Integrative Scalable Computing Laboratory
A research group at the Department of Information Technology, Uppsala Universtity.
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…
Read More “FedQAS: Federated machine reading comprehension based on FEDn” »
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…
Read More “Proactive Autoscaling for Edge Computing Systems with Kubernetes” »
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…
Read More “Smart Resource Management for Data Streaming using an Online Bin-packing Strategy” »
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…
(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…
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…
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…
Read More “StochSS 1.4: Introducing Spatial Stochastic Modeling and Simulations” »
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…
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…
