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42 results for “efficient coding”
Source code and simulation results for the computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators
<p><strong>Summary</strong></p> <p>Data and source code relate to the article "Computation of eigenfrequency sensitivities using Riesz projections for<br> efficient optimization of nanophotonic resonators" [<a href="https://doi.org/10.1038/s42005-022-00977-1">1</a>]. It combines direct differentiation of scattering problems with a contour integral method [<a href="https://doi.org/10.1016/j.jcp.2020.109678">2</a>] to compute eigenfrequency sensitivities. An optimization is used to demonstrate the relevance of the method.</p> <p><strong>Structure</strong></p> <p>The most important elements of this publication are the MATLAB scripts 'sensitivities.m' and 'optimization.m', which can be used to reproduce the most important results of the paper. The directories <strong>code</strong>, <strong>scattering</strong> and <strong>results </strong>contain the software RPExpand [<a href="https://doi.org/10.1016/j.softx.2021.100763">3</a>], input files for JCMsuite [<a href="https://doi.org/10.1002/pssb.200743192">4</a>] and results produced with the scripts, respectively. Furthermore, the latter contains the subfolder <strong>tabulated,</strong> which contains text files tabulating data presented in Figures 2 and 4 of the paper. Eventually, the function 'code/observation.m' evaluates the target for the optimization.</p> <p><strong>Additional Information</strong></p> <p>The applicaton is based on an example from the literature [<a href="https://doi.org/10.1126/science.aaz3985">5</a>]. Using apriori knowledge about the eigenmode of interest, we chose the scalar observable, as defined in Section B of the paper, to be the component of the electric field normal to the plane defining the solid of revolution.</p> <p>The convergence studies are based on the discrete, circular contour <span>\(\tilde{C} = \big\{ c_n~|~ c_n=r_0 e^{2\pi i n/8}, n \in \{0,1,...,7\}\big\}\)</span> with center <span>\(\omega_0 = 2 \pi c/(1600~\mathrm{nm})\)</span> and radius <span>\(r_0 = \omega_0\times10^{-2}\)</span>. For finite element degrees <span>\(d\)</span> higher than 5, the error saturates. For this reason, the differences between results for <span>\(d=5\)</span> and <span>\(d = 6\)</span> may depend on the hardware architecture.</p> <p>A larger radius <span>\(r = 4\times10^{13}\)</span> has been chosen for the optimization to include information from poles located further away from the frequency of interest. The target function <span>\(t(p_1,\dots,p_5) = -q_n \left(1 - \frac{(\omega_n-\omega_0)^2}{r^2} \right)\)</span>is minimized. The first factor is the negative <em>Q-</em>Factor and the second factor ensures that the target is zero at the boundary. If no eigenfrequency <span>\(\omega_n\)</span> is located inside the contour, the target is set to zero. For the purpose of this data publication some numerical parameters have been improved. This resulted in a faster convergence of the optimization.</p> <p><strong>Requirements</strong></p> <ul> <li>JCMsuite (version 5.2.0 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holders in the files by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>. </p> <p><strong>References</strong></p> <p>[1] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p> <p>[2] Felix Binkowski, Lin Zschiedrich, Sven Burger, A Riesz-projection-based method for nonlinear eigenvalue problems, Journal of Computational Physics <strong>419</strong>, 109678 (2020), https://doi.org/10.1016/j.jcp.2020.109678</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763</p> <p>[4] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192</p> <p>[5] Kirill Koshelev, Sergey Kruk, Elizaveta Melik-Gaykazyan, Jae-Hyuck Choi, Andrey Bogdanov, Hong-Gyu Park, Yuri Kivshar, Subwavelength dielectric resonators for nonlinear nanophotonics, Science <strong>367</strong>, 288 (2020), http://dx.doi.org/%2010.1126/science.aaz3985</p>
Database and model code for "Material efficiency and climate change mitigation of passenger vehicles"
<p>This record provides all data points and model code necessary to compute the results presented in P. Wolfram, Q. Tu, N. Heeren, S. Pauliuk, E. Hertwich (2020) "Material efficiency and climate change mitigation of passenger vehicles", published in Journal of Industrial Ecology. All data is described in section 2 of the manuscript. The code can be run in MATLAB. </p>
Dataset for EASY: Efficient Arbiter SYnthesis from Multi-threaded Code
<p>High-level synthesis (HLS) is an increasingly popular method for generating hardware from a description written in a software language like C/C++. Traditionally, HLS tools have operated on sequential code, however in recent years there has been a drive to synthesise multi-threaded code. In this context, a major challenge facing HLS tools is how to automatically partition memory among parallel threads to fully exploit the bandwidth available on an FPGA device and minimise memory contention. Existing partitioning approaches require inefficient arbitration circuitry to serialise accesses to each bank because they make conservative assumptions about which threads might access which memory banks. In this work, we design a static analysis that can prove certain memory banks are only accessed by certain threads and use this analysis to simplify or even remove the arbiters while preserving correctness. We show how this analysis can be implemented using the Microsoft Boogie verifier, a tool named EASY for automatic formal verification using an SMT solver. Our work supports arbitrary input code with any irregular memory access patterns and indirect array addressing forms. We implement our approach in LLVM, integrate it with the LegUp HLS tool, and show that for a set of typical application benchmarks we can achieve up to 87% (avg. 61%) area savings and up to 39% (avg. 23%) improvement in execution time, with little additional compilation time relative to a long time in hardware synthesis.</p> <p>This repository includes all the measured results for this work.</p>
Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection'. </p> <p>The following files are included:</p> <ul> <li>best_algo.csv : labels for the classification and median performance of algorithms;</li> <li>ML_models.ipyb : jupyter notebook with the definition of the neural networks for both classifiers;</li> <li>pickle.zip : pickled models for the hardness classification and the algorithm selector;</li> <li>trajectories.zip : raw data files containing parts of the trajectories of each algorithm;</li> <li>Results.zip : results obtained using the approach on the stream of instances.</li> </ul>
Source code and simulation results: Efficient rational approximation of optical response functions with the AAA algorithm
<p>This publication provides data published in the article "Efficient rational approximation of optical response functions with the AAA algorithm" [1] in tabulated form along with the Matlab scripts that have been used to produce them. These scripts interface the finite element method solver JCMsuite [2,3]. The article presents rational approximations of optical response functions based on an extended version of the AAA algorithm [4] that allows to efficiently reconstruct sensitivty spectra and gives access to sensitivities of poles, residues, and zeros. Furthermore, the rational approximation of a scalar observalbe is used to construct solutions of the source free Maxwell's equation, i.e., a nonlinear eigenvalue problem. </p> <p><strong>The physical Structure</strong></p> <p>The example is based on the chiral metasurface introduced in [5]. For the sake of simplicity we added infinite layers of SiO\(_2\) to the top and the bottom of the structure. The original structure has a SiO\(_2\) substrate and a layer of PMMA polymethyl methacrylate (PMMA) deposited on top. PMMA can be modelled with the same refractive index of 1.45 as SiO\(_2\). Furthermore, our simulations include the 13 nm indium tin oxide (ITO) coating which drastically reduces the Q-factor as it is slightly absorbing. The accuracy of the discrete model is verified by assessing reflection, transmission, and absorption at 241 evenly spaced points within the specified range. Energy conservation requires that the discrepancy between their sum and the energy entering the system is zero. The numerical discretization is chosen such that the maximum relative error is less than \(3\times10^{−5}\).</p> <p><strong>Dispersion</strong></p> <p>Tabulated data for ITO has been taken from the <a href="https://refractiveindex.info/?shelf=other&book=In2O3-SnO2&page=Konig">refractiveindex.info</a> database (T. A. F. König et al., 2014, https://doi.org/10.1021/nn501601e) and the data for TiO2 was kindly provided the authors of [5]. The permittivity \(\varepsilon = (n+ik)^2\) is locally approximated as a rational function, i.e., only data in a vicinity of the frequency range of interest is considered. As we aim for a function with the symmetry \(f^\ast(\omega) = f(-\omega^\ast)\) we add the complex conjugated data at negative frequencies and enforce the symmetry in a second step. The partial fraction decomposition of the required function is of the form: \(\varepsilon(\omega) = \varepsilon_\infty + \sum_{j=1}^{4}a_j/(\omega-\omega_j) - a_j^\ast/(\omega+\omega_j^\ast)\) with the residues \(a_j\) and the poles \(\omega_j\). We expect 4 pairs of poles to sufficiently approximate the data within the range of interest (4 with positive and 4 with negative real parts).</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (at least 6.2.0)</li> <li>MATLAB (tested with version R2023b)</li> </ul> <p>In order to run the simulations with JCMsuite you must replace corresponding place holders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <p><strong>Usage</strong></p> <p>With the content of 'spectra.zip' you can reproduce results presented in the paper. Running the script 'plots.m' will not start any expensive simulation but use the provided data. With 'dispersion.m' the fits to the material data can be reproduced. Additionally, tabulated data is contained in 'data/ascii'. The archive 'eigenmodes.zip' must be extracted in the same directory as 'spectra.zip'.</p> <p><strong>References</strong></p> <p>[1] Fridtjof Betz, Martin Hammerschmidt, Lin Zschiedrich, Sven Burger, Felix Binkowski: Efficient rational approximation of optical response functions<br>with the AAA algorithm, https://doi.org/10.48550/arXiv.2403.19404.</p> <p>[2] Jan Pomplun, Sven Burger, Lin Zschiedrich, Frank Schmidt, Adaptive finite element method for simulation of optical nano structures, Physica Status Solidi B <strong>244</strong>, 3419 (2007), http://dx.doi.org/10.1002/pssb.200743192.</p> <p>[3] Fridtjof Betz, Felix Binkowski, Sven Burger, RPExpand: Software for Riesz projection expansion of resonance phenomena, SoftwareX <strong>15</strong>, 100763 (2021), https://doi.org/10.1016/j.softx.2021.100763.</p> <p>[4] Y. Nakatsukasa, O. Sète, and L. N. Trefethen, The AAA Algorithm for Rational Approximation, SIAM Journal on Scientific Computing <strong>40</strong>, A1494 (2018), http://dx.doi.org/10.1137/16M1106122.</p> <p>[5] X. Zhang, Y. Liu, J. Han, Y. Kivshar, and Q. Song, Chiral emission from resonant metasurfaces, Science <strong>377</strong>, 1215 (2022), http://dx.doi.org/%2010.1126/science.abq7870.</p>
Sequential and efficient neural-population coding of complex task information
<p>Condensed neural and behavioral data, included secondary results from model fits and analyses. This dataset contains all information required to recreate figures from the paper.</p>
Efficient coding of natural scenes improves neural system identification
<p>Dataset for <a href="https://www.biorxiv.org/content/10.1101/2022.01.10.475663v3">Qiu et al., 2022</a>.</p> <blockquote> <p>This work was supported by the German Research Foundation (DFG; SFB 1233, Robust Vision: Inference Principles and Neural Mechanisms, projects 10 and 12, project number 276693517; GRK2381, project number 335549539), the Germany’s Excellence Strategy (EXC 2064/1, project number 390727645), the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (agreement No 674901); the Max Planck Society (M.FE.A.KYBE0004); the German Ministry of Education and Research (BMBF; FKZ: 01GQ1002), and the Tübingen AI Center (FKZ: 01IS18039A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p> </blockquote>
U.S. building energy efficiency and flexibility as an electric grid resource (Data and Code)
<p><strong>* New in Version 2.1 *</strong></p> <ul> <li> <p>All residential measure savings shapes data (<strong>Latest_Res_Shapes.zip</strong> and residential measures in <strong>Latest_BM_Shapes.zip</strong>) were updated to correct post-processing errors present in version 2.</p> </li> <li> <p>The raw baseline-case data that are used in Scout to estimate sector-level baseline hourly loads (file <a href="https://github.com/trynthink/scout/blob/master/supporting_data/tsv_data/tsv_load.gz">tsv_load</a>) are now included in this data resource (see files <strong>Latest_Res_Baselines.zip</strong> and <strong>Latest_Com_Baselines.zip</strong>).</p> </li> <li> <p>Additional residential measure run documentation is available (<a href="https://github.com/NREL/resstock/blob/e2a98b7345d5c453ba35341b70af2f8859dd22fe/GEB_Potential.yml">here</a> for all except water heating efficiency plus flexibility (EE+DF) measure and <a href="https://github.com/NREL/resstock/blob/9611d92388e1e23466c9dc451e115c21321b4012/GEB_Potential_v2.5.0_appl_ee_dr.yml">here</a> for the water heating EE+DF measure).</p> </li> <li>A guide to reading and/or preparing savings shapes CSVs is available <a href="https://scout-bto.readthedocs.io/_/downloads/en/latest/pdf/">in the Scout documentation</a>, p. 36. The documentation also summarizes the net system load conditions that measures with flexibility (DF) characteristics respond to (Table 1, p. 37).</li> </ul> <p><strong>* New in Version 2 *</strong></p> <p>All hourly savings shapes CSV files that support the original <a href="https://doi.org/10.1016/j.joule.2021.06.002">analysis</a> have been updated to reflect the following improvements:</p> <ul> <li> <p>Generate residential data using ResStock v2.5.0 and commercial data using DOE Commercial Prototypes generated with OpenStudio v3.3.0.</p> </li> <li> <p>Residential and commercial measures with flexibility (DF) features respond to updated grid conditions (net peak/low load periods) that are consistent with projections from the EIA 2022 Annual Energy Outlook (AEO) “Low renewables cost” <a href="https://www.eia.gov/outlooks/aeo/tables_side_xls.php">side case</a>.</p> </li> <li> <p>Residential baseline loads and load savings are now distinguished by three building types (single family, multi family, and mobile homes).</p> </li> </ul> <p>Updated savings shape CSVs are organized into three ZIP files that may be separately downloaded depending on user interests:</p> <p><strong>Latest_BM_Shapes.zip</strong> includes only the subset of savings shape CSVs needed to execute the <a href="https://doi.org/10.5281/zenodo.3158929">Scout Benchmark Scenarios</a>.</p> <p><strong>Latest_Res_Shapes.zip</strong> includes all residential savings shape CSVs.</p> <p><strong>Latest_Com_Shapes.zip</strong> includes all commercial savings shape CSVs.</p> <p>Baseline load shapes in Scout have also been updated based on the same versions of ResStock and the DOE Commercial Prototypes, and peak/take period impact calculations have been updated to reflect the 2022 AEO system conditions. These updated data are contained in <a href="https://github.com/trynthink/scout/releases/tag/v0.8">Scout v0.8</a> (see ./supporting_data/tsv_data).</p> <p><br> <strong>Summary of Original Data Files</strong></p> <p>These data underpin an analysis of the near- and long-term technical potential bulk power grid resource offered by best available U.S. building efficiency and flexibility measures. Using multiple openly-available modeling frameworks supported by the U.S. Department of Energy, including <a href="https://scout.energy.gov/">Scout</a>, <a href="https://resstock.nrel.gov/">ResStock</a>, and the <a href="https://www.energycodes.gov/development/commercial/prototype_models">Commercial Building Prototype Models</a>, we pair bottom-up simulations of measures' building-level impacts with regional representations of the building stock and its projected electricity use to estimate the impacts of multiple building efficiency and flexibility scenarios on hourly regional system loads across the contiguous U.S. in 2030 and 2050. We find that demand-side management via building efficiency and flexibility could avoid up to nearly ⅓ of annual fossil-fired generation and ½ of fossil-fired capacity additions after 2020.<strong> </strong>Results are reported at both the national and regional scales and are disaggregated by building type and end use, facilitating a quantitative understanding of the role that buildings as a whole and specific building technologies or operational approaches can play in the future evolution of the U.S. electricity system.</p> <p>The four ZIP files that make up this data record are interpreted as follows:</p> <p><strong>Measure_Data.zip: </strong>Includes the Scout energy conservation measure (ECM) JSON definitions that were used to generate the main baseline and efficient/flexible scenario results ("Baseline_Measures" and "Efficiency_Flexibility_Measures", respectively), as well as side cases that assess the sensitivity of results to higher levels of variable renewable penetration ("High_RE_Sensitivity_Analysis") and a high degree of building load electrification ("High_Electrification_Measures"). Each measure set includes supporting 8760 load savings shapes in the sub-folder "Savings_Shapes". Additional details about defining and interpreting Scout measures with time-sensitive analysis features are available <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#time-sensitive-valuation">here</a>.</p> <p><strong>Results_Data.zip: </strong>Includes the main and side case results data. Baseline-case outcomes, which are consistent with the <a href="https://www.eia.gov/outlooks/archive/aeo19/">EIA 2019 Annual Energy Outlook</a>, are stored in "Baseline_Loads". Efficient/flexible scenario results are stored in "Efficiency_Flexibility_Measure_Impacts_Individual" and "Efficiency_Flexibility_Measure_Impacts_Portfolio," respectively, where the former includes results for individual measures in our analysis without considering any interactions across measures, and the latter includes results for aggregations of energy efficiency (EE), demand flexibility (DF), and efficiency and flexibility (EE+DF) portfolios that do consider interactions across measures in each portfolio. Results for the high electrification side case are stored in the "High_Electrification" sub-folder in the EE+DF case only. Results for the high renewable sensitivity analysis are stored in "High_RE_Sensitivity_Analysis", and residential and commercial 8760 savings shape outcomes for each of the EE, DF, and EE+DF measure portfolios and five of the 2019 EIA Electricity Market Module (EMM) <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">regions</a> (p.6) of focus are stored in "Sector_Level_8760s".</p> <p><strong>Source_Code.zip: </strong>Includes the source code needed to translate the measure inputs provided in "Measures_Data.zip" into the outputs provided in "Results_Data.zip". The core set of files required to execute the main analysis results is stored in "Base_Code_Package", while variants to certain files in the core package needed to execute the high renewable sensitivity and high electrification side cases are stored in "Code_Variants". In general, the process of running an analysis is as described in the Scout <a href="https://scout-bto.readthedocs.io/en/latest/quick_start_guide.html">Quick Start Guide</a>; however, the file "ecm_prep_batch.py" should be substituted for "ecm_prep.py" and the file "run_batch.py" should be substituted for "run.py". These batch files execute multiple versions of "ecm_prep.py" and "run.py" that are tailored to generate individual measure and whole portfolio results for annual, net peak summer and winter, and net off-peak summer and winter metrics (individual measures: "ecm_prep.json," "ecm_prep_spa," "ecm_prep_wpa," "ecm_prep_sta," "ecm_prep_wta"; whole portfolio: "ecm_results.json," "ecm_results_spa.json," "ecm_results_wpa.json," and "ecm_results_sta.json," and "ecm_results_wta.json"). Results for the side cases are generated by replacing the versions of the "ecm_prep" and "run" files included in the "Base_Code_Package" folder with those in the "Code_Variants" folder. Sector-level 8760 shapes are generated using the "--sect_shapes" command line option as described <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#sector-level-hourly-energy-loads">here</a>. See Scout's <a href="https://scout-bto.readthedocs.io/en/latest/tutorials.html#local-execution-tutorials">Local Execution Tutorials</a> for more details on how to develop Scout inputs and outputs.</p> <p><strong>Supporting_Data.zip: </strong>Includes supplemental data files provided by EIA that describe key inputs and outputs to the <a href="https://www.eia.gov/outlooks/aeo/nems/documentation/archive/pdf/m068(2018).pdf">Electricity Market Module</a> in the AEO 2019 run of the National Energy Modeling System ("EIA EMM Data (AEO 2019)"), as well as raw EnergyPlus outputs that were used to develop the baseline Scout hourly load shape file found in "./Source_Code/Base_Code_Package/supporting_data/tsv_data/tsv_load.json". </p>
Supplementary codes and datasets for "Efficient numerical method for reliable upper and lower bounds on homogenized parameters"
<p>This repository supports L. Gaynutdinova, M. Ladecký, A. Nekvinda, I. Pultarová, and J. Zeman, <em>Efficient numerical method for reliable upper and lower bounds on homogenized parameters</em> (first announced as the arXiv preprint <a href="http://arxiv.org/abs/2208.09940">2208.09940</a>).</p> <p>In particular, it contains MATLAB source files for reproducing the results presented in Examples 1 and 2 of the manuscript for discretizations with <span class="math-tex">\(N_1 = N_2 = N_3 = 6, 12, 24\)</span>. For other parameters, the code needs to be modified manually.</p> <p>The most recent version of the codes is available in the <a href="https://gitlab.com/ul_bounds_homog_CTU/3d-fem-homogenized-parameters">GitLab repository</a>.</p>
Data and code from: Efficient inference of macrophylogenies: Insights from the avian tree of life
Open the record for dataset details and reuse information.
Sequential and efficient neural-population coding of complex task information
Open the record for dataset details and reuse information.
The MATLAB code for "A kinematic model for understanding rain formation efficiency of a convective cell"
<p>Please check the code for the 1D model and the plotting commands. Please start from main.m </p>
Code and scripts used to generate results for: Designing transmissible viral vaccines for evolutionary robustness and maximum efficiency
<p>The danger posed by infectious agents necessitates the development of new tools to both predict and manage emerging diseases. One promising approach is the development of recombinant viral vaccines that are themselves infectious. Transmissible vaccines have been shown to greatly reduce the effort required to control the spread of zoonotic pathogens in their animal reservoirs, thereby limiting the chances of human infection. While this approach can be immensely useful in combating emerging diseases, the ability to self-replicate exposes vaccines to evolutionary change. As a recombinant transmissible vaccine mutates, selection is expected to favor variants with reduced efficacy against the pathogen and increased transmission rates. This creates a trade-off in vaccine design priorities between efficacy, reduction in transmission, and evolutionary stability. Here we ask how such trade-offs influence the overall performance of transmissible vaccines. We find that evolutionary instability can dramatically reduce performance, even for vaccine candidates with ideal efficacy and transmission dynamics. One method to increase functional stability is through the inclusion of multiple redundant antigens. We show that the inclusion of a second antigen can increase functional stability in the face of evolutionary pressures. However, the benefit of genetic redundancy plus any gain in efficacy must outweigh the reduction in transmission for dual-gene designs to be effective. Our results suggest that the successful application of recombinant transmissible vaccines will require consideration of evolutionary dynamics and epistatic effects, as well as basic measurements of epidemiological features.</p>
Data and codes for manuscript "A study on radiative efficiencies and warming effects of halocarbons"
<p>Data and codes for “A study on radiative efficiencies and warming effects of halocarbons”, including the 998-band radiative transfer model for calculating the radiative efficiency, scripts for plotting and corresponding data.</p>
Code and Data for "Routescore: Punching the Ticket to More Efficient Materials Development"
<p>Code and dataset for the manuscript "Routescore: Punching the Ticket to More Efficient Materials Development" by Seifrid, <em>et al.</em></p>
Principal component decomposition of acoustic and neural representations of time-varying pitch reveals adaptive efficient coding of speech covariation patterns
<p>Data and scripts reported by Llanos and colleagues in their Brain and Language study </p>
The code and data for the paper entitled 'Facilitating Efficient Discovery: A GUI-Oriented Approach for Exploring Functionality using Machine Learning Model'
<h2><strong>Code</strong></h2> <p><strong><span>dataCollection.py</span></strong></p> <p><span>The collection of app data is primarily accomplished by processing and storing XML files and screenshots of the app. Firstly, the uiautomator is utilized to connect to the smart device and obtain screenshots and XML files. Subsequently, the XML files are analyzed to identify clickable functions within the GUI, and the textual information contained within the functions, along with their specific locations, is extracted. Finally, the Python file also includes handling of interface elements, such as determining if elements are obscured and verifying the legitimacy of the text.</span></p> <p><strong><span>tagData.py</span></strong></p> <p><span>The main implementation involves volunteers annotating app functionalities, including HTML generation, user data analysis, and retrieval. Flask framework is employed, presenting one GUI to the user each time while randomly prompting them to click on three functionalities. Ultimately, the time taken by users to locate these three functionalities is collected.</span></p> <p><strong><span>userPersonalization.py</span></strong></p> <p><span>Separating out the data annotated by each user facilitates personalized analysis. This process involves extraction, storage, and loading of individual user annotations.</span></p> <p><strong><span>dataPreprocessing.py</span></strong></p> <p><span>For a user-annotated functionality, completing the conversion from user time to either "hard-to-find" or "easy-to-find" involves several steps. First, the functionalities are vectorized, extracting relevant parameters from the XML files and computing their correlation with the time users spent searching for the functionalities. These parameters are then normalized to obtain feature vectors for the functionalities. Additionally, an initial determination is made regarding whether the annotated functionalities are "hard-to-find" or "easy-to-find" for each user. Subsequently, clustering is performed on all annotated data from users, and based on the clustering results, the outcomes are filtered and adjusted.</span></p> <p><strong><span>difficultFindClassifier.py</span></strong></p> <p><span>Train the classifier and use it to predict "hard-to-find" functionalities, then display the results.</span></p> <h2><span>Data</span></h2> <p><span>The data is located in the "static" folder:</span></p> <p><span>- The "persistentData" folder contains the trained classifier.</span></p> <p><span>- The "picture" folder contains screenshots of the app.</span></p> <p><span>- The "requestTime" folder stores data for when volunteers annotate only one function in a GUI.</span></p> <p><span>- The "threeResponseTime" folder saves data for when volunteers annotate three functions in a GUI.</span></p> <p><span>- The "userData" folder stores personalized user data.</span></p> <p><span><span>- The "xml_information" folder stores XML files of the app.</span></span></p>
Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study
<p>Este conjunto de dados foi gerado como parte do estudo "Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study". O estudo focou na avaliação do desempenho das ferramentas Copilot, ChatGPT e Gemini, utilizando problemas do LeetCode em quatro linguagens de programação: Python, Java, JavaScript e C.</p> <p>O conjunto de dados atualizado está organizado nas seguintes pastas:</p> <ol> <li> <p><strong>c_programs</strong>: Esta pasta contém os scripts Python utilizados para calcular a complexidade ciclomática e a complexidade cognitiva do código C gerado pelas ferramentas.</p> <ul> <li><code>calculate_cyclomatic_complexity.py</code>: Script para calcular a complexidade ciclomática.</li> <li><code>calculate_cognitive_complexity.py</code>: Script para calcular a complexidade cognitiva.</li> </ul> </li> <li> <p><strong>codes_suggested_by_the_tools</strong>: Esta pasta contém as sugestões de código geradas pelo Copilot, ChatGPT e Gemini para cada problema do LeetCode.</p> <ul> <li>Subpastas: <code>ChatGPT</code>, <code>Copilot</code>, <code>Gemini</code>, cada uma contendo as sugestões de código correspondentes nos formatos das linguagens.</li> </ul> </li> <li> <p><strong>complexity_of_codes</strong>: Esta pasta contém dois arquivos CSV que fornecem os resultados da análise de complexidade para o código gerado.</p> <ul> <li><code>AI analysis results table - Cognitive.csv</code>: Resultados da complexidade cognitiva do código gerado.</li> <li><code>AI analysis results table - Cyclomatic.csv</code>: Resultados da complexidade ciclomática do código gerado.</li> </ul> </li> </ol> <p>Este conjunto de dados atualizado oferece insights valiosos sobre o desempenho das ferramentas de geração de código com IA e pode ser utilizado para análises futuras ou estudos de replicação.</p>
Efficiency of thermoremanent magnetization acquisition in vortex-state particle assemblies: Dataset and Codes
<p>When magma cools, crystallized magnetic minerals can record the ambient magnetic field. If these mineral grains are sufficiently small, they may retain that magnetic record for billions of years. This phenomenon enables paleomagnetists to study the direction and intensity of ancient magnetic fields on Earth and other planets. A key task is determining the number of magnetic particles required to reliably record such a field, as well as understanding the influence of grain size and shape on this process. Here, we present a dataset from the study titled "Efficiency of Thermoremanent Magnetization Acquisition in Vortex-State Particle Assemblies," which integrates micromagnetic modeling and probabilistic magnetic state partitioning to address these questions. The dataset includes codes for the micromagnetic simulations and data from all the simulations conducted.</p>
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