Artificial Intelligence
We released TrafficLLM's training datasets, which contain over 0.4M traffic data and 9K human instructions for LLM adaptation across different traffic analysis tasks.
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The painting style data sets were constructed by searching, selecting and collecting the public painting works on the internet, treating the painting style and artists' names as keywords. The data set collected 750 painting works in all, including five kinds of styles. They were receptively Cubism, Op Art, Color Field Painting, Post Impressionism and Rococo.
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Amid global climate change, rising atmospheric methane (CH4) concentrations significantly influence the climate system, contributing to temperature increases and atmospheric chemistry changes. Accurate monitoring of these concentrations is essential to support global methane emission reduction goals, such as those outlined in the Global Methane Pledge targeting a 30% reduction by 2030. Satellite remote sensing, offering high precision and extensive spatial coverage, has become a critical tool for measuring large-scale atmospheric methane concentrations.
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Computational experiments within metaverse service ecosystems enable the identification of social risks and governance crises, and the optimization of governance strategies through counterfactual inference to dynamically guide real-world service ecosystem operations. The advent of Large Language Models (LLMs) has empowered LLM-based agents to function as autonomous service entities capable of executing diverse service operations within metaverse ecosystems, thereby facilitating the governance of metaverse service ecosystem with computational experiments.
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Laboratory experiments are fundamental to science education, yet resource constraints often limit students’ access to hands-on learning experiences. While object detection technology offers promising solutions for automated material identification and assistance, existing datasets like CABD (21 classes) and Chemical Experiment Image Dataset (7 classes) are limited in scope. We present two comprehensive datasets for laboratory material detection: a Chemistry dataset comprising 1,191 images across 60 classes and a Physics dataset containing 1,749 images across 76 classes.
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This is the sample data from a switched-capacitor single-input multiple-output (SC-SIMO) converter, which can be utilized to train an artificial neural network (ANN) model. In the dataset, the current references IL3ref, IL2ref, IL1ref, and IL0ref are recorded and applied to the switched-capacitor single-input multiple-output (SC-SIMO) converter, and the introduced inductor currents IL3, IL2, IL1, and IL0 are recorded. During the ANN training process, the inductor currents are considered as the inductor currents references, which are the four inputs of the ANN model.
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Ensemble clustering, which integrates multiple base clusterings to enhance robustness and accuracy, is commonly evaluated on over 10 benchmark datasets. These include 6 synthetic datasets (e.g., 3MC,atom,Chainlink,Flame,Jain,wingnut) designed to test algorithms on nonlinear separability and density variations.
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This research introduces a novel text-to-speech (TTS) system for the endangered Sylheti Nagri language, spoken primarily in the Sylhet region of Bangladesh. The Sylheti Nagri script, dating back to the 1500s, is largely obsolete today, replaced by the Bangla script. To aid in its preservation, we present the Sylheti Nagri TTS Corpus, a dataset containing over 15 hours of audio recordings, including 8,268 sentences spoken by a professional voice artist.
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The Forbes 2022 Billionaires List dataset contains information about the world's wealthiest individuals, including their net worth, industry, country, and key business ventures. The dataset provides structured details such as rankings, company associations, and financial status, making it useful for various NLP tasks like table-to-text generation, entity recognition, and financial analysis.
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To establish a versatile RSFM adaptable to diverse tasks, RingMoE requires a comprehensive and diverse pre-training dataset that accounts for significant variations in imaging modalities, spatial resolutions, temporal dynamics, geographic regions, and scene complexities. To meet this challenge, we curate RingMOSS, a large-scale multi-modal RS dataset comprising 400 million images from nine satellite platforms, covering a broad spectrum of Earth observation scenarios.
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