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9,882 results for “Scale”
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_1 (4), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 1 (4) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_1 (3), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 1 (3) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_6 (4), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 6 (4) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_6 (3), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 6 (3) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (3), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (3) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (4), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (4) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (2), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (2) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
【outputs_best_number_tokens_to_span_vectors_large_scales】/best_number_tokens_log_part_11 (1), only two 8B LLMs, 30 (span_vectors) = 30 files
<ul> <li>Part 11 (1) </li> <li>only two 8B LLMs, </li> <li>only valid results (both MP p value and CI),</li> <li>all permutation controls,</li> <li>all seeds,</li> <li>all linguistic spans,</li> <li>all 5 tasks, </li> <li>30 (span_vectors) = 30 files</li> </ul>
Maps for Human-scale Accessibility in London Central Activity Zone
<p>This dataset is the result of individual research aimed at developing a new metric, Human-Scale Accessibility (HSA), to assess the level of 15-minute cities locally. The basic unit of the dataset is streets, with HSA values calculated for all streets, along with the contributing variables.</p>
Multi-scale lead-lag relationship between the stock and futures markets: New evidence from Malaysia based on 15-minute intraday data
<p>KCOM: FBM KLCI(15 Minutes)<br> KLINF1: KLOFFE FUTURE(15 Minutes)</p> <p>From 15/04/2009 to 12/01/2021</p>
FIGURE 5 in Cissococcus Cockerell (Hemiptera: Coccoidea: Coccidae), a unique gall-inducing soft scale genus on Vitaceae from South Africa, with description of a new species
FIGURE 5. Cissococcus fulleri Cockerell, third-instar female. Lettering as for Figure 4.
FIGS 1 in An unusual genus and species of Encyrtidae (Hymenoptera: Chalcidoidea) from Australia reared from soft scale insects (Hemiptera: Coccidae)
FIGS 1±3. Arketypon vaderi. (1) Habitus /; (2) head, frontal view, /; (3) head, side view, /.
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
<p>Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf</p> <p>In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:</p> <ul> <li><a href="https://zenodo.org/deposit/7262581">labeled data (500CT+100MRI)</a></li> <li><a href="https://zenodo.org/record/7262757#.Y2iSQ9JBwYs">unlabeled data Part I (900CT)</a></li> <li><a href="https://zenodo.org/record/7295661#.Y2iR_9JBwYs">unlabeled data Part II (1100CT)</a> (Now there are 1000CT, we will replenish to 1100CT)</li> <li><a href="https://zenodo.org/record/7295816">unlabeled data Part III (1200MRI)</a></li> </ul> <p>if you found this dataset useful for your research, please cite:</p> <blockquote> <pre>@inproceedings{NEURIPS2022_ee604e1b, author = {Ji, Yuanfeng and Bai, Haotian and GE, Chongjian and Yang, Jie and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhanng, Lingyan and Ma, Wanling and Wan, Xiang and Luo, Ping}, booktitle = {Advances in Neural Information Processing Systems}, editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh}, pages = {36722--36732}, publisher = {Curran Associates, Inc.}, title = {AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation}, url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/ee604e1bedbd069d9fc9328b7b9584be-Paper-Datasets_and_Benchmarks.pdf}, volume = {35}, year = {2022} } </pre> </blockquote> <p> </p>
Dataset related to: "Determinants of improvement in functional abilities in peripheral neuropathy patients undergoing rehabilitation: the role of Berg Balance Scale items"
<p>We provide the raw data used for the following article:</p> <p>Gialanella B, Comini L, Prometti P. <strong>Determinants of improvement in functional abilities in peripheral neuropathy patients undergoing rehabilitation: the role of Berg Balance Scale items</strong>. Acta Neurol Belg. 2023 Feb;123(1):173-179. doi: 10.1007/s13760-021-01806-y. </p> <p>Abstract<br> Purpose This prospective observational cohort study aimed to verify whether single Berg Balance Scale (BBS) items were<br> important determinants of improvement in functional abilities in patients with peripheral neuropathy (PN).<br> Methods Data were collected in 75 older patients with PN performing a standard motor rehabilitation program. Backward<br> stepwise multiple regression analyses were performed to identify determinants of outcome measures. Gain in total and motor<br> Functional Independence Measure (FIM) and gain in motor-FIM areas were the outcome measures.<br> Results Among BBS items, at the end of rehabilitation, the score was higher in "sitting unsupported" (3.94 ± 0.22) and lower<br> in "standing on one foot" (1.18 ± 1.06). The gain was higher in "standing to sitting" (1.02 ± 0.67) and "standing unsupported"<br> (1.00 ± 1.00), and lower in "sitting unsupported" (0.22 ± 0.60) and "standing on one foot" (0.65 ± 0.77). "Standing unsupported"<br> was the only determinant of gain in motor-FIM (beta − 0.36, p = 0.002) and gain in self-care (beta − 0.37, p = 0.002).<br> The R2 value of the models was 0.13 and 0.14, respectively. No independent variable was a determinant of gain in total-FIM.<br> Conclusions The study shows that "unsupported standing" is an important determinant of results of ADL rehabilitation in<br> PN patients and indicates that ability to stand without support for a fairly long time is an essential requirement to achieve<br> with rehabilitation higher gain levels in functional abilities in PN patients.</p>
STAVER: A Standardized Dataset-Based Algorithm for Efficient Variation Reduction in Large-Scale DIA MS Data
<p>This project focuses on the development and application of STAVER, a standardized dataset-based algorithm designed to reduce variation in large-scale data-independent acquisition (DIA) mass spectrometry data. By using a reference dataset to standardize mass spectrometry signals, STAVER effectively reduces noise and enhances protein quantification accuracy, especially in the context of multi-library search. The effectiveness of STAVER is demonstrated in several large-scale DIA datasets, showing improved identification and quantification of thousands of proteins. The project aims to promote the adoption of multi-library search and improve the quality of DIA proteomics data through the open-source STAVER software package.</p>
Cross-cultural Validation of the Arabic 10-Factors Big Five Scale Using Factor Analysis
<p>In Arab World, the big five aspect scale (BFAS) with 10 factors domain currently lacks validating evidence. The BFAS structure was investigated in Libyan adult sample (N = 1163). The sample completed the BFAS, and the International Personality Item Pool (IPIP) to assess the BFAS validity. Results found that The BFAS Conscientiousness, Extraversion, Introversion Openness/Intellect are correlated positively with IPIP Conscientiousness, Extraversion, Introversion, Intellect, The BFAS Neuroticism was negatively correlated with IPIP emotional stability. Results found that the BFAS scales includes its 10 domains, has good internal consistency, and is strongly related to major personality dimensions assessed by the leading questionnaire. To conclude, the findings indicate that the Arabic BFAS is reliable, valid, and comparable to the original English version. This study provides valuable insights into an individual's personality and behavior, which can have implications for personal, professional, and clinical contexts.</p>
Estimated seismicity of Venusian wrinkle ridges based on fault scaling relationships
<p>These files contain all lengths of wrinkle ridges from Bilotti & Suppe (1999), and the code I used to calculate scalar moment release from these faults. We apply segmentation and apply a maximum vertical slip extent. We calculate a moment release rate, or annual moment release, by summing the scalar moments and dividing by a deformation time. The fault lengths are in the last (3rd) column of the Excel file (wrinkleridges_midXY_frank.xlsx) in meters.</p> <p>Please see this reference for more information:<br> Sabbeth, L., Smrekar, S.E., and Stock, J.M. "Estimated seismicity of Venusian wrinkle ridges based on fault scaling<br> relationships." <em>Earth and Planetary Science Letters</em>, 619 (2023): 118308. https://doi.org/10.1016/j.epsl.2023.118308.</p> <p>We develop the method in this paper: https://doi.org/10.1029/2022JE007686</p>
Codes for A hidden connection of meso-scale convective rainfall and large-scale circulations over the eastern Pacific
<h4><strong>README</strong></h4><h4><strong>System requirments</strong></h4><ul><li><strong>Figure 1, ED_Figure_1, and ED_FIGURE_3</strong>: IDL (v8.5) on Linux OS</li><li><strong>Figure 3, Figure 4, ED_Figure_4, and ED_FIGURE_5</strong>: Python (v3.7.3) on Linux OS</li><li><strong>Figure 2 and ED_Figure_2</strong>: Sigmaplot (v10.0)</li></ul><h4><strong>Installation guide</strong></h4><p>The modules needed to run are in each directory. Each code requires execution time of less than 1 minute.</p><h4><strong>Demo</strong></h4><p>Sample data, code, and expected output are located in their repective directories.</p><h4><strong>Instruction for use</strong></h4><p>IDL code) type <i>idl</i> to enter IDL mode</p><p>idl> .r (figure name).pro </p><p>Python code) </p><p>> python (figurename).py</p>
MR2-Net: Retinal OCTA Image Stitching via Multi-Scale Representation Learning and Dynamic Location Guidance
<p>These datasets are released for academic research use only.</p>
In vivo organization of bacterial transcription initiation complexes at a genome-scale [RNA-seq]
GEO Series GSE54405. Escherichia coli str. K-12 substr. MG1655. 2 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
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.