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212 results for “fine tuning”
(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>
Data from: Endothelial and systemic upregulation of miR-34a-5p fine-tunes senescence in progeria
<p>Endothelial defects significantly contribute to cardiovascular pathology in the premature aging disease Hutchinson-Gilford progeria syndrome (HGPS). Using an endothelium-specific progeria mouse model, we identify a novel, endothelium-specific microRNA (miR) signature linked to the p53-senescence pathway and a senescence-associated secretory phenotype (SASP). Progerin-expressing endothelial cells exert profound cell-non-autonomous effects initiating senescence in non-endothelial cell populations and causing immune cell infiltrates around blood vessels. Comparative miR expression analyses revealed unique upregulation of senescence-associated miR34a-5p in endothelial cells with strong accumulation at atheroprone aortic arch regions but also, in whole cardiac- and lung tissues as well as in the circulation of progeria mice. Mechanistically, miR34a-5p knockdown reduced not only p53 levels but also late-stage senescence regulator p16 with no effect on p21 levels, while p53 knockdown reduced miR34a-5p and partially rescued p21-mediated cell cycle inhibition with a moderate effect on SASP. These data demonstrate that miR34a-5p reinforces two separate senescence regulating branches in progerin-expressing endothelial cells, the p53- and p16-associated pathways, which synergistically maintain a senescence phenotype that contributes to cardiovascular pathology. Thus, the key function of circulatory miR34a-5p in endothelial dysfunction-linked cardiovascular pathology offers novel routes for diagnosis, prognosis and treatment for cardiovascular aging in HGPS and potentially geriatric patients.</p>
CodonTransformer - Genomic and CodonTransformer-Generated Sequences for Fine-tuned Organisms
<p>This dataset is used in creating Fig. 2a and Supplementary Figs. 2-16 of the paper, mainly including the predictions of base (pretrained) and finetuend CodonTransformer model along with various metrics. </p>
Fine Tune, Sample of Training Set
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Data from: Fine-tuning biodiversity assessments: A framework to pair eDNA metabarcoding and morphological approaches
<p><span>Accurate quantification of biodiversity can be demanding and expensive. Although environmental DNA (eDNA) metabarcoding can facilitate biodiversity assessments through non-invasive, cost-efficient, and rapid surveys, the approach struggles to outperform traditional morphological approaches in providing reliable quantitative estimates for surveyed species (e.g., abundance and biomass).</span></p> <p><span>We present an integrated methodology for improving biodiversity surveys that pairs eDNA metabarcoding with morphological data, following a series of taxonomic and geographic filters. We demonstrate its power by applying it to a new spatiotemporal dataset generated on an Iberian-wide distributed aquatic mesocosm infrastructure that spans a wide biogeographic gradient.</span></p> <p><span>By building upon the strengths that these two approaches offer, our framework improved taxonomic resolution for 30% of the taxa and enabled species' traits (e.g., body-size) and abundance to be assigned to 85% of the taxa in hybrid datasets.</span></p> <p><span>These results indicate that eDNA-based assessments can complement, but not always replace, conventional approaches. Integrating conventional and modern eDNA metabarcoding approaches, already available in the ecologist's toolbox, will greatly enhance biodiversity assessments.</span></p>
The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
<p>Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.</p>
Supplementary Tables and Datasets for publication: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream
<p>This is a data dump of the tables, genomes, and .faa files that were too large to submit as part of the publication titled: Spatial and finely tuned temporal metagenomics of river compartments reveals viral community dynamics in an urban stream</p> <p> </p> <p>Files here include:</p> <p>-Fasta file containing 1230 vMAGs.</p> <p>-Zip file containing individual fasta files for 125 MAGs</p> <p>-Annotations output for DRAM and DRAM-v for all MAGs and vMAGs</p> <p>-.faa proteins file for the full Freshwater / Wastewater / TARA Oceans dataset that was used for vContact2 biogeography analyses</p>
Data from: Fine-tuning the nested structure of pollination networks by adaptive interaction switching, biogeography and sampling effect in the Galápagos Islands
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Supplementary material data for: Unstable environmental conditions constrain the fine-tune between opsin sensitivity and underwater light in an Amazon forest stream fish
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Novel image-analytic approach reveals new insights in fine tuning of slime mould network adaptation
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Data from: High-fidelity parameter-efficient fine-tuning for joint recognition and linking of diagnoses to ICD-10 in non-standard primary care notes
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The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics
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Data from: Fine-tuning biodiversity assessments: A framework to pair eDNA metabarcoding and morphological approaches
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How small deviations in kinematics and body form dictate muscle performances in the finely tuned avian downstroke
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Summary statistics data for "Genetic variation and microRNA targeting of A-to-I RNA editing fine tune human tissue transcriptomes"
<p>edQTL and ASED summary statistics data for the manuscript titled "Genetic variation and microRNA targeting of A-to-I RNA editing fine tune human tissue transcriptomes"</p>
Data from: Causes and consequences of repeatability, flexibility and individual fine-tuning of migratory timing in pike
1. Many organisms undertake migrations between foraging and breeding habitats and while it is assumed that reproductive timing affects fitness, little is known about the degree of individual consistency, and about the causes and consequences of individual variation in migratory timing in organisms other than birds. 2. Here, we report on a 6-year mark–recapture study, including 2048 individuals, of breeding migration in anadromous pike (Esox lucius), an iteroparous top-predatory fish that displays homing behaviour. By repeated sampling across years at a breeding site, we first quantify individual variation both within and between breeding events and then investigate phenotypic correlates and fitness consequences of arrival timing to the breeding site. 3. Our data demonstrate that males arrive before females, that large males arrive later than small males, that the timing of breeding migration varies among years and that individuals are consistent in their timing across years relative to other individuals in the population. 4. Furthermore, data on return rates indicate that arrival time is under stabilizing viability selection, and that individuals who are more flexible in their timing of arrival during the first reproductive years survive longer compared with less flexible individuals. Finally, longitudinal data demonstrate that individuals consistently fine-tune their arrival timing across years, showing that the timing of arrival to breeding sites is influenced by experience. 5. These findings represent rare evidence of how between- and within-individual variations in migratory timing across breeding events are correlated with phenotypic and fitness traits in an ecologically important keystone species. Our results emphasize the importance of considering variation in migratory timing both between and within individuals in studies investigating the fitness consequences of migratory behaviour and have implications for future management.
Pre-training and fine-tuning dataset for transformers consisting of basic blocks and their execution times (average, minimum, and maximum) along with the execution context of these blocks, for various Cortex processors M7, M4, A53, and A72.
<p>We are making public the dataset used for training CAWET, a tool for estimating the Worst-Case Execution Time (WCET) of basic blocks using the Transformer XL model. CAWET leverages the Transformer architecture for accurate WCET predictions, and its training involves two main phases: self-supervised pre-training and fine-tuning.</p><p>CAWET undergoes a pre-training process on a substantial corpus of basic blocks to enable the Transformer to grasp the intricacies of the assembly language in focus. For this, we utilized CodeNet \cite{codenet}, a comprehensive collection of publicly submitted solutions to competitive programming challenges, comprising roughly 900,000 C programs. These programs were cross-compiled to the target architecture and subsequently disassembled using GNU binary utilities with objdump. The textual output from objdump, post a series of basic parsing operations (e.g., address extraction, separation of basic blocks), serves as the foundation for an extensive pre-training dataset. We employed this dataset to develop a vocabulary model utilizing sentence piece \cite{sentencepiece}. Following the completion of the sentence piece model's training, it becomes ready for use in tokenizing any binary programs written in the target instruction set.</p><p>The fine-tuning phase of CAWET involves its adaptation to basic blocks along with their contextual information. Here, we used a varied and openly accessible collection of programs, namely, The Algorithms (accessible at: <a href="https://github.com/TheAlgorithms/C">https://github.com/TheAlgorithms/C</a>), MiBench \cite{mibench}, and Polybench \cite{polybench}.</p><p>The provided zip file encompasses the following directories:</p><p>Fine_Tuning: This includes four distinct files, each tailored for a specific processor: Cortex_M4, Cortex_M7, Cortex_A53, and Cortex_72. Each file encompasses the basic block under analysis (bbUA), the preceding 10 basic blocks executed prior to it, and timing information related to the bbUA (mean, min, max, normalization, etc.).</p><p>Pre_Training: This comprises two extensive files, dataset_CortexA and dataset_CortexM, utilized for pre-training the transformers for the Masked Language Modeling Task (MLM). Additionally, it includes the sentence piece model and the necessary code to facilitate accurate tokenization.</p><p>For additional information, please refer to the CAWET paper or contact us at <a href="mailto:ea_amalou@esi.dz">ea_amalou@esi.dz</a></p><p> </p><p>Citation:</p><p>@inproceedings{amalou2023cawet,</p><p> title={CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers},</p><p> author={Amalou, Abderaouf N and Fromont, Elisa and Puaut, Isabelle},</p><p> booktitle={35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},</p><p> year={2023},</p><p> organization={Schloss Dagstuhl-Leibniz-Zentrum f{\"u}r Informatik}</p><p>}</p><p> </p><p><strong>Bibliography</strong>:</p><p>codenet</p><p>@article{codenet2021,</p><p> title={CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks},</p><p> author={Puri, Ruchir and Kung, David S and Janssen, Geert and Zhang, Wei and Domeniconi, Giacomo and Zolotov, Vladimir and Dolby, Julian and Chen, Jie and Choudhury, Mihir and Decker, Lindsey and others},</p><p> journal={arXiv preprint arXiv:2105.12655},</p><p> year={2021}</p><p>}</p><p>sentencepiece</p><p>@article{sentencepiece2018,</p><p> title={Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing},</p><p> author={Kudo, Taku and Richardson, John},</p><p> journal={arXiv preprint arXiv:1808.06226},</p><p> year={2018}</p><p>}</p><p>mibench</p><p>@inproceedings{polybench2014,</p><p> title={Understanding polybench/c 3.2 kernels},</p><p> author={Yuki, Tomofumi},</p><p> booktitle={International workshop on polyhedral compilation techniques (IMPACT)},</p><p> pages={1--5},</p><p> year={2014}</p><p>}</p><p>polybench: </p><p>@inproceedings{mibench,</p><p> title={MiBench: A free, commercially representative embedded benchmark suite},</p><p> author={Guthaus, Matthew R and Ringenberg, Jeffrey S and Ernst, Dan and Austin, Todd M and Mudge, Trevor and Brown, Richard B},</p><p> booktitle={4th IEEE international workshop on workload characterization},</p><p> year={2001}</p><p>}</p>
"An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model" train and test data
<ul><li>Model for the article "An efficient ptychography reconstruction strategy through fine-tuning of large pre-trained deep learning model".</li><li>The .pth file is the pre-trained PtyNet-S model and the fine-tuned PtyNet-B model.</li><li>Please contact panxy@ihep.ac.cn if you have any questions.</li></ul>
Supplementary material of article "Stem-loop-induced ribosome queuing in the uORF2/ATF4 overlap fine-tunes stress-induced human ATF4 translational control"
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Protein sequences for Dephosphorylation sites and fine-tuning notebook
<p>Protein sequences for Dephosphorylation sites</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.