Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task. Methods to overcome this problem often involve robust least-squares regression, but few methods perform well when confronted with severe adaptive adversarial attacks. In many applications, prior knowledge is often available from historical data or engineering experience, and by incorporating prior information into a robust regression method, we develop an effective robust regression method that can resist adaptive adversarial attacks. First, we propose the novel TRIP (hard Thresholding approach to Robust regression with sImple Prior) algorithm, which improves the breakdown point when facing adaptive adversarial attacks. Then, to improve the robustness and reduce the estimation error caused by the inclusion of priors, we use the idea of Bayesian reweighting to construct the more robust BRHT (robust Bayesian Reweighting regression via Hard Thresholding) algorithm. We prove the theoretical convergence of the proposed algorithms under mild conditions, and extensive experiments show that under different types of dataset attacks, our algorithms outperform other benchmark ones. Finally, we apply our methods to a data-recovery problem in a real-world application involving a space solar array, demonstrating their good applicability.

Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: For this retrospective study, we collected 3,166 three-dimensional (3D) double-echo steady-state magnetic resonance (MR) images from the Osteoarthritis Initiative dataset and 50 3D turbo/fast spin-echo MR images from our institute (in 2020 and 2021) as the source and target datasets, respectively. For each patient, the degree of knee OA was initially graded according to the MRI Osteoarthritis Knee Score (MOAKS) before being converted to binary OA phenotype labels. The proposed UDA pipeline included (a) pre-processing, which involved automatic segmentation and region-of-interest cropping; (b) source classifier training, which involved pre-training phenotype classifiers on the source dataset; (c) target encoder adaptation, which involved unsupervised adaption of the source encoder to the target encoder and (d) target classifier validation, which involved statistical analysis of the target classification performance evaluated by the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity and accuracy. Additionally, a classifier was trained without UDA for comparison. Results: The target classifier trained with UDA achieved improved AUROC, sensitivity, specificity and accuracy for both knee OA phenotypes compared with the classifier trained without UDA. Conclusion: The proposed UDA approach improves the performance of automated knee OA phenotype classification for small target datasets by utilising a large, high-quality source dataset for training. The results successfully demonstrated the advantages of the UDA approach in classification on small datasets.

Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge.

Deep neural networks are vulnerable to adversarial attacks. Ideally, a robust model shall perform well on both the perturbed training data and the unseen perturbed test data. It is found empirically that fitting perturbed training data is not hard, but generalizing to perturbed test data is quite difficult. To better understand adversarial generalization, it is of great interest to study the adversarial Rademacher complexity (ARC) of deep neural networks. However, how to bound ARC in multi-layers cases is largely unclear due to the difficulty of analyzing adversarial loss in the definition of ARC. There have been two types of attempts of ARC. One is to provide the upper bound of ARC in linear and one-hidden layer cases. However, these approaches seem hard to extend to multi-layer cases. Another is to modify the adversarial loss and provide upper bounds of Rademacher complexity on such surrogate loss in multi-layer cases. However, such variants of Rademacher complexity are not guaranteed to be bounds for meaningful robust generalization gaps (RGG). In this paper, we provide a solution to this unsolved problem. Specifically, we provide the first bound of adversarial Rademacher complexity of deep neural networks. Our approach is based on covering numbers. We provide a method to handle the robustify function classes of DNNs such that we can calculate the covering numbers. Finally, we provide experiments to study the empirical implication of our bounds and provide an analysis of poor adversarial generalization.

Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologists adopt two-field fundus photography as the dominating tool, where the information from each field (i.e.,macula-centric and optic disc-centric) is highly correlated and complementary, and benefits comprehensive decisions. However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies. In this work, we first construct a new benchmark dataset (DRTiD) for DR grading, consisting of 3,100 two-field fundus images. To the best of our knowledge, it is the largest public DR dataset with diverse and high-quality two-field images. Then, we propose a novel DR grading approach, namely Cross-Field Transformer (CrossFiT), to capture the correspondence between two fields as well as the long-range spatial correlations within each field. Considering the inherent two-field geometric constraints, we particularly define aligned position embeddings to preserve relative consistent position in fundus. Besides, we perform masked cross-field attention during interaction to flter the noisy relations between fields. Extensive experiments on our DRTiD dataset and a public DeepDRiD dataset demonstrate the effectiveness of our CrossFiT network. The new dataset and the source code of CrossFiT will be publicly available at https://github.com/FDU-VTS/DRTiD.

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.

大多数图形神经网络（GNN）通过学习输入图和标签之间的相关性来预测看不见的图的标签。但是，通过对具有严重偏见的训练图进行图形分类调查，我们发现GNN始终倾向于探索伪造的相关性以做出决定，即使因果关系始终存在。这意味着在此类偏见的数据集中接受培训的现有GNN将遭受概括能力差。通过在因果观点中分析此问题，我们发现从偏见图中解开和去偏置因果和偏见的潜在变量对于偏见至关重要。在此鼓舞下，我们提出了一个普遍的分解GNN框架，分别学习因果子结构和偏见子结构。特别是，我们设计了一个参数化的边蒙版生成器，以将输入图明确分为因果和偏置子图。然后，分别由因果/偏见感知损失函数监督的两个GNN模块进行培训，以编码因果关系和偏置子图表中的相应表示。通过分离的表示，我们合成了反事实无偏的训练样本，以进一步脱离因果变量和偏见变量。此外，为了更好地基于严重的偏见问题，我们构建了三个新的图形数据集，这些数据集具有可控的偏置度，并且更容易可视化和解释。实验结果很好地表明，我们的方法比现有基线实现了优越的概括性能。此外，由于学习的边缘面膜，该拟议的模型具有吸引人的解释性和可转让性。代码和数据可在以下网址获得：https：//github.com/googlebaba/disc。

作为最成功的AI驱动应用程序之一，推荐系统的目的是通过在我们生活的许多方面提供个性化建议，以有效而有效的方式帮助人们做出适当的决定，尤其是针对各种面向人类的在线服务，例如E-商务平台和社交媒体网站。在过去的几十年中，推荐系统的快速发展通过创造经济价值，节省时间和精力以及促进社会利益，从而使人类受益匪浅。但是，最近的研究发现，数据驱动的推荐系统可能会对用户和社会构成严重威胁，例如传播虚假新闻以操纵社交媒体网站中的公众舆论，扩大不公平为代表性不足的团体或在工作匹配服务中的个人，或从建议结果中推断隐私信息。因此，系统的可信赖性一直吸引着各个方面的关注，以减轻推荐系统引起的负面影响，以增强公众对推荐系统技术的信任。在这项调查中，我们提供了可信赖的推荐系统（TREC）的全面概述，特别关注六个最重要的方面；即安全与鲁棒性，非歧视与公平，解释性，隐私，环境福祉以及问责制和可审计性。对于每个方面，我们总结了最近的相关技术，并讨论了潜在的研究方向，以帮助未来实现值得信赖的推荐系统。

在本文中，我们介绍了2022年多模式情感分析挑战（MUSE）的解决方案，其中包括Muse-Humor，Muse-Rection和Muse Surns Sub-Challenges。 2022年穆斯穆斯（Muse 2022）着重于幽默检测，情绪反应和多模式的情感压力，利用不同的方式和数据集。在我们的工作中，提取了不同种类的多模式特征，包括声学，视觉，文本和生物学特征。这些功能由Temma和Gru融合到自发机制框架中。在本文中，1）提取了一些新的音频功能，面部表达功能和段落级文本嵌入以进行准确的改进。 2）我们通过挖掘和融合多模式特征来显着提高多模式情感预测的准确性和可靠性。 3）在模型培训中应用有效的数据增强策略，以减轻样本不平衡问题并防止模型形成学习有偏见的主题字符。对于博物馆的子挑战，我们的模型获得了0.8932的AUC分数。对于Muse Rection子挑战，我们在测试集上的Pearson相关系数为0.3879，它的表现优于所有其他参与者。对于Muse Surst Sub-Challenge，我们的方法在测试数据集上的唤醒和价值都优于基线，达到了0.5151的最终综合结果。

本文研究了深入的增强学习（DRL），以解决多个无人驾驶汽车（UAV）的任务调度问题。当前的方法通常使用精确的启发式算法来解决该问题，而随着任务量表的增长，计算时间迅速增加，并且启发式规则需要手动设计。作为一种自学方法，DRL可以在没有手工设计的规则的情况下快速获得高质量的解决方案。但是，巨大的决策空间使得在大规模任务的情况下，对DRL模型的培训变得不稳定。在这项工作中，为了解决大规模的问题，我们开发了一个基于鸿沟和征服的框架（DCF），以将原始问题与任务分配和无人机路由计划子问题分配，并在上层和下层解决，分别。基于DCF，提出了双层深钢筋学习方法（DL-DRL），其中高层DRL模型被设计为将任务分配给适当的无人机和下层DRL模型[即广泛使用的注意力模型（AM）]应用于生成可行的无人机路由。由于上层模型确定了低层模型的输入数据分布，并且在培训期间通过低层模型计算其奖励，因此我们制定了交互式训练策略（ITS），其中整个训练过程由PRE组成 - 培训，强化培训和替代培训过程。实验结果表明，我们的DL-DRL胜过基于主流学习和大多数传统方法的主体，并且与最新的启发式方法[即OR-Tools]具有竞争力，尤其是在大规模问题上。通过测试针对较大较大的模型学习的模型，还可以验证DL-DRL的巨大概括性。此外，一项消融研究表明，我们的它可以达到模型性能和训练持续时间之间的妥协。