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DEEP as a Service (DEEPaaS) is a REST API that is focused on providing access to machine learning models. By using DEEPaaS users can easily run a REST API in fron of their model, thus accesing its functionality via HTTP calls. Quickstart: The best way to quickly try the DEEPaaS API is trough: make run. This command will install a virtualenv (in the `virtualenv` directory) with DEEPaaS and all its dependencies and will run the DEEPaaS REST API, listening on `127.0.0.1:5000`. If you browse to `http://127.0.0.1:5000` you will get the swagger documentation page. Develop mode: If you want to run the code in develop mode (i.e. `pip install -e`), you can issue the following command before: make develop. DEEP as a Service (DEEPaaS) is a REST API that is focused on providing access to machine learning models. By using DEEPaaS users can easily run a REST API in fron of their model, thus accesing its functionality via HTTP calls. Proyecto: Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud (DEEP-HybridDataCloud). Peer reviewed

Python script for rewriting long expressions in Jordan algebras, used for proving the fundamental equality

First release of the the MagnetoTellurics time series data publication (MTtsdp) codes. The MTtsdp_v0 codes were developed as part of the Magnetotellurics (MT) component of the 2017-2020 Australian Research Data Commons (ARDC) funded Geoscience Data enhanced Virtual Laboratory (GeoDeVL) project (https://zenodo.org/record/4278997#.ZAgaIuJBzao). The MTtsdp_v1 codes were developed as part of the 2021-2023 Australian Research Data Commons (ARDC), AuScope, NCI, TERN co-funded 2030 Geophysics Collections project: https://doi.org/10.47486/XN002.

Ocean acidification may have deleterious effects on many species, but anticipating long-term changes in the abundance of populations will require an understanding of ocean acidification as an evolutionary force. Here I show that ocean acidification alters natural selection on offspring size and is likely to drive contemporary evolution. In a detailed study of a coastal fish species (California Grunion), I demonstrate that larval mortality is highly sensitive to ocean acidification and that mortality rates are lower for larger larvae. However, these effects are countered by tradeoffs between offspring size and number, suggesting that measurements of maternal fitness are critical for quantifying selection through ocean acidification. Measurements of selection and genetic variation were used to project the evolution of larval size as seawater conditions changed incrementally over many decades. Results for California Grunion suggest that contemporary evolution may offset the projected decline in reproductive success by about 50%. Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: OCE-1948975

Assembles to a reference sequence. Developed with Applied Biosystems colourspace genomic representation in mind. Authors are Michael Brudno and Stephen Rumble at the University of Toronto. Works with data in letterspace (Roche, Illumina), colourspace (AB) and Helicos space.

Added support for printf-style formatting.

Find broken SSL certificate validation in Android Apps