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- An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning398
An end-to-end automatic CDB tuning system, CDBTune, using deep reinforcement learning (RL), which enables end- to-end learning and accelerates the convergence speed of the model and improves efficiency of online tuning.
- Rational Design and Synthesis of Novel Dual PROTACs for Simultaneous Degradation of EGFR and PARP189
Gefitinib, olaparib, and CRBN or VHL E3 ligands were used as substrates to synthesize novel dual PROTACs that successfully degraded both the epidermal growth factor receptor (EGFR) and poly(ADP-ribose) polymerase (PARP) simultaneously in cancer cells.
- Healthcare workers’ stress when caring for COVID-19 patients: An altruistic perspective174
A low stress level among healthcare workers indicated their professional devotion and altruism during COVID-19 epidemic, which will be a lesson to other countries that awaking healthcare workers’ inside motivation and providing necessary support from government and society were significant.
- Influence of perceived stress and workload on work engagement in front‐line nurses during COVID‐19 pandemic168
Front‐line nurses perceived low stress and workload, but high work engagement, especially in self‐dedication, however, infection control, PPE discomfort and frustration were negatively associated with nurses’Work engagement, while mental demand and good performance were positively associated with Nurses’ work engagement.
- Emergency management of nursing human resources and supplies to respond to coronavirus disease 2019 epidemic108
The emergency management of nursing human resources and material resources for COVID-19 of the hospital is successful, but several deficiencies were identified, which indicated that the hospital needs to establish an efficient emergency management system and pay attention to the practice of nursing emergency plans to enhance coping capacities in public health emergencies.
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- Combination of Cloud Computing and Internet of Things (IOT) in Medical Monitoring Systems85
This paper study and analyze the application of cloud computing and the Internet of Things on the field of medical environment and proposes the hospital medical information service cloud system monitoring and management application.
- HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements71
An online CDB tuning system called HUNTER, which uses samples generated by Genetic Algorithm to warm-start the finer grained exploration of deep reinforcement learning, and employs Principal Component Analysis, Random Forest, and Fast Exploration Strategy to reduce the search space and the update time of the learning model.
- Learning Where to Focus for Efficient Video Object Detection70
A novel module called Learnable Spatio-Temporal Sampling (LSTS) has been proposed to learn semantic-level correspondences among adjacent frame features accurately and achieves state-of-the-art performance on the ImageNet VID dataset with less computational complexity and real-time speed.
- Deep Self-taught Hashing for Image Retrieval68
A deep self-taught hashing (DSTH) framework to combine deep structures with hashing to improve the retrieval performance by automatically learning robust visual features and hash functions and applies CNNs and Multi-layer Perceptron under deep learning scheme.
- Mining high utility itemsets by dynamically pruning the tree structure67
This paper proposes an efficient concurrent algorithm, called CHUI-Mine (Concurrent High Utility Itemsets Mine), for mining high utility itemsets by dynamically pruning the tree structure, and implements dynamicCHUI-Tree pruning, and discusses the rationality thereof.
- An Evolutionary Algorithm Based on Cloud Model64
The successful integrating of the cloud model and evolution principle expands the research fields of Cloud Theory and also indicates a new way for the research of evolutionary computation.
- Human emotion recognition with a microcomb‐enabled integrated optical neural network54
The proposed MIONN provides a high-speed and energy-efficient neuromorphic computing hardware for deep learning models with emotional interaction capabilities for fast and low-power computing.
- Fast Graph Convolution Network Based Multi-label Image Recognition via Cross-modal Fusion54
Experimental results demonstrate the MFB component efficiently fuses image representations and label co-occurrence embeddings and thus greatly improves the convergence efficiency of the model and proposes F-GCN, a fast graph convolution network (GCN) based multi-label image recognition model.
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- DeepIndex for Accurate and Efficient Image Retrieval44
This work attempts to introduce deep features into inverted index based image retrieval and proposes the DeepIndex framework, which finds the optimal integration of one midlevel deep feature and one high- level deep feature, from two different CNN architectures separately.
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- UniNet: Unified Architecture Search with Convolution, Transformer, and MLP40
This work studies the learnable combination of convolution, transformer, and MLP by proposing a novel unified architecture search approach, and proposes context-aware downsampling modules (DSMs) to mitigate the gap between the different types of operators.
- Unsupervised deep hashing with node representation for image retrieval40
NRDH, an unsupervised method with extensive experiments on CIFAR-10, MS-COCO and FLICKR25K show NRDH can achieve higher performance and outperform the state-of-the-art un supervised deep hashing methods.
- Label-Attended Hashing for Multi-Label Image Retrieval34
A Label-Attended Hashing (LAH) algorithm is proposed which enables an end-to-end hash model with inter-dependency feature extraction and the usage of co-occurrence relationship and MFB not only promotes the precision of hash codes but also accelerates the hash learning.
- Exploring Hierarchical Information in Hyperbolic Space for Self-Supervised Image Hashing33
This paper proposes a new self-supervised hashing method named Hierarchical Hyperbolic Contrastive Hashing (HHCH), making breakthroughs in three aspects, including to embed continuous hash codes into hyperbolic space for accurate semantic expression and to exploit the hierarchical semantic structures in hyperbolic space.
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- Context-Aware Temporal Knowledge Graph Embedding33
This work decomposes the target fact into two parts, relation and entities, and measures the usefulness of a context for each part respectively, and adopts deep neural networks to encode contexts and score the temporal consistency.
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 2026-10-16. 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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