具有诸如LSTM或GRU之类的门控机制的经常性神经网络是模拟顺序数据的强大工具。在机制中，最近被引入到RNN中的隐藏状态下控制信息流的忘记门被重新解释为状态的时间尺度的代表，即RNN保留信息的时间在输入上。在此解释的基础上，已经提出了几种参数初始化方法，以利用数据依赖于数据中的时间依赖性的知识，以提高可读性。然而，解释依赖于各种不切实际的假设，例如在一定时间点之后没有输入。在这项工作中，我们重新考虑了忘记门的解释，更现实的环境。我们首先概括了所存在的网格RNN理论，以便我们可以考虑连续给出输入的情况。然后，我们争论作为时间表示的忘记门的解释是有效的，当随着时间的推移时，当相对于国家的损失的梯度减小时是有效的。我们经验证明现有的RNNS在初始训练阶段满足了几个任务的初始训练阶段，这与先前的初始化方法很好。在此发现的基础上，我们提出了一种构建新的RNN的方法，可以代表比传统模型更长的时间级，这将提高长期顺序数据的可读性。通过使用现实世界数据集的实验，我们验证了我们的方法的有效性。

Search algorithms for the bandit problems are applicable in materials discovery. However, the objectives of the conventional bandit problem are different from those of materials discovery. The conventional bandit problem aims to maximize the total rewards, whereas materials discovery aims to achieve breakthroughs in material properties. The max K-armed bandit (MKB) problem, which aims to acquire the single best reward, matches with the discovery tasks better than the conventional bandit. Thus, here, we propose a search algorithm for materials discovery based on the MKB problem using a pseudo-value of the upper confidence bound of expected improvement of the best reward. This approach is pseudo-guaranteed to be asymptotic oracles that do not depends on the time horizon. In addition, compared with other MKB algorithms, the proposed algorithm has only one hyperparameter, which is advantageous in materials discovery. We applied the proposed algorithm to synthetic problems and molecular-design demonstrations using a Monte Carlo tree search. According to the results, the proposed algorithm stably outperformed other bandit algorithms in the late stage of the search process when the optimal arm of the MKB could not be determined based on its expectation reward.

Utilizing the latest advances in Artificial Intelligence (AI), the computer vision community is now witnessing an unprecedented evolution in all kinds of perception tasks, particularly in object detection. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. However, current cooperative object detection methods mainly focus on ego-vehicle efficiency without considering the practical issues of system-wide costs. In this paper, we introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet is the first CP method designed from the standpoint of large-scale system-level implementation and can be divided into three main phases: 1) Global Pre-Processing and Lightweight Feature Extraction which prepare the data into global style and extract features for cooperation in a lightweight manner; 2) Two-Stream Fusion which fuses the features from scalable and heterogeneous perception nodes; and 3) Central Feature Backbone and 3D Detection Head which further process the fused features and generate cooperative detection results. A cooperative perception platform is designed and developed for CP dataset acquisition and several baselines are compared during the experiments. The experimental analysis shows that VINet can achieve remarkable improvements for pedestrians and cars with 2x less system-wide computational costs and 12x less system-wide communicational costs.

Telework "avatar work," in which people with disabilities can engage in physical work such as customer service, is being implemented in society. In order to enable avatar work in a variety of occupations, we propose a mobile sales system using a mobile frozen drink machine and an avatar robot "OriHime", focusing on mobile customer service like peddling. The effect of the peddling by the system on the customers are examined based on the results of video annotation.

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

针对目标的对话任务的先前研究缺乏关键观念，该观念在以目标为导向的人工智能代理的背景下进行了深入研究。在这项研究中，我们提出了目标引导的开放域对话计划（TGCP）任务的任务，以评估神经对话代理是否具有目标对话计划的能力。使用TGCP任务，我们研究了现有检索模型和最新强生成模型的对话计划能力。实验结果揭示了当前技术面临的挑战。

感知环境是实现合作驾驶自动化（CDA）的最基本关键之一，该关键被认为是解决当代运输系统的安全性，流动性和可持续性问题的革命性解决方案。尽管目前在计算机视觉的物体感知领域正在发生前所未有的进化，但由于不可避免的物理遮挡和单辆车的接受程度有限，最先进的感知方法仍在与复杂的现实世界流量环境中挣扎系统。基于多个空间分离的感知节点，合作感知（CP）诞生是为了解锁驱动自动化的感知瓶颈。在本文中，我们全面审查和分析了CP的研究进度，据我们所知，这是第一次提出统一的CP框架。审查了基于不同类型的传感器的CP系统的体系结构和分类学，以显示对CP系统的工作流程和不同结构的高级描述。对节点结构，传感器模式和融合方案进行了审查和分析，并使用全面的文献进行了详细的解释。提出了分层CP框架，然后对现有数据集和模拟器进行审查，以勾勒出CP的整体景观。讨论重点介绍了当前的机会，开放挑战和预期的未来趋势。

游戏中的学习理论在AI社区中很突出，这是由多个不断上升的应用程序（例如多代理增强学习和生成对抗性网络）的动机。我们提出了突变驱动的乘法更新（M2WU），以在两人零和零正常形式游戏中学习平衡，并证明它在全面和嘈杂的信息反馈设置中都表现出了最后的题融合属性。在全信息反馈设置中，玩家观察了实用程序功能的确切梯度向量。另一方面，在嘈杂的信息反馈设置中，他们只能观察到嘈杂的梯度向量。现有的算法，包括众所周知的乘法权重更新（MWU）和乐观的MWU（OMWU）算法，未能收敛到具有嘈杂的信息反馈的NASH平衡。相反，在两个反馈设置中，M2WU表现出最后的近期收敛到NASH平衡附近的固定点。然后，我们证明它通过迭代地适应突变项来收敛到精确的NASH平衡。我们从经验上确认，M2WU在可剥削性和收敛速率方面胜过MWU和OMWU。

避免产生与先前环境相矛盾的响应是对话响应产生的重大挑战。一种可行的方法是后处理，例如从最终的n-最佳响应列表中滤除矛盾的响应。在这种情况下，n-最佳列表的质量极大地影响了矛盾的发生，因为最终响应是从该最佳列表中选择的。这项研究定量地分析了使用N最佳列表的一致性对神经反应产生模型的上下文矛盾意识。特别是，我们将极性问题用作简洁和定量分析的刺激输入。我们的测试说明了最近的神经反应产生模型和方法的矛盾意识，然后讨论了它们的性质和局限性。

我们介绍了Realtime QA，这是一个动态的问答（QA）平台，该平台宣布问题并定期评估系统（此版本每周）。实时质量检查询问当前世界，质量检查系统需要回答有关新事件或信息的问题。因此，它挑战了QA数据集中的静态，常规假设，并追求瞬时应用。我们在包括GPT-3和T5在内的大型语言模型上建立了强大的基线模型。我们的基准是一项持续的努力，该初步报告在过去一个月中提出了实时评估结果。我们的实验结果表明，GPT-3通常可以根据新的退休文档正确更新其生成结果，从而突出了最新信息检索的重要性。尽管如此，我们发现GPT-3倾向于在检索文件时返回过时的答案，这些文件没有提供足够的信息来找到答案。这表明了未来研究的重要途径：开放式域质量检查系统是否可以确定无法回答的案例，并与用户甚至检索模块进行通信以修改检索结果？我们希望实时质量检查能够刺激问题答案及其他问题的瞬时应用。