张量分解因其在多维数据中捕获潜在因素的固有能力而获得了越来越多的兴趣，该数据具有许多应用程序，例如推荐系统和电子健康记录（EHR）挖掘。已经提出了Parafac2及其变体来解决不规则的张量，其中一种张量模式不对齐，例如，EHR中推荐系统或患者的不同用户可能具有不同的记录。 PARAFAC2已成功应用于EHRS，用于提取有意义的医学概念（表型）。尽管有最近的进步，但当前模型的可预测性和可解释性并不令人满意，这限制了其用于下游分析的效用。在本文中，我们提出了多个多任务学习的多个监督不规则张量分解。多个多个可以灵活地包含静态（例如，院内死亡率预测）和连续或动态（例如，通风的需求）任务。通过通过下游预测任务监督张量分解并利用来自多个相关预测任务的信息，Multipar不仅可以产生更有意义的表型，而且可以为下游任务提供更好的预测性能。我们在两个现实世界中的EHR数据集上进行了广泛的实验，以证明Multipar是可扩展的，并且与现有的最新方法相比，具有更有意义的亚组和更强的预测性能，可以更好地张紧张量。

许多现代机器学习算法，例如生成的对抗网络（GANS）和对抗性培训可以制定为最低限度优化。梯度下降上升（GDA）是由于其简单性导致的最常用的算法。但是，GDA可以收敛到非最佳Minimax点。我们提出了一个新的最低限度优化框架GDA-AM，将GDadynamics视为固定点迭代，并使用Anderson混合来解决局部imemax。它解决了同时GDA的发散问题加速了交替GDA的收敛性。我们从理论上显示了该算法可以在温和条件下实现Bilinear问题的全局收敛性。我们还经验证明GDA-AMSOLVES各种极少问题，并改善了几个数据集的GaN训练

手语制作（SLP）旨在将语言的表达方式转化为手语的相应语言，例如基于骨架的标志姿势或视频。现有的SLP型号是自动回旋（AR）或非自动入口（NAR）。但是，AR-SLP模型在解码过程中遭受了回归对均值和误差传播的影响。 NSLP-G是一种基于NAR的模型，在某种程度上解决了这些问题，但会带来其他问题。例如，它不考虑目标符号长度，并且会遭受虚假解码启动的影响。我们通过知识蒸馏（KD）提出了一种新型的NAR-SLP模型，以解决这些问题。首先，我们设计一个长度调节器来预测生成的符号姿势序列的末端。然后，我们采用KD，该KD从预训练的姿势编码器中提取空间语言特征以减轻虚假解码的启动。广泛的实验表明，所提出的方法在特里切特的手势距离和背面翻译评估中都显着优于现有的SLP模型。

唇裂是一种先天性异常，需要专家手术修复。外科医生必须具有丰富的经验和理论知识才能进行手术，并且已经提出了人工智能（AI）方法来指导外科医生改善手术结局。如果可以使用AI来预测修复的唇唇的外观，那么外科医生可以将其用作辅助手术技术来调整其手术技术并改善结果。为了在保护患者隐私时探索这个想法的可行性，我们提出了一种基于深度学习的图像镶嵌方法，该方法能够覆盖唇裂，并产生唇彩，而无需裂缝。我们的实验是在两个现实世界中的裂口数据集上进行的，并由专家cleft唇外科医生评估，以证明该方法的可行性。

目的：目的是将先前验证的深度学习算法应用于新的甲状腺结节超声图像数据集，并将其性能与放射科医生进行比较。方法：先前的研究提出了一种能够检测甲状腺结节，然后使用两个超声图像进行恶性分类的算法。从1278个结节训练了多任务深度卷积神经网络，最初用99个单独的结节进行了测试。结果与放射科医生相当。与培训案例相比，使用来自不同制造商和产品类型的超声计算机成像的378个结节进一步测试了该算法。要求四名经验丰富的放射科医生评估结节，以与深度学习进行比较。结果：用参数，二维估计计算了深度学习算法和四个放射科医生的曲线（AUC）面积。对于深度学习算法，AUC为0.70（95％CI：0.64-0.75）。放射科医生的AUC为0.66（95％CI：0.61-0.71），0.67（95％CI：0.62-0.73），0.68（95％CI：0.63-0.73）和0.66（95％CI：95％CI：0.61-0.71）。结论：在新的测试数据集中，深度学习算法与所有四个放射科医生都达到了类似的性能。

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.