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Dataset results
225 results for “GIST”
DA-TCA test data sets (KIAPS-GIST)
<p><strong>About the data sets:</strong></p> <p>The test datasets from SMOS/ASCAT will be utilized to validate the efficacy of SMAP data assimilation in numerical weather forecasting. This project is being conducted in collaboration with the Korea Institute of Atmospheric Prediction Systems (KIAPS) and the Gwangju Institute of Science and Technology (GIST) in South Korea.</p> <p><strong>About the project:</strong></p> <p>Soil moisture assimilation from satellites can significantly improve weather prediction accuracy by providing valuable information about the moisture content in the soil. When integrated into numerical weather forecasting models, satellite-derived soil moisture data enhances the representation of land surface processes and interactions between the atmosphere and the Earth's surface.</p> <p>Here's how soil moisture assimilation benefits weather prediction:</p> <ol> <li> <p>Initialization of Models: Accurate initial conditions are crucial for reliable weather forecasting. Satellite-derived soil moisture data serves as an important source of information for initializing models. By incorporating this data, forecast models can start with more realistic representations of the state of the land surface, enabling a better starting point for predictions.</p> </li> <li> <p>Land-Atmosphere Interaction: Soil moisture plays a significant role in land-atmosphere interactions. It influences the partitioning of energy, the transfer of moisture, and the formation of clouds and precipitation. Assimilating satellite-derived soil moisture data allows forecast models to better capture these interactions and improve the representation of feedback mechanisms between the land surface and the atmosphere.</p> </li> <li> <p>Precipitation Forecasting: Soil moisture assimilation also enhances precipitation forecasting. The availability of accurate soil moisture data helps in understanding soil moisture-atmosphere feedback processes, which impact the formation, intensity, and movement of precipitation systems. By incorporating this information into forecasting models, more accurate precipitation predictions can be made.</p> </li> <li> <p>Drought and Flood Monitoring: Satellite-derived soil moisture data aids in monitoring and predicting droughts and floods. Real-time updates of soil moisture conditions can be integrated into forecasting models, allowing for timely and accurate assessments of soil moisture deficits or surpluses. This information is crucial for managing water resources, agriculture, and mitigating the impacts of extreme weather events.</p> </li> </ol> <p><strong>Related code in Github</strong>:</p> <p>https://github.com/Hyunglok-Kim/HydroAI/blob/main/Ex_in_TCA.ipynb</p>
Rechallenge of Imatinib in GIST Having no Effective Treatment: RIGHT
ClinicalTrials.gov study NCT01151852. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Phase 3 Study of DCC-2618 vs Placebo in Advanced GIST Patients Who Have Been Treated With Prior Anticancer Therapies
ClinicalTrials.gov study NCT03353753. IPD Sharing: NO. Countries: 13. Publications: 3.
(VOYAGER) Study of Avapritinib vs Regorafenib in Patients With Locally Advanced Unresectable or Metastatic GIST
ClinicalTrials.gov study NCT03465722. IPD Sharing: Not stated. Countries: 18. Publications: 3.
Study of Regorafenib as a 3rd-line or Beyond Treatment for Gastrointestinal Stromal Tumors (GIST)
ClinicalTrials.gov study NCT01271712. IPD Sharing: Not stated. Countries: 17. Publications: 4.
Phase Ib Study of SUnitinib Alternating With REgorafenib in Patients With Metastatic and/or Unresectable GIST
ClinicalTrials.gov study NCT02164240. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Ripretinib vs Sunitinib in Advanced GIST Patients After Treatment With Imatinib
ClinicalTrials.gov study NCT03673501. IPD Sharing: NO. Countries: 22. Publications: 5.
A Phase II Trial of the DNA Methyl Transferase Inhibitor, Guadecitabine (SGI-110), in Children and Adults With Wild Type GIST,Pheochromocytoma and Paraganglioma Associated With Succinate Dehydrogenase
ClinicalTrials.gov study NCT03165721. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study To Assess The Safety And Efficacy Of SU11248 In Patients With Gastrointestinal Stromal Tumor(GIST)
ClinicalTrials.gov study NCT00075218. IPD Sharing: Not stated. Countries: 11. Publications: 1.
Study of Imatinib and Peginterferon α-2b in Gastrointestinal Stromal Tumor (GIST) Patients
ClinicalTrials.gov study NCT00585221. IPD Sharing: Not stated. Countries: 1. Publications: 1.
BGJ398 in Combination With Imatinib Mesylate in Patients With Untreated Advanced Gastrointestinal Stromal Tumor (GIST)
ClinicalTrials.gov study NCT02257541. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Registry For Temsirolimus, Sunitinib, And Axitinib Treated Patients With Metastatic Renal Cell Carcinoma (mRCC), Mantle Cell Lymphoma (MCL), And Gastro-Intestinal Stroma Tumor (GIST) [STAR-TOR]
ClinicalTrials.gov study NCT00700258. IPD Sharing: NO. Countries: 1. Publications: 3.
Study of Dose Escalation Versus no Dose Escalation of Imatinib in Metastatic Gastrointestinal Stromal Tumors (GIST) Patients
ClinicalTrials.gov study NCT01031628. IPD Sharing: Not stated. Countries: 1. Publications: 2.
(NAVIGATOR) Study of BLU-285 in Patients With Gastrointestinal Stromal Tumors (GIST) and Other Relapsed and Refractory Solid Tumors
ClinicalTrials.gov study NCT02508532. IPD Sharing: Not stated. Countries: 10. Publications: 6.
Five Year Adjuvant Imatinib Mesylate (Gleevec®) in Gastrointestinal Stromal Tumor (GIST)
ClinicalTrials.gov study NCT00867113. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Biological Activity of Cediranib (AZD2171) in Gastro-Intestinal Stromal Tumours(GIST).
ClinicalTrials.gov study NCT00385203. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Nilotinib Versus Imatinib in GIST Patients
ClinicalTrials.gov study NCT00785785. IPD Sharing: Not stated. Countries: 35. Publications: 1.
GIST: Generated Inputs Sets Transferability in Deep Learning (Part 2)
<p>Part2 of the Replication Package for the paper "GIST: Generated Inputs Sets Transferability in Deep Learning"</p> <p>Contains RoBERTa models and data for the KMNC property.</p> <p>Github link: https://github.com/FlowSs/GIST</p> <p>Part1 can be found here: https://zenodo.org/records/10028594</p> <p>Abstract:</p> <p> To foster the verifiability and testability of Deep Neural Networks (DNN), an increasing number of methods<br>for test case generation techniques are being developed.<br> When confronted with testing DNN models, the user can apply any existing test generation technique.<br>However, it needs to do so for each technique and each DNN model under test, which can be expensive.<br>Therefore, a paradigm shift could benefit this testing process: rather than regenerating the test set independently<br>for each DNN model under test, we could transfer from existing DNN models.<br> This paper introduces GIST (Generated Inputs Sets Transferability), a novel approach for the efficient<br>transfer of test sets. Given a property selected by a user (e.g., neurons covered, faults), GIST enables the<br>selection of good test sets from the point of view of this property among available test sets. This allows the<br>user to recover similar properties on the transferred test sets as he would have obtained by generating the<br>test set from scratch with a test cases generation technique. Experimental results show that GIST can select<br>effective test sets for the given property to transfer. Moreover, GIST scales better than reapplying test case<br>generation techniques from scratch on DNN models under test.</p>
Study of Crenolanib for the Treatment of Patients With Advanced GIST With the D842-related Mutations and Deletions in the PDGFRA Gene
ClinicalTrials.gov study NCT01243346. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Dose-finding Study of a Combination of Imatinib and BYL719 in the Treatment of 3rd Line GIST Patients
ClinicalTrials.gov study NCT01735968. IPD Sharing: NO. Countries: 8. Publications: 1.
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International Brain Laboratory public data
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OpenNeuro
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