Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.

In this paper, we introduce a novel optimization algorithm for machine learning model training called Normalized Stochastic Gradient Descent (NSGD) inspired by Normalized Least Mean Squares (NLMS) from adaptive filtering. When we train a high-complexity model on a large dataset, the learning rate is significantly important as a poor choice of optimizer parameters can lead to divergence. The algorithm updates the new set of network weights using the stochastic gradient but with $\ell_1$ and $\ell_2$-based normalizations on the learning rate parameter similar to the NLMS algorithm. Our main difference from the existing normalization methods is that we do not include the error term in the normalization process. We normalize the update term using the input vector to the neuron. Our experiments present that the model can be trained to a better accuracy level on different initial settings using our optimization algorithm. In this paper, we demonstrate the efficiency of our training algorithm using ResNet-20 and a toy neural network on different benchmark datasets with different initializations. The NSGD improves the accuracy of the ResNet-20 from 91.96\% to 92.20\% on the CIFAR-10 dataset.

Segmenting humans in 3D indoor scenes has become increasingly important with the rise of human-centered robotics and AR/VR applications. In this direction, we explore the tasks of 3D human semantic-, instance- and multi-human body-part segmentation. Few works have attempted to directly segment humans in point clouds (or depth maps), which is largely due to the lack of training data on humans interacting with 3D scenes. We address this challenge and propose a framework for synthesizing virtual humans in realistic 3D scenes. Synthetic point cloud data is attractive since the domain gap between real and synthetic depth is small compared to images. Our analysis of different training schemes using a combination of synthetic and realistic data shows that synthetic data for pre-training improves performance in a wide variety of segmentation tasks and models. We further propose the first end-to-end model for 3D multi-human body-part segmentation, called Human3D, that performs all the above segmentation tasks in a unified manner. Remarkably, Human3D even outperforms previous task-specific state-of-the-art methods. Finally, we manually annotate humans in test scenes from EgoBody to compare the proposed training schemes and segmentation models.

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.

在科学计算的许多领域越来越流行的人工神经网络（ANN）的大量使用迅速增加了现代高性能计算系统的能源消耗。新型的神经形态范式提供了一种吸引人的替代方案，它直接在硬件中实施了ANN。但是，对于科学计算中用例使用ANN在神经形态硬件上运行ANN的实际好处知之甚少。在这里，我们提出了一种方法，用于测量使用常规硬件的ANN来计算推理任务的时间。此外，我们为这些任务设计了一个体系结构，并根据最先进的模拟内存计算（AIMC）平台估算了相同的指标，这是神经形态计算中的关键范例之一。在二维凝结物质系统中的量子多体物理学中的用例比较两种方法，并在粒子物理学中大型强子对撞机上以40 MHz的速率以40 MHz的速率进行异常检测。我们发现，与传统硬件相比，AIMC最多可以达到一个较短的计算时间，最高三个数量级的能源成本。这表明使用神经形态硬件进行更快，更可持续的科学计算的潜力。

众所周知，从像素观察中进行的非质量增强学习（RL）是不稳定的。结果，许多成功的算法必须结合不同领域的实践和辅助损失，以在复杂的环境中学习有意义的行为。在这项工作中，我们提供了新颖的分析，表明这些不稳定性是通过卷积编码器和低质量奖励进行时间差异学习而产生的。我们表明，这种新的视觉致命三合会导致不稳定的训练和过早的融合归化解决方案，这是一种现象，我们将灾难性的自相传为。基于我们的分析，我们提出了A-LIX，这是一种为编码器梯度提供适应性正则化的方法，该梯度明确防止使用双重目标防止灾难性的自我抗辩发生。通过应用A-LIX，我们在DeepMind Control和Atari 100K基准测试方面显着优于先前的最先进，而无需任何数据增强或辅助损失。

深度展开是一种基于深度学习的图像重建方法，它弥合了基于模型和纯粹的基于深度学习的图像重建方法之间的差距。尽管深层展开的方法实现了成像问题的最新性能，并允许将观察模型纳入重建过程，但它们没有提供有关重建图像的任何不确定性信息，这严重限制了他们在实践中的使用，尤其是用于安全 - 关键成像应用。在本文中，我们提出了一个基于学习的图像重建框架，该框架将观察模型纳入重建任务中，并能够基于深层展开和贝叶斯神经网络来量化认知和核心不确定性。我们证明了所提出的框架在磁共振成像和计算机断层扫描重建问题上的不确定性表征能力。我们研究了拟议框架提供的认知和态度不确定性信息的特征，以激发未来的研究利用不确定性信息来开发更准确，健壮，可信赖，不确定性，基于学习的图像重建和成像问题的分析方法。我们表明，所提出的框架可以提供不确定性信息，同时与最新的深层展开方法实现可比的重建性能。

本文为我们最近在端到端优化的层次阶段性视频压缩方面提供了改进和新颖的补充，以进一步推进学到的视频压缩中的最新时间。作为改进，我们将运动估计和预测模块结合在一起，并压缩精制的残留运动向量，以提高速率延伸性能。作为新颖的添加，我们将提出的图像压缩的增益单元改编为柔性率视频压缩以两种方式：首先，增益单元使单个编码器模型能够以多速度距离操作点运行；其次，我们利用增益单元来控制内部编码与双向编码框架之间的位分配，通过微调相应的模型，用于真正的灵活率学习的视频编码。实验结果表明，我们获得的最先进的利率延伸性能超过了学到的视频编码中所有先前艺术的效果。

在本文中，我们提出了一个神经端到端系统，用于保存视频的语音，唇部同步翻译。该系统旨在将多个组件模型结合在一起，并以目标语言的目标语言与目标语言的原始扬声器演讲的视频与目标语音相结合，但在语音，语音特征，面对原始扬声器的视频中保持着重点。管道从自动语音识别开始，包括重点检测，然后是翻译模型。然后，翻译后的文本由文本到语音模型合成，该模型重新创建了原始句子映射的原始重点。然后，使用语音转换模型将结果的合成语音映射到原始扬声器的声音。最后，为了将扬声器的嘴唇与翻译的音频同步，有条件的基于对抗网络的模型生成了相对于输入面图像以及语音转换模型的输出的适应性唇部运动的帧。最后，系统将生成的视频与转换后的音频结合在一起，以产生最终输出。结果是一个扬声器用另一种语言说话的视频而不真正知道。为了评估我们的设计，我们介绍了完整系统的用户研究以及对单个组件的单独评估。由于没有可用的数据集来评估我们的整个系统，因此我们收集了一个测试集并在此测试集上评估我们的系统。结果表明，我们的系统能够生成令人信服的原始演讲者的视频，同时保留原始说话者的特征。收集的数据集将共享。

卷积一直是现代深层神经网络的核心运作。众所周知，可以在傅立叶变换域中实现卷积。在本文中，我们建议使用二进制块WALSH-HATAMARD变换（WHT）而不是傅里叶变换。我们使用基于WHT的二进制层来替换深度神经网络中的一些常规卷积层。我们本文利用了一维（1-D）和二维（2-D）二进制WHT。在两个1-D和2-D层中，我们计算输入特征图的二进制WHT，并使用非线性去噪该WHT域系数，该非线性通过将软阈值与TanH函数组合而获得的非线性。在去噪后，我们计算反相WHT。我们使用1d-wht来取代$ 1 \ times 1 $卷积层，2d-wht层可以取代3 $ \ times $ 3卷积层和挤压和激发层。具有可培训重量的2D-WHT层也可以在全局平均池（间隙）层之前插入以辅助致密层。通过这种方式，我们可以显着降低可训练参数的衡量参数的数量。在本文中，我们将WHT层实施到MobileNet-V2，MobileNet-V3大，并重新阅读，以显着降低参数的数量，以可忽略不计的精度损失。此外，根据我们的速度测试，2D-FWWHT层的运行大约是常规3美元3美元3美元的速度大约为19.51次较少的RAM使用率在NVIDIA Jetson Nano实验中的使用率。