在本文中，我们介绍链接，一个1亿个自由度平面连锁机制和11亿个耦合器曲线的数据集，其比任何现有的平面机制数据库大1000倍以上，并且不仅限于特定种类的机制，例如作为四杆，六个栏，\ etc，通常是大多数数据库所包含的内容。链接由各种组件组成，包括1亿个机制，每种机制的仿真数据，每种机制生成的标准化路径，一组策划的路径，用于生成数据和模拟机制的代码以及用于交互式的实时Web演示连锁机制的设计。提供了策划的路径作为消除通过机制生成的路径中的偏差的量度，从而使设计空间表示更加均匀。在本文中，我们讨论了如何生成如此大的数据集以及如何通过此类量表克服重大问题的细节。为了能够生成如此大的数据集，我们介绍了一个新的操作员来生成1-DOF机制拓扑，此外，我们采取了许多步骤来加快机制的慢速模拟，并在大量线程中并行模拟器并行将模拟器并行处理。导致模拟的速度比简单的模拟算法快800倍。这是平均必须给出的，生成的500名候选者中有1个是有效的〜（所有必须模拟以确定其有效性），这意味着必须对本数据集的生成进行数十亿个模拟。然后，我们通过基于双向倒角距离的形状检索研究来证明数据集的深度，在该研究中，我们显示如何直接使用数据集来找到可以非常接近所需目标路径的路径的机制。

结构拓扑优化旨在找到最大化机械性能的最佳物理结构，在航空航天，机械和土木工程中的工程设计应用中至关重要。生成对抗网络（GAN）最近成为传统迭代拓扑优化方法的流行替代品。但是，这些模型通常很难训练，具有有限的概括性，并且由于它们的目标是模仿最佳拓扑，忽视生产性和诸如机械合规性之类的性能目标。我们提出了TopoDiff，这是一种有条件的基于扩散模型的体系结构，以执行克服这些问题的性能感知和可制造性感的拓扑优化。我们的模型介绍了基于替代模型的指导策略，该策略积极利用依从性低和良好的制造性的结构。我们的方法通过将物理性能的平均误差降低了8倍，并且产生的不可行样本少11倍，从而极大地超过了最先进的条件gan。通过将扩散模型引入拓扑优化，我们表明条件扩散模型也具有在工程设计合成应用中的表现。我们的工作还提出了使用扩散模型以及外部性能和约束意识指导的工程优化问题的一般框架。

由于其学习能力和模仿复杂的数据分布的能力，深层生成机器学习模型（DGM）在整个设计社区的流行一直在越来越受欢迎。 DGM经过常规培训，以最大程度地减少分布与生成数据的分布与对其训练的数据集的分布之间的统计差异。尽管足以生成“现实”的假数据的任务，但该目标通常不足以设计综合任务。相反，设计问题通常要求遵守设计要求，例如性能目标和约束。在工程设计中推进DGM需要新的培训目标，以促进工程设计目标。在本文中，我们介绍了第一个同时优化性能，可行性，多样性和目标成就的深层生成模型。我们在八个评估指标上针对几个深层生成模型的拟议方法的性能进行了基准性能，这些模型着重于设计性能目标的可行性，多样性和满意度。在具有挑战性的多目标自行车框架设计问题上测试了方法，并具有偏斜的不同数据类型的多模式数据。在八个指标中的六个指标中，提出的框架被发现胜过所有深层生成模型。

This paper demonstrates how Automated Machine Learning (AutoML) methods can be used as effective surrogate models in engineering design problems. To do so, we consider the challenging problem of structurally-performant bicycle frame design and demonstrate across-the-board dominance by AutoML in regression and classification surrogate modeling tasks. We also introduce FRAMED -- a parametric dataset of 4500 bicycle frames based on bicycles designed by practitioners and enthusiasts worldwide. Accompanying these frame designs, we provide ten structural performance values such as weight, displacements under load, and safety factors computed using finite element simulations for all the bicycle frame designs. We formulate two challenging test problems: a performance-prediction regression problem and a feasibility-prediction classification problem. We then systematically search for optimal surrogate models using Bayesian hyperparameter tuning and neural architecture search. Finally, we show how a state-of-the-art AutoML method can be effective for both regression and classification problems. We demonstrate that the proposed AutoML models outperform the strongest gradient boosting and neural network surrogates identified through Bayesian optimization by an improved F1 score of 24\% for classification and reduced mean absolute error by 12.5\% for regression. Our work introduces a dataset for bicycle design practitioners, provides two benchmark problems for surrogate modeling researchers, and demonstrates the advantages of AutoML in machine learning tasks. The dataset and code are provided at \url{http://decode.mit.edu/projects/framed/}.

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

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.

Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue's future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

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.