Data Set Information: Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
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Open Dataset: Mobility Scenario FIMU
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Several datasets are fostering innovation in higher-level functions for everyone, everywhere. By providing this repository, we hope to encourage the research community to focus on hard problems. In this repository, we present the real results severity (BIRADS) and pathology (post-report) classifications provided by the Radiologist Director from the Radiology Department of Hospital Fernando Fonseca while diagnosing several patients (see dataset-uta4-dicom) from our User Tests and Analysis 4 (UTA4) study. Here, we provide a dataset for the measurements of both severity (BIRADS) and pathology classifications concerning the patient diagnostic. Work and results are published on a top Human-Computer Interaction (HCI) conference named AVI 2020 (page). Results were analyzed and interpreted from our Statistical Analysis charts. The user tests were made in clinical institutions, where clinicians diagnose several patients for a Single-Modality vs Multi-Modality comparison. For example, in these t
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FinBench is a benchmark for evaluating the performance of machine learning models with both tabular data inputs and profile text inputs.
The data used in - "Radio Galaxy Zoo EMU: Towards a Semantic Radio Galaxy Morphology Taxonomy" (Bowles et al. submitted) - "A New Task: Deriving Semantic Class Targets for the Physical Sciences" (Bowles et al. 2022: https://arxiv.org/abs/2210.14760) accepted at the Fifth Workshop on Machine Learning and the Physical Sciences, Neural Information Processing Systems 2022.
This dataset was acquired in a retrospective study from a cohort of pediatric patients admitted with abdominal pain to Children’s Hospital St. Hedwig in Regensburg, Germany. Multiple abdominal B-mode ultrasound images were acquired for most patients, with the number of views varying from 1 to 15. The images depict various regions of interest, such as the abdomen’s right lower quadrant, appendix, intestines, lymph nodes and reproductive organs. Alongside multiple US images for each subject, the dataset includes information encompassing laboratory tests, physical examination results, clinical scores, such as Alvarado and pediatric appendicitis scores, and expert-produced ultrasonographic findings. Lastly, the subjects were labeled w.r.t. three target variables: diagnosis (appendicitis vs. no appendicitis), management (surgical vs. conservative) and severity (complicated vs. uncomplicated or no appendicitis). The study was approved by the Ethics Committee of the University of Regensburg (
Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. However, there is a noticeable absence of research on applying GNNs to supply chain networks. Supply chain networks are inherently graphlike in structure, making them prime candidates for applying GNN methodologies. This opens up a world of possibilities for optimizing, predicting, and solving even the most complex supply chain problems. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problem using GNNs. To address the issue, we present a real-world benchmark dataset for temporal tasks, obtained from one of the leading FMCG companies in Bangladesh, focusing on supply chain planning for production purposes. The dataset includes temporal data as node features to enable sales predictions, production planning, and the identification of fact
In our benchmark WHYSHIFT, we explore distribution shifts on 5 real-world tabular datasets from the economic and traffic sectors with natural spatiotemporal distribution shifts.We only pick 7 typical settings out of 22 settings and select only one representative target domain for each setting. In our benchmark, we specify the distribution shift pattern for each setting, and we provide the tools to identify risky regions with large $Y|X$ shifts and to diagnose the performance degradation.