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  • Added Added a flowclient Query class to represent a FlowKit query #1980. Added method flowclient.Connection.update_token, to replace the API token for an existing connection. Changed The names of flowclient functions for generating query specifications have been renamed to _spec (e.g. flowclient.modal_location is now flowclient.modal_location_spec). flowclient.get_status now returns "not_running" (instead of raising FileNotFoundError) if a query is not running or completed. Flowclient functions location_event_counts_spec, meaningful_locations_aggregate_spec, meaningful_locations_between_label_od_matrix_spec, meaningful_locations_between_dates_od_matrix_spec, flows_spec, unique_subscriber_counts_spec, location_introversion_spec, total_network_objects_spec, aggregate_network_objects_spec, spatial_aggregate_spec and joined_spatial_aggregate_spec have moved to the flowclient.aggregates submodule.
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  • This replication package contains the raw dataset, scripts to produce all plots, and documentation on how to replicate our MLR study on FaaS performance evaluation. Paper J. Scheuner and P. Leitner, “The State of Research on Function-as-a-Service Performance Evaluation: A Multivocal Literature Review,” 2020, Preprint, arXiv:2004.03276. Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing event-triggered code snippets (i.e., functions). Many studies that empirically evaluate the performance of such FaaS platforms have started to appear but we are currently lacking a comprehensive understanding of the overall domain. In our work, we survey existing research on FaaS performance evaluation and present results from a multivocal literature review (MLR) covering 112 studies from academic (51) and grey literature (61). We find that existing work heavily focuses on AWS Lambda and prevalently evaluates micro-benchmarks using simple functions to measure CPU speed and FaaS platform overhead (i.e., container cold starts). Further, we identify a mismatch between academic and industrial sources on tested platform configurations, conclude that function triggers remain insufficiently studied, and list HTTP API gateways and cloud storages as the most used external service integrations. Following existing guidelines on experimentation in cloud systems, we discover many flaws threatening the reproducibility of experiments presented in the surveyed studies. We conclude with a discussion of gaps in literature and highlight methodological suggestions that may serve to improve future FaaS performance evaluation studies. Dataset All extracted data originating from academic and grey literature studies is available as machine-readable CSV (./data/faas_mlr_raw.csv) and human-readable XLSX (./data/faas_mlr_raw.xlsx). The Excel file also contains all 700+ comments with guidance, decision rationales, and extra information. It is configured with a filtered view to display only relevant sources but contains the complete data (i.e., including discussion for sources considered to be not relevant in our context). Find further documentation in the README.md or on Github: https://github.com/joe4dev/faas-performance-mlr/
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  • Change models Added Coral Reefs Optimization in Evolutionary-based group BaseCRO OCRO (Opposition-based CRO) Change others models_history.csv: Update history of meta-heuristic algorithms examples: Added new examples of: BaseCRO and CRO
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  • 13.12.3 (2020-04-05) Bug Fixes ImageProperty: Return true/false on set methods (f7e08a6)
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  • Analytic pipeline for Mahgoub et al. 2020 (Zcwpw1)
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  • Check added by @vinisalazar looking for duplicates in user-provided mapping file, previously would run entire analysis, not produce a tree, and have duplicate entries in the alignment file. Now it exits at start with notice.
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  • Open Authorization for MyProxy.
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  • A BIDS-App to preprocess anatomical (T1w, T2w and FLAIR) data, providing workflows for reuse in NIPreps (NeuroImaging PREProcesssing tools) such as fMRIPrep.
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  • Several changes, most important: theoretical framework: Corrected likelihood: Forgot non-constant K term when K=K(theta): c8c0046153b1eb269d00280b572f742b1a3cf4d7 parameters and choices: unfolding parameters: 0fcafe2ff7770be8c2bb107256201af79739cdb3 unfolder and fg method use remove negatives only, no fill: 9edb48537cca1f88c3120a73fa8eb92f6ebb5177 Randomize p0 for decomposition 77dec9db9a3a34d5fd6195752c84cfbca0c26c39 implementation and convenience: different save/load for vectors e5f7e52ce13cff04e8b23f50a00902be1d098bfc and parent commits Enable pickling of normalizer instances via dill: 896b352686594a8c7dbe52904645cc5b900ba800
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  • Data for Mahgoub et al. analytic pipeline
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