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615 results for “tuning”
Dataset for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study"
<p># Data and code for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study."</p><p>## Contents</p><p>* data-{type}/*: reproducible data</p><p>* job.job : example slurm script</p><p> </p><p>## Description of the data</p><p>The data are organized in subdirectories *data-{type}/{system}/* corresponding to the considered molecules and simulation type:</p><p>* data-gs: Ground state calculations</p><p>* data-td: TD-DFT calculations</p><p>The contents of each subdirectory are:</p><p>* data-gs/{system}/structure.xyz: physical atomic structure</p><p>* data-td/{system}/td-dft/td_uvvis.txt: photoabsorption spectrum</p><p>The spectrum plots in the article correspond to the first (x values) and second (y values) columns of the spectrum files.</p><p> </p><p>## Reproduction of the data</p><p>The data were produced using Gaussian version g16.A.01</p><p>The calculation of the data of a system consists of the following steps:</p><p>1. Ground-state (gs) calculation:</p><p> * Prepare the input file for the gs by adjusting the parameters of the ground state calculations:</p><p> * "# opt b3lyp/6-311+g(d,p) scrf=(smd,solvent=chloroform) geom=connectivity empiricaldispersion=gd3bj out=wfn"</p><p> * out = wfn keyword to create a wfn file of the ground state that will be used for EDD and RDG investigations</p><p> * Submit the job.job file for the gs calculation as appropriate for the particular input file of the system</p><p> * The optimized sturctures are visualised using GaussView</p><p>2. Time-propagation calculation:</p><p> * Requires finished ground-state calculation</p><p> * Set up the TD-DFT calculation parameters as necessary:</p><p> * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density out=wfn"</p><p> * density out = wfn keywords to create a wfn file of the excited state that will be used for EDD investigation</p><p> * Submit the job.job file for TD-DFT calculation as appropriate for the particular system</p><p> * The photoabsoption specta are visualised using GaussView</p><p>3. RDG calculation:</p><p> * Put the .wfn file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 3.23.1 in the manual</p><p>4. DOS calculation:</p><p> * The dos curves are plotted starting from the .fchk of the ground state geometry, select the atoms index corresponding to the diffrents subpart of the studied molecules (donor, acceptor, pi-spacer)</p><p> * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the structure in Section 4.10.1 in the manual</p><p> * the output generates .chk file which is transformed to .fchk file : formchk .chk .fch</p><p>5. TDM calculations:</p><p> * Requires finished ground-state calculation</p><p> * Set up the TD-DFT calculaton parameters as necessary</p><p> * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density transition=1 iop(6/8=3) out=wfn"</p><p> * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 4.18.8 in the manual</p><p>6. EDD calculation:</p><p> * Edd plots are plotted based on the es.wfn and gs.wfn following Section 4.18.1 in the manual</p><p> </p>
Supplementary material data for: Unstable environmental conditions constrain the fine-tune between opsin sensitivity and underwater light in an Amazon forest stream fish
<p>Visual adaptations can stem from variations in amino acid composition, chromophore utilization, and differential opsin gene expression levels, enabling individuals to adjust their light sensitivity to environmental lighting conditions. In stable environments, adaptations often involve amino acid substitutions, whereas in unstable conditions, differential gene expression may be a more relevant mechanism. Amazon forest streams present diverse underwater lighting conditions and experience short-term water colour fluctuations. In these environments, it is less likely for genetic and amino acid sequences to undergo modifications that tailor opsin proteins to the prevailing lighting conditions, particularly in species having several copies of the same gene. The sailfin tetra, <em>Crenuchus spilurus</em>, inhabits black and clear water Amazon forest streams. The long wavelength sensitivity (LWS) is an important component for foraging and courtship. Here, we investigated LWS opsin genes in the <em>sailfin tetra</em>. Three copies of LWS1 and two copies of LWS2 genes were found. The maximum absorbance wavelength (λmax) estimated from the amino acid sequences of LWS1 genes exhibited variation among the different copies. In contrast, the copies of LWS2 genes showed identical expected λmax values. Although the amino acid positions affecting λmax varied among LWS genes, they remained consistent among populations living in different water colours. The relative expression levels of LWS genes differed between gene copies. While not formally tested, our results suggest that in fluctuating environments, visual adaptations may primarily stem from alterations in gene expression profiles and/or chromophore usage rather than precise genetic tuning of protein light sensitivity to environmental lighting conditions.</p>
Ancient insect vision tuned for flight amongst rocks and plants underpins natural flower colour diversity - rock, mineral, stick, bark, leaf, bird- and insect-flower petal reflectance spectra
<p>Understanding the origins of flower colour signalling to pollinators is fundamental to evolutionary biology and ecology. Flower colour evolves under pressure from visual systems of pollinators, like birds and insects, to establish global signatures among flowers with similar pollinators. However, an understanding of the ancient origins of this relationship remains elusive. Here, we employ computer simulations to generate artificial flower backgrounds assembled from real material sample spectra of rocks, leaves, and dead plant materials, against which to test flowers' visibility to birds and bees. Our results indicate how flower colours differ from their backgrounds in strength, and the distributions of salient reflectance features when perceived by these key pollinators, to reveal the possible origins of their colours. Since Hymenopteran visual perception evolved before flowers, the terrestrial chromatic context for its evolution to facilitate flight and orientation consisted of rocks, leaves, sticks, and bark. Flowers exploited these pre-evolved visual capacities of their visitors, and in response evolved chromatic features to signal to bees, and differently to birds, against a backdrop of other natural materials. Consequently, it appears that today's flower colours may be an evolutionary response to the vision of diurnal pollinators navigating their world millennia prior to the first flowers.</p>
Selective dynamic band gap tuning in metamaterials using graded photoresponsive resonator arrays
<p>Raw Data for figures:</p> <p>Fig. 2: Dispersion diagrams for non-illuminated (off) and illuminated (on) pillars of different heights (hp). hp1 = 7 mm, hp2 = 9 mm, hp3 = 11 mm, hp4 = 13 mm; p = 0 (1) for purely in- (out-of-plane) behavior</p> <p>Fig. 4: Computed transmission spectrum of a finite structure. a) Numerically simulated transmission spectrum for the considered 8-pillar specimen, both without ("Laser off") and with laser illumination ("Laser on 7th pillar").</p> <p>Fig. 5: Transmission spectrum of the finite structure considered experimentally. a) Measured spectra before (blue) and after (red) illumination of pillar 1. Band gaps are highlighted in light blue and numbered from I to IV; b) Corresponding colour map representing transmission vs. frequency and time (vertical axis) when switching laser illumination on (t = 700 s) and off (t = 2300 s); c) same as a), with illumination of pillar 6; d) same as b), with illumination on pillar 6.</p> <p>Fig. 6: Dynamic modulation of signal frequencies (f1 = 21.5 kHz, f2 = 71.5 kHz) in a graded pillar structure. The different temporal intervals depict tunable suppression and enhancement of specific frequencies through selective pillar illumination.</p>
PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data
<p>Describes the taxonomy used in the paper "PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data", presented at the Second Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data<em> </em>at the Extended Semantic Web Conference (ESWC) 2024.</p>
How small deviations in kinematics and body form dictate muscle performances in the finely tuned avian downstroke
<p>Avian takeoff requires peak pectoralis muscle power to generate sufficient aerodynamic force during the downstroke. Subsequently the much smaller supracoracoideus recovers the wing during the upstroke. How the pectoralis work loop is tuned to power flight is unclear. We integrate wingbeat-resolved muscle, kinematic and aerodynamic recordings <em>in vivo</em> with a new mathematical model to disentangle how the pectoralis muscle overcomes wing inertia and generates aerodynamic force during takeoff in doves. Doves reduce the incidence of their wing mid-downstroke to efficiently generate aerodynamic force, resulting in an aerodynamic power dip, that allows transferring excess pectoralis power into tensioning the supracoracoideus tendon to assist the upstroke—improving the pectoralis work loop efficiency simultaneously. Integrating extant bird data, our model shows how the pectoralis of birds with faster wingtip speed need to generate proportionally more power. Finally, birds with disproportionally larger wing inertia need to activate the pectoralis earlier to tune their downstroke.</p>
data supporting ''Tuning the mechanical properties of organophilic clay dispersions: Particle composition and preshear history effects''
Open the record for dataset details and reuse information.
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)
<p>Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)</p>
Raw datasets and media accompanying the manuscript: Homogenous high enhancement surface-enhanced Raman scattering (SERS) substrates by simple hierarchical tuning of gold nanofoams
<p>Raw datasets and media accompanying the manuscript: Homogenous high enhancement surface-enhanced Raman scattering (SERS) substrates by simple hierarchical tuning of gold nanofoams</p>
Tuning VASP (ver. 6.2.1) performance on AMD supercomputers
<p>Here are provided several test results for the VASP code version 6.2.1 on Karolina supercomputer (karolina.it4i.cz) at IT4Innovations, VSB - Technical University of Ostrava, Czech Republic (it4i.cz) compiled with different compilers, compiler options, and numerical libraries. Tests have been done for two small systems (folders SMALL-1 and SMALL-2), and two<br> average systems (folders AVERAGE-1 and AVERAGE-2).<br> </p>
Tyrosine phosphorylation tunes chemical and thermal sensitivity of TRPV2 ion channel
<p><span>Transient receptor potential vanilloid 2 (TRPV2) is a multimodal ion channel implicated in diverse physiopathological processes.</span><span> Its important involvement in immune responses has been suggested such as in the macrophages' phagocytosis process. However, the endogenous signaling cascades controlling the gating of TRPV2 remain to be understood. Here, we report that enhancing tyrosine phosphorylation remarkably alters the chemical and thermal sensitivities of TRPV2 endogenously expressed in</span><span> rat bone marrow-derived macrophages. We identify that the </span><span>protein tyrosine kinase </span><span>JAK1 mediates TRPV2 phosphorylation at the molecular sites Tyr(335), Tyr(471), and Tyr(525). JAK1 phosphorylation is required for maintaining TRPV2 activity and the phagocytic ability of macrophages. We further show that TRPV2 phosphorylation is dynamically balanced by protein tyrosine phosphatase</span><span> non-receptor type 1 (</span><span>PTPN1). PTPN1 inhibition increases TRPV2 phosphorylation, further reducing the activation temperature threshold. Our data thus unveil an intrinsic mechanism where the phosphorylation/dephosphorylation dynamic balance sets the basal chemical and thermal sensitivity of TRPV2. Targeting this pathway will aid therapeutic interventions in physiopathological contexts.</span></p>
Transient Absorption Spectroscopy for "The Other Dimension - Tuning Hole Extraction via Nanorod Width"
<p>This is the transient absorption spectroscopy dataset for the publication "The Other Dimension - Tuning Hole Extraction via Nanorod Width". It contains the originally obtained data, chirp corrected 2D datasets, and the Gauss fittings of spectra obtained at delay times of 20 ps.</p>
Paris catacombs - "Looney Tunes" graffiti
Just a cool and colorfull graffiti with Will E. Coyote and the road runner from "Looney Tunes", one of my favorite cartoon when I was a kid. Scanned with the iPhone12 Pro and Scaniverse. Please feel free to follow my collections of daily scans ([link](https://skfb.ly/6YuwK)) as well as my scans in San Francisco, Paris, or in the catacombs: [link](https://sketchfab.com/edemaistre/collections) Source: Objaverse 1.0 / Sketchfab
Comprehensive large-scale datasets for 26 viral families for fine-tuning BERT-infect models
<p>These datasets were constructed in the paper "Hidden Challenges in Evaluating Spillover Risk of Zoonotic Viruses using Machine Learning Models" (doi: https://doi.org/10.1101/2024.04.25.591033). The details were also described in the git-hub (https://github.com/Junna-Kawasaki/BERT-infect_2024).</p> <ul> <li>The compressed files, such as ${virus}.tar.xz, contain fasta and genbank files.</li> </ul>
April 7, 2024 (v1) Image Open Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 5 relative to Figure 7 - Figure Supplement 4
<p>Source data file relative to <strong><span>Figure 7 – figure supplement 4 Panel A</span></strong></p> <p><span>Raw image of agarose gel showing the 2 alternative mRNAs encoding for ArhGEF11 in control animals (left track, control) and after injection of the MO at the one cell stage (right track, +MO at 2 and 5ng). The source data includes the raw files (native format .scn and open source format .tiff) as well as a pdf file showing both the full scale image and the cropped image selected for the figure.<br></span></p>
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
<p>Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applications, such as physics discovery, cancer research and material sciences. A key challenge with autonomous accelerator tuning remains that the most capable algorithms require an expert in optimisation, machine learning or a similar field to implement the algorithm for every new tuning task. In this work, we propose the use of large language models (LLMs) to tune particle accelerators. We demonstrate on a proof-of-principle example the ability of LLMs to successfully and autonomously tune a particle accelerator subsystem based on nothing more than a natural language prompt from the operator, and compare the performance of our LLM-based solution to state-of-the-art optimisation algorithms, such as Bayesian optimisation (BO) and reinforcement learning-trained optimisation (RLO). In doing so, we also show how LLMs can perform numerical optimisation of a highly non-linear real-world objective function. Ultimately, this work represents yet another complex task that LLMs are capable of solving and promises to help accelerate the deployment of autonomous tuning algorithms to the day-to-day operations of particle accelerators.</p>
(supplementary material) Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation
<div> <div> <div> <div>Supplementary material for paper <strong>"Fine-Tuning and Prompt Engineering for Large Language Models-based Code Review Automation"</strong></div> <div> </div> <div> <div> <div>The script for the paper can be found in this GitHub repository: https://github.com/awsm-research/LLM-for-code-review-automatiton</div> </div> </div> </div> </div> </div>
Dataset for publication "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation"
<p>This is a dataset for a paper "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation". All details about the data are included in the readme file.</p>
Tuning the electronic properties of Zr UiO-66 through defect-functionalised multivariate modulation
<p>Raw data supporting the article 'Tuning the electronic properties of Zr UiO-66 through defect-functionalised multivariate modulation'.</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.