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FedQAS: Federated machine reading comprehension based on FEDn

Posted on February 14, 2022February 14, 2022 By admin No Comments on FedQAS: Federated machine reading comprehension based on FEDn
FedQAS: Federated machine reading comprehension based on FEDn

Machine reading comprehension (MRC) of text data is a complex NLP problem with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD). It is considered to be an effort to teach computers how to “understand” a text, and then to be able to answer questions about it using…

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Data-Intensive Computing, Federated Learning, News, publication, Software

Proactive Autoscaling for Edge Computing Systems with Kubernetes

Posted on December 21, 2021December 21, 2021 By Salman Toor No Comments on Proactive Autoscaling for Edge Computing Systems with Kubernetes
Proactive Autoscaling for Edge Computing Systems with Kubernetes

Happy to announce our newly accepted article: Accepted at the 14th IEEE/ACM International Conference on Utility and Cloud Computing UCC 2021. Abstract With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge computing…

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Applied Cloud Computing, Data Science, Data-Intensive Computing, HASTE, News, Software

Smart Resource Management for Data Streaming using an Online Bin-packing Strategy

Posted on December 16, 2020September 13, 2021 By admin No Comments on Smart Resource Management for Data Streaming using an Online Bin-packing Strategy
Smart Resource Management for Data Streaming using an Online Bin-packing Strategy

The stream processing framework HarmonicIO is a prototype that addresses the needs for processing streams based on relatively large individual objects. In this regard, it is a specialized streaming framework well-suited for scientific workflows. Salman Toor and Oliver Stein presented this work, and our latest publication  Smart Resource Management for Data Streaming using an Online…

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Applied Cloud Computing, Data-Intensive Computing, HASTE, News, publication, Software

try.stochss.org: Try StochSS as a Service

Posted on June 22, 2016June 22, 2016 By admin No Comments on try.stochss.org: Try StochSS as a Service

To make it easy to try StochSS, our software for rapid model development and simulation of stochastic regulatory networks, we are now providing it as a service on http://try.stochss.org. To use it, simply follow the link to set up your account. Since this only require a modern browser and a valid email address (no software installation), we hope…

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News, Software, Stochastic Chemical Kinetics, StochSS

Open Posititions: up to two PhD students

Posted on January 19, 2016March 14, 2016 By admin No Comments on Open Posititions: up to two PhD students

(Call for applications is closed. ) We are expanding the group with up to two PhD students in the areas of Applied Mathematics and Applied Cloud Computing: CIM, the Center for Interdisciplinary Mathematics are hiring students.  We are, in collaboration with the Spjuth lab offering the project, Smart and Efficient Scientific Software for Exploration of Vast Amounts…

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Applied Cloud Computing, Data Science, Data-Intensive Computing, Open Positions, Software, StochSS

StochSS 1.6 is now officially released!

Posted on July 6, 2015November 6, 2015 By admin No Comments on StochSS 1.6 is now officially released!

New capabilities of Version 1.6 include: Spatial visualization now supports animation, wireframe rendering, and mesh slicing FlexCloud: run ‘cloud’ jobs on dedicated hardware (in addition to using EC2) Import SBML models Many bug fixes and stability enhancements Details and instructions on how to obtain the code can be found on the Download page Tutorials are…

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News, PyURDME, Software, Stochastic Chemical Kinetics, StochSS

StochSS 1.4: Introducing Spatial Stochastic Modeling and Simulations

Posted on September 1, 2014September 1, 2014 By admin No Comments on StochSS 1.4: Introducing Spatial Stochastic Modeling and Simulations

We are excited to announce the release of StochSS 1.4 Version 1.4 includes spatial stochastic simulation capabilities powered by  PyURDME (http://www.pyurdme.org/). Details and instructions on how to obtain the code can be found on the Download page Tutorials are available on the Documentation page Linda Petzold and Chandra Krintz – University of California Santa Barbara Per Lotstedt and Andreas Hellander – Uppsala…

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News, PyURDME, Reaction Diffusion Master Equation, Software, Stochastic Chemical Kinetics, StochSS

PyURDME 1.0

Posted on June 25, 2014June 25, 2014 By admin No Comments on PyURDME 1.0

After more than a year of development, we are happy to announce the release of PyURDME 1.0.0! PyURDME is a Python module for spatial stochastic simulation model development and simulation. PyURDME is connected to URDME in that is uses a modified version of its core solver. While URDME is mainly designed as an interactive Matlab…

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News, PyURDME, Reaction Diffusion Master Equation, Software, StochSS, URDME

ECMTB 2014 and Computational Software

Posted on June 25, 2014June 25, 2014 By admin No Comments on ECMTB 2014 and Computational Software

I attended this years ECMTB 2014. Apart from many interesting sessions on mathematical modeling, I attended this minisymposia on software for multicellular simulations. All the speakers had done a great job developing software for the application community, but the impression was that they managed to do this despite being productive methods researchers, not as a…

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News, Software, StochSS, URDME

Data-and simulation-driven life science. Much of our work in eScience and applied ML has applications in life science, and in Systems Biology in particular. We aim to enable data-and simulation-driven scientific discovery.

HASTE - a cloud native framework for intelligent processing of image streams: http://haste.research.it.uu.se/

Follow us on twitter

Andreas HellanderFollow

Andreas Hellander
A_HellanderAndreas Hellander@A_Hellander·
11 May

Are you using StochSS? Please help us gather insights into what is working well and what can be improved by filling in this short survey https://forms.gle/mEqfASuUd3MDWuPS9

@LindaPetzold @briandrawert @mhucka

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A_HellanderAndreas Hellander@A_Hellander·
9 May

Apply to this PhD student position in the eSSENCE and SciLifeLab graduate school in data-intensive science!

This project is the intersection of cybersecurity and big data with main supervisor @sztoor.
https://www.uu.se/en/about-uu/join-us/details/?positionId=501061

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A_HellanderAndreas Hellander@A_Hellander·
28 Apr

PhD position in the eSSENCE/@scilifelab graduate school in data-intensive science with @cnettel: https://uu.se/en/about-uu/join-us/details/?positionId=501716 Apply and be part of a new interdiciplinary research effort @UU_University!

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A_HellanderAndreas Hellander@A_Hellander·
23 Apr

If you are a current user of StochSS please let us know your thoughts by filling out this brief user survey: https://forms.gle/3r836iph8gqFEpZX7

#systemsbiology #stochss @LindaPetzold @briandrawert @prashant_rsingh

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Retweet on TwitterAndreas Hellander Retweeted
AssistSweASSIST Sweden@AssistSwe·
14 Apr

Soon the partners in the ASSIST project will attend a workshop on federated learning arranged by Scaleout. Partners from different countries (Sweden, Belgium, Netherlands, Turkey) will contribute with nodes that train a segmentation network.

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Decentralized AI, Federated Learning. One focus area of the group is development of methods and software to address decentralized and privacy-preserving AI. We are core contributors to the FEDn open source framework for scalable federated machine learning:

https://github.com/scaleoutsystems/fedn
Introduction to Federated Learning by Andreas Hellander
Join the discussion on Decentralized AI:

Scaleout Systems is a spin-out from ISCL on a mission to enable decentralized AI and federated learning to production.

https://www.scaleoutsystems.com/

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