不同的代理需要进行预测。他们观察到相同的数据，但有不同的模型：他们预测使用不同的解释变量。我们研究哪个代理商认为它们具有最佳的预测能力 - 通过最小的主观后均匀平均平方预测误差来衡量 - 并且显示它如何取决于样本大小。使用小样品，我们呈现结果表明它是使用低维模型的代理。对于大型样品，通常是具有高维模型的代理，可能包括无关的变量，但从未排除相关的变量。我们将结果应用于拍卖生产资产拍卖中的获胜模型，以争辩于企业家和具有简单模型的投资者将在新部门过度代表，并了解解释横断面变异的“因素”的扩散资产定价文学中的预期股票回报。

Ithaca is a Fuzzy Logic (FL) plugin for developing artificial intelligence systems within the Unity game engine. Its goal is to provide an intuitive and natural way to build advanced artificial intelligence systems, making the implementation of such a system faster and more affordable. The software is made up by a C\# framework and an Application Programming Interface (API) for writing inference systems, as well as a set of tools for graphic development and debugging. Additionally, a Fuzzy Control Language (FCL) parser is provided in order to import systems previously defined using this standard.

In this paper, we present an evolved version of the Situational Graphs, which jointly models in a single optimizable factor graph, a SLAM graph, as a set of robot keyframes, containing its associated measurements and robot poses, and a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between those elements. Our proposed S-Graphs+ is a novel four-layered factor graph that includes: (1) a keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robot's pose and its map, simultaneously constructing and leveraging the high-level information of the environment. To extract such high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets including, simulations of distinct indoor environments, on real datasets captured over several construction sites and office environments, and on a real public dataset of indoor office environments. S-Graphs+ outperforms relevant baselines in the majority of the datasets while extending the robot situational awareness by a four-layered scene model. Moreover, we make the algorithm available as a docker file.

The circular coordinates algorithm of de Silva, Morozov, and Vejdemo-Johansson takes as input a dataset together with a cohomology class representing a $1$-dimensional hole in the data; the output is a map from the data into the circle that captures this hole, and that is of minimum energy in a suitable sense. However, when applied to several cohomology classes, the output circle-valued maps can be "geometrically correlated" even if the chosen cohomology classes are linearly independent. It is shown in the original work that less correlated maps can be obtained with suitable integer linear combinations of the cohomology classes, with the linear combinations being chosen by inspection. In this paper, we identify a formal notion of geometric correlation between circle-valued maps which, in the Riemannian manifold case, corresponds to the Dirichlet form, a bilinear form derived from the Dirichlet energy. We describe a systematic procedure for constructing low energy torus-valued maps on data, starting from a set of linearly independent cohomology classes. We showcase our procedure with computational examples. Our main algorithm is based on the Lenstra--Lenstra--Lov\'asz algorithm from computational number theory.

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

安全可靠的自主驾驶堆栈（AD）的设计是我们时代最具挑战性的任务之一。预计这些广告将在具有完全自主权的高度动态环境中驱动，并且比人类更大的可靠性。从这个意义上讲，要高效，安全地浏览任意复杂的流量情景，广告必须具有预测周围参与者的未来轨迹的能力。当前的最新模型通常基于复发，图形和卷积网络，在车辆预测的背景下取得了明显的结果。在本文中，我们探讨了在生成模型进行运动预测中注意力的影响，考虑到物理和社会环境以计算最合理的轨迹。我们首先使用LSTM网络对过去的轨迹进行编码，该网络是计算社会背景的多头自我发言模块的输入。另一方面，我们制定了一个加权插值来计算最后一个观测框中的速度和方向，以便计算可接受的目标点，从HDMAP信息的可驱动的HDMAP信息中提取，这代表了我们的物理环境。最后，我们的发电机的输入是从多元正态分布采样的白噪声矢量，而社会和物理环境则是其条件，以预测可行的轨迹。我们使用Argoverse运动预测基准1.1验证我们的方法，从而实现竞争性的单峰结果。

建筑行业的机器人可以使用高精度数据捕获来通过不断监视工作进度来降低成本。准确的数据捕获需要在环境中精确的移动机器人定位。在本文中，我们介绍了有关机器人本地化的新颖作品，该工作以墙壁和房间的形式提取了从建筑计划中提取几何，语义以及拓扑信息，并创建了情境图的拓扑和度量语言层（S-图）在环境中导航之前。当机器人在施工环境中导航时，它使用机器人的探光仪和从3D LIDAR测量中提取的平面壁的形式的感觉观测来估算其依靠粒子过滤器方法的姿势，并利用先前构建的情境图和它可用的几何，语义和拓扑信息。我们在将其与基于传统几何的本地化技术进行比较时，在实际持续的施工站点上捕获的模拟和真实数据集中验证了我们的方法。

语言随着时间的流逝而演变，单词含义会发生相应的变化。在社交媒体中尤其如此，因为它的动态性质会导致语义转移的速度更快，这使得NLP模型在处理新内容和趋势方面具有挑战性。但是，专门解决这些社交平台动态性质的数据集和模型的数量很少。为了弥合这一差距，我们提出了Tempowic，这是一种新的基准，尤其是旨在加快基于社交媒体的含义转变的研究。我们的结果表明，即使对于最近发行的专门从事社交媒体的语言模型，Tempowic是一个具有挑战性的基准。

软机器人抓手具有许多优势，可以解决动态空中抓握方面的挑战。最近展示的用于空中抓握的典型多指的软握把高度依赖于成功抓握的目标对象的方向。这项研究通过开发一种用于自主空气操纵的全向系统来推动动态空中抓地力的边界。特别是，该论文研究了一种新型，高度集成，模块化，传感器富含通用的握把的设计，制造和实验验证，专为空中应用而设计。提出的抓手利用粒子堵塞和软颗粒材料的最新发展产生了强大的握持力，同时非常轻巧，节能，并且只需要低激活力。我们表明，通过在膜的硅硅混合物中添加添加剂，可以将持有力提高多达50％。实验表明，即使没有几何互锁，我们的轻质抓地力也可以以低至2.5n的激活力发育高达15n的持有力。最后，通过将抓地力安装到多旋风的情况下，在实际条件下执行了一个选择和释放任务。开发的空中抓握系统具有许多有用的属性，例如对碰撞的弹性和鲁棒性以及将无人机与环境脱离的固有的被动合规性。