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1. Building the SLATE Platform

   https://doi.org/10.1145/3219104.3219144  

   Breen, Joe ; McKee, Shawn ; Riedel, Benedikt ; Stidd, Jason ; Truong, Luan ; Vukotic, Ilija ; Bryant, Lincoln ; Carcassi, Gabriele ; Chen, Jiahui ; Gardner, Robert W. ; et al ( July 2018 , Proceedings of the Practice and Experience on Advanced Research Computing)

   We describe progress on building the SLATE (Services Layer at the Edge) platform. The high level goal of SLATE is to facilitate creation of multi-institutional science computing systems by augmenting the canonical Science DMZ pattern with a generic, "programmable", secure and trusted underlayment platform. This platform permits hosting of advanced container-centric services needed for higher-level capabilities such as data transfer nodes, software and data caches, workflow services and science gateway components. SLATE uses best-of-breed data center virtualization and containerization components, and where available, software defined networking, to enable distributed automation of deployment and service lifecycle management tasks by domain experts. As such it will simplify creation of scalable platforms that connect research teams, institutions and resources to accelerate science while reducing operational costs and development cycle times. 

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2. 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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3. Collections Management and High-Throughput Digitization using Distributed Cyberinfrastructure Resources

   https://doi.org/10.3897/biss.2.25643  

   Motz, Gary ; Zimmerman, Alexander ; Cook, Kimberly ; Bancroft, Alyssa ( July 2018 , Biodiversity Information Science and Standards)

   Collections digitization relies increasingly upon computational and data management resources that occasionally exceed the capacity of natural history collections and their managers and curators. Digitization of many tens of thousands of micropaleontological specimen slides, as evidenced by the effort presented here by the Indiana University Paleontology Collection, has been a concerted effort in adherence to the recommended practices of multifaceted aspects of collections management for both physical and digital collections resources. This presentation highlights the contributions of distributed cyberinfrastructure from the National Science Foundation-supported Extreme Science and Engineering Discovery Environment (XSEDE) for web-hosting of collections management system resources and distributed processing of millions of digital images and metadata records of specimens from our collections. The Indiana University Center for Biological Research Collections is currently hosting its instance of the Specify collections management system (CMS) on a virtual server hosted on Jetstream, the cloud service for on-demand computational resources as provisioned by XSEDE. This web-service allows the CMS to be flexibly hosted on the cloud with additional services that can be provisioned on an as-needed basis for generating and integrating digitized collections objects in both web-friendly and digital preservation contexts. On-demand computing resources can be used for the manipulation of digital images for automated file I/O, scripted renaming of files for adherence to file naming conventions, derivative generation, and backup to our local tape archive for digital disaster preparedness and long-term storage. Here, we will present our strategies for facilitating reproducible workflows for general collections digitization of the IUPC nomenclatorial types and figured specimens in addition to the gigapixel resolution photographs of our large collection of microfossils using our GIGAmacro system (e.g., this slide of conodonts). We aim to demonstrate the flexibility and nimbleness of cloud computing resources for replicating this, and other, workflows to enhance the findability, accessibility, interoperability, and reproducibility of the data and metadata contained within our collections. 

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4. Science Gateway Development to aid Cyber and Software Automation for Neuroscience Researchers and Educators

   Calyam, P. ; Nair, S.S ( January 2018 , 13th Gateway Computing Environments Conference (Gateways))

   Neuroscientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. This requires expert knowledge of programming and cyberinfrastructure configuration, which is beyond the repertoire of most neuroscience programs. This paper presents early experiences from a one-credit graduate research training course titled ECE 8001 “Software and Cyber Automation in Neuroscience” at the University of Missouri for engendering multi-disciplinary collaborations between computational neuroscience and cyberinfrastructure students and faculty. Specifically, we discuss the course organization and exemplar outcomes involving a next-generation science gateway for training novice users on exemplar neuroscience use cases that involve using tools such as NEURON and MATLAB on local as well as Neuroscience Gateway resources. We also discuss our vision towards a course sequence curriculum for graduate/undergraduate students from biological/psychological sciences and computer science/engineering to jointly build “self- service” training modules using Jupyter Notebook platforms. Thus, our efforts show how we can create scalable and sustainable cyber and software automation for fulfilling a broad set of neuroscience research and education use cases. 

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5. Extending Tapis Workflow Management Framework with Elastic Google Cloud Distributed System using CloudyCluster by Omnibond

   Eric Lam ; Sean Cleveland ; Cole Mcknight ; Boyd Wilson ; Richard Cardone ; Maytal Dahan ; Joe Stubbs ; Gwen A. Jacobs ( October 2022 , Science Gateways 2022)

   The goal of a robust cyberinfrastructure (CI) ecosystem is to catalyse discovery and innovation. Tapis does this through offering a sustainable production-quality set of API services to support modern science and engineering research, which increasingly span geographically distributed data centers, instruments, experimental facilities, and a network of national and regional CI. Leveraging frameworks, such as Tapis, enables researchers to accomplish computational and data-intensive research in a secure, scalable, and reproducible way and allows them to focus on their research instead of the technology needed to accomplish it. This project aims to enable the integration of the Google Cloud Platform (GCP) and CloudyCluster resources into Tapis- supported science gateways to provide on-demand scaling needed by computational workflows. The new functionality uses Tapis event-driven Abaco Actors and CloudyCluster to create an elastic distributed cloud computing system on demand. This integration allows researchers and science gateways to augment cloud resources on top of existing local and national computing resources. 

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