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Found 7 papers, 3 papers with code
Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion
Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion.
WLC-Net: a robust and fast deep-learning wood-leaf classification method
Wood-leaf classification is an essential and fundamental prerequisite in the analysis and estimation of forest attributes from terrestrial laser scanning (TLS) point clouds, including critical measurements such as diameter at breast height(DBH), above-ground biomass(AGB), wood volume. To address this, we introduce the Wood-Leaf Classification Network(WLC-Net), a deep learning model derived from PointNet++, designed to differentiate between wood and leaf points within tree point clouds. WLC-Net enhances classification accuracy, completeness, and speed by incorporating linearity as an inherent feature, refining the input-output framework, and optimizing the centroid sampling technique. WLC-Net was trained and assessed using three distinct tree species datasets, comprising a total of 102 individual tree point clouds:21 Chinese ash trees, 21 willow trees, and 60 tropical trees. For comparative evaluation, five alternative methods, including PointNet++, DGCNN, Krishna Moorthy's method, LeWoS, and Sun's method, were also applied to these datasets. The classification accuracy of all six methods was quantified using three metrics:overall accuracy(OA), mean Intersection over Union(mIoU), and F1-score. Across all three datasets, WLC-Net demonstrated superior performance, achieving OA scores of 0. 9778, 0. 9712, and 0. 9508;mIoU scores of 0. 9761, 0. 9693, and 0. 9141;and F1-scores of 0. 8628, 0. 7938, and 0. 9019, respectively. The time costs of WLC-Net were also recorded to evaluate the efficiency. The average processing time was 102. 74s per million points for WLC-Net. In terms of visual inspect, accuracy evaluation and efficiency evaluation, the results suggest that WLC-Net presents a promising approach for wood-leaf classification, distinguished by its high accuracy.
Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain
While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms.
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
The advent of Large Language Models (LLMs) has paved the way for complex tasks such as role-playing, which enhances user interactions by enabling models to imitate various characters.
Query2GMM: Learning Representation with Gaussian Mixture Model for Reasoning over Knowledge Graphs
Research along this line suggests that using multi-modal distribution to represent answer entities is more suitable than uni-modal distribution, as a single query may contain multiple disjoint answer subsets due to the compositional nature of multi-hop queries and the varying latent semantics of relations.
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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Optimal configuration of cooperative stationary and mobile energy storage considering ambient temperature: A case for Winter Olympic Game
The international mega-event, such as the Winter Olympic Game, has been considered as one of the most carbon intensive activities worldwide.
