AutoEncoder技术在减少秩序建模中发现越来越常见的用途作为创建潜在空间的手段。这种缩小的订单表示为与时间序列预测模型集成时的非线性动力系统提供了模块化数据驱动建模方法。在这封信中，我们提出了一个非线性适当的正交分解（POD）框架，它是一个端到端的Galerkin的模型，组合AutoEncoders，用于动态的长短期内存网络。通过消除由于Galerkin模型的截断导致的投影误差，所提出的非流体方法的关键推动器是在POD系数的全级扩展和动态发展的潜空间之间的非线性映射的运动结构。我们测试我们的模型减少对流主导系统的框架，这通常是针对减少订单模型的具有挑战性。我们的方法不仅提高了准确性，而且显着降低了培训和测试的计算成本。

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.

Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.

Recent advances in deep learning (dl) have led to the release of several dl software libraries such as pytorch, Caffe, and TensorFlow, in order to assist machine learning (ml) practitioners in developing and deploying state-of-the-art deep neural networks (DNN), but they are not able to properly cope with limitations in the dl libraries such as testing or data processing. In this paper, we present a qualitative and quantitative analysis of the most frequent dl libraries combination, the distribution of dl library dependencies across the ml workflow, and formulate a set of recommendations to (i) hardware builders for more optimized accelerators and (ii) library builder for more refined future releases. Our study is based on 1,484 open-source dl projects with 46,110 contributors selected based on their reputation. First, we found an increasing trend in the usage of deep learning libraries. Second, we highlight several usage patterns of deep learning libraries. In addition, we identify dependencies between dl libraries and the most frequent combination where we discover that pytorch and Scikit-learn and, Keras and TensorFlow are the most frequent combination in 18% and 14% of the projects. The developer uses two or three dl libraries in the same projects and tends to use different multiple dl libraries in both the same function and the same files. The developer shows patterns in using various deep-learning libraries and prefers simple functions with fewer arguments and straightforward goals. Finally, we present the implications of our findings for researchers, library maintainers, and hardware vendors.

从不同扫描仪/部位的有丝分裂数字的检测仍然是研究的重要主题，这是由于其潜力协助临床医生进行肿瘤分级。有丝分裂结构域的概括（MIDOG）2022挑战旨在测试从多种扫描仪和该任务的多种扫描仪和组织类型中看不见数据的检测模型的鲁棒性。我们提供了TIA中心团队采用的方法来应对这一挑战的简短摘要。我们的方法基于混合检测模型，在该模型中，在该模型中进行了有丝分裂候选者，然后被深度学习分类器精炼。在训练图像上的交叉验证在初步测试集上达到了0.816和0.784的F1得分，这证明了我们模型可以从新扫描仪中看不见的数据的普遍性。

肿瘤浸润淋巴细胞（TIL）的定量已被证明是乳腺癌患者预后的独立预测因子。通常，病理学家对含有tils的基质区域的比例进行估计，以获得TILS评分。乳腺癌（Tiger）挑战中肿瘤浸润淋巴细胞旨在评估计算机生成的TILS评分的预后意义，以预测作为COX比例风险模型的一部分的存活率。在这一挑战中，作为Tiager团队，我们已经开发了一种算法，以将肿瘤与基质与基质进行第一部分，然后将肿瘤散装区域用于TILS检测。最后，我们使用这些输出来生成每种情况的TILS分数。在初步测试中，我们的方法达到了肿瘤 - 细胞瘤的加权骰子评分为0.791，而淋巴细胞检测的FROC得分为0.572。为了预测生存，我们的模型达到了0.719的C索引。这些结果在老虎挑战的初步测试排行榜中获得了第一名。

病理诊所中癌症的诊断，预后和治疗性决策现在可以基于对多吉吉像素组织图像的分析，也称为全斜图像（WSIS）。最近，已经提出了深层卷积神经网络（CNN）来得出无监督的WSI表示。这些很有吸引力，因为它们不太依赖于繁琐的专家注释。但是，一个主要的权衡是，较高的预测能力通常以解释性为代价，这对他们的临床使用构成了挑战，通常通常期望决策中的透明度。为了应对这一挑战，我们提出了一个基于Deep CNN的手工制作的框架，用于构建整体WSI级表示。基于有关变压器在自然语言处理领域的内部工作的最新发现，我们将其过程分解为一个更透明的框架，我们称其为手工制作的组织学变压器或H2T。基于我们涉及各种数据集的实验，包括总共5,306个WSI，结果表明，与最近的最新方法相比，基于H2T的整体WSI级表示具有竞争性能，并且可以轻松用于各种下游分析任务。最后，我们的结果表明，H2T框架的最大14倍，比变压器模型快14倍。

本文提出了一种新的方法，用于可重新配置智能表面（RIS）和发射器 - 接收器对的联合设计，其作为一组深神经网络（DNN）培训，以优化端到端通信性能接收者。 RIS是一种软件定义的单位单元阵列，其可以根据散射和反射轮廓来控制，以将来自发射机的传入信号集中到接收器。 RIS的好处是通过克服视线（LOS）链路的物理障碍来提高无线通信的覆盖率和光谱效率。 RIS波束码字（从预定义的码本）的选择过程被配制为DNN，而发射器 - 接收器对的操作被建模为两个DNN，一个用于编码器（在发射器）和另一个一个用于AutoEncoder的解码器（在接收器处），通过考虑包括由in之间引起的频道效应。底层DNN共同训练，以最小化接收器处的符号误差率。数值结果表明，所提出的设计在各种基线方案中实现了误差性能的主要增益，其中使用了没有RIS或者将RIS光束的选择与发射器 - 接收器对的设计分离。

核毒素和eosin染色组织学图像中的核分段，分类和定量使得能够提取可解释的细胞基特征，该特征可用于计算病理（CPATH）中的下游可解释模型。然而，对不同核的自动识别面临着主要的挑战，因为有几种不同类型的核，其中一些呈现出大的内部变异性。为了帮助推动CPATH中自动核认可的前进研究和创新，我们组织了结肠核识别和计数（圆锥）挑战。挑战鼓励研究人员开发在CPATH中，在CPATH中，在CPATH中进行当前最大已知的公知的核级数据集进行分割，分类和计数，其中包含大约一半的标记的核。因此，锥形挑战利用核数量超过10倍的核，作为核识别的前一大挑战数据集。如果我们希望在临床环境中部署它们，则对输入变体具有强大的算法很重要。因此，作为这一挑战的一部分，我们还将测试每个提交算法对某些输入变化的敏感性。