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We are pleased to submit our manuscript entitled ​​"Hetero-modal Template Guide Search Region for RGBT Tracking"​​ for consideration for publication in IEEE Transactions on Consumer Electronics. This work presents a novel framework for robust RGB-Thermal (RGBT) object tracking, addressing critical challenges in consumer electronics applications such as smart security systems, autonomous navigation, and augmented reality devices.

This dataset was synthesized through a large model. We deployed two large models, one playing the role of an elderly patient and the other playing the role of a nurse. The scenario we define is as follows: the patient lies on a deformed bed, conveying clear or unclear needs to the nurse. The nurse analyzes the patient's true needs based on their language, provides a humanized and personalized response to the patient, and issues control commands to the deformed bed. The dataset consists of two parts: patient statements - answers and patient statements - control commands.

Numerous studies have demonstrated that microbes play a vital role in human health, making the identification of potential microbe-drug associations critical for drug discovery and clinical treatment. In this manuscript, we proposed a novel prediction model named GTDEKAN by integrating an aware Transformer network with a Dual Cross-Attention (DCA) module (including a Channel Cross-Attention and a Spatial Cross-Attention) and an Enhanced Kolmogorov-Arnold Network (EKAN) to infer potential microbe-drug associations.

A dataset incldes 841 nodes. This dataset includes 841 nodes in a mobile social network, used to simulate the process of users being interconnected and influencing each other within the mobile social network. Each row of data consists of two numbers, representing the current location of the node. In the process of information dissemination, each user is a node, influenced by their neighboring nodes and also influencing those neighbors in return.