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Found 6 papers, 3 papers with code
Segment Anything Model for Road Network Graph Extraction
We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) for extracting large-scale, vectorized road network graphs from satellite imagery.
StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows
In StateFlow, we distinguish between "process grounding" (via state and state transitions) and "sub-task solving" (through actions within a state), enhancing control and interpretability of the task-solving procedure.
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks
Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks.
Deep Learning for Genomics: A Concise Overview
Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines.
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction
We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction.
WristAuthen: A Dynamic Time Wrapping Approach for User Authentication by Hand-Interaction through Wrist-Worn Devices
This paper presents a novel user authentication system through wrist-worn devices by analyzing the interaction behavior with users, which is both accurate and efficient for future usage.
