Artificial Intelligence

The 3D-SPAD dataset we proposed is preprocessed and stored in the JSON file. If you want to visualize, you can visit this webpage to get the original 3D scene data.

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The raw MTBS data were downloaded from the official website (https://www.mtbs.gov/direct-download), and we select the pre-fire images, post-fire images, and thematic burn severity layers from 2010 to 2019 (over 7000 fires) across the conterminous United States as the data source of Landsat-BSA dataset. The raw MTBS dataset is preprocessed, reformed, and relabeled to produce the Landsat-BSA dataset with a sample size of 256 × 256.

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Data were obtained from the established Clinical Data Repository (CDR) of Yichang Central People's Hospital, and the master index dataset of AKI sample cases was derived by combining the inclusion-exclusion conditions of the study samples with a query of the relevant conditions in the CDR. The data from the CDR and the AKI contain protected health information and are not subject to public sharing. Currently, 500 pieces of data were randomly selected as a sample for public disclosure, details of which are attached to the paper.

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<p>we consider GPT-4 to generate dialogues according to designed causal structures. Specifically, we, for simplicity, assumed all dialogues are composed of 4 utterances rotating from 2 speakers and designed 4 causal structures to manifest different causal relationships. Along with the designed generating rules according to 4 causal structures, we adopted gpt-4-0314 and Chat Completion api to commence dialogue generation. Below is an example dialogue of the Chain IV from our dataset:
1. Have you seen a dog around here recently?

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In the context of the FLAMENCO project, we have released a dataset designed for predicting potential deficiencies in children's communication skills, tailored for Federated Learning. This dataset specifically focuses on addressing two prevalent deficiencies in communication skill development in children: autism and intellectual disability. For each deficiency, two CSV files are provided—one for training machine learning models and another for testing them. Each entry in these CSV files includes the following details:

 

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Since the majority of people have smartphones, the Hb level can be determined using the smartphone's video through PPG signal as opposed to the traditional approaches, which still require the use of a needle to puncture a vein. This study enrolled 108 subjects who underwent a clinical test, with their hemoglobin (Hb) level within the range of 6.6 to 16.5 g/dL.

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Since the majority of people have smartphones, the Hb level can be determined using the smartphone's video through PPG signal as opposed to the traditional approaches, which still require the use of a needle to puncture a vein. This study enrolled 108 subjects who underwent a clinical test, with their hemoglobin (Hb) level within the range of 6.6 to 16.5 g/dL.

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Path information in knowledge graphs can provide explicit explanations for recommendation decisions, thus becoming a focus in explainable recommendation research.Path information in knowledge graphs can provide explicit explanations for recommendation decisions, thus becoming a focus in explainable recommendation research.Path information in knowledge graphs can provide explicit explanations for recommendation decisions, thus becoming a focus in explainable recommendation research.Path information in knowledge graphs can provide explicit explanations for recommendation decisions, thus becom

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Dual-polarization (dual-pol) radar can measure additional parameters that provide more microphysical information of precipitation systems than those provided by conventional Doppler radar. The dual-pol parameters have been successfully utilized to investigate precipitation microphysics and improve radar quantitative precipitation estimation (QPE). The recent progress in dual-pol radar research and applications in China is summarized in four aspects. Firstly, the characteristics of several representative dual-pol radars are reviewed.

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Our released weight dataset for fusion results in edge-cloud collaborative inference contains the corresponding weighted summation weights under 50,000 edge-cloud collaborative DNN inference tasks, listing the five heterogeneous NVIDIA edge devices they use (NVIDIA Jetson Nano, TX2, NX, Orin NX, and AGX Orin), computing power (1.9~275TOPS), DNN model type (EfficientNet-B0, ViT-B16), and network bandwidth (0.5~8Mbps).

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