Previous work has shown that a neural network with the rectified linear unit (ReLU) activation function leads to a convex polyhedral decomposition of the input space. These decompositions can be represented by a dual graph with vertices corresponding to polyhedra and edges corresponding to polyhedra sharing a facet, which is a subgraph of a Hamming graph. This paper illustrates how one can utilize the dual graph to detect and analyze adversarial attacks in the context of digital images. When an image passes through a network containing ReLU nodes, the firing or non-firing at a node can be encoded as a bit ($1$ for ReLU activation, $0$ for ReLU non-activation). The sequence of all bit activations identifies the image with a bit vector, which identifies it with a polyhedron in the decomposition and, in turn, identifies it with a vertex in the dual graph. We identify ReLU bits that are discriminators between non-adversarial and adversarial images and examine how well collections of these discriminators can ensemble vote to build an adversarial image detector. Specifically, we examine the similarities and differences of ReLU bit vectors for adversarial images, and their non-adversarial counterparts, using a pre-trained ResNet-50 architecture. While this paper focuses on adversarial digital images, ResNet-50 architecture, and the ReLU activation function, our methods extend to other network architectures, activation functions, and types of datasets.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

物理知识的神经网络（PINN）将问题领域的物理知识作为对损失函数的软限制，但最近的工作表明这可能导致优化困难。在这里，我们研究了搭配点的位置对这些模型训练性的影响。我们发现，随着训练的进行，可以通过适应搭配点的位置来显着提高香草·皮恩的性能。具体而言，我们提出了一种新型的自适应搭配方案，该方案逐渐将更多的搭配点（不增加数量）分配给模型正在造成更高误差的区域（基于域中损失函数的梯度）。加上在任何优化失速过程中对训练的明智重新启动（通过简单地重新采样搭配点以调整损失景观）会导致预测错误的更好估计。我们提出了一些问题的结果，包括具有不同强迫函数的2D泊松和扩散 - 辅助系统。我们发现，针对这些问题的训练香草PINN可以导致解决方案中的预测误差高达70％，尤其是在低搭配点的状态下。相比之下，我们的自适应方案可以达到较小误差的顺序，其计算复杂性与基线相似。此外，我们发现自适应方法始终如一地执行PAR或比香草Pinn方法稍好，即使对于大型搭配点方案也是如此。所有实验的代码都是开源的。

物理建模对于许多现代科学和工程应用至关重要。从数据科学或机器学习的角度来看，更多的域 - 不可吻合，数据驱动的模型是普遍的，物理知识 - 通常表示为微分方程 - 很有价值，因为它与数据是互补的，并且可能有可能帮助克服问题例如数据稀疏性，噪音和不准确性。在这项工作中，我们提出了一个简单但功能强大且通用的框架 - 自动构建物理学，可以将各种微分方程集成到高斯流程（GPS）中，以增强预测准确性和不确定性量化。这些方程可以是线性或非线性，空间，时间或时空，与未知的源术语完全或不完整，等等。基于内核分化，我们在示例目标函数，方程相关的衍生物和潜在源函数之前构建了GP，这些函数全部来自多元高斯分布。采样值被馈送到两个可能性：一个以适合观测值，另一个符合方程式。我们使用美白方法来逃避采样函数值和内核参数之间的强依赖性，并开发出一种随机变分学习算法。在模拟和几个现实世界应用中，即使使用粗糙的，不完整的方程式，自动元素都显示出对香草GPS的改进。

自动编码器已被广泛用作降低数据维度的非线性工具。虽然自动编码器不使用标签信息，但质心编码器（CE）\ cite {ghosh20222superpised}在其学习过程中使用类标签。在这项研究中，我们提出了使用Centroid-编码器结构进行稀疏优化，以确定一组最小的特征，以区分两个或多个类别。所得的算法，稀疏的质心编码器（SCE），使用稀疏性诱导$ \ ell_1 $ - norm提取歧视性特征，同时将点映射到其类质心。 SCE的一个关键属性是，它可以从多模式数据集（即其类似乎具有多个群集的数据集）中提取信息性功能。该算法应用于各种现实世界数据集，包括单细胞数据，高维生物学数据，图像数据，语音数据和加速度计传感器数据。我们将我们的方法与各种最先进的特征选择技术进行了比较，包括监督的混凝土自动编码器（SCAE），功能选择网络（FSNET），深度特征选择（DFS），随机门（STG）和Lassonet。我们从经验上表明，SCE特征通常比隔离测试集中的其他方法产生更好的分类精度。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

最近在科学机器学习的工作已经开发出所谓的物理信息的神经网络（Pinn）模型。典型方法是将物理域知识纳入经验丢失功能的软限制，并使用现有的机器学习方法来培训模型。我们展示了，虽然现有的Pinn方法可以学习良好的模型，但它们可以轻松地未能学习相关的物理现象，甚至更复杂的问题。特别是，我们分析了众多不同的普遍物理兴趣的情况，包括使用对流，反应和扩散运营商学习微分方程。我们提供了证据表明Pinns中的软正规化，涉及基于PDE的差分运营商，可以引入许多微妙的问题，包括使问题更加不良。重要的是，我们表明，这些可能的失败模式不是由于NN架构中缺乏富有效力，但Pinn的设置使得损失景观很难优化。然后，我们描述了两个有希望的解决方案来解决这些故障模式。第一种方法是使用课程正则化，其中Pinn的丢失项从简单的PDE正则化开始，并且随着NN训练而变得逐渐变得更加复杂。第二种方法是将问题构成为序列到序列的学习任务，而不是学习一次性地预测整个时空。广泛的测试表明，与常规Pinn训练相比，我们可以通过这些方法实现最多1-2个数量级。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.