背景：基于其可变的历史视觉记录，对青少年的球形等效物进行定量预测。方法：从2019年10月到2022年3月，我们检查了来自中国成都成都6-20岁的37,586名青少年的双眼未校正视力，轴向长度，角膜曲率和轴向75,172眼。 80 \％样品由训练集和剩余的20 \％组成测试集。时间感知的长期短期记忆被用来定量预测青少年在两年半内的球形当量。结果：球形当量的测试集的平均绝对预测误差为0.273-0.257，如果我们考虑不同的历史记录和不同的预测持续时间，则从0.189-0.160到0.596-0.473。结论：时间感知时间长的短期记忆被应用于不规则采样时间序列中的时间特征，这更符合实际数据的特征，因此具有更高的适用性，并有助于较早地识别近视的进展。总体误差0.273远小于临床上可接受预测的标准，例如0.75。

深度神经网络（DNN）已在脑病变检测和分割中广泛采用。但是，在2D MRI切片中定位小病变是具有挑战性的，需要在3D上下文聚集的粒度和计算复杂性之间取得平衡。在本文中，我们提出了一种新型的视角变压器，以增强MRI特征的提取，以进行更准确的肿瘤检测。首先，所提出的变压器在3D脑扫描中收获了不同位置之间的远程相关性。其次，变压器将一堆切片功能堆叠为多个2D视图，并增强这些特征的视图，该功能大致以有效的方式实现了3D相关计算。第三，我们将提出的变压器模块部署在变压器主链中，该模块可以有效地检测到脑损伤周围的2D区域。实验结果表明，我们提出的观看式变压器在具有挑战性的大脑MRI数据集上对大脑病变检测表现良好。

基于图像补丁重建的自我监督学习方法在培训自动编码器方面取得了巨大的成功，其预训练的权重可以转移到微调图像理解的其他下游任务。但是，现有方法很少研究重建斑块的各种重要性和解剖结构的对称性，当它们应用于3D医学图像时。在本文中，我们提出了一种基于3D脑MRI分割任务的视觉变压器（VIT）的新颖的对称自动编码器（ASA）。我们猜想，强迫自动编码器恢复信息性图像区域可以收获更多的判别性表示，而不是恢复光滑的图像贴片。然后，我们采用基于梯度的指标来估计每个图像补丁的重要性。在预训练阶段，提议的自动编码器更多地注意根据梯度指标重建信息贴片。此外，我们求助于大脑结构的先验，并开发一种对称位置编码（SPE）方法，以更好地利用远距离但空间对称区域之间的相关性以获得有效的特征。实验结果表明，我们提出的细心对称自动编码器的表现优于三个大脑MRI分割基准的最先进的自我监督学习方法和医学图像分割模型。

Bimanual activities like coffee stirring, which require coordination of dual arms, are common in daily life and intractable to learn by robots. Adopting reinforcement learning to learn these tasks is a promising topic since it enables the robot to explore how dual arms coordinate together to accomplish the same task. However, this field has two main challenges: coordination mechanism and long-horizon task decomposition. Therefore, we propose the Mixline method to learn sub-tasks separately via the online algorithm and then compose them together based on the generated data through the offline algorithm. We constructed a learning environment based on the GPU-accelerated Isaac Gym. In our work, the bimanual robot successfully learned to grasp, hold and lift the spoon and cup, insert them together and stir the coffee. The proposed method has the potential to be extended to other long-horizon bimanual tasks.

因果发现旨在从观察数据中学习因果图。迄今为止，大多数因果发现方法需要将数据存储在中央服务器中。但是，数据所有者逐渐拒绝分享他们的个性化数据以避免隐私泄漏，使这项任务通过切断第一步来更加麻烦。出现拼图：$ \ texit {如何从分散数据的原因关系推断出来自分散数据的因果关系？} $本文，具有数据的添加性噪声模型假设，我们参加了开发基于渐变的学习框架命名为DAG共享的渐变学习框架联邦因果发现（DS-FCD），可以在不直接触摸本地数据的情况下学习因果图，并自然地处理数据异质性。 DS-FCD受益于每个本地模型的两级结构。第一级别学习因果图并与服务器通信以获取来自其他客户端的模型信息，而第二级别近似于因果机制，并且从其自身的数据逐步更新以适应数据异质性。此外，DS-FCD通过利用平等的非循环性约束，将整体学习任务制定为连续优化问题，这可以通过梯度下降方法自然地解决。对合成和现实世界数据集的广泛实验验证了所提出的方法的功效。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.