单像人类的重新构成旨在通过将输入图像分解为反照率，形状和照明，以在新的照明条件下重新确定目标人。尽管可以实现合理的重新确定结果，但以前的方法均遭受反照率和照明之间的纠缠以及缺乏硬阴影的纠缠，这大大降低了现实主义。为了解决这两个问题，我们提出了一个几何学意识到的单像人类重心框架，该框架利用单位图几何重建来共同部署传统的图形渲染和神经渲染技术。对于脱光灯，我们探索了UNET架构的缺点，并提出了修改后的HRNET，从而在反照率和照明之间获得了更好的分解。为了获得重新，我们引入了一个基于射线跟踪的每个像素照明表示形式，该表示明确地对高频阴影进行了建模，并提出了一个基于学习的阴影修补模块，以恢复来自射线追踪的阴影图的逼真的逼真的阴影（包括硬铸造阴影）。我们的框架能够生成照片逼真的高频阴影，例如在挑战性的照明条件下铸造阴影。广泛的实验表明，我们提出的方法在合成图像和真实图像上都优于先前的方法。

多目标定向运动问题（MO-OPS）是经典的多目标路由问题，在过去几十年中，人们一直受到很多关注。这项研究旨在通过问题分解框架解决MO-OPS，即MO-OP分解为多目标背包问题（MOKP）和旅行推销员问题（TSP）。然后，MOKP和TSP分别通过多目标进化算法（MOEA）和深钢筋学习（DRL）方法来解决。虽然MOEA模块用于选择城市，但DRL模块用于计划这些城市的哈密顿路径。这两个模块的迭代使用将人口驱动到Mo-ops的帕累托前沿。在各种类型的MO-OP实例上，将提出方法的有效性与NSGA-II和NSGA-III进行了比较。实验结果表明，我们的方法几乎在所有测试实例上表现出最佳性能，并且表现出强大的概括能力。

当有大量的计算资源可用时，AutoAttack（AA）是评估对抗性鲁棒性的最可靠方法。但是，高计算成本（例如，比项目梯度下降攻击的100倍）使AA对于具有有限计算资源的从业者来说是不可行的，并且也阻碍了AA在对抗培训中的应用（AT）。在本文中，我们提出了一种新颖的方法，即最小利润率（MM）攻击，以快速可靠地评估对抗性鲁棒性。与AA相比，我们的方法可实现可比的性能，但在广泛的实验中仅占计算时间的3％。我们方法的可靠性在于，我们使用两个目标之间的边缘来评估对抗性示例的质量，这些目标可以精确地识别最对抗性的示例。我们方法的计算效率在于有效的顺序目标排名选择（星形）方法，以确保MM攻击的成本与类数无关。 MM攻击开辟了一种评估对抗性鲁棒性的新方法，并提供了一种可行且可靠的方式来生成高质量的对抗示例。

我们研究了密集和互动人群中安全和意图意识到的机器人导航的问题。大多数以前的强化学习（RL）方法无法考虑所有代理之间的不同类型的相互作用或忽略人的意图，从而导致绩效降级。在本文中，我们提出了一个新型的复发图神经网络，具有注意机制，以通过空间和时间捕获代理之间的异质相互作用。为了鼓励长远的机器人行为，我们通过预测其未来的轨迹在几个时间段中来推断动态代理的意图。预测被纳入无模型的RL框架中，以防止机器人侵入其他试剂的预期路径。我们证明我们的方法使机器人能够在挑战人群导航方案中实现良好的导航性能和无侵入性。我们成功地将模拟中学到的政策转移到了现实世界中的Turtlebot 2i。

一般而言，融合是人类驱动因素和自治车辆的具有挑战性的任务，特别是在密集的交通中，因为合并的车辆通常需要与其他车辆互动以识别或创造间隙并安全合并。在本文中，我们考虑了强制合并方案的自主车辆控制问题。我们提出了一种新的游戏 - 理论控制器，称为领导者跟随者游戏控制器（LFGC），其中自主EGO车辆和其他具有先验不确定驾驶意图的车辆之间的相互作用被建模为部分可观察到的领导者 - 跟随游戏。 LFGC估计基于观察到的轨迹的其他车辆在线在线，然后预测其未来的轨迹，并计划使用模型预测控制（MPC）来同时实现概率保证安全性和合并目标的自我车辆自己的轨迹。为了验证LFGC的性能，我们在模拟和NGSIM数据中测试它，其中LFGC在合并中展示了97.5％的高成功率。

标签昂贵，有时是不可靠的。嘈杂的标签学习，半监督学习和对比学习是三种不同的设计，用于设计需要更少的注释成本的学习过程。最近已经证明了半监督学习和对比学习，以改善使用嘈杂标签地址数据集的学习策略。尽管如此，这些领域之间的内部连接以及将它们的强度结合在一起的可能性仅开始出现。在本文中，我们探讨了融合它们的进一步方法和优势。具体而言，我们提出了CSSL，统一的对比半监督学习算法和Codim（对比DivideMix），一种用嘈杂标签学习的新算法。 CSSL利用经典半监督学习和对比学习技术的力量，并进一步适应了Codim，其从多种类型和标签噪声水平鲁莽地学习。我们表明Codim带来了一致的改进，并在多个基准上实现了最先进的结果。

We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+1D lattice compact quantum electrodynamics with finite density dynamical fermions. The gauge field is represented by a neural network which parameterizes a discretized flow-based transformation of the amplitude while the fermionic sign structure is represented by a neural net backflow. This approach directly represents the $U(1)$ degree of freedom without any truncation, obeys Guass's law by construction, samples autoregressively avoiding any equilibration time, and variationally simulates Gauge-Fermion systems with sign problems accurately. In this model, we investigate confinement and string breaking phenomena in different fermion density and hopping regimes. We study the phase transition from the charge crystal phase to the vacuum phase at zero density, and observe the phase seperation and the net charge penetration blocking effect under magnetic interaction at finite density. In addition, we investigate a magnetic phase transition due to the competition effect between the kinetic energy of fermions and the magnetic energy of the gauge field. With our method, we further note potential differences on the order of the phase transitions between a continuous $U(1)$ system and one with finite truncation. Our state-of-the-art neural network approach opens up new possibilities to study different gauge theories coupled to dynamical matter in higher dimensions.

Most multimodal multi-objective evolutionary algorithms (MMEAs) aim to find all global Pareto optimal sets (PSs) for a multimodal multi-objective optimization problem (MMOP). However, in real-world problems, decision makers (DMs) may be also interested in local PSs. Also, searching for both global and local PSs is more general in view of dealing with MMOPs, which can be seen as a generalized MMOP. In addition, the state-of-the-art MMEAs exhibit poor convergence on high-dimension MMOPs. To address the above two issues, in this study, a novel coevolutionary framework termed CoMMEA for multimodal multi-objective optimization is proposed to better obtain both global and local PSs, and simultaneously, to improve the convergence performance in dealing with high-dimension MMOPs. Specifically, the CoMMEA introduces two archives to the search process, and coevolves them simultaneously through effective knowledge transfer. The convergence archive assists the CoMMEA to quickly approaching the Pareto optimal front (PF). The knowledge of the converged solutions is then transferred to the diversity archive which utilizes the local convergence indicator and the $\epsilon$-dominance-based method to obtain global and local PSs effectively. Experimental results show that CoMMEA is competitive compared to seven state-of-the-art MMEAs on fifty-four complex MMOPs.

Objective: Thigh muscle group segmentation is important for assessment of muscle anatomy, metabolic disease and aging. Many efforts have been put into quantifying muscle tissues with magnetic resonance (MR) imaging including manual annotation of individual muscles. However, leveraging publicly available annotations in MR images to achieve muscle group segmentation on single slice computed tomography (CT) thigh images is challenging. Method: We propose an unsupervised domain adaptation pipeline with self-training to transfer labels from 3D MR to single CT slice. First, we transform the image appearance from MR to CT with CycleGAN and feed the synthesized CT images to a segmenter simultaneously. Single CT slices are divided into hard and easy cohorts based on the entropy of pseudo labels inferenced by the segmenter. After refining easy cohort pseudo labels based on anatomical assumption, self-training with easy and hard splits is applied to fine tune the segmenter. Results: On 152 withheld single CT thigh images, the proposed pipeline achieved a mean Dice of 0.888(0.041) across all muscle groups including sartorius, hamstrings, quadriceps femoris and gracilis. muscles Conclusion: To our best knowledge, this is the first pipeline to achieve thigh imaging domain adaptation from MR to CT. The proposed pipeline is effective and robust in extracting muscle groups on 2D single slice CT thigh images.The container is available for public use at https://github.com/MASILab/DA_CT_muscle_seg

Multi-modal robots expand their operations from one working media to another, land to air for example. The majorities multi-modal robots mainly refer to platforms that operate in two different media. However, for all-terrain tasks, there is seldom research to date in the literature. In this paper, we proposed a triphibian robotic platform aiming at solving the challenges of different propulsion systems and immensely varied working media. In our design, three ducted fans are adopted to unify the propulsion system and provide the robot with driving forces to perform all-terrain operations. A morphable mechanism is designed to enable the transition between different motion modes, and specifically, a cylindrical body is implemented as the rolling mechanism in land mode. Detailed design principles of different mechanisms and the transition between various locomotion modes are analyzed in detail. Finally, a triphibian robot prototype is fabricated and tested in various working media with mono-modal and multi-modal functionalities. Experiments have verified our platform, and the results show promising adaptions for future exploration tasks in different working scenarios.