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Release candidate 2 of supporting code for paper * Jan Blechta, Josef Málek, and Martin Vohralík. *Localization of the $W^{-1,q}$ norm for local a posteriori efficiency.* In preparation, 2016.

Systematic literature mapping is an essential part of research methodology. Conducting a systematic literature mapping is challenging. Researchers query publications from various sources, which need to be filtered, categorized, and cleared of duplicates. It is usually the case that the number of publications ranges between hundreds to thousands. The whole process is often performed iteratively and repeatedly, especially at the start of the mapping study, which only further increases the effort. When a team of researchers conducts a mapping study, the members may have different opinions on filtering and categorizing papers, which must be resolved. To our knowledge, this problem is very poorly supported by open-source tools. To address these issues, we present a tool called Robin which facilitates managing the steps of conducting a mapping study within a team. It provides search tools, categorization, and a platform for team members to define their criteria for including and excluding papers. In addition, Robin is connected to publicly available publications search platforms such as IEEE API and Scopus API. Robin is written in Python-Django and can be installed as a web application. Written in Python-Django, it is also possible to download the source https://github.com/smartarch/robin

3.2.0 (2021-11-10) Features Added einfra template (c66e361)

This repository contains electronic notebooks (Jupyter) for reproducing the simulation results presented in the research article doi:10.1021/acs.jpcb.3c01085

0.3.5 Changelog New Contributor: Malte Mues New Contributor: Bob Myhill Bugfix: Fixed a bug with the index structure for the documentation example galleries. Expanded test suite for a text coverage bump. Started testing against and supporting Python 3.12. pyrolite.geochem PR Merged: Malte Mues contributed a PR to allow series and dataframes with Pm data to be used with the pyrolite.pyrochem REE accessors (i.e., the column will not be dropped if it exists; #100). This PR also included an update for the pyrolite.util.lambdas functions to be more flexible in terms of access, including as individual series (allowing performant usage with pandarallel. Updates to pyrolite.pyrochem accessors to be more flexible, generally allowing usage with both DataFrame and Series objects (e.g. df.pyrochem.REE, ser.pyrochem.REE). pyrolite.plot PR Merged: Bob Myhill contributed a series of pull requests to fix formatting of TAS diagram labels (#91), update the TAS field names in the JSON template (#92), improve the formatting and scaling of TAS diagrams (#93), add an option to add field labels in the visual centre of polygons (rather than the 'centroid'; #94 ), update some usage of matplotlib (#96), and allow selective plotting of individual fields when adding a classification diagram to axes (#98). pyrolite.mineral Suppressed pandas performance warnings related to sequential construction of pandas.DataFrames in pyrolite.mineral.normative.CIPW_norm.

This is the implementation of "GitSEED: A Git-backed Automated Assessment Tool for Software Engineering Education" accepted at SIGCSE Virtual 2024. GitSEED a language-agnostic automated assessment tool designed for Programming Education and Software Engineering (SE) and backed by GitLab. The students interact with GitSEED through GitLab.Using GitSEED, students in Computer Science (CS) and SE can master the fundamentals of git while receiving personalized feedback on their programming assignments and projects. Furthermore, faculty members can easily tailor GitSEED's pipeline by integrating various code evaluation tools (e.g., memory leak detection, fault localization, program repair, etc.) to offer personalized feedback that aligns with the needs of each CS/SE course. ## Contact * Author: pmorvalho* Email: gitseed AT arsr.inesc-id.pt

We describe a simple and efficient Python code to perform Bayesian forecasting for gravitational waves produced by Extreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for an efficient parallelised computation of thousands of waveforms and sampling of the posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MC generates EMRI waveforms based on the so-called kludge scheme, and propagates it to the observer accounting for cosmological effects in the observed waveform due to modified gravity/dark energy. Extending the code to more accurate schemes for the generation of the waveform is straightforward. Despite the known limitations of the kludge formalism, we believe that the code can provide a helpful resource for the community working on forecasts for interferometry missions in the milli-Hz scale, predominantly, the satellite-mission LISA. Main updates of version v2: Added new GPU parallelisation for the likelihood computation. Improved the overall efficiency of the code. Added new functionality for executing the code. Updated the README file. Uploaded an updated manual of the code, planned to appear in the journal "SciPost Codebases" - See the file "PAPER.pdf".

Most research on aposematism has focused on chemically defended prey, but the signaling difficulty of capture remains poorly explored. Similar to classical Batesian and Müllerian mimicry related to distastefulness, such 'evasive aposematism' may also lead to convergence in warning colours, known as evasive mimicry. A prime candidate group for evasive mimicry areAdelphabutterflies, which are agile insects and show remarkable colour pattern convergence. We tested the ability of naive blue tits to learn to avoid and generalizeAdelphawing patterns associated with the difficulty of capture and compared their response to that of birds that learned to associate the same wing patterns with distastefulness. Birds learned to avoid all wing patterns tested and generalized their aversion to other prey to some extent, but learning was faster with evasive prey compared to distasteful prey. Our results on generalization agree with longstanding observations of striking convergence in wing colour patterns amongAdelphaspecies, since, in our experiments, perfect mimics of evasive and distasteful models were always protected during generalization and suffered the lowest attack rate. Moreover, generalization on evasive prey was broader compared to that on distasteful prey. Our results suggest that being hard to catch may deter predators at least as effectively as distastefulness. This study provides empirical evidence for evasive mimicry, a potentially widespread but poorly understood form of morphological convergence driven by predator selection. Funding provided by: Academy of FinlandCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002341Award Number: project no. 319124Funding provided by: Muséum National d'Histoire Naturelle - Sorbonne Université*Crossref Funder Registry ID: Award Number: TRANSHUMANCE mobility grantFunding provided by: Secretaría de Educación Superior, Ciencia, Tecnología e InnovaciónCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004299Award Number: Graduate fellowshipFunding provided by: Grantová Agentura České RepublikyCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001824Award Number: Junior GAČR grant GJ20-18566YFunding provided by: Akademie Věd České RepublikyCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004240Award Number: Fellowship grant L200961951Funding provided by: Doctoral School 227 Muséum National d'Histoire Naturelle - Sorbonne UniversitéCrossref Funder Registry ID: Award Number: Transhumance mobility grantFunding provided by: Secretaría de Educación Superior, Ciencia, Tecnología e Innovación SenescytCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100004299Award Number: Graduate fellowshipFunding provided by: The Academy of FinlandCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100002341Award Number: Project no. 319124Funding provided by: Muséum National d'Histoire Naturelle - Sorbonne UniversitéCrossref Funder Registry ID: Award Number: TRANSHUMANCE mobility grant