以任务为导向的通信，主要是使用基于学习的联合源通道编码（JSCC），旨在通过将与任务相关的信息传输到接收方来设计通信有效的边缘推理系统。但是，只有在不引入任何冗余的情况下传输与任务相关的信息可能会导致由于渠道变化引起的学习鲁棒性问题，而JSCC将源数据直接映射到连续的通道输入符号中会对现有数字通信系统提出兼容性问题。在本文中，我们通过首先调查编码表示形式的信息性与接收到的信息失真的鲁棒性之间的固有权衡解决这两个问题，然后提出一种具有任务调制的导向的通信方案，名为Inveete Task-定向的JSCC（DT-JSCC），其中发射器将功能编码为离散表示形式，并使用数字调制方案将其传输到接收器。在DT-JSCC方案中，我们开发了一个可靠的编码框架，称为强大的信息瓶颈（rib），以改善对信道变化的稳健性，并使用变量近似来得出肋骨目标的可拖动变异上限，以克服克服相互信息的计算棘手性。实验结果表明，所提出的DT-JSCC比具有低通信延迟的基线方法更好的推理性能更好，并且由于施加的肋骨框架而表现出对通道变化的鲁棒性。

我们提供了一种差异化私有算法，用于同时生成多个任务的合成数据：边际查询和多任务机器学习（ML）。我们算法中的一个关键创新是能够直接处理数值特征的能力，与许多相关的先验方法相反，这些方法需要首先通过{binning策略}将数值特征转换为{高基数}分类特征。为了提高准确性，需要较高的分子粒度，但这会对可伸缩性产生负面影响。消除对套在一起的需求使我们能够产生合成数据，以保留大量统计查询，例如数值特征的边际和条件线性阈值查询。保留后者意味着在特定半空间上方的每个类标记的点的比例在实际数据和合成数据中都大致相同。这是在多任务设置中训练线性分类器所需的属性。我们的算法还使我们能够为混合边缘查询提供高质量的合成数据，这些数据结合了分类和数值特征。我们的方法始终比最佳可比技术快2-5倍，并在边缘查询和混合型数据集的线性预测任务方面提供了显着的准确性改进。

为了更好地利用搜索日志和建模用户的行为模式，提出了许多点击模型来提取用户的隐式交互反馈。大多数传统点击模型都是基于概率图形模型（PGM）框架，该框架需要手动设计的依赖项，并且可能会过度简化用户行为。最近，提出了基于神经网络的方法来通过增强表达能力并允许灵活的依赖性来提高用户行为的预测准确性。但是，他们仍然遭受数据稀疏性和冷启动问题的困扰。在本文中，我们提出了一个新颖的图形增强点击模型（GraphCM），用于Web搜索。首先，我们将每个查询或文档视为顶点，并分别针对查询和文档提出新颖的均匀图构造方法，以完全利用会议内和会议间信息，以解决稀疏性和冷启动问题。其次，在考试假设之后，我们分别对吸引力估计量和检查预测值进行了建模，以输出吸引力得分和检查概率，在该分数中，应用图形神经网络和邻居相互作用技术用于提取在预构建的同质图中编码的辅助信息。最后，我们将组合功能应用于将考试概率和吸引力得分整合到点击预测中。在三个现实世界会话数据集上进行的广泛实验表明，GraphCM不仅胜过了最先进的模型，而且还可以在解决数据稀疏性和冷启动问题方面取得卓越的性能。

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment. Inspired by the success of Retinal Fundus Glaucoma Challenge (REFUGE) we held previously, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus \& OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, top-10 teams were selected to the final stage. We analysis their results and summarize their methods in the paper. Since all these teams submitted their source code in the challenge, a detailed ablation study is also conducted to verify the effectiveness of the particular modules proposed. We find many of the proposed techniques are practical for the clinical diagnosis of glaucoma. As the first in-depth study of fundus \& OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will be an essential starting point for future research.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there is no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion-batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

Hybrid unmanned aerial vehicles (UAVs) integrate the efficient forward flight of fixed-wing and vertical takeoff and landing (VTOL) capabilities of multicopter UAVs. This paper presents the modeling, control and simulation of a new type of hybrid micro-small UAVs, coined as lifting-wing quadcopters. The airframe orientation of the lifting wing needs to tilt a specific angle often within $ 45$ degrees, neither nearly $ 90$ nor approximately $ 0$ degrees. Compared with some convertiplane and tail-sitter UAVs, the lifting-wing quadcopter has a highly reliable structure, robust wind resistance, low cruise speed and reliable transition flight, making it potential to work fully-autonomous outdoor or some confined airspace indoor. In the modeling part, forces and moments generated by both lifting wing and rotors are considered. Based on the established model, a unified controller for the full flight phase is designed. The controller has the capability of uniformly treating the hovering and forward flight, and enables a continuous transition between two modes, depending on the velocity command. What is more, by taking rotor thrust and aerodynamic force under consideration simultaneously, a control allocation based on optimization is utilized to realize cooperative control for energy saving. Finally, comprehensive Hardware-In-the-Loop (HIL) simulations are performed to verify the advantages of the designed aircraft and the proposed controller.