我们提出了一个公平的衡量标准，以放松流行的平等赔率公平制度中的平等条件。我们设计了一种迭代，模型，基于网格的启发式启发式，该启发式校准了每个敏感属性值的结果以符合度量。该启发式旨在处理高Arity属性值，并执行跨不同受保护属性值的结果的每个属性消毒。我们还将启发式方法扩展到多个属性。强调了我们激励的应用，欺诈检测，我们表明所提出的启发式能够在单个受保护的属性，多个受保护的属性的多个值中实现公平性。与当前关注两组的公平技术相比，我们在几个公共数据集中实现了可比的性能。

Recently, Smart Video Surveillance (SVS) systems have been receiving more attention among scholars and developers as a substitute for the current passive surveillance systems. These systems are used to make the policing and monitoring systems more efficient and improve public safety. However, the nature of these systems in monitoring the public's daily activities brings different ethical challenges. There are different approaches for addressing privacy issues in implementing the SVS. In this paper, we are focusing on the role of design considering ethical and privacy challenges in SVS. Reviewing four policy protection regulations that generate an overview of best practices for privacy protection, we argue that ethical and privacy concerns could be addressed through four lenses: algorithm, system, model, and data. As an case study, we describe our proposed system and illustrate how our system can create a baseline for designing a privacy perseverance system to deliver safety to society. We used several Artificial Intelligence algorithms, such as object detection, single and multi camera re-identification, action recognition, and anomaly detection, to provide a basic functional system. We also use cloud-native services to implement a smartphone application in order to deliver the outputs to the end users.

This paper presents a state-of-the-art optimal controller for quadruped locomotion. The robot dynamics is represented using a single rigid body (SRB) model. A linear time-varying model predictive controller (LTV MPC) is proposed by using linearization schemes. Simulation results show that the LTV MPC can execute various gaits, such as trot and crawl, and is capable of tracking desired reference trajectories even under unknown external disturbances. The LTV MPC is implemented as a quadratic program using qpOASES through the CasADi interface at 50 Hz. The proposed MPC can reach up to 1 m/s top speed with an acceleration of 0.5 m/s2 executing a trot gait. The implementation is available at https:// github.com/AndrewZheng-1011/Quad_ConvexMPC

Graph neural networks (GNNs) have recently emerged as a promising learning paradigm in learning graph-structured data and have demonstrated wide success across various domains such as recommendation systems, social networks, and electronic design automation (EDA). Like other deep learning (DL) methods, GNNs are being deployed in sophisticated modern hardware systems, as well as dedicated accelerators. However, despite the popularity of GNNs and the recent efforts of bringing GNNs to hardware, the fault tolerance and resilience of GNNs has generally been overlooked. Inspired by the inherent algorithmic resilience of DL methods, this paper conducts, for the first time, a large-scale and empirical study of GNN resilience, aiming to understand the relationship between hardware faults and GNN accuracy. By developing a customized fault injection tool on top of PyTorch, we perform extensive fault injection experiments to various GNN models and application datasets. We observe that the error resilience of GNN models varies by orders of magnitude with respect to different models and application datasets. Further, we explore a low-cost error mitigation mechanism for GNN to enhance its resilience. This GNN resilience study aims to open up new directions and opportunities for future GNN accelerator design and architectural optimization.

We apply reinforcement learning (RL) to robotics. One of the drawbacks of traditional RL algorithms has been their poor sample efficiency. One approach to improve it is model-based RL. We learn a model of the environment, essentially its dynamics and reward function, use it to generate imaginary trajectories and backpropagate through them to update the policy, exploiting the differentiability of the model. Intuitively, learning more accurate models should lead to better performance. Recently, there has been growing interest in developing better deep neural network based dynamics models for physical systems, through better inductive biases. We focus on robotic systems undergoing rigid body motion. We compare two versions of our model-based RL algorithm, one which uses a standard deep neural network based dynamics model and the other which uses a much more accurate, physics-informed neural network based dynamics model. We show that, in environments that are not sensitive to initial conditions, model accuracy matters only to some extent, as numerical errors accumulate slowly. In these environments, both versions achieve similar average-return, while the physics-informed version achieves better sample efficiency. We show that, in environments that are sensitive to initial conditions, model accuracy matters a lot, as numerical errors accumulate fast. In these environments, the physics-informed version achieves significantly better average-return and sample efficiency. We show that, in challenging environments, where we need a lot of samples to learn, physics-informed model-based RL can achieve better asymptotic performance than model-free RL, by generating accurate imaginary data, which allows it to perform many more policy updates. In these environments, our physics-informed model-based RL approach achieves better average-return than Soft Actor-Critic, a SOTA model-free RL algorithm.

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym environments from RDDL declerative description. The discrete time step evolution of variables in RDDL is described by conditional probability functions, which fits naturally into the Gym step scheme. Furthermore, since RDDL is a lifted description, the modification and scaling up of environments to support multiple entities and different configurations becomes trivial rather than a tedious process prone to errors. We hope that pyRDDLGym will serve as a new wind in the reinforcement learning community by enabling easy and rapid development of benchmarks due to the unique expressive power of RDDL. By providing explicit access to the model in the RDDL description, pyRDDLGym can also facilitate research on hybrid approaches for learning from interaction while leveraging model knowledge. We present the design and built-in examples of pyRDDLGym, and the additions made to the RDDL language that were incorporated into the framework.

We discuss a platform that has both software and hardware components, and whose purpose is to support research into characterizing and mitigating the sim-to-real gap in robotics and vehicle autonomy engineering. The software is operating-system independent and has three main components: a simulation engine called Chrono, which supports high-fidelity vehicle and sensor simulation; an autonomy stack for algorithm design and testing; and a development environment that supports visualization and hardware-in-the-loop experimentation. The accompanying hardware platform is a 1/6th scale vehicle augmented with reconfigurable mountings for computing, sensing, and tracking. Since this vehicle platform has a digital twin within the simulation environment, one can test the same autonomy perception, state estimation, or controls algorithms, as well as the processors they run on, in both simulation and reality. A demonstration is provided to show the utilization of this platform for autonomy research. Future work will concentrate on augmenting ART/ATK with support for a full-sized Chevy Bolt EUV, which will be made available to this group in the immediate future.

在辅助和自动驾驶系统的各种传感器中，即使在不利的天气或照明条件下，汽车雷达也被认为是一种健壮且低成本的解决方案。随着雷达技术的最新发展和开源的注释数据集，带有雷达信号的语义分割变得非常有前途。但是，现有的方法在计算上是昂贵的，或者通过平均将其减少到2D平面，从原始3D雷达信号中丢弃了大量的有价值的信息。在这项工作中，我们引入了Erase-Net，这是一个有效的雷达分割网络，以语义上的原始雷达信号。我们方法的核心是新型的检测到原始雷达信号的段方法。它首先检测每个对象的中心点，然后提取紧凑的雷达信号表示，最后执行语义分割。我们表明，与最新技术（SOTA）技术相比，我们的方法可以在雷达语义分割任务上实现卓越的性能。此外，我们的方法需要减少20倍的计算资源。最后，我们表明所提出的擦除网络可以被40％压缩而不会造成大幅损失，这比SOTA网络大得多，这使其成为实用汽车应用的更有希望的候选人。

与标准动态范围（SDR）视频相比，高动态范围（HDR）视频可以代表更大的亮度和色彩范围，并且正迅速成为行业标准。与传统SDR视频相比，HDR视频具有更具挑战性的捕获，传输和显示要求。凭借其更大的深度，高级的电流传输功能以及更广泛的颜色范围，因此需要专门设计用于预测HDR视频质量的视频质量算法。为此，我们介绍了HDR视频的首次公开发布的大规模主观研究。我们研究扭曲的影响，例如压缩和混叠对HDR视频质量的影响。我们还通过在黑暗实验室环境和更明亮的客厅环境中进行研究来研究环境照明对HDR视频感知质量的影响。总共有66名受试者参加了这项研究，并收集了20,000多个意见分数，这使得这成为有史以来最大的HDR视频质量研究。我们预计，该数据集将成为研究人员为HDR视频开发更好的感知质量模型的宝贵资源。

我们通过策略提取（MSVIPER）提出了多种可验证的增强学习，这是一种策略蒸馏到决策树以改进机器人导航的新方法。 MSVIPER使用任何强化学习（RL）技术来学习一项“专家”政策，涉及学习国家行动映射，然后使用模仿学习来从中学习决策树策略。我们证明，MSVIPER会导致有效的决策树，并可以准确模仿专家政策的行为。此外，我们提出了有效的政策蒸馏和树修改技术，这些技术利用决策树结构，可以改进政策而无需再培训。我们使用我们的方法来改善用于室内和室外场景的基于RL的机器人导航算法的性能。我们证明了在减少冻结和振荡行为（减少95 \％降低）方面的好处。