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357 results for “Functional performance”
Experimental data for the motor learning study performed: "Towards functional robotic training: Motor learning of dynamic tasks is enhanced by haptic rendering but hampered by robotic assistance"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. The details of the study are described in [doi: ]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses and population metrics are stored as “CSV” files. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Device Performance Metrics as Function of Absorption Onset
<p>Updated database and overview plot based on previously published version in J. Mater. Chem. A, 2017, 5, 11401 (Unger et al.)</p>
Dataset and Experiment Scripts for "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance"
<p>This repository contains the Experiment Results and Collection scripts for our ESEC/FSE 2023 submission, "When Function Inlining Meets WebAssembly: A Counterintuitive Effect on Runtime Performance"</p> <p>Our runtime experiment data is located in the <em>Experiment Results</em> directory. This directory contains two subdirectories, <em>All Experiment Results</em> and <em>Counterintuitive Results Only</em>. In <em>All Experiment Results</em>, we present the runtime results from our Experiments 1-5 and the Libsodium.js case study as CSV files. For the Chromium and Firefox results, each CSV file list the sample names and multiple columns for each of the four optimization levels, O0-O3. Under each optimization level, we list the runtime (in milliseconds) with inlining enabled (from the Baseline experiment), the runtime with inlining enabled (from one of Experiment #1-5), and percent change in runtime after disabling inlining.</p> <p>The <em>Counterintuitive Results Only</em> directory contains CSV files presenting only the samples from each Experiment #1-5 that meet our threshold of at least a 5% decrease in runtime after disabling inlining.</p> <p>The Excel file, <em>Wasm Function Inlining Experiment Data.xlsx</em>, contains all of these results in a single workbook, as well as formatting applied to highlight the counterintuitive runtime values presented in our paper.</p> <p>The raw data collected from our experiments, including the generated WebAssembly, HTML, and JS files to run the samples, is found under the <em>RawCollectedData</em> directory. This folder contains a zipped file that, when extracted, contains subdirectories for each sample's collected data.</p> <p>The <em>CollectionScripts</em> directory contains the scripts necessary to run our experiments. The <em>PatchFiles</em> directory contains the files from the Binaryen and LLVM infrastructures with the changes that we introduced to enable and disable select optimization passes through environment variables. The <em>Scripts</em> directory contains the scripts we used to run our experiments. The main file within this directory that serves as the entry point is <em>opt_level_inlining.py</em>. This file uses the other scripts to run Experiments 1-5 in our study. The script <em>libsodiumjs_script.py</em> is used to run our Libsodium.js case study.</p> <p>The scripts are written in Python and Node.js, and the dependencies to run the scripts are Node.js, Python, and MySQL. To download the necessary dependencies for the scripts, run the command `pip install -r requirements.txt` in <em>Scripts </em>folder<em>, and run `npm install` in the Scripts/performance-measurement-tool</em> folder. Then, import the included MySQL schemas under the<em> Scripts </em>folder into a MySQL database. Update the MySQL connection details in the <em>Scripts\__db_utils.py</em> and <em>Scripts\performance-measurement-tool\src\MySQLConnector.ts</em> files, and then run `npm run build` in the <em>Scripts\performance-measurement-tool</em> folder.</p>
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
Replication Package for "TriggerBench: A Performance Benchmark for Serverless Function Triggers"
<p>This replication package contains the code (`aws-triggers` and `azure-trigger`), data analysis scripts (`data-analysis`), and dataset (`data`) of the TriggerBench cross-provider serverless benchmark.</p> <p>It also bundles a customized extension of the `serverless-benchmarker` tool to automate and analyze serverless performance experiments.</p> <p><strong>TriggerBench</strong></p> <p>The Github repository <a href="https://github.com/joe4dev/trigger-bench/">joe4dev/trigger-bench</a> contains the last version of TriggerBench. This replication package describes the version for the paper "TriggerBench: A Performance Benchmark for Serverless Function Triggers".</p> <p>TriggerBench currently supports three triggers on AWS and eight triggers on Microsoft Azure.</p> <p><strong>Dataset</strong></p> <p>The `data/aws` and `data/azure` directories contain data from benchmark executions from April 2022.</p> <p>Each execution is a separate directory with a timestamp in the format `yyyy-mm-dd-HH-MM-SS` (e.g., `2022-04-15_21-58-52`) and contains the following files:</p> <ul> <li>`k6_metrics.csv`: Load generator HTTP client logs in CSV format (see [K6 docs](https://k6.io/docs/results-visualization/csv/))</li> <li>`sb_config.yml`: serverless benchmarker execution configuration including experiment label.</li> <li>`trigger.csv`: analyzer output CSV per trace. <ul> <li>`root_trace_id`: The trace id created by k6 and adopted by the invoker function</li> <li>`child_trace_id`: The trace id newly created by the receiver function if trace propagation is not supported (this is the case for most asynchronous triggers)</li> <li>`t1`-`t4`: Timestamps following the trace model (see paper)</li> <li>`t5`-`t9`: Additional timestamps for measuring timestamping overhead</li> <li>`coldstart_f1=True|False`: coldstart status for invoker (f1) and receiver (f2) functions</li> <li>`trace_ids.txt`: text file with each pair of `root_trace_id` and `child_trace_id` on a new line.</li> </ul> </li> <li>`traces.json`: raw trace JSON representation as retrieved from the provider tracing service. For AWS, see [X-Ray segment docs](https://docs.aws.amazon.com/xray/latest/devguide/xray-api-segmentdocuments.html). For Azure, see [Application Insights telemetry data model](https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-model).</li> <li>`workload_options.json`: [K6 load scenario](https://k6.io/docs/using-k6/scenarios/) configuration.</li> </ul> <p><strong>Replicate Data Analysis</strong></p> <p><strong>Installation</strong></p> <p>1. Install [Python](https://www.python.org/downloads/) 3.10+</p> <p>2. Install Python dependencies `pip install -r requirements.txt`</p> <p><strong>Create Plots</strong></p> <p>1. Run `python plots.py` generates the plots and the statistical summaries presented in the paper.</p> <p>By default, the plots will be saved into a `plots` sub-directory.<br> An alternative output directory can be configured through the environment variable `PLOTS_PATH`.</p> <p>> Hint: For interactive development, we recommend the VSCode [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) in [interactive mode](https://youtu.be/lwN4-W1WR84?t=107).</p> <p> </p> <p><strong>Replicate Cloud Experiments</strong></p> <p>The following experiment plan automates benchmarking experiments with different types workloads (constant and bursty).</p> <p>This generates a new dataset in the same format as described above.</p> <p> </p> <ol> <li> Set up a load generator as vantage point following the description in [LOADGENERATOR](./serverless-benchmarker/docs/LOADGENERATOR.md).</li> <li> Choose the `PROVIDER` (aws or azure) in the [constant.py](./experiment-plans/constant.py) experiment plan</li> <li> Run the [constant.py](./experiment-plans/constant.py) experiment plan <ol> <li> Open tmux</li> <li> Activate virtualenv `source sb-env/bin/activate`</li> <li> Run `./constant.py 2>&1 | tee -a constant.log`</li> </ol> </li> </ol> <p><strong>Contributors</strong></p> <p>The initial trigger implementations for AWS and Azure are based on two master thesis projects at Chalmers University of Technology in Sweden supervised by Joel:</p> <ul> <li>AWS + Azure: <a href="https://odr.chalmers.se/handle/20.500.12380/302822">Performance Comparison of Function-as- a-Service Triggers: A Cross-Platform Performance Study of Function Triggers in Function-as-a-Service</a> by Marcus Bertilsson and Oskar Grönqvist, 2021.</li> <li>Azure Extension: <a href="https://odr.chalmers.se/handle/20.500.12380/305138">Serverless Function Triggers in Azure: An Analysis of Latency and Reliability</a> by Henrik Lagergren and Henrik Tao, 2022.</li> </ul> <p>Joel contributed many improvements to their original source code as documented in the import commits <a href="https://github.com/joe4dev/trigger-bench/commit/a00b67a1dd8476ca77d026e59adf2674c7807e68">a00b67a</a> and <a href="https://github.com/joe4dev/trigger-bench/commit/6d2f5ef8bda0596b3f295cb6c6cbeba212c6ef43">6d2f5ef</a> and developed TriggerBench as an integrated benchmark suite (see commit history for detailed changelog).</p>
Dataset of Reaction Times for the Study of Differential Functional Changes in Visual Performance During Acute Exposure to Microgravity Analogue and their Potential Links with Spaceflight-Associated Neuro-Ocular Syndrome
<p><strong>Title</strong>: "Dataset of Reaction Times for the Study of Differential Functional Changes in Visual Performance During Acute Exposure to Microgravity Analogue and their Potential Links with Spaceflight-Associated Neuro-Ocular Syndrome"<br>Zenodo DOI: 10.5281/zenodo.11840654</p> <p><strong>Contains</strong>: simple-reaction-times-VBRHDT-data.csv<br>Dateset of SRT (simple reaction time) values to visual stimuli in different positions in visual field, binocularly observed, in a microgravity analogue study, in four body positions (vertical, horizontal, -6 deg tild, -15 deg tilt). <br>Total records contained: 3584.</p> <p><strong>Institutional Review Board Statement</strong>: The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee “Carol Davila” University of Medicine and Pharmacy Bucharest, Romania, 14877/26.05.2023. Data was collected in SpaceMed Laboratory , CIeH (Center for Innovation and eHealth) of UMF Carol Davila Bucharest, Romania.</p> <p><strong>Citation and</strong> <strong>Detailed description:</strong> see "<em>Differential functional changes in visual performance during acute exposure to microgravity analogue and their potential links with Spaceflight-Associated Neuro-Ocular Syndrome</em>", 2024, by Iftime A, Tofolean IT, Pintilie V, Călinescu O, Busnatu S, Papacocea IR, Diagnostics ,2024; 14(17):1918. doi: 10.3390/diagnostics14171918 <br>https://pubmed.ncbi.nlm.nih.gov/39272703/</p> <p><strong>Structure</strong>: Dataset is in CSV (Comma-Separated Values) format, UTF-8 encoded, text field delimited with quotation marks, in tidy format; one row is one record from the SRT task, variables values are in columns). The first row is the variable name. The variable are:</p> <p>1) "live_row"<br>- type: integer numbers, sequential;<br>- values: the index (order) of each SRT measurement performed in a body position, by a participant (ID). The value is C-based (first measurement is "0", second measurement is "1", etc).</p> <p>2) "response_time"<br>- type: real numbers, continuous values;<br>- values: response time recorded from the participant; values in miliseconds.</p> <p>3) "target_position_x_deg"<br>- type: real numbers, categorical (4 values: +/- 18.478, +/- 0.75);<br>- values: horizontal visual angle, in visual degrees in the visual field, of the position of the stimulus. Screen coordinates are computed from "0" position, foveal fixation, positive values to the right, negative values to the left.</p> <p>4) "target_position_y_deg"<br>- type: real numbers, categorical (2 values: -0.75, -7.654);<br>- values: vertical visual angle, in visual degrees in the visual field, of the position of the stimulus. Screen coordinates are computed from "0" position, foveal fixation, positive values downward, negative values upward.<br>-Note: For the equivalent polar coordinates (as in planimetry testing) see Figure 2 of the mentioned paper. </p> <p>5) "Contrast_Weber_calibrated"<br>- type: real numbers, categorical (2 values: 50.58, 99.3);<br>- values: Measured Weber contrast of the visual stimulus shown, values in percents.</p> <p>6) "hand"<br>- type: categorical, 1 value ("right");<br>- values: hand used by the participant during SRT task.</p> <p>7) "body_position"<br>- type: categorical (4 values: "vertical (90 deg)", "horizontal (0 deg)", "inclined (-6 deg)", "inclined (-15 deg)" );<br>- values: Body position during the SRT task.</p> <p>8) "ID(anon)"<br>- type: integer number, categorical, 8 values;<br>- values: anonymized ID of the participants in the study (8 persons).</p>
Multisensor measurement of healthy adult performance during standardised motor function test battery
<p>This dataset contains inertial data from 4 wearable sensor nodes and 1 wearable patch worn by 20 healthy adult participants performing a series of physical functioning tests (including the short physical performance battery, the timed up and go test, a walking test and balance tests). Details of patient demographics, the physical functioning tests and of each sensor are contained in files in the main folder.</p> <p>Inertial data (accelerometer and gyroscope) is contained in two folders relating to each sensor type. The start and end time for each sensor can be taken from the details in each folder structure, as detailed below. The times given are specific to each sensor's monitoring system which are not exactly synchronised. As such, a manual synchronisation shaking protocol was followed where all sensors were strapped together and shaken three times in succession at the start of each data collection period. The physical functioning test times will also need to be synchronised.</p> <p>-> Inertial sensor data / (subject id).zip / (subject id) / (date_time_crossTest_SD_session#) /<br> -> Wearable inertial patch / (subject id) / (date)T(time) /</p>
Agronomic performance of cultivar mixtures and pure stands of 8 winter wheat varieties, obtained from mixture field trials in Switzerland from 2021 to 2023, together with associated functional traits measurements
<p>This dataset contains agronomic performance data for 8 Swiss winter wheat cultivars, grown in pure stands and in mixtures at 3 locations in Switzerland during 3 growing seasons (2021-2023). The dataset has been used to analyse the effects of cultivar mixtures on agronomic performance and stability, which is published in <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eja.2024.127504" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.eja.2024.127504</a>. </p> <p>The dataset contains notably grain yield, protein content, thousand kernel weight, specific weight, and Zeleny sedimentation value, as well as functional traits measured at flowering for each mixture and pure stand plot. </p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP. </p> <h3>Methods </h3> <p> <em>Field trials </em></p> <div>Field trials were set up over the course of three growing seasons – 2020/2021, 2021/2022 and 2022/2023 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Utzenstorf (47°97′ N 7°33′ E, 483m a.s.l.). </div> <div>Experimental communities consisted of pure stand plots, 2-cultivars mixtures, and one plot with the 8 cultivars mixed. We sowed every possible combination of 2-cultivar mixtures, amounting to a total of 28 2-cultivar mixtures treatments, to which we added the 8-cultivar mixture. Each community was grown in a plot of 7.1 m<sup>2</sup> (1.5m∗4.7m). We used a complete randomized block design, with 3 replicates, the plots being randomized at each site within each block. Sowing was performed with a small plot drill (Wintersteiger plotseed TC). Density of sowing was 350 viable seeds/m<sup>2</sup>. For the mixtures, seeds were mixed beforehand at a 2 × 50 % mass ratio for 2-cultivars mixtures and 8 × 12.5 % for the 8-cultivar mixture. We chose this method of mixing as this is what is commonly done by farmers in Switzerland. Plots were sowed mechanically each autumn and fertilized with ammonium nitrate at a rate of 140 N/ha in 3 applications (40 N/ha at tillering stage/BBCH 22–29; 60 N/ha at the beginning of stem elongation/BBCH 30–31; 40 N/ha at booting stage/BBCH 45–47). The trials were grown according to the Swiss <em>Extenso</em> scheme, i.e. without any fungicide, insecticide, and growth regulator. Weeds were regulated twice or thrice per season with the application of herbicides commonly used in Switzerland.</div> <div> </div> <div><em>Ear density</em></div> <div> </div> <div>Before harvest, we manually harvested horizontal bands of 1.5 × 0.3 square meters per plot. The location of the band was randomly chosen but we avoided plot edges (i.e. the band was located at more than 0.5 m from the lower and upper edge of each plot). We counted the heads, and obtained ear density from the head counts.</div> <div> </div> <div><em>Trait measurements </em></div> <div> </div> <div>At flowering time, we randomly sampled 6 healthy leaves per plot. We immediately wrapped this leaf in moist cotton; this was stored overnight at room temperature in open plastic bags. The following day, we removed excess surface water on the leaf and weighted it to obtain its water saturated weight. This leaf was then scanned with a flatbed scanner (Perfection V39II, Epson), oven-dried in a paper envelope at 80°C for 72 hours, and subsequently weighed again to obtain its dry weight. Leaf Dry Matter Content (LDMC) was calculated as the ratio of leaf dry mass (g) to water saturated leaf mass (g). Using the leaf scans, we measured leaf area with the image processing software ImageJ. Specific Leaf Area (SLA) was calculated as the ratio of leaf area (cm2) to leaf dry mass (g).</div> <div> </div> <div><em>Phenology and height </em></div> <div> </div> <div>For each plot, we recorded the heading date as the day of the year, in which 50 % of the ears of the plot had fully emerged from the flag leaf. Plant height was measured in each plot at BBCH 59–75, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</div> <div> </div> <p><em>Harvest and post harvest measurements</em></p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were dried when needed, weighed a first time, then sorted and cleaned by air and with a sieve cleaner, and subsequently weighted again. We measured hectoliter weight (test weight, HLW, kg/hl) and water content at the plot level using a Dickey-John machine (GAC 2100). Grain yield was subsequently standardized to 15 % of humidity. Protein content (% of dry matter) was measured at the site level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g) was measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany). Zeleny sedimentation value was measured by the laboratory of Delley Seeds and Plants. </p> <p> </p>
Individualized mental fatigue does not impact neuromuscular function and exercise performance
<p>Previous work has shown that mental fatigue may have negative consequences on cognitive or physical performance, although recent reports question this previous empirical evidence. Here, we investigate the critical role of inter-individual differences in susceptibility to develop mental fatigue by measuring neurophysiological and physical responses to an individualized mental fatigue task. We expected mental load to alter both subjective, i.e., increased subjective perception of fatigue, and objective markers of fatigue, i.e., impaired knee extensor neuromuscular function, impaired corticospinal excitability and reduced cerebral oxygenation. Even though all participants performed a similar mental effort, their performance in a subsequent exercise did not differ. Furthermore, even if there was an elevated subjective feeling of mental fatigue, none of the neurophysiological parameters were affected. The study provides new insights into an issue that has grown in popularity in recent years without questioning individual differences and which has taken for granted the detrimental effect of acute mental fatigue on performance.</p>
Data for "Influences of Glyphosate Contaminations and Concentrate Feed on Performance, Blood Parameters, Blood Cell Functionality and DNA Damage Properties in Fattening Bulls"
<p>The deposited data consist of three data tables and three tables containing legends for the data:</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint.txt">oneTimepoint.txt</a> contains data from statistical tests within one timepoint; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint_legend.txt">oneTimepoint_legend.txt</a> contains the corresponding legend.</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints.txt">threeTimepoints.txt</a> contains data from statistical tests inclduing three distinct timepoints; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints_legend.txt">threeTimepoints_legend.txt</a> contains the corresponding legend.</p> <p><a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance.txt">Performance.txt</a> contains data from statistical tests inclduing two time periods in fattening; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance_legend.txt">Performance_legend.txt</a> contains the corresponding legend.</p>
Functional traits of young seedlings predict trade-offs in seedling performance in three neotropical forests
<ol> <li>Understanding the mechanisms that promote the coexistence of hundreds of species over small areas in tropical forest remains a challenge. Many tropical tree species are presumed to be functionally equivalent shade-tolerant species that differ in performance trade-offs between survival in shade and the ability to quickly grow in sunlight.</li> <li>Variation in plant functional traits related to resource acquisition is thought to predict variation in performance among species, perhaps explaining community assembly across habitats with gradients in resource availability. Many studies have found low predictive power, however, when linking trait measurements to species demographic rates.</li> <li>Seedlings face different challenges recruiting on the forest floor and may exhibit different traits and/or performance trade-offs than older individuals face in the eventual adult niche. Seed mass is the typical proxy for seedling success, but species also differ in cotyledon strategy (reserve vs photosynthetic) or other seedling traits. These can cause species with the same average seed mass to have divergent performance in the same habitat.</li> <li>We combined long-term studies of seedling dynamics with functional trait data collected at a standard developmental stage in three diverse neotropical forests to ask whether variation in coordinated suites of traits predicts variation among species in demographic performance.</li> <li>Across hundreds of species in Ecuador, Panama, and Puerto Rico, we found seedlings displayed correlated suites of leaf, stem, and root traits, which strongly correlated with seed mass and cotyledon strategy. Variation among species in seedling functional traits, seed mass, and cotyledon strategy were strong predictors of trade-offs in seedling growth and survival.</li> <li>Our findings highlight the importance of cotyledon strategy in addition to seed mass as a key component of seed and seedling biology. These results also underscore the importance of matching the ontogenetic stage of the trait measurement to the stage of demographic dynamics.</li> <li> <em>Synthesis</em>: With strikingly consistent patterns across three tropical forests, we find strong evidence for the promise of functional traits to provide mechanistic links between seedling form and demographic performance. </li> </ol>
Functional traits of young seedlings predict trade-offs in seedling performance in three neotropical forests
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Data from: Form-function relationships within species are uncoupled from those across species in swimming and jumping performance in arboreal frogs
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Replication Package for "Function-as-a-Service Performance Evaluation: A Multivocal Literature Review"
<p><strong>Paper</strong></p> <p>J. Scheuner and P. Leitner, “Function-as-a-Service Performance Evaluation: A Multivocal Literature Review,” Accepted at the <a href="https://www.journals.elsevier.com/journal-of-systems-and-software">Journal of Systems and Software (JSS)</a>, Preprint: <a href="https://arxiv.org/abs/2004.03276">arXiv:2004.03276</a>.</p> <blockquote> <p>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. To address this gap, we conducted a multivocal literature review (MLR) covering 112 studies from academic (51) and grey (61) literature. We find that existing work mainly studies the AWS Lambda platform and focuses on micro-benchmarks using simple functions to measure CPU speed and FaaS platform overhead (i.e., container cold starts). Further, we discover a mismatch between academic and industrial sources on tested platform configurations, find that function triggers remain insufficiently studied, and identify 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.</p> </blockquote> <pre>@article{scheuner:20-jss, author = {Joel Scheuner and Philipp Leitner}, title = {Function-as-a-Service Performance Evaluation: A Multivocal Literature Review}, journal = {J. Syst. Softw.}, volume = {170}, pages = {110708}, year = {2020}, doi = {10.1016/j.jss.2020.110708} }</pre> <p><strong>Dataset</strong></p> <p>All extracted data originating from academic and grey literature studies are available as machine-readable CSV (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.csv">./data/faas_mlr_raw.csv</a>) and human-readable XLSX (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.xlsx">./data/faas_mlr_raw.xlsx</a>). 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 <em>relevant</em> sources but contains the complete data (i.e., including discussion for sources considered to be <em>not relevant</em> in our context).</p> <p>Find further documentation in the README.md or on Github: <a href="https://github.com/joe4dev/faas-performance-mlr/">https://github.com/joe4dev/faas-performance-mlr/</a></p> <p>The latest version is also available online as an interactive Google spreadsheet (GSheet): <a href="https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8">https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8</a></p> <p> </p>
Data from: Along with intraspecific functional trait variation, individual performance is key to resolving community assembly processes
<ol> <li>Species contributing high proportions to community biomass strongly influence ecosystem processes within the community. Studies have shown that dominant species may serve as nurse plants, helping to ensure biomass stability of the subordinate species under stress conditions. The question is widely debated as to whether either niche differentiation or neutral processes drive the net outcome of plant interactions within a subordinate plant community. To answer this question, requires precise estimates of individual variation in functional traits and performance.</li> <li>In a five-year mesocosm experiment, the functional responses of a subordinate plant community to the removal of the dominant species were evaluated across two drought-stress scenarios. Small scale (i.e., large pots) wetland communities were constructed comprising one dominant species (Carex elata) and three subordinate species. Removal of the dominant species allowed evaluation of the net effects of drought and interspecific interactions. We estimated the functional divergences for three traits (specific leaf area, leaf dry matter content and height growth allocation) and compared these with performance differences quantified individually. This enabled distinctions to be made between deterministic (i.e., niche differentiation) and neutral processes driving the drought response of the subordinate community.</li> <li>We showed that the dominant species decreased relative performance differences within the subordinate plant community under conditions of permanent drought stress. These changes were associated with the convergence of traits related to resource acquisition and growth. The dominant species equalised species performance differences by supressing relatively drought-tolerant species with low competitive ability and by supporting the less drought-tolerant species with relatively high competitive ability. Meanwhile, under conditions of interannual drought, the subordinate species likely coexisted due to differentiation in resource-use strategies and the interaction with the dominant species.</li> <li>Inclusion of individual variation in performance with a functional trait approach provides valuable insights into the processes structuring plant communities. Ours is the first study to provide evidence that subordinate species exposed to drought may coexist via neutral processes arising from their interactions with the dominant species, leading to functional convergence of traits associated with the trade-off between stress tolerance and competitive ability.</li> </ol>
Apresentação para Sessão Oral - CBEB2020: Evaluation of the Motor Performance of People with Parkinson's Disease through the Autocorrelation Function Estimated from Sinusoidal Drawings
<p>The objective of this work was to use the autocorrelation function to assess the fine motor performance of a healthy control group (CG) and people with Parkinson's disease (PD), whose fluctuations caused by the disease compromise motor skills, performed mainly by the hands and fingers essential for daily activities such as dressing, carrying objects and taking care of personal hygiene. For this, accelerometer signals were collected while the volunteers drew a sinusoidal pattern. The correlogram was estimated and two features were calculated from it: (i) the normalized area under the curve (𝑨𝒖𝑪); (ii) the difference between matching peaks and valleys from the reference and estimated correlograms (𝒗𝒎𝒎). These features allowed for the discrimination between drawing patterns of CG (𝒗𝒎𝒎= 0.154±0.063, 𝑨𝒖𝑪 = 0.204±0.040) and PD (𝒗𝒎𝒎 = 0.239±0.085, 𝑨𝒖𝑪 = 0.179±0.033) groups. Following the verification of normality (Shapiro-Wilk, p > 0.05), the t-test was applied to confirm the significant differences between groups (p < 0.05). The Cohen’s d effect size (p < 0.05) was medium (0.730) for 𝑨𝒖𝑪 and large (1.252) for 𝒗𝒎𝒎. This further confirms the differences between the features extracted from the groups. Therefore, the features 𝑨𝒖𝑪 and 𝒗𝒎𝒎 estimated from the autocorrelation function are effective for the assessment of the motor performance of healthy individuals and people with PD. </p>
Apresentação para o Prêmio Iniciação Científica - CBEB2020: Evaluation of the Motor Performance of People with Parkinson's Disease through the Autocorrelation Function Estimated from Sinusoidal Drawings
<p>The objective of this work was to use the autocorrelation function to assess the fine motor performance of a healthy control group (CG) and people with Parkinson's disease (PD), whose fluctuations caused by the disease compromise motor skills, performed mainly by the hands and fingers essential for daily activities such as dressing, carrying objects and taking care of personal hygiene. For this, accelerometer signals were collected while the volunteers drew a sinusoidal pattern. The correlogram was estimated and two features were calculated from it: (i) the normalized area under the curve (𝑨𝒖𝑪); (ii) the difference between matching peaks and valleys from the reference and estimated correlograms (𝒗𝒎𝒎). These features allowed for the discrimination between drawing patterns of CG (𝒗𝒎𝒎= 0.154±0.063, 𝑨𝒖𝑪 = 0.204±0.040) and PD (𝒗𝒎𝒎 = 0.239±0.085, 𝑨𝒖𝑪 = 0.179±0.033) groups. Following the verification of normality (Shapiro-Wilk, p > 0.05), the t-test was applied to confirm the significant differences between groups (p < 0.05). The Cohen’s d effect size (p < 0.05) was medium (0.730) for 𝑨𝒖𝑪 and large (1.252) for 𝒗𝒎𝒎. This further confirms the differences between the features extracted from the groups. Therefore, the features 𝑨𝒖𝑪 and 𝒗𝒎𝒎 estimated from the autocorrelation function are effective for the assessment of the motor performance of healthy individuals and people with PD. </p>
Data from: Reconfiguration of functional brain networks and metabolic cost converge during task performance
<p>The ability to solve cognitive tasks depends upon adaptive changes in the organization of whole-brain functional networks. However, the link between task-induced network reconfigurations and their underlying energy demands is poorly understood. We address this by multimodal network analyses integrating functional and molecular neuroimaging acquired concurrently during a complex cognitive task. Task engagement elicited a marked increase in the association between glucose consumption and functional brain network reorganization. This convergence between metabolic and neural processes was specific to feedforward connections linking the visual and dorsal attention networks, in accordance with task requirements of visuo-spatial reasoning. Further increases in cognitive load above initial task engagement did not affect the relationship between metabolism and network reorganization but only modulated existing interactions. Our findings show how the upregulation of key computational mechanisms to support cognitive performance unveils the complex, interdependent changes in neural metabolism and neuro-vascular responses.</p>
Data from: Will like replace like? linking thermal performance to ecological function across predator and herbivore populations
The inability of species to adapt to changing climate may cause ecological communities to disassemble and lose ecological functioning. However, theory suggests that communities may be resilient whenever populations within species exhibit variation in thermal plasticity or adaptation whereby thermally tolerant populations replace thermally sensitive ones. But will they maintain the functional roles of the populations being replaced? This study evaluated whether "like replaces like" functionally by measuring how four populations of a grasshopper herbivore and its co-occurring spider predator cope with environmental warming. The study occurred across a latitudinal gradient bounded by southerly, warmer Connecticut and northerly, cooler New Hampshire, USA. The study compared the survival rates, thermal performance, habitat usage, and food chain interactions of each grasshopper and spider population between its home field site (field of origin) and a Connecticut transplant site, and the native Connecticut population. Three grasshopper populations exhibited physiological plasticity by adjusting metabolic rates. The fourth population selected cooler habitat locations. Spider populations did not alter their metabolism, and instead selected cooler habitat locations thereby altering spatial overlap with their prey and food chain interactions. Grasshopper populations that coped physiologically consumed plants in different ratios than the fourth population and the Connecticut population. Hence "like may not replace like" whenever populations adapt physiologically to warming.
Data for the article "Performance of SCAN density functional method for a set of ionic liquids"
<p>The repository (https://github.com/vilab-tartu/SCAN) contains the database, geometries and an illustrative ipython notebook supporting the article "Performance of SCAN density functional method for a set of ionic liquids". </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.