiring remote access to shared/distributed memory. We focus instead on scenarios where data is partitioned across machines. In this partitioned scenario, we explore two parallelisation models: PDMS, inspired by the traditional master-slave model, and PDMD, based on island models. We adopt the two models to distribute BioHEL, a popular large-scale single-node GA classifier, using the Spark distributed data processing platform. We investigate the effect of GA control parameters (population size and migration frequency).We study the accuracy, time performance and scalability of the proposed models. Our results show that our distributed genetic algorithm design provides a good tradeoff between accuracy and time. We then extend the two models using automatic termination and population sizing to enhance the distributed genetic algorithm ease-of-use. Moreover, after testing this strategy on both models, we show that the applied automation offers a promising enhancement on the performance of the initially designed GA models

Scaling Genetic Algorithms to Large Distributed Datasets

Analysing large-scale data brings promises of new levels of scientific discovery and economic value. However, the fact that such a volume of data is by its nature distributed and the need for new computational methods to be effective in the face of significant changes in data complexity and size has led to the need to develop large-scale data analytics. Genetic algorithms (GAs) have proven their flexibility in many application areas, and substantial research has been dedicated to improving their performance through parallelisation. In contrast with most previous efforts, we reject approaches based on the centralisation of data in the main memory of a single node or requiring remote access to shared/distributed memory. We focus instead on scenarios where data is partitioned across machines. In this partitioned scenario, we explore two parallelisation models: PDMS, inspired by the traditional master-slave model, and PDMD, based on island models. We adopt the two models to distribute BioHEL, a popular large-scale single-node GA classifier, using the Spark distributed data processing platform. We investigate the effect of GA control parameters (population size and migration frequency). We study the accuracy, time performance and scalability of the proposed models. Our results show that our distributed genetic algorithm design provides a good tradeoff between accuracy and time

Research and Creative Activity, July 01, 2021-June 30, 2022: Major Sponsored Programs and Faculty Accomplishments in Research and Creative Activity, University of Nebraska-Lincoln

Foreword by Bob Wilhelm, Vice Chancellor for Research and Economic Development: This booklet highlights successes in research, scholarship and creative activity by University of Nebraska–Lincoln faculty during the fiscal year running July 1, 2021, to June 30, 2022. It lists investigators, project titles and funding sources on major grants and sponsored awards that were active during the year; fellowships and other recognitions and honors bestowed on our faculty; books, chapters and creative literature published by faculty; performances, exhibitions and other examples of creative activity; patents and licensing agreements; and conference presentations. In recognition of the important role faculty play in the undergraduate experience at Nebraska, this booklet notes the students and mentors participating in the Undergraduate Creative Activities and Research Experience (UCARE) and the First-Year Research Experience (FYRE) programs. Increasing impact through research and creative activity is one of the six core aims of the N2025 strategic plan. A few measurements of progress made this year: • UNL achieved a record $321 million in total research expenditures in FY 2021, a 31• Our faculty earned 1,560 sponsored research awards in FY 2021. N2025 aims also include contributing to economic growth throughout the state and broadening Nebraska’s engagement in community, industry and global partnerships. These are some measures of our efforts to commercialize university-sponsored research and partner with industry: • Nebraska Innovation Campus created 2,127 jobs statewide. The cumulative impact of NIC investments totals$328.9 million. • Industry sponsorship supported $19.8 million in research expenditures. • NUtech Ventures brought in$6.36 million in licensing income. I want to thank the Nebraska Research community for its willingness to collaborate, mentor and redefine success in research and creative activity. Your leadership is paving the way for future growth and providing an unparalleled educational experience. At Nebraska, it is the people who make the place. Because of your dedication and expertise, Nebraska is positioned to solve some of the world’s most wicked problems. I am impressed by your commitment to the Grand Challenges initiative, a strategic investment of up to $40 million over four years for projects in the high-impact areas of anti-racism and racial equity; climate resilience; early childhood education and development; health equity; quantum science and engineering; science and technology literacy for society; and sustainable food and water security. More than 180 faculty, staff and students are contributing to projects funded in Year 1. Another N2025 aim is to create a climate that emphasizes, prioritizes and expands inclusive excellence and diversity. In the Office of Research and Economic Development, we continue to seek ways to remove barriers to success and ensure all Nebraska researchers have the resources they need to thrive. Thank you for the feedback you’ve thoughtfully provided. I am pleased to present this record of accomplishments. Contents Awards of$5 Million or More Awards of $1 Million to$4,999,999 Awards of $250,000 to$999,999 Early Career Awards Arts and Humanities Awards of $250,000 or More Arts and Humanities Awards of$50,000 to $249,999 Arts and Humanities Awards of$5,000 to $49,999 Patents License Agreements National Science Foundation Innovation Corps Teams Creative Activity Books Recognitions and Honors Journal Articles Conference Presentations UCARE and FYRE Projects Glossar

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services

Advanced photonic and electronic systems WILGA 2016

Young Researchers Symposium WILGA on Photonics Applications and Web Engineering has been organized since 1998, two times a year. Subject area of the Wilga Symposium are advanced photonic and electronic systems in all aspects: theoretical, design and application, hardware and software, academic, scientific, research, development, commissioning and industrial, but also educational and development of research and technical staff. Each year, during the international Spring edition, the Wilga Symposium is attended by a few hundred young researchers, graduated M.Sc. students, Ph.D. students, young doctors, young research workers from the R&D institutions, universities, innovative firms, etc. Wilga, gathering through years the organization experience, has turned out to be a perfect relevant information exchange platform between young researchers from Poland with participation  of international guests, all active in the research areas of electron and photon technologies, electronics, photonics, telecommunications, automation, robotics and information technology, but also technical physics. The paper summarizes the achievements of the 38th Spring Edition of 2016 WILGA Symposium, organized in Wilga Village Resort owned by Warsaw University of technology

Research and Creative Activity, July 1, 2020-June 30, 2021: Major Sponsored Programs and Faculty Accomplishments in Research and Creative Activity, University of Nebraska-Lincoln

Foreword by Bob Wilhelm, Vice Chancellor for Research and Economic Development, University of Nebraska-Lincoln: This booklet highlights successes in research, scholarship and creative activity by University of Nebraska–Lincoln faculty during the fiscal year running July 1, 2020, to June 30, 2021. It lists investigators, project titles and funding sources on major grants and sponsored awards received during the year; fellowships and other recognitions and honors bestowed on our faculty; books and chapters published by faculty; performances, exhibitions and other examples of creative activity; patents and licensing agreements issued; National Science Foundation I-CORPS teams; and peer-reviewed journal articles and conference presentations. In recognition of the important role faculty have in the undergraduate experience at Nebraska, this booklet notes the students and mentors participating in the Undergraduate Creative Activities and Research Experience (UCARE) and the First-Year Research Experience (FYRE) programs. While metrics cannot convey the full impact of our work, they are tangible measures of growth. A few achievements of note: • UNL achieved a record $320 million in total research expenditures in FY 2020, a 43• Our faculty earned 1,508 sponsored research awards in FY 2020. University-sponsored industry activity also spurred economic growth for Nebraska. • Nebraska Innovation Campus created 1,948 jobs statewide and had a total economic impact of$372 million. • Industry sponsorship supported $19.2 million in research expenditures. • NUtech Ventures brought in$6.48 million in licensing income. I applaud the Nebraska Research community for its determination and commitment during a challenging year. Your hard work has made it possible for our momentum to continue growing. Our university is poised for even greater success. The Grand Challenges initiative provides a framework for developing bold ideas to solve society’s greatest issues, which is how we will have the greatest impact as an institution. Please visit research.unl.edu/grandchallenges to learn more. We’re also renewing our campus commitment to a journey of anti-racism and racial equity, which is among the most important work we’ll do. I am pleased to present this record of accomplishments. Contents Awards of $5 Million or More Awards of$1 Million to $4,999,999 Awards of$250,000 to $999,999 50 Early Career Awards Arts and Humanities Awards of$250,000 or More Arts and Humanities Awards of $50,000 to$249,999 Arts and Humanities Awards of $5,000 to$49,999 Patents License Agreements National Science Foundation Innovation Corps Teams Creative Activity Books Recognitions and Honors Journal Articles 105 Conference Presentations UCARE and FYRE Projects Glossar

2012 IMSAloquium, Student Investigation Showcase

Through SIR and its partnerships, IMSA students engage in rich opportunities to pursue compelling questions of interest, conduct investigations, engage with extraordinary advisors, communicate findings, and ultimately impact society.https://digitalcommons.imsa.edu/archives_sir/1004/thumbnail.jp

Coded Territories

This collection of essays provides a historical and contemporary context for Indigenous new media arts practice in Canada. The writers are established artists, scholars, and curators who cover thematic concepts and underlying approaches to new media from a distinctly Indigenous perspective. Through discourse and narrative analysis, the writers discuss a number of topics ranging from how Indigenous worldviews inform unique approaches to new media arts practice to their own work and specific contemporary works. Contributors include: Archer Pechawis, Jackson 2Bears, Jason Edward Lewis, Steven Foster, Candice Hopkins, and Cheryl L'Hirondelle