由于严重的阻塞，快速身体运动和复杂的相互作用引起的歧义，多人运动捕获可能具有挑战性。现有的框架以2D姿势估算为基础，并通过推理多相机观测值的外观，轨迹和几何一致性来对3D坐标进行三角测量。但是，由于观察角有限，2D联合检测通常不完整，并且由于观察角有限而导致错误的身份分配，这会导致噪音3D三角测量结果。为了克服这个问题，我们建议使用变压器探索骨骼运动的短距离自回归特征。首先，我们提出了一个自适应，身份感知的三角剖分模块，以重建3D关节并确定每个身份的缺失关节。为了产生完整的3D骨骼运动，我们提出了一个双掩模的自动编码器（D-MAE），该自动编码器（D-MAE）用骨骼结构和时间位置编码轨迹完成的骨骼结构和时间位置编码关节状态。 D-MAE的灵活掩蔽和编码机制使任意骨骼定义可以方便地在同一框架下部署。为了证明所提出的模型在处理严重的数据丢失方案方面的能力，我们为多人相互作用与严重遮挡的高临界性和挑战性运动捕获数据集。对基准和我们的新数据集的评估都证明了我们提出的模型的效率，以及其对其他最新方法的优势。

临床前和临床领域中的结构化（表格）数据包含有关个人的有价值信息，有效的表格到文本摘要系统可以大大减少手动努力，以将该数据凝结到报告中。但是，实际上，该问题受到最先进的自然语言生成模型（包括T5，Pegasus和GPT-NEO）的数据稀疏性和无法产生准确可靠的输出的严重阻碍。在本文中，我们提出了一种新颖的桌面到文本方法，并通过新颖的两步结构解决这些问题，通过自动校正，复制机制和合成数据增强来增强这些问题。研究表明，所提出的方法从结构化数据中选择了显着的生物医学实体和值，以提高精度（最高0.13个绝对增加），以复制表格值，以生成相干和准确的文本以进行测定验证报告和毒理学报告。此外，我们还通过微调示例进行微调来展示提出的系统对新数据集的轻量重量改编。我们模型的输出在人类的场景中得到了人类专家的验证。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

随着高通量实验技术的快速发展，可以从临床样品中产生不同类型的OMIC（例如基因组学，基因组，转录组织，蛋白质组学和代谢组学）数据。不同OMICS类型之间的相关性吸引了大量的研究兴趣，而STDUY对基因组宽的OMCIS数据转换（即，来自另一种类型的OMIC数据的一种类型的OMIC数据）几乎是空白的。生成的对策网络和变体是最先进的深度学习技术之一，在这里表现出巨大的成功，在此提出的图像到图像转换等。在这里，我们提出了奥贝纳人，a深度学习框架采用了生成的对抗网络的想法，实现了具有有前途的结果的Omics-to-Omics翻译。如在实验中所证明的那样，奥硝化能够忠于从DNA甲基化数据重建从DNA甲基化数据的基因表达谱。

目的：临床票据含有其他地方未存在的信息，包括药物反应和症状，所有这些都在预测急性护理患者的关键结果时非常重要。我们提出了从临床笔记中的表型作为一种捕获基本信息的方法的自动注释，这与通常使用生命体征和实验室测试结果的互补性，以预测重症监护单元（ICU）中的结果。方法：我们开发一种新颖的表型注释模型，用于注释患者的表型特征，然后用作预测模型的输入特征，以预测ICU患者结果。我们展示并验证了我们的方法对三个ICU预测任务进行实验，包括使用MIMIC-III数据集的医院死亡率，生理失效和超过24,000名患者的逗留时间。结果：掺入表型信息的预测模型实现0.845（AUC-ROC），以预测医院死亡率，0.839（AUC-ROC）的生理失代偿和0.430（Kappa），所有这些都始终胜过基线模型利用只有生命的迹象和实验室测试结果。此外，我们进行了彻底的解释性研究，表明表型在患者和队列水平方面提供了有价值的见解。结论：该方法表明表型信息是传统上使用生命体征和实验室测试结果的补充，改善了ICU中的结果的重要预测。

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance of the original dense models, but also sometimes even slightly boost the generalization performance. Theoretical understanding for such experimental observations are yet to be developed. This work makes the first attempt to study how different pruning fractions affect the model's gradient descent dynamics and generalization. Specifically, this work considers a classification task for overparameterized two-layer neural networks, where the network is randomly pruned according to different rates at the initialization. It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance. More surprisingly, the generalization bound gets better as the pruning fraction gets larger. To complement this positive result, this work further shows a negative result: there exists a large pruning fraction such that while gradient descent is still able to drive the training loss toward zero (by memorizing noise), the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. Up to our knowledge, this is the \textbf{first} generalization result for pruned neural networks, suggesting that pruning can improve the neural network's generalization.

Time-series anomaly detection is an important task and has been widely applied in the industry. Since manual data annotation is expensive and inefficient, most applications adopt unsupervised anomaly detection methods, but the results are usually sub-optimal and unsatisfactory to end customers. Weak supervision is a promising paradigm for obtaining considerable labels in a low-cost way, which enables the customers to label data by writing heuristic rules rather than annotating each instance individually. However, in the time-series domain, it is hard for people to write reasonable labeling functions as the time-series data is numerically continuous and difficult to be understood. In this paper, we propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system, which enables a user to improve the results of unsupervised anomaly detection by performing only a small amount of interactions with the system. To achieve this goal, the system integrates weak supervision and active learning collaboratively while generating labeling functions automatically using only a few labeled data. All of these techniques are complementary and can promote each other in a reinforced manner. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions in both weak supervision and active learning areas. Also, the system has been tested in a real scenario in industry to show its practicality.

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.

The task of video prediction and generation is known to be notoriously difficult, with the research in this area largely limited to short-term predictions. Though plagued with noise and stochasticity, videos consist of features that are organised in a spatiotemporal hierarchy, different features possessing different temporal dynamics. In this paper, we introduce Dynamic Latent Hierarchy (DLH) -- a deep hierarchical latent model that represents videos as a hierarchy of latent states that evolve over separate and fluid timescales. Each latent state is a mixture distribution with two components, representing the immediate past and the predicted future, causing the model to learn transitions only between sufficiently dissimilar states, while clustering temporally persistent states closer together. Using this unique property, DLH naturally discovers the spatiotemporal structure of a dataset and learns disentangled representations across its hierarchy. We hypothesise that this simplifies the task of modeling temporal dynamics of a video, improves the learning of long-term dependencies, and reduces error accumulation. As evidence, we demonstrate that DLH outperforms state-of-the-art benchmarks in video prediction, is able to better represent stochasticity, as well as to dynamically adjust its hierarchical and temporal structure. Our paper shows, among other things, how progress in representation learning can translate into progress in prediction tasks.