视觉域的适应性（DA）试图将经过训练的模型转移到分发转移的未看到的，未标记的域，但是方法通常着重于适应卷积神经网络体系结构，并使用有监督的成像网表示。在这项工作中，我们将重点转移到将现代体系结构改编成对象识别的重点 - 越来越流行的视觉变压器（VIT）以及基于自我监督的学习（SSL）的现代预测。受到最新SSL方法的启发，该方法是基于通过掩盖或裁剪生成的部分图像输入的学习的 - 要么通过学习预测缺失的像素或学习代表性的不断增强来进行这种增强 - 我们提出了简单的两阶段适应性PACMAC自我监督VIT的算法。 PACMAC首先在汇总源和目标数据上执行内域SSL，以学习任务歧视性特征，然后探究该模型的预测一致性，这些歧视性的一致性是通过新的注意力条件掩盖策略生成的一组部分目标输入，以识别自我候选者的可靠候选者-训练。我们的简单方法导致对使用VIT和对标准对象识别基准的自我监督初始化的竞争方法的性能一致。可在https://github.com/virajprabhu/pacmac上找到代码

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

We propose an end-to-end inverse rendering pipeline called SupeRVol that allows us to recover 3D shape and material parameters from a set of color images in a super-resolution manner. To this end, we represent both the bidirectional reflectance distribution function (BRDF) and the signed distance function (SDF) by multi-layer perceptrons. In order to obtain both the surface shape and its reflectance properties, we revert to a differentiable volume renderer with a physically based illumination model that allows us to decouple reflectance and lighting. This physical model takes into account the effect of the camera's point spread function thereby enabling a reconstruction of shape and material in a super-resolution quality. Experimental validation confirms that SupeRVol achieves state of the art performance in terms of inverse rendering quality. It generates reconstructions that are sharper than the individual input images, making this method ideally suited for 3D modeling from low-resolution imagery.

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 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.

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。

在本文中，我们提出了一个模型，以执行语音转换为歌声。与以前的基于信号处理的方法相反，基于信号处理的方法需要高质量的唱歌模板或音素同步，我们探索了一种数据驱动的方法，即将自然语音转换为唱歌声音的问题。我们开发了一种新型的神经网络体系结构，称为Symnet，该结构将输入语音与目标旋律的一致性建模，同时保留了说话者的身份和自然性。所提出的符号模型由三种类型层的对称堆栈组成：卷积，变压器和自发层。本文还探讨了新的数据增强和生成损耗退火方法，以促进模型培训。实验是在NUS和NHSS数据集上进行的，这些数据集由语音和唱歌语音的平行数据组成。在这些实验中，我们表明所提出的SYMNET模型在先前发表的方法和基线体系结构上显着提高了客观重建质量。此外，主观听力测试证实了使用拟议方法获得的音频质量的提高（绝对提高了0.37的平均意见分数测度量度比基线系统）。

尖峰神经网络（SNN）正在受到越来越多的关注，作为开发“生物学上合理”的机器学习模型的一种手段。这些网络模仿人大脑中的突触连接并产生尖峰列车，可以通过二进制值近似，从而排除了浮点算术电路的高计算成本。最近，已经引入了卷积层与SNNS的计算效率相结合的卷积层。在本文中，研究了使用脑电图（EEG）使用卷积尖峰神经网络（CSNN）作为分类器的可行性。脑电图数据是在一个实验中收集的，该实验参与者在旨在模拟城市环境的测试台上操作遥控车辆。参与者通过音频倒计时通知了进入传入的制动事件，以引起预期潜力，然后使用脑电图测量。将CSNN的性能与标准的卷积神经网络（CNN）和三个图形神经网络（GNN）进行了比较。结果表明，CSNN的表现优于其他神经网络。