Stress has a great effect on people's lives that can not be understated. While it can be good, since it helps humans to adapt to new and different situations, it can also be harmful when not dealt with properly, leading to chronic stress. The objective of this paper is developing a stress monitoring solution, that can be used in real life, while being able to tackle this challenge in a positive way. The SMILE data set was provided to team Anxolotl, and all it was needed was to develop a robust model. We developed a supervised learning model for classification in Python, presenting the final result of 64.1% in accuracy and a f1-score of 54.96%. The resulting solution stood the robustness test, presenting low variation between runs, which was a major point for it's possible integration in the Anxolotl app in the future.

Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting large-scale robotic data is much more expensive and slower as each operator can control only a single robot at a time. To make this costly data collection process efficient and scalable, we propose Policy Assisted TeleOperation (PATO), a system which automates part of the demonstration collection process using a learned assistive policy. PATO autonomously executes repetitive behaviors in data collection and asks for human input only when it is uncertain about which subtask or behavior to execute. We conduct teleoperation user studies both with a real robot and a simulated robot fleet and demonstrate that our assisted teleoperation system reduces human operators' mental load while improving data collection efficiency. Further, it enables a single operator to control multiple robots in parallel, which is a first step towards scalable robotic data collection. For code and video results, see https://clvrai.com/pato

深度学习的成功激发了人们对大脑是否使用基于梯度的学习来学习层次结构表示的兴趣。但是，目前在深层神经网络中基于梯度的信用分配的生物学上合理的方法需要无限的小反馈信号，这在生物学上现实的嘈杂环境中是有问题的，并且与神经科学的实验证据不符，表明自上而下的反馈可以显着影响神经活动。在最近提出的一种信用分配方法的深度反馈控制（DFC）的基础上，我们结合了对神经活动的强烈反馈影响与基​​于梯度的学习，并表明这自然会导致对神经网络优化的新看法。权重更新并没有逐渐将网络权重转换为具有低输出损失的配置，而是逐渐最大程度地减少了将网络驱动到监督输出标签的控制器所需的反馈量。此外，我们表明，在DFC中使用强反馈的使用允许同时学习和反馈连接，并在时空中完全本地学习规则。我们通过对标准计算机视觉基准测试的实验来补充我们的理论结果，显示了反向传播的竞争性能以及对噪声的鲁棒性。总体而言，我们的工作提出了一种从根本上新颖的学习视图，作为控制最小化，同时避开了生物学上不切实际的假设。

在本文中，我们提出了帕托 - 一种可生产性感知拓扑优化（至）框架，以帮助有效地探索使用金属添加剂制造（AM）制造的部件的设计空间，同时确保相对于裂化的可制造性。具体地，通过激光粉末融合制造的部件由于从构建过程中产生的陡峭热梯度产生的高残余应力值而易于诸如翘曲或裂缝的缺陷。为这些零件的设计成熟并规划其制作可能跨越几年，通常涉及设计和制造工程师之间的多种切换。帕托基于先验的无裂缝设计的发现，使得优化部分可以在一开始就自由缺陷。为确保设计在优化期间无裂缝，可以在使用裂缝指数的标准制剂中明确地编码生产性。探索多个裂缝指数并使用实验验证，最大剪切应变指数（MSSI）被显示为准确的裂缝指数。模拟构建过程是耦合的多物理计算，并将其结合在循环中可以计算上禁止。我们利用了深度卷积神经网络的当前进步，并基于基于关注的U-Net架构的高保真代理模型，以将MSSI值预测为部分域上的空间变化的字段。此外，我们采用自动差异来直接计算关于输入设计变量的最大MSSI的梯度，并使用基于性能的灵敏度字段增强，以优化设计，同时考虑重量，可制造性和功能之间的权衡。我们通过3D基准研究以及实验验证来证明所提出的方法的有效性。