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- Research of Target Detection and Classification Techniques Using Millimeter-Wave Radar and Vision Sensors56
A robust object detection and classification algorithm based on millimeter-wave (MMW) radar and camera fusion is proposed, which is up to 89.42% more accurate than the traditional radar signal algorithm and up to 32.76% higher than Faster R-CNN, especially in the environment of low light and strong electromagnetic clutter.
- Clinical decision support system for hypertension medication based on knowledge graph37
The proposed CDSS has functions of medication knowledge graph management and hypertension medication decision support and knowledge management with elaborate design on knowledge representation, knowledge management is intuitive and convenient.
- CP-Prompt: Composition-Based Cross-modal Prompting for Domain-Incremental Continual Learning30
A simple yet effective framework to instruct a pre-trained model to learn new domains and avoid forgetting existing feature distributions, CP-Prompt, which shows superiority compared with state-of-the-art baselines among three widely evaluated DIL tasks.
- PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning28
The proposed PM-MoE architecture integrates a mixture of personalized modules and an energy-based personalized modules denoising, enabling each client to select beneficial personalized parameters from other clients, achieving performance improvements with minimal additional training.
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- Hippocampal Subregion and Gene Detection in Alzheimer’s Disease Based on Genetic Clustering Random Forest8
A novel method to construct fusion features and a classification method based on the random forest for identifying the important features of hippocampal subregions and genes to Alzheimer’s disease groups are proposed.
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- CADA: A Flexible and Elastic DDoS Mitigation Architecture4
A genetic algorithm-based quantitative deployment algorithm is proposed that generates deployment strategies for each service chain through a fitness function that quantifies response time and resource utilization in the Cloud-native Anti-DDoS Architecture.
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- MVKTrans: Multi-View Knowledge Transfer for Robust Multiomics Classification4
The multi-view knowledge transfer learning (MVKTrans) framework is proposed, which transfers intra- and inter-omics knowledge in an adaptive manner by reviewing data heterogeneity and suppressing bias transfer, thereby enhancing classification performance.
- TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning3
TuneAgent is presented, an agentic Linux kernel tuning framework powered by rule-based reinforcement learning (RL) that formulates the kernel space as a constrained RL environment, enabling large language models to autonomously explore the kernel while enforcing valid and precise configuration modifications.
- CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model3
A novel approach to domain-specific relation extraction (DSCRE) tasks that are semantically more complex by combining LLMs with triples is introduced, based on fine-tuning open-source LLMs, such as Llama-2, ChatGLM2, and Baichuan2.
- Trustworthy Enhanced Multi-view Multi-modal Alzheimer’s Disease Prediction with Brain-wide Imaging Transcriptomics Data3
This work proposes TMM, a trusted multiview multimodal graph attention framework for AD diagnosis, using extensive brain-wide transcriptomics and imaging data and demonstrates the superiority of the method in identifying AD, EMCI, and LMCI compared to state-of-the-arts.
- DDASR: Domain-Distance Adapted Super-Resolution Reconstruction of MR Brain Images3
This framework leverages the ability to learn abstract representations of arbitrary unpaired images and adapt to the domain gap, making it feasible to identify realistic down-sampling and outperforming state-of-the-art SR approaches in both perceptual and quantitative evaluations.
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- A Module-Level Polygenic Risk Score-Based NetWAS Framework for Identifying AD Genetic Modules Mediated by Amygdala: An ADNI Study1
A module-level polygenic risk score (MPRS)-based NetWAS framework is proposed to uncover genetic modules associated with Alzheimer’s disease (AD) through the mediation of an iQT, using amygdala density as a case study to identify AD-relevant modules (ADMs) influenced by iQT-associated genetic variants.
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- FulBM: Fast Fully Batch Maintenance for Landmark-based 3-hop Cover Labeling1
FulBM is composed of two algorithms: InsBM and DelBM, which are designed to handle batch edge insertions and deletions, respectively, and is motivated by the insight that batch maintenance for edge insertions are much more time-efficient and the fact that most edge updates in the real world are incremental.
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- Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage1
Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets, and an ensemble of top SNPs, SNP-pairs and SNP- sets, whose effects have the largest neuroanatomic coverage.
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- Deep learning-enabled high-performance multiphoton fluorescence vascular imaging using clinically approved fluorescent probes–
A deep learning-based method is developed, trained on previously reported MPFI images enabled by aggregation-induced emissions luminogens nanoparticles, for high-performance MPFI using commercial q800 quantum dots and a clinically approved indocyanine green (ICG) probe.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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