According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

在自主驾驶环境中，同时保证实时和准确的对象检测至关重要。但是，现有的对象检测神经网络系统的特征是计算时间和准确性之间的权衡，因此必须优化这种权衡。幸运的是，在许多自动驾驶环境中，图像以连续的形式出现，提供了使用光流的机会。在本文中，我们利用光流估计来提高对象检测神经网络的性能。此外，我们提出了一个lyapunov优化框架，以实现稳定性的时间平均性能最大化。它可以自适应地确定是否使用光流程适合动态车辆环境，从而确保车辆的队列稳定性和同时的时间平均最高性能。为了验证关键思想，我们使用各种对象检测神经网络和光流估计网络进行数值实验。此外，我们通过Yolov3微小和Flownet2-S展示了可自配置的稳定检测，它们分别是实时对象检测网络和光流估计网络。在演示中，我们提出的框架将准确性提高了3.02％，检测到的对象数量增加了59.6％，并且用于计算功能的队列稳定性。

现有的图像到图像翻译技术通常遭受了两个关键问题：严重依赖按样本域注释和/或无法处理每个图像的多个属性。最近的方法采用聚类方法来轻松以无监督的方式提供样本注释。但是，他们无法解释现实环境。一个样本可能具有多个属性。此外，集群的语义不容易与人类的理解相结合。为了克服这些，我们提出了一种语言驱动的图像到图像翻译模型，称为LANIT。我们利用文本中给出的易于访问的候选域注释，并在培训期间共同优化它们。目标样式是通过根据多热域分配汇总多域样式向量来指定的。由于最初的候选域文本可能不准确，因此我们将候选域文本设置为可学习的，并在培训期间共同对其进行微调。此外，我们引入了一个松弛域，以涵盖候选域未覆盖的样品。对几个标准基准测试的实验表明，LANIT与现有模型具有可比性或优越的性能。

最近求解深卷积神经网络（CNNS）内的光致风格转移的技术通常需要大规模数据集的密集训练，从而具有有限的适用性和揭示图像或风格的普遍性能力差。为了克服这一点，我们提出了一种新颖的框架，称为深度翻译（DTP），通过对给定输入图像对的测试时间训练来实现光致风格转移，与未经培训的网络一起学习特定于图像对的翻译，从而更好地产生性能和泛化。为风格转移进行此类测试时间培训量身定制，我们提出了新颖的网络架构，具有两个对应和生成模块的子模块，以及由对比含量，样式和循环一致性损耗组成的损耗功能。我们的框架不需要离线培训阶段进行风格转移，这是现有方法中的主要挑战之一，但网络将在测试期间仅了解。实验结果证明我们的框架具有更好的概念图像对的概括能力，甚至优于最先进的方法。

当将同时映射和本地化（SLAM）调整到现实世界中的应用程序（例如自动驾驶汽车，无人机和增强现实设备）时，其内存足迹和计算成本是限制性能和应用程序范围的两个主要因素。在基于稀疏特征的SLAM算法中，解决此问题的一种有效方法是通过选择可能对本地和全局捆绑捆绑调整（BA）有用的点来限制地图点大小。这项研究提出了用于大量系统中稀疏地图点的有效图优化。具体而言，我们将最大姿势可见度和最大空间多样性问题作为最小成本最大流量图优化问题。提出的方法是现有SLAM系统的附加步骤，因此可以在常规或基于学习的SLAM系统中使用。通过广泛的实验评估，我们证明了所提出的方法以大约1/3的MAP点和1/2的计算实现了更准确的相机姿势。

我们引入了统一的歧管近似值，具有两相优化（UMATO），这是一种降低尺寸（DR）技术，可改善UMAP，以更准确地捕获高维数据的全局结构。在Umato中，优化分为两个阶段，因此所得的嵌入可以可靠地描绘出全球结构，同时以足够的精度保留局部结构。在第一阶段，识别并预测集线器点以构建全局结构的骨骼布局。在第二阶段，剩余点添加到保存地方区域特征的嵌入中。通过定量实验，我们发现Umato（1）在保留全局结构方面优于广泛使用的DR技术，而（2）在代表局部结构方面产生了竞争精度。我们还验证了Umato在鲁棒性方面比各种初始化方法，时期数量和亚采样技术优选。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly being replaced by individual re-identification using image analysis. This paper introduces a contrastive learning-based model for identifying individuals. The model uses the first parts of the Inception v3 network, supported by a projection head, and we use contrastive learning to find similar or dissimilar image pairs from a collection of uniform photographs. We apply this technique for corkwing wrasse, Symphodus melops, an ecologically and commercially important fish species. Photos are taken during repeated catches of the same individuals from a wild population, where the intervals between individual sightings might range from a few days to several years. Our model achieves a one-shot accuracy of 0.35, a 5-shot accuracy of 0.56, and a 100-shot accuracy of 0.88, on our dataset.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.