Artificial Intelligence
RLED contains 80,400 images and corresponding events, we utilized a photometer to continuously measure scene illumination and calculate the illumination value after attenuation at the event camera. The capture scenes included city (35.0%), suburbs (10.3%), town (14.5%), village (17.8%), and valley (22.4%). Half of the RLED frames are captured at a frame rate of 25 fps, and the other half at 10 fps. The exposure time is set to 1ms, 3ms, and 5ms based on the varying environmental illumination.
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The small rectal cancer dataset only consists of 190 MR images (including ADC, DCE, DWI and T2WI images), which can be categorized into 2 classes, i.e., T2 and T3. Fig. 2 illustrates some of the original MR images about T2 and T3 in the rectal cancer dataset. The ground-truth labels about tumor and rectum for all MR images are defined by one gastrointestinal radiologist.
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CARLA-AOD is a novel aerial object detection dataset created using the CARLA autonomous driving simulator. It comprises 2,160 images from 18 diverse urban and rural scenarios, featuring 4 vehicle categories, 24 viewpoints, and 5 scales. This dataset aims to support research in aerial object detection, offering comprehensive viewpoint coverage and rich environmental diversity to enhance model performance and generalization.
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Abstract—In massive Internet of Things (IoT) deployments,
the efficient allocation of computing resources to IoT devices
while preserving devices’ data poses a significant challenge.
This paper proposes a new online probabilistic model to address
uncertainties in demand and resource allocation for IoT
networks, where the task computing of requesting devices is
addressed by serving devices. The model incorporates uncertainty
and formulates an optimization problem, concerning available
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Biopsy information and protein Prostate-Specific Antigen (PSA) levels are the most robust information available to oncologists worldwide to diagnose and decide therapies for prostate cancer patients. However, prostate cancer presents a high risk of recurrence, and the technologies used to evaluate it demand more complex resources. This paper aims to predict Biochemical Recurrence (BCR) based on Whole Slide Images (WSI) of biopsies, Gleason scores, and PSA levels.
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<p class="MsoNormal"><span lang="EN-US">The Text2RDF dataset is primarily designed to facilitate the transformation from text to RDF. It contains 1,000 annotated text segments, encompassing a total of 7,228 triplets. Utilizing this dataset to fine-tune large language models enables the models to extract triplets from text, which can ultimately be used to construct knowledge graphs. </span></p>
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An experimental setup is developed in which shape memory alloy actuator is working againt a linear tension spring as a bias mechanism. The experimental data is collected via microcontroller-based embedded system for training and validation of a self-sensing approach for a shape memory alloy actuator. The provided dataset includes 'electrical power' in watt that is applied to actuate a spring-biased shape memory alloy actuator. measured 'electrical resistance' in ohm, resulting 'displacement' in mm and 'time' stamps in second.
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Modern deep neural networks are overparameterized and thus require data augmentation techniques to prevent over-fitting and improve generalization ability. Generative adversarial networks (GANs) are famous for generating visually realistic images. However, the generated images lack diversity and have uncertain class labels. On the other hand, recent methods mix labels proportionally to the salient region.
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This dataset contains precomputed MS-COCO and Flickr30K Faster R-CNN image features, which are all the data needed for reproducing the experiments in "Stacked Cross Attention for Image-Text Matching", our ECCV 2018 paper. We use splits produced by Andrej Karpathy.
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The ultrasound video data were collected from two sets of neck ultrasound videos of ten healthy subjects at the Ultrasound Department of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine. Each subject included video files of two groups of LSCM, LSSCap, RSCM, and RSSCap. The video format is avi.
The MRI training data were sourced from three hospitals: Longhua Hospital, Shanghai University of Traditional Chinese Medicine; Huadong Hospital, Fudan University; and Shenzhen Traditional Chinese Medicine Hospital.
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