Generally, regularization-based continual learning models limit access to the previous task data to imitate the real-world setting which has memory and privacy issues. However, this introduces a problem in these models by not being able to track the performance on each task. In other words, current continual learning methods are vulnerable to attacks done on the previous task. We demonstrate the vulnerability of regularization-based continual learning methods by presenting simple task-specific training time adversarial attack that can be used in the learning process of a new task. Training data generated by the proposed attack causes performance degradation on a specific task targeted by the attacker. Experiment results justify the vulnerability proposed in this paper and demonstrate the importance of developing continual learning models that are robust to adversarial attack.

本文提出了一种以完全分布式方式工作的协同环境学习算法。多机器人系统比单个机器人更有效，但它涉及以下挑战：1）使用多个机器人在线分布式学习环境地图; 2）基于学习地图的安全和有效的探索路径的产生; 3）对机器人数量的维持能力。为此，我们将整个过程划分为环境学习和路径规划的两个阶段。在每个阶段应用分布式算法并通过相邻机器人之间的通信组合。环境学习算法使用分布式高斯过程，路径规划算法使用分布式蒙特卡罗树搜索。因此，我们构建一个可扩展系统，而无需对机器人数量的约束。仿真结果证明了所提出的系统的性能和可扩展性。此外，基于实际数据集的仿真验证了我们算法在更现实的方案中的实用程序。

尽管等级加固学习的进步，但其在高速公路上自动驾驶中的路径规划的应用是具有挑战性的。一个原因是传统的等级加强学习方法由于其危险而无法自动驾驶，因此代理必须移动避免多个障碍物，例如高度不可预测的其他代理，因此安全区域较小，散射，随着时间的推移而变化。为了克服这一挑战，我们提出了一种用于国家空间和政策空间的空间分层加强学习方法。高级策略不仅选择行为子策略，而且选择在国家空间中和政策空间中的概要中致力于思维的区域。随后，低级政策阐述了代理在由高级命令选择的区域的轮廓内的短期目标位置。我们的方法中建议的网络结构和优化与单级方法一样简洁。各种形状的道路环境的实验表明，我们的方法发现了早期发作的几乎最佳的政策，优于基线等级加强学习方法，特别是在狭窄和复杂的道路上。在道路上产生的轨迹类似于人类策略对行为规划水平的策略。

Attention networks in multimodal learning provide an efficient way to utilize given visual information selectively. However, the computational cost to learn attention distributions for every pair of multimodal input channels is prohibitively expensive. To solve this problem, co-attention builds two separate attention distributions for each modality neglecting the interaction between multimodal inputs. In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly. BAN considers bilinear interactions among two groups of input channels, while low-rank bilinear pooling extracts the joint representations for each pair of channels. Furthermore, we propose a variant of multimodal residual networks to exploit eight-attention maps of the BAN efficiently. We quantitatively and qualitatively evaluate our model on visual question answering (VQA 2.0) and Flickr30k Entities datasets, showing that BAN significantly outperforms previous methods and achieves new state-of-the-arts on both datasets.

在腿部机器人技术中，计划和执行敏捷的机动演习一直是一个长期的挑战。它需要实时得出运动计划和本地反馈政策，以处理动力学动量的非物质。为此，我们提出了一个混合预测控制器，该控制器考虑了机器人的致动界限和全身动力学。它将反馈政策与触觉信息相结合，以在本地预测未来的行动。由于采用可行性驱动的方法，它在几毫秒内收敛。我们的预测控制器使Anymal机器人能够在现实的场景中生成敏捷操作。关键要素是跟踪本地反馈策略，因为与全身控制相反，它们达到了所需的角动量。据我们所知，我们的预测控制器是第一个处理驱动限制，生成敏捷的机动操作以及执行低级扭矩控制的最佳反馈策略，而无需使用单独的全身控制器。

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.

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.