Jintai Chen

Yixuan Wu, Kaiyuan Hu, Qian Shao et al.

The advent of telemedicine represents a transformative development in leveraging technology to extend the reach of specialized medical expertise to remote surgeries, a field where the immediacy of expert guidance is paramount. However, the intricate dynamics of Operating Room (OR) scene pose unique challenges for telemedicine, particularly in achieving high-fidelity, real-time scene reconstruction and transmission amidst obstructions and bandwidth limitations. This paper introduces TeleOR, a pioneering system designed to address these challenges through real-time OR scene reconstruction for Tele-intervention. TeleOR distinguishes itself with three innovative approaches: dynamic self-calibration, which leverages inherent scene features for calibration without the need for preset markers, allowing for obstacle avoidance and real-time camera adjustment; selective OR reconstruction, focusing on dynamically changing scene segments to reduce reconstruction complexity; and viewport-adaptive transmission, optimizing data transmission based on real-time client feedback to efficiently deliver high-quality 3D reconstructions within bandwidth constraints. Comprehensive experiments on the 4D-OR surgical scene dataset demostrate the superiority and applicability of TeleOR, illuminating the potential to revolutionize tele-interventions by overcoming the spatial and technical barriers inherent in remote surgical guidance.

Hanjing Zhou, Mingze Yin, Wei Wu et al.

Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in various downstream applications. However, these works were still unable to replicate the extraordinary success of language-supervised visual foundation models due to the ineffective usage of aligned protein-text paired data and the lack of an effective function-informed pre-training paradigm. To address these issues, this paper curates a large-scale protein-text paired dataset called ProtAnno with a property-driven sampling strategy, and introduces a novel function-informed protein pre-training paradigm. Specifically, the sampling strategy determines selecting probability based on the sample confidence and property coverage, balancing the data quality and data quantity in face of large-scale noisy data. Furthermore, motivated by significance of the protein specific functional mechanism, the proposed paradigm explicitly model protein static and dynamic functional segments by two segment-wise pre-training objectives, injecting fine-grained information in a function-informed manner. Leveraging all these innovations, we develop ProtCLIP, a multi-modality foundation model that comprehensively represents function-aware protein embeddings. On 22 different protein benchmarks within 5 types, including protein functionality classification, mutation effect prediction, cross-modal transformation, semantic similarity inference and protein-protein interaction prediction, our ProtCLIP consistently achieves SOTA performance, with remarkable improvements of 75% on average in five cross-modal transformation benchmarks, 59.9% in GO-CC and 39.7% in GO-BP protein function prediction. The experimental results verify the extraordinary potential of ProtCLIP serving as the protein multi-modality foundation model.

Jintai Chen, Zhen Lin, Qiyuan Chen et al.

Tabular data from different tables exhibit significant diversity due to varied definitions and types of features, as well as complex inter-feature and feature-target relationships. Cross-dataset pretraining, which learns reusable patterns from upstream data to support downstream tasks, have shown notable success in various fields. Yet, when applied to tabular data prediction, this paradigm faces challenges due to the limited reusable patterns among diverse tabular datasets (tables) and the general scarcity of tabular data available for fine-tuning. In this study, we fill this gap by introducing a cross-table pretrained Transformer, XTFormer, for versatile downstream tabular prediction tasks. Our methodology insight is pretraining XTFormer to establish a "meta-function" space that encompasses all potential feature-target mappings. In pre-training, a variety of potential mappings are extracted from pre-training tabular datasets and are embedded into the "meta-function" space, and suited mappings are extracted from the "meta-function" space for downstream tasks by a specified coordinate positioning approach. Experiments show that, in 190 downstream tabular prediction tasks, our cross-table pretrained XTFormer wins both XGBoost and Catboost on 137 (72%) tasks, and surpasses representative deep learning models FT-Transformer and the tabular pre-training approach XTab on 144 (76%) and 162 (85%) tasks.