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In this work, we provide new, tighter proofs for the $T_{\mathit{RH}}$-transformation by Jiang et al. (ASIACRYPT 2023), which converts OW-CPA secure PKEs into KEMs with IND-1CCA security, a variant of typical IND-CCA security where only a single decapsulation ...${}_{\mathit{}}$$\mathcal{}\mathrm{}$$\mathcal{}$$\mathcal{}{}^{\mathrm{}}$${}_{}$${}_{}$${}_{\mathit{}}$

With the rapid development of Internet of Things (IoT) technology, the deep integration of IoT devices and social networks has given rise to the field of Social Internet of Things (SIoT), where the significance of SIoT service recommendation is ...

DNA methylation is a crucial epigenetic modification that can influence gene expression by adding methyl groups to DNA without altering its sequence. The primary types include 4-methylcytosine (4mC), 5-hydroxymethylcytosine (5hmC), and N6-methyladenosine ...

Deep learning-based object detectors often demand abundant annotated data for training. However, in practice, only limited training data are available, making Few-Shot Object Detection (FSOD) an attractive research topic. Existing two-stage ...

• We identified comparison mechanisms as the bottleneck in few-shot object detection.
• We proposed two contrast blocks for training and testing, unlike previous approaches.
• We based on our blocks to create a new state-of-the-art few-...

Visible–infrared person re-identification (VI Re-ID) is an essential and challenging task. Existing studies mainly focus on learning the unified modality-invariant representations directly from visible and infrared images. However, it is hard to ...

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• A novel attention-guided cross-modality correlation approach for VI Re-ID.
• A modality-aware attention mechanism is utilized to mine modality-shared regions and discriminative patterns.
• An attention-guided channel and spatial ...

The intelligent soft robot has received wide attention from both academia and the industry due to its remarkable adaptability. It performs intelligent behavioral learning and evolved morphologies in unpredictable environmental conditions. However, ...

Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional ...

Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of ...$$

Interpretability is essential to MRI brain studies relying on deep learning models for neuroscientific discovery. One way to facilitate the interpretability of a deep learning model is to ensure the samples are arranged in the model’s latent space ...

Cancer is a complex genomic mutation disease, and identifying cancer driver genes promotes the development of targeted drugs and personalized therapies. The current computational method takes less consideration of the relationship among features and the ...

Direction of arrival (DOA) estimation is widely used in many applications. Traditional DOA estimation generally adopts the second-order statistics and the uniform linear array (ULA) structure. However, the second-order statistics and the uniform array ...

Gene mutations are crucial in cancer development. We introduce a method called Diffusion Graph Contrastive Learning (DGCL) to accurately identify cancer driver genes. This approach involves generating a diffusion network from protein-protein ...

Accurately predicting drug response is crucial for personalized cancer treatment. Current graph neural network methods mainly focus on pairwise relationships between cell lines and drugs, neglecting higher-order interactions. In this work, we ...

Predicting patient responses to anticancer drugs is essential for the development of effective treatment strategies. Tumors, intricate structures comprised of diverse cell types, exhibit considerable cellular heterogeneity. Leveraging single-cell ...

Crystal-Dilithium is a post-quantum signature scheme and has earned recognition as one of NIST’s selected digital signature standards. Compared with other candidates, Dilithium is well-suited for hardware implementation, primarily due to its ...

In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is proposed. Firstly, upon completion of the arrangement of network nodes, network clustering is ...

The data-driven modular deep fuzzy model has demonstrated excellent forecasting performance due to its clear architecture and powerful fuzzy inference ability. However, the fixed structure predesigned for specific types of datasets limits the ...