As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy sensitive applications such as autonomous driving and biometric authentication, it is critical to understand the fault-tolerance nature of DNNs. Prior work primarily focuses on metrics such as Failures In Time (FIT) rate and the Silent Data Corruption (SDC) rate, which quantify how often a device fails. Instead, this paper focuses on quantifying the DNN accuracy given that a transient error has occurred, which tells us how well a network behaves when a transient error occurs. We call this metric Resiliency Accuracy (RA). We show that existing RA formulation is fundamentally inaccurate, because it incorrectly assumes that software variables (model weights/activations) have equal faulty probability under hardware transient faults. We present an algorithm that captures the faulty probabilities of DNN variables under transient faults and, thus, provides correct RA estimations validated by hardware. To accelerate RA estimation, we reformulate RA calculation as a Monte Carlo integration problem, and solve it using importance sampling driven by DNN specific heuristics. Using our lightweight RA estimation method, we show that transient faults lead to far greater accuracy degradation than what todays DNN resiliency tools estimate. We show how our RA estimation tool can help design more resilient DNNs by integrating it with a Network Architecture Search framework.

因子图是代表概率分布函数分解的图形，并且已在许多自动机器计算任务中使用，例如本地化，跟踪，计划和控制等。我们正在开发一个架构，其目标是将因子图用作一个对于大多数（如果不是），所有自主机计算任务的常见抽象。如果成功，则该体系结构将为基础计算硬件提供映射自动机函数的非常简单的接口。作为此类尝试的第一步，本文介绍了我们最新的工作，即开发用于LIDAR惯性射测（LIO）的因子图加速器（LIO），这是许多自动机器（例如自动驾驶汽车和移动机器人）的重要任务。通过将LIO建模为因子图，所提出的加速器不仅支持多传感器融合，例如LIDAR，惯性测量单元（IMU），GPS等，还可以解决批处理或增量模式的机器人导航的全局优化问题。我们的评估表明，拟议的设计显着提高了自动机器导航系统的实时性能和能源效率。最初的成功表明，将因子图体系结构概括为自动机器计算的常见抽象的潜力，包括跟踪，计划和控制等。

自主机时代的一个主要技术挑战是自动驾驶机器的编程，它要求跨多个领域的协同作用，包括基本的计算机科学，计算机架构和机器人技术，并且需要学术界和行业的专业知识。本文讨论了与生产现实生活自动驾驶机器相关的编程理论和实践，并在特定功能要求，性能期望和自主机的实施约束的背景下涵盖了从高级概念到低级代码生成的各个方面。

本文是第一个提供全面的系统设计概述以及融合方法选择标准的现实世界合作自动驾驶系统的选择标准，该标准为基础架构增强自主驾驶或IAAD。我们在路边和车辆侧计算和通信平台上介绍了IAAD硬件和软件的深入介绍。我们在现实部署方案的背景下广泛地表征了IAAD系统，并观察到沿着道路波动的网络状况是目前是合作自动驾驶的主要技术障碍。为了应对这一挑战，我们提出了新的融合方法，称为“框架间融合”和“计划融合”，以补充当前最新的“框架内融合”。我们证明，每种融合方法都有其自身的好处和约束。

随着医疗保健成本的攀升，我们今天面临着全球医疗危机，但是随着人口老龄化，政府财政收入一直在下降。为了建立更有效和有效的医疗保健系统，立即出现了三个技术挑战：医疗保健访问，医疗保健和医疗保健效率。自主移动诊所通过通过患者的指尖向患者带来医疗服务来解决医疗保健问题。然而，要启用通用自主移动诊所网络，需要实现三阶段的技术路线图：在第一阶段，我们专注于通过结合自动驾驶和远程医疗来解决现有医疗保健系统中的不平等挑战。在第二阶段，我们开发了一位初级保健的AI医生，我们从婴儿期到成年，并使用干净的医疗保健数据培养。使用AI医生，我们可以解决效率低下的问题。在第三阶段，在我们证明自主移动诊所网络可以真正解决目标临床用例之后，我们将为所有医疗垂直行业打开平台，从而通过整个新系统实现普遍的医疗保健。

作为自动驾驶空间的初创公司，我们经历了四年的痛苦经验，处理广泛的监管要求。与软件行业规范相比，这花费了13％的整体预算，我们被迫花费42％的预算是合作的。我们的情况并不孤单，在某种程度上反映了人工智能（AI）监管景观的困境。根本原因是立法和行政部门缺乏AI专业知识，导致行业缺乏标准化。在本文中，我们分享了我们的第一手经验，并倡导建立FDA样机构来管理AI。

商业自主机器是一个蓬勃发展的扇区，它可能是下一个无处不在的计算平台，它是在个人计算机（PC），云计算和移动计算之后的。然而，缺少适用于自动机器的合适计算基板，许多公司被迫开发既不原则也不可扩展的临时计算解决方案。通过分析自动机器计算的需求，本文提出了数据流加速器体系结构（DAA），这是经典数据流原理的现代实例化，与自动机器软件的特性相匹配。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.