The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.

Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most co-design frameworks either explore a limited search space or employ suboptimal exploration techniques for simultaneous design decision investigations of the ML model and the accelerator. Furthermore, training the ML model and simulating the accelerator performance is computationally expensive. To address these limitations, this work proposes a novel neural architecture and hardware accelerator co-design framework, called CODEBench. It is composed of two new benchmarking sub-frameworks, CNNBench and AccelBench, which explore expanded design spaces of convolutional neural networks (CNNs) and CNN accelerators. CNNBench leverages an advanced search technique, BOSHNAS, to efficiently train a neural heteroscedastic surrogate model to converge to an optimal CNN architecture by employing second-order gradients. AccelBench performs cycle-accurate simulations for a diverse set of accelerator architectures in a vast design space. With the proposed co-design method, called BOSHCODE, our best CNN-accelerator pair achieves 1.4% higher accuracy on the CIFAR-10 dataset compared to the state-of-the-art pair, while enabling 59.1% lower latency and 60.8% lower energy consumption. On the ImageNet dataset, it achieves 3.7% higher Top1 accuracy at 43.8% lower latency and 11.2% lower energy consumption. CODEBench outperforms the state-of-the-art framework, i.e., Auto-NBA, by achieving 1.5% higher accuracy and 34.7x higher throughput, while enabling 11.0x lower energy-delay product (EDP) and 4.0x lower chip area on CIFAR-10.

Robots have been steadily increasing their presence in our daily lives, where they can work along with humans to provide assistance in various tasks on industry floors, in offices, and in homes. Automated assembly is one of the key applications of robots, and the next generation assembly systems could become much more efficient by creating collaborative human-robot systems. However, although collaborative robots have been around for decades, their application in truly collaborative systems has been limited. This is because a truly collaborative human-robot system needs to adjust its operation with respect to the uncertainty and imprecision in human actions, ensure safety during interaction, etc. In this paper, we present a system for human-robot collaborative assembly using learning from demonstration and pose estimation, so that the robot can adapt to the uncertainty caused by the operation of humans. Learning from demonstration is used to generate motion trajectories for the robot based on the pose estimate of different goal locations from a deep learning-based vision system. The proposed system is demonstrated using a physical 6 DoF manipulator in a collaborative human-robot assembly scenario. We show successful generalization of the system's operation to changes in the initial and final goal locations through various experiments.

Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification. Due to the large volume of XRD samples from high-throughput XRD experiments, it has become impossible for domain scientists to process them manually. Recently, they have started leveraging standard clustering techniques, to reduce the XRD pattern representations requiring manual efforts for labeling and verification. Nevertheless, these standard clustering techniques do not handle problem-specific aspects such as peak shifting, adjacent peaks, background noise, and mixed phases; hence, resulting in incorrect composition-phase diagrams that complicate further steps. Here, we leverage data mining techniques along with domain expertise to handle these issues. In this paper, we introduce an incremental phase mapping approach based on binary peak representations using a new threshold based fuzzy dissimilarity measure. The proposed approach first applies an incremental phase computation algorithm on discrete binary peak representation of XRD samples, followed by hierarchical clustering or manual merging of similar pure phases to obtain the final composition-phase diagram. We evaluate our method on the composition space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta. Our results are verified by domain scientists and closely resembles the manually computed ground-truth composition-phase diagrams. The proposed approach takes us closer towards achieving the goal of complete end-to-end automated XRD analysis.

Dynamic movement primitives are widely used for learning skills which can be demonstrated to a robot by a skilled human or controller. While their generalization capabilities and simple formulation make them very appealing to use, they possess no strong guarantees to satisfy operational safety constraints for a task. In this paper, we present constrained dynamic movement primitives (CDMP) which can allow for constraint satisfaction in the robot workspace. We present a formulation of a non-linear optimization to perturb the DMP forcing weights regressed by locally-weighted regression to admit a Zeroing Barrier Function (ZBF), which certifies workspace constraint satisfaction. We demonstrate the proposed CDMP under different constraints on the end-effector movement such as obstacle avoidance and workspace constraints on a physical robot. A video showing the implementation of the proposed algorithm using different manipulators in different environments could be found here https://youtu.be/hJegJJkJfys.

与计算机视觉合并的基于无人机的遥感系统（UAV）遥感系统具有协助建筑物建设和灾难管理的潜力，例如地震期间的损害评估。可以通过检查来评估建筑物到地震的脆弱性，该检查考虑到相关组件的预期损害进展以及组件对结构系统性能的贡献。这些检查中的大多数是手动进行的，导致高利用人力，时间和成本。本文提出了一种通过基于无人机的图像数据收集和用于后处理的软件库来自动化这些检查的方法，该方法有助于估算地震结构参数。这里考虑的关键参数是相邻建筑物，建筑计划形状，建筑计划区域，屋顶上的对象和屋顶布局之间的距离。通过使用距离测量传感器以及通过Google Earth获得的数据进行的现场测量，可以验证所提出的方法在估计上述参数估算上述参数方面的准确性。可以从https://uvrsabi.github.io/访问其他详细信息和代码。

在监督的机器学习中，使用正确的标签对于确保高精度非常重要。不幸的是，大多数数据集都包含损坏的标签。在此类数据集上训练的机器学习模型不能很好地概括。因此，检测其标签错误可以显着提高其功效。我们提出了一个名为CTRL的新型框架（标签错误检测的聚类训练损失），以检测多级数据集中的标签错误。它基于模型以不同方式学习干净和嘈杂的标签的观察结果，以两个步骤检测标签错误。首先，我们使用嘈杂的训练数据集训练神经网络，并为每个样本获得损失曲线。然后，我们将聚类算法应用于训练损失，将样本分为两类：已标记和噪声标记。标签误差检测后，我们删除带有嘈杂标签的样品并重新训练该模型。我们的实验结果表明，在模拟噪声下，图像（CIFAR-10和CIFAR-100和CIFAR-100）和表格数据集上的最新误差检测准确性。我们还使用理论分析来提供有关CTRL表现如此出色的见解。

对象探测器对于许多现代计算机视觉应用至关重要。但是，即使是最新的对象探测器也不是完美的。在两个看起来与人眼类似的图像上，同一探测器可以做出不同的预测，因为摄像机传感器噪声和照明变化等小图像变形。这个问题称为不一致。现有的准确性指标不能正确解释不一致的情况，并且在该领域的类似工作仅针对人造图像扭曲的改善。因此，我们提出了一种使用非人工视频框架来测量对象检测一致性，随着时间的流逝，跨帧的方法来测量对象检测一致性。使用此方法，我们表明，来自多个对象跟踪挑战的不同视频数据集，现代对象检测器的一致性范围从83.2％至97.1％。最后，我们表明应用图像失真校正（例如.WEBP图像压缩和UNSHARP遮罩）可以提高一致性多达5.1％，而准确性没有损失。

通常使用卷积神经网络（CNN）进行计算机视觉。 CNN是计算密集型的，并且在移动和互联网（IoT）设备等电力控制系统上部署。 CNN是计算密集型的，因为它们不加选择地计算输入图像的所有像素上的许多特征。我们观察到，鉴于计算机视觉任务，图像通常包含与任务无关的像素。例如，如果任务正在寻找汽车，那么天空中的像素不是很有用。因此，我们建议对CNN进行修改以仅在相关像素上操作以节省计算和能量。我们提出了一种研究三个流行的计算机视觉数据集的方法，发现48％的像素无关紧要。我们还提出了重点卷积，以修改CNN的卷积层，以拒绝明显无关的像素。在嵌入式设备上，我们没有观察到准确性的损失，而推论潜伏期，能耗和倍增add计数均减少了约45％。

免疫反应是一个动态过程，通过该过程，身体决定抗原是自我还是非自然。这种动态过程的状态由构成该决策过程的炎症和监管参与者的相对平衡和种群定义。免疫疗法的目的，例如因此，类风湿关节炎（RA）是为了使免疫状态偏向于监管参与者，从而在反应中关闭自身免疫性途径。尽管有几种已知的免疫疗法方法，但治疗的有效性将取决于这种干预措施如何改变该状态的演变。不幸的是，此过程不仅取决于该过程的动力学，而且是在干预时的系统状态决定的 - 这种状态在应用治疗之前很难确定即使不是不可能的状态。