Question Answering (QA) is a task that entails reasoning over natural language contexts, and many relevant works augment language models (LMs) with graph neural networks (GNNs) to encode the Knowledge Graph (KG) information. However, most existing GNN-based modules for QA do not take advantage of rich relational information of KGs and depend on limited information interaction between the LM and the KG. To address these issues, we propose Question Answering Transformer (QAT), which is designed to jointly reason over language and graphs with respect to entity relations in a unified manner. Specifically, QAT constructs Meta-Path tokens, which learn relation-centric embeddings based on diverse structural and semantic relations. Then, our Relation-Aware Self-Attention module comprehensively integrates different modalities via the Cross-Modal Relative Position Bias, which guides information exchange between relevant entities of different modalities. We validate the effectiveness of QAT on commonsense question answering datasets like CommonsenseQA and OpenBookQA, and on a medical question answering dataset, MedQA-USMLE. On all the datasets, our method achieves state-of-the-art performance. Our code is available at http://github.com/mlvlab/QAT.

The current reinforcement learning algorithm uses forward-generated trajectories to train the agent. The forward-generated trajectories give the agent little guidance, so the agent can explore as much as possible. While the appreciation of reinforcement learning comes from enough exploration, this gives the trade-off of losing sample efficiency. The sampling efficiency is an important factor that decides the performance of the algorithm. Past tasks use reward shaping techniques and changing the structure of the network to increase sample efficiency, however these methods require many steps to implement. In this work, we propose novel reverse curriculum reinforcement learning. Reverse curriculum learning starts training the agent using the backward trajectory of the episode rather than the original forward trajectory. This gives the agent a strong reward signal, so the agent can learn in a more sample-efficient manner. Moreover, our method only requires a minor change in algorithm, which is reversing the order of trajectory before training the agent. Therefore, it can be simply applied to any state-of-art algorithms.

最近的高精度亚次光学光学扫描仪的开发允许将3D键盘检测器和功能描述符在海底环境中的点云扫描上利用。但是，文献缺乏一项全面的调查，无法确定在这些挑战和新颖的环境中使用的检测器和描述符的最佳组合。本文旨在使用使用商业水下激光扫描仪收集的具有挑战性的现场数据集确定最佳的检测器/描述符对。此外，研究表明，合并纹理信息扩展几何特征为合成数据集的特征匹配增添了鲁棒性。本文还提出了一种与水下激光扫描融合图像以产生有色点云的新方法，该方法用于研究6D点云描述符的有效性。

当训练数据稀缺时，用人为生成的数据进行训练可以是一种替代方法，但由于较大的域间隙，培训数据的概括性能差。在本文中，我们通过使用因果框架进行数据生成来表征域间隙。我们假设真实和合成数据具有常见的内容变量，但样式变量不同。因此，随着模型了解滋扰样式变量，对合成数据集训练的模型可能具有较差的概括。为此，我们提出了因果不变性学习，该学习鼓励模型学习一种风格不变的表示，从而增强了SYN到真实的概括。此外，我们提出了一种简单而有效的特征蒸馏方法，以防止灾难性地忘记对真实领域的语义知识。总而言之，我们将我们的方法称为指导性因果不变的syn到现实概括，从而有效地提高了syn到真实的概括的性能。我们从经验上验证了所提出的方法的有效性，尤其是我们的方法在视觉SYN到现实的域概括任务（例如图像分类和语义分割）上实现了最新的方法。

对比表示学习旨在通过估计数据的多个视图之间的共享信息来获得有用的表示形式。在这里，数据增强的选择对学会表示的质量很敏感：随着更难的应用，数据增加了，视图共享更多与任务相关的信息，但也可以妨碍表示代表的概括能力。在此激励的基础上，我们提出了一种新的强大的对比度学习计划，即r \'enyicl，可以通过利用r \'enyi差异来有效地管理更艰难的增强。我们的方法建立在r \'enyi差异的变异下限基础上，但是由于差异很大，对变异方法的使用是不切实际的。要应对这一挑战，我们提出了一个新颖的对比目标，该目标是进行变异估计的新型对比目标偏斜r \'enyi的分歧，并提供理论保证，以确保偏差差异如何导致稳定训练。我们表明，r \'enyi对比度学习目标执行先天的硬性负面样本和易于选择的阳性抽样学习有用的功能并忽略滋扰功能。通过在Imagenet上进行实验，我们表明，r \'enyi对比度学习具有更强的增强性能优于其他自我监督的方法，而无需额外的正则化或计算上的开销。图形和表格，显示了与其他对比方法相比的经验增益。

虽然最近的基于NERF的生成模型实现了不同的3D感知图像的产生，但这些方法在生成包含用户指定特征的图像时具有限制。在本文中，我们提出了一种新颖的模型，称为条件生成神经辐射场（CG-NERF），其可以生成反映诸如图像或文本的额外输入条件的多视图图像。在保留给定输入条件的常见特征的同时，所提出的模型以精细的细节生成不同的图像。我们提出：1）一种小说统一的架构，它从各种形式和2）以各种形式和2）给出的姿势一致的分集损失，用于在保持视图的一致性的同时产生姿势 - 一致的分集损失。实验结果表明，与现有的基于NERF的生成模型相比，该方法对各种情况类型的图像质量保持一致的图像质量，并实现了卓越的保真度和多样性。

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.