Surface Current Analysis from High Frequency Radar Measurement off the Coast of Yantai, China

Remote Sensing and Field Monitorin

CTRL: Connect Tabular and Language Model for CTR Prediction

Traditional click-through rate (CTR) prediction models convert the tabular data into one-hot vectors and leverage the collaborative relations among features for inferring user's preference over items. This modeling paradigm discards the essential semantic information. Though some recent works like P5 and M6-Rec have explored the potential of using Pre-trained Language Models (PLMs) to extract semantic signals for CTR prediction, they are computationally expensive and suffer from low efficiency. Besides, the beneficial collaborative relations are not considered, hindering the recommendation performance. To solve these problems, in this paper, we propose a novel framework \textbf{CTRL}, which is industrial friendly and model-agnostic with high training and inference efficiency. Specifically, the original tabular data is first converted into textual data. Both tabular data and converted textual data are regarded as two different modalities and are separately fed into the collaborative CTR model and pre-trained language model. A cross-modal knowledge alignment procedure is performed to fine-grained align and integrate the collaborative and semantic signals, and the lightweight collaborative model can be deployed online for efficient serving after fine-tuned with supervised signals. Experimental results on three public datasets show that CTRL outperforms the SOTA CTR models significantly. Moreover, we further verify its effectiveness on a large-scale industrial recommender system

MicroRNA expression profiling in human acute organophosphorus poisoning and functional analysis of dysregulated miRNAs

Objective: Acute organophosphorus(OP) pesticide poisoning is associated with dysfunctions in multiple organs, especially skeletal muscles, the nervous system and the heart. However, little is known about the specific microRNA (miRNA) changes that control the pathophysiological processes of acute OP poisoning damage. We aimed to explore miRNA expression profiles and gain insight into molecular mechanisms of OP toxic effects.Methods: MicroRNA expression was analyzed by TaqMan Human MicroRNA Array analysis and subsequent validated with quantitive PCR. The targets of the significantly different miRNAs were predicted with miRNA prediction databases, and pathway analysis of the predicted target genes was performed using bioinformatics resources.Results: 37 miRNAs were significantly different in the sera of poisoned patients compared to the healthy controls, including 29 miRNAs that were up-regulated and 8 miRNAs that were down-regulated. Functional analysis indicated that many pathways potentially regulated by these miRNAs are involved in skeletal muscle, nervous system and heart disorders.Conclusion: This study mapped changes in the serum miRNA expression profiles of poisoning patients and predicted functional links between miRNAs and their target genes in the regulation of acute OP poisoning. Our findings are an important resource for further understanding the role of these miRNAs in the regulation of OP-induced injury.Keywords: MicroRNA, expression profiles, human, acute organophosphorus pesticide poisoning, signaling pathways

LanPose: Language-Instructed 6D Object Pose Estimation for Robotic Assembly

Comprehending natural language instructions is a critical skill for robots to cooperate effectively with humans. In this paper, we aim to learn 6D poses for roboticassembly by natural language instructions. For this purpose, Language-Instructed 6D Pose Regression Network (LanPose) is proposed to jointly predict the 6D poses of the observed object and the corresponding assembly position. Our proposed approach is based on the fusion of geometric and linguistic features, which allows us to finely integrate multi-modality input and map it to the 6D pose in SE(3) space by the cross-attention mechanism and the language-integrated 6D pose mapping module, respectively. To validate the effectiveness of our approach, an integrated robotic system is established to precisely and robustly perceive, grasp, manipulate and assemble blocks by language commands. 98.09 and 93.55 in ADD(-S)-0.1d are derived for the prediction of 6D object pose and 6D assembly pose, respectively. Both quantitative and qualitative results demonstrate the effectiveness of our proposed language-instructed 6D pose estimation methodology and its potential to enable robots to better understand and execute natural language instructions.Comment: 8 page