为了协助游戏开发人员制作游戏NPC，我们展示了EvolvingBehavior，这是一种新颖的工具，用于基因编程，以在不真实的引擎4中发展行为树4.在初步评估中，我们将演变的行为与我们的研究人员设计的手工制作的树木和随机的树木进行了比较 - 在3D生存游戏中种植的树木。我们发现，在这种情况下，EvolvingBehavior能够产生行为，以实现设计师的目标。最后，我们讨论了共同创造游戏AI设计工具的探索的含义和未来途径，以及行为树进化的挑战和困难。

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.

Nine language-vision AI models trained on web scrapes with the Contrastive Language-Image Pretraining (CLIP) objective are evaluated for evidence of a bias studied by psychologists: the sexual objectification of girls and women, which occurs when a person's human characteristics are disregarded and the person is treated as a body or a collection of body parts. A first experiment uses standardized images of women from the Sexual OBjectification and EMotion Database, and finds that, commensurate with prior research in psychology, human characteristics are disassociated from images of objectified women: the model's recognition of emotional state is mediated by whether the subject is fully or partially clothed. Embedding association tests (EATs) return significant effect sizes for both anger (d >.8) and sadness (d >.5). A second experiment measures the effect in a representative application: an automatic image captioner (Antarctic Captions) includes words denoting emotion less than 50% as often for images of partially clothed women than for images of fully clothed women. A third experiment finds that images of female professionals (scientists, doctors, executives) are likely to be associated with sexual descriptions relative to images of male professionals. A fourth experiment shows that a prompt of "a [age] year old girl" generates sexualized images (as determined by an NSFW classifier) up to 73% of the time for VQGAN-CLIP (age 17), and up to 40% of the time for Stable Diffusion (ages 14 and 18); the corresponding rate for boys never surpasses 9%. The evidence indicates that language-vision AI models trained on automatically collected web scrapes learn biases of sexual objectification, which propagate to downstream applications.

We present AI-SDC, an integrated suite of open source Python tools to facilitate Statistical Disclosure Control (SDC) of Machine Learning (ML) models trained on confidential data prior to public release. AI-SDC combines (i) a SafeModel package that extends commonly used ML models to provide ante-hoc SDC by assessing the vulnerability of disclosure posed by the training regime; and (ii) an Attacks package that provides post-hoc SDC by rigorously assessing the empirical disclosure risk of a model through a variety of simulated attacks after training. The AI-SDC code and documentation are available under an MIT license at https://github.com/AI-SDC/AI-SDC.

Semantic segmentation of aerial point cloud data can be utilised to differentiate which points belong to classes such as ground, buildings, or vegetation. Point clouds generated from aerial sensors mounted to drones or planes can utilise LIDAR sensors or cameras along with photogrammetry. Each method of data collection contains unique characteristics which can be learnt independently with state-of-the-art point cloud segmentation models. Utilising a single point cloud segmentation model can be desirable in situations where point cloud sensors, quality, and structures can change. In these situations it is desirable that the segmentation model can handle these variations with predictable and consistent results. Although deep learning can segment point clouds accurately it often suffers in generalisation, adapting poorly to data which is different than the training data. To address this issue, we propose to utilise multiple available open source fully annotated datasets to train and test models that are better able to generalise. In this paper we discuss the combination of these datasets into a simple training set and challenging test set. Combining datasets allows us to evaluate generalisation performance on known variations in the point cloud data. We show that a naive combination of datasets produces a model with improved generalisation performance as expected. We go on to show that an improved sampling strategy which decreases sampling variations increases the generalisation performance substantially on top of this. Experiments to find which sample variations give this performance boost found that consistent densities are the most important.

我们提供了奖励黑客的第一个正式定义，即优化不完美的代理奖励功能的现象，$ \ Mathcal {\ tilde {r}} $，根据真实的奖励功能，$ \ MATHCAL {R} $导致性能差。 。我们说，如果增加预期的代理回报率永远无法减少预期的真实回报，则代理是不可接受的。直觉上，可以通过从奖励功能（使其“较窄”）中留出一些术语或忽略大致等效的结果之间的细粒度区分来创建一个不可接受的代理，但是我们表明情况通常不是这样。一个关键的见解是，奖励的线性性（在州行动访问计数中）使得无法实现的状况非常强烈。特别是，对于所有随机策略的集合，只有在其中一个是恒定的，只有两个奖励函数才能是不可接受的。因此，我们将注意力转移到确定性的政策和有限的随机政策集中，在这些策略中，始终存在非平凡的不可动摇的对，并为简化的存在建立必要和充分的条件，这是一个重要的不被限制的特殊情况。我们的结果揭示了使用奖励函数指定狭窄任务和对齐人类价值的AI系统之间的紧张关系。

我们提出了一种新的拒绝期权分类技术，以识别和删除给定神经分类器和数据集的决策空间中不确定性区域。这种现有的配方采用了学习的拒绝（删除）/选择功能，并且需要拒绝示例的已知成本或对所选示例的准确性或覆盖范围的强大限制。我们通过分析互补拒绝区域并采用验证集来学习每个级别的软马克斯阈值来考虑替代公式。目的是最大程度地提高所选示例的准确性，但在被拒绝的示例上受到自然随机性津贴（拒绝比正确的预测更不正确）。我们提供的结果显示了所提出的方法比Na \“ ively阈值校准/未校准/未校准的SoftMax分数，使用2-D点，图像和文本分类数据集使用最先进的预读型模型。源代码可在https上获得。 ：//github.com/osu-cvl/learning-idk。

已经开发了各种方法来结合多组结果的推理，以在集合和共识聚类文献中进行无监督的聚类。从几个候选聚类模型中的一个“最佳”模型报告结果的方法通常忽略了由模型选择产生的不确定性，并且导致对所选择的特定模型和参数敏感的推论，以及制作的假设，尤其是在小样本中所做的假设。尺寸或小簇尺寸。贝叶斯模型平均（BMA）是一种在多种模型中结合结果的流行方法，这些模型在这种情况下提供了一些有吸引力的好处，包括对组合集群结构的概率解释和基于模型的不确定性的量化。在这项工作中，我们介绍了ClusterBMA，该方法可以通过多种无监督聚类算法进行加权模型平均。我们将聚类内部验证标准的组合用作后验模型概率的新近似值，以加权每个模型的结果。从代表跨模型的聚类溶液的加权平均值的组合后相似性矩阵，我们应用对称的单纯形矩阵分解来计算最终的概率群集分配。此方法在随附的R软件包中实现。我们通过案例研究探索这种方法的性能，该案例研究旨在根据脑电图（EEG）数据识别个体的概率簇。我们还使用仿真数据集探索所提出的技术识别稳健的集成簇具有不同级别的集成簇，并在子组之间的分离水平变化，并且模型之间的簇数量变化。

对黑暗时代和系外行星（Farside）进行无线电科学调查的遥远阵列是对Lunar Far Side的拟议任务概念，试图在100正方形的区域内部署和操作128双极化的阵列，偶极天线公里。所得的干涉射电望远镜将提供遥远恒星系统的前所未有的无线电图像，从而可以研究冠状质量弹出和能量颗粒事件的微弱无线电特征，还可以导致在其母星的居住区内检测到磁层周围的磁层。同时，Farside还将在一系列红移（z大约50-100）中以全球21厘米信号的全局信号来测量早期宇宙的“黑暗年龄”。阵列中的每个离散天线节点都通过通信和电源系绳连接到中央集线器（位于降落器）。节点是由Cold =可操作的电子设备驱动的，该电子设备连续监测极宽的频率（200 kHz至40 MHz），该频率超过了基于地球的望远镜的能力，该望远镜的功能由两个数量级。实现这种开创性的能力需要在月球表面上制定强大的部署策略，这对于现有高的TRL技术（演示或正在积极发展）是可行的，并且能够在下一代商业地面上传递到地​​表，例如蓝色Origin的蓝月亮着陆器。本文介绍了一种天线包装，放置和表面部署贸易研究，该研究利用了NASA的Jet Propuls实验室开发的束缚移动机器人的最新进展，该机器人用于部署平坦的，天线隔离的，带有光学通信和电源传输的磁带。功能。

研究过程自动化 - 对科学仪器，计算机，数据存储和其他资源的可靠，高效和可重复执行的可靠，高效和可重复执行，这是现代科学的基本要素。我们在此处报告Globus研究数据管理平台内的新服务，该服务可以将各种研究过程的规范作为可重复使用的动作集，流量以及在异质研究环境中执行此类流动的集合。为了以广泛的空间范围（例如，从科学仪器到远程数据中心）和时间范围（从几秒钟到几周），这些Globus自动化服务功能：1）云托管以可靠地执行长期持久的流量，尽管零星的失败，但这些Globus自动化服务功能：1） ; 2）声明性符号和可扩展的异步行动提供商API，用于定义和执行涉及任意资源的各种行动和流动规范； 3）授权授权机制，用于安全调用动作。这些服务允许研究人员将广泛的研究任务的管理外包和自动化为可靠，可扩展和安全的云平台。我们向Globus自动化服务提供用例