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1. Novel Network Services for Supporting Big Data Science Research

   Chung, J. ; Donovan, S. ; Bezerra, J. ; Morgan, H. ; Ibarra, J. ; Clark, R. ; Owen, H ( October 2017 , Gateways 2017)

   To interconnect research facilities across wide geographic areas, network operators deploy science networks, also referred to as Research and Education (R&E) networks. These networks allow experimenters to establish dedicated network connections between research facilities for transferring large amounts of data. Recently, R&E networks have started using Software-Defined Networking (SDN) and Software Defined Exchanges (SDX) for deploying these connections. AtlanticWave/SDX is a response to the growing demand to support end-to-end network services spanning multiple SDN domains. However, requesting these services is a challenging task for domain-expert scientists, because the interfaces of the R&E networks have been developed by network operators for network operators. In this paper, we propose interfaces that allow domain expert scientists to reserve resources of the scientific network using abstractions that focus on their data transfer needs for scientific workflow management. Recent trends in the networking field pursue better interfaces for requesting network services (e.g., intent-based networking). Although intents are sufficient for the needs of network operations, they are not abstract enough in most cases to be used by domain-expert scientists. This is an issue we are addressing in the AtlanticWave/SDX design: network operators and domain-expert scientists will have their own interfaces focusing on their specific needs. 

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2. Orchestrating Intercontinental Advance Reservations with Software-Defined Exchanges

   Chung, J. ; Kettimuthu, R. ; Clark, R. ( November 2017 , The 4th Innovating the Network for Data-Intensive Science workshop (INDIS17))

   To interconnect research facilities across wide geographic areas, network operators deploy science networks, also referred to as Research and Education (R&E) networks. These networks allow experimenters to establish dedicated circuits between research facilities for transferring large amounts of data, by using advanced reservation systems. Intercontinental dedicated circuits typically require coordination between multiple administrative domains, which need to reach an agreement on a suitable advance reservation. The success rate of finding an advance reservation decreases as the number of participant domains increases for traditional systems because the circuit is composed over a single path. To improve provisioning of multi-domain advance reservations, we propose an architecture for end-to-end service orchestration in multi-domain science networks that leverages software-defined exchanges (SDX) for providing multi-path, multi-domain advance reservations. We have implemented an orchestrator for multi-path, multi-domain advance reservations and an SDX to support these services. Our orchestration architecture enables multi-path, multi-domain advance reservations and improves the reservation success rate from 50% in single path systems to 99% when four path are available. 

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3. Responding to the demands of big data scientific instruments through the development of an international software defined exchange point (SDX)

   Ibarra, J. ; Bezerra, J. ; Lopez, L.F. ; Morgan, L ; Cox, D. ( April 2016 , UbuntuNet Connect-Connect 2016)

   Science is being conducted in an era of information abundance. The rate at which science data is generated is increasing, both in volume and variety. This phenomenon is transforming how science is thought of and practiced. This transformation is being shaped by new scientific instruments that are being designed and deployed that will dramatically increase the need for large, real-time data transfers among scientists throughout the world. One such instrument is the Square Kilometer Array (SKA) being built in South Africa that will transmit approximately 160Gbps of data from each radio dish to a central processor. This paper describes a collaborative effort to respond to the demands of big data scientific instruments through the development of an international software defined exchange point (SDX) that will meet the network provisioning needs for science applications. This paper discusses the challenges of end-to-end path provisioning across multiple research and education networks using OpenFlow/SDN technologies. Furthermore, it refers to the AtlanticWave-SDX, a project at Florida International University and the Georgia Institute of Technology, funded by the US National Science Foundation (NSF), along with support from Brazil’s NREN, Rede Nacional de Ensino e Pesquisa (RNP, and the Academic Network of Sao Paulo (ANSP). Future work explores the feasibility of establishing an SDX in West Africa, in collaboration with regional African RENs, based on the planned availability of submarine cable spectrum for use by research and education communities. 

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4. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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5. AARCLight – New opportunities for South Atlantic R&E Network collaboration between Africa, Brazil, and the U.S.

   Morgan, Heidi ; Ibarra, Julio ; Bezerra, Jeronimo ; Lopez, Luis Fernandez ; Chergarova, Vasilka ; Cox III, Donald A.“Chip” ; Alvarez, Gabriella E. ; Stanton, Michael ; Hazin, Aluizio ; Lotz, Len ; et al ( January 2018 , Proceedings and report of the ... UbuntuNet Alliance annual conference)

   Higher education and research science is being conducted in an era of information abundance. Sharing educational resources (e.g. Libraries, Curriculums, Online courses) and science resources, such as data commons, instrumentation, technology, and best practices, across national borders, can promote expanded global education goals and scientific inquiry and has the potential to advance discovery. Providing robust diverse Research and Education Networks (RENs) linking the U.S., Brazil (S. America) and African researcher and education communities is an increasingly strategic priority. Africa has developed research and education communities with unique biological, environmental, geological, anthropological, and cultural resources. Research challenges in atmospheric and geosciences, materials sciences, tropical diseases, biology, astronomy, and other disciplines will benefit by enhancing the technological and social connections between the research and education communities of these three continents via an S. Atlantic route to complement the existing North Atlantic routes via Europe. This paper will discuss the availability of new submarine cable spectrum for RENs via SACS between Luanda, Angola and Fortaleza, Brazil and the Monet cable between Fortaleza and Florida in the U.S. for use by research and education communities. This new infrastructure creates an unprecedented opportunity for the stakeholders to coordinate planning efforts to strategically make use of the offered spectrum towards serving the broadest communities of interest in research and education. The new links will be a foundational layer for the employment of R&E networks outfitted with leading-edge technologies (e.g. Science DMZ, SDN, SDX, cybersecurity etc.). The paper seeks to leverage a discussion of opportunities for a new R&E Exchange point at Luanda, Angola, other connectivity options, and to further promote discussion and identify synergies with UbuntuNet members. Florida International University and AmLight consortium partners are planning, designing, and defining a strategy for high capacity connectivity research and education network connectivity between the US and Southwest Africa, called Americas Africa Research and eduCation Lightpaths (AARCLight). Furthermore, the other “end” of the SACS cable is being connected to an Open Fortaleza R&E Exchange point in Brazil. The new academic exchange point, South Atlantic Crossroads (SAX), is managed by Rede Nacional de Ensino e Pesquisa (RNP), where AmLight connects and continues on the Monet spectrum to Boca Raton Miami Florida. Having the transport service opened in Fortaleza will allow RENs from South America to collaborate with partners in Africa with significantly less delay, (at least 150ms lower) than using the current paths available. Interactive high-resolution video and big data applications will benefit from the establishment of the SAX international exchange point in Fortaleza. 

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