Machine Learning

This dataset contains high-resolution wind measurement data collected from 22 channels at varying heights, providing valuable insights for wind energy assessment, atmospheric research, and meteorological studies. The dataset includes wind speed, wind direction, and environmental parameters measured at multiple altitudes ranging from 10m to 120m. Each channel records parameters such as average wind speed, standard deviation, minimum and maximum values, gust speed, and wind vane direction. Additionally, atmospheric parameters such as temperature, relative humidity, and pressure are included.

<p>This meteorological data is provided by the Inner Mongolia Meteorological Bureau and includes data from three stations.

<p>This meteorological data is provided by the Inner Mongolia Meteorological Bureau and includes data from three stations.

We present a dataset of histopathology images from OSCC patients treated at Sun Yat-sen Memorial Hospital (2015–2022). Each case includes two tissue sections (core and boundary), with six images per patient captured at ×200, ×400, and ×1000 magnifications (2592×1944 pixels). Key histopathological features—such as cancer cells, nests, keratin pearls, nuclear atypia, and necrosis—are included. The study was approved by the Ethics Committee with a waiver of informed consent, and patient-level diagnosis and prognosis annotations were obtained from electronic records.

Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a smaller language model (student) from a larger one (teacher). Previous research has introduced several distillation methods for both generating training data and training the student model.

Artificial Intelligence (AI) has increasingly influenced modern society, recently in particular through significant advancements in Large Language Models (LLMs). However, high computational and storage demands of LLMs still limit their deployment in resource-constrained environments. Knowledge distillation addresses this challenge by training a smaller language model (student) from a larger one (teacher). Previous research has introduced several distillation methods for both generating training data and training the student model.

Shape completion remains a fundamental challenge in computer vision and image processing, particularly for tasks involving hand-drawn sketches and occluded objects. Traditional deep learning methods such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) often suffer from high computational costs and poor generalization on sparse, abstract structures.

This dataset is designed for the classification of textual transcriptions of spoken conversations in Shanghai dialect and Mandarin Chinese. It consists of high-quality, manually transcribed texts from natural dialogues, annotated with corresponding language labels (Shanghai dialect: 1, Mandarin: 0). The dataset aims to facilitate research in text-based dialect classification, natural language processing (NLP), and linguistic variation analysis.

• This dataset comprises license plate information captured by Automatic Number Plate Recognition (ANPR) devices as vehicles either entered or left the smart village area of Alpujarra, which encompasses the towns of Pampaneira, Capileira, and Bubión.
• The sensor network includes four Hikvision IP cameras that use deep learning-based ANPR technology to cover all vehicular movements.