Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generating art, question answering (such as ChatGPT), etc. Such a dramatic progress raises the question: how generalizable are neural networks in solving problems that demand broad skills? To answer this question, we propose SMART: a Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101 dataset, for evaluating the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed specifically for children in the 6-8 age group. Our dataset consists of 101 unique puzzles; each puzzle comprises a picture and a question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning, among others. To scale our dataset towards training deep neural networks, we programmatically generate entirely new instances for each puzzle while retaining their solution algorithm. To benchmark the performance on the SMART-101 dataset, we propose a vision and language meta-learning model using varied state-of-the-art backbone neural networks. Our experiments reveal that while powerful deep models offer reasonable performances on puzzles that they are trained on, they are not better than random accuracy when analyzed for generalization. We also evaluate the recent ChatGPT large language model on a subset of our dataset and find that while ChatGPT produces convincing reasoning abilities, the answers are often incorrect.

In this paper, we study the implementation of a model predictive controller (MPC) for the task of object manipulation in a highly uncertain environment (e.g., picking objects from a semi-flexible array of densely packed bins). As a real-time perception-driven feedback controller, MPC is robust to the uncertainties in this environment. However, our experiment shows MPC cannot control a robot to complete a sequence of motions in a heavily occluded environment due to its myopic nature. It will benefit from adding a high-level policy that adaptively adjusts the optimization problem for MPC.

我们提出了Blenderbot 3，这是一个175B参数对话模型，能够通过访问Internet和长期内存进行开放域对话，并接受了大量用户定义的任务的培训。我们同时发布了模型权重和代码，还将模型部署在公共网页上，以与有机用户进行交互。该技术报告描述了该模型的构建方式（建筑，模型和培训计划）以及其部署的细节，包括安全机制。人类评估表明，它优于现有的开放域对话代理，包括其前身（Roller等，2021； Komeili等，2022）。最后，我们使用部署收集的数据详细介绍了持续学习的计划，该数据也将公开发布。因此，该研究计划的目标是使社区能够研究通过互动学习的不断改进的负责任的代理商。

我们提出了Neuricam，这是一种基于钥匙帧的视频超分辨率和着色系统，可从双模式IoT摄像机获得低功耗视频捕获。我们的想法是设计一个双模式摄像机系统，其中第一个模式是低功率（1.1〜MW），但仅输出灰度，低分辨率和嘈杂的视频，第二种模式会消耗更高的功率（100〜MW），但输出会输出。颜色和更高分辨率的图像。为了减少总能源消耗，我们在高功率模式下高功率模式仅输出图像每秒一次。然后将来自该相机系统的数据无线流传输到附近的插入网关，在那里我们运行实时神经网络解码器，以重建更高的分辨率颜色视频。为了实现这一目标，我们基于每个空间位置的特征映射和输入框架的内容之间的相关性，引入了一种注意力特征滤波器机制，该机制将不同的权重分配给不同的特征。我们使用现成的摄像机设计无线硬件原型，并解决包括数据包丢失和透视不匹配在内的实用问题。我们的评估表明，我们的双摄像机硬件可减少相机的能耗，同时在先前的视频超级分辨率方法中获得平均的灰度PSNR增益为3.7〜db，而在现有的颜色传播方法上，我们的灰度尺度PSNR增益为3.7 〜db。开源代码：https：//github.com/vb000/neuricam。

Systemic Lupus红斑（SLE）是一种罕见的自身免疫疾病，其特征是令人无法预测的耀斑和缓解的速度，具有不同的表现形式。狼疮性肾炎，SLE用于器官损伤和死亡率的主要疾病表现之一，是卢布斯分类标准的关键组成部分。因此，准确地鉴定电子健康记录（EHRS）中的狼疮性肾炎将使大型队列观察研究和临床试验有益于患者人口的表征对于招聘，研究设计和分析至关重要。可以通过程序代码和结构化数据来认可狼疮肾炎，例如实验室测试。然而，记录狼疮肾炎的其他关键信息，例如来自肾脏活检和先前的医学史叙事的组织学报告，需要复杂的文本处理，以从病理报告和临床笔记中挖掘信息。在这项研究中，我们开发了使用EHR数据识别鉴定狼疮肾炎的血管肾炎，而不使用自然语言处理（NLP）。我们开发了四种算法：仅使用结构化数据（基线算法）和使用不同NLP模型的三种算法的规则的算法。这三种NLP模型基于正则化逻辑回归，并使用不同的特征集，包括积极提及概念独特标识符（Cue），耐备的外观数量，以及三个部件的混合物。基线算法和最佳执行的NLP算法在Vanderbilt University Center（VUMC）的数据集上验证了外部验证。我们最佳地执行来自结构化数据，正则表达式概念和映射的特征的NLP模型，与基线狼疮性肾炎算法相比，在NMEDW（0.41 VS 0.79）和VUMC（0.62 VS 0.96）数据集中有所改善。

尽管当前的视觉算法在许多具有挑战性的任务上都表现出色，但尚不清楚他们如何理解现实世界环境的物理动态。在这里，我们介绍了Physion，一种数据集和基准，用于严格评估预测物理场景如何随着时间而发展的能力。我们的数据集具有对各种物理现象的现实模拟，包括刚性和软体体碰撞，稳定的多对象配置，滚动，滑动和弹丸运动，因此比以前的基准提供了更全面的挑战。我们使用Physion来基准一套模型，其体系结构，学习目标，投入输出结构和培训数据各不相同。同时，我们在同一场景上获得了人类预测行为的精确测量，从而使我们能够直接评估任何模型能够近似人类行为的效果。我们发现，学习以对象为中心的表示的视觉算法通常优于那些没有人的表现，但仍未达到人类绩效。另一方面，绘制具有直接访问物理状态信息的神经网络的表现效果更好，并且做出与人类制作的预测更相似。这些结果表明，提取场景的物理表征是在视力算法中实现人类水平和类似人类的物理理解的主要瓶颈。我们已公开发布了所有数据和代码，以促进使用物理以完全可重现的方式对其他模型进行基准测试，从而使对视觉算法的进度进行系统的评估，这些算法像人们一样坚固地了解物理环境。

对控制框架的兴趣越来越大，能够将机器人从工业笼子转移到非结构化环境并与人类共存。尽管某些特定应用（例如，医学机器人技术）有了显着改善，但仍然需要一个一般控制框架来改善鲁棒性和运动动力学。被动控制者在这个方向上显示出令人鼓舞的结果。但是，他们通常依靠虚拟能源储罐，只要它们不耗尽能量，就可以保证被动性。在本文中，提出了一个分形吸引子来实施可变的阻抗控制器，该控制器可以保留不依赖能箱的无源性。控制器使用渐近稳定电位场在所需状态周围生成一个分形吸引子，从而使控制器稳健地对离散化和数值集成误差。结果证明它可以在相互作用过程中准确跟踪轨迹和最终效应力。因此，这些属性使控制器非常适合需要在最终效应器上进行鲁棒动态相互作用的应用。

Understanding the relationship between structure and sentiment is essential in highlighting future operations with online social networks. More specifically, within popular conversation on Twitter. This paper provides a development on the relationship between the two variables: structure, defined as the composition of a directed network, and sentiment, a quantified value of the positive/negative connotations of a conversation. We highlight thread sentiment to be inversely proportional to the strength and connectivity of a network. The second portion of this paper highlights differences in query types, specifically how the aforementioned behavior differs within four key query types. This paper focuses on topical, event-based, geographic, and individual queries as orientations which have differing behavior. Using cross-query analysis, we see that the relationship between structure and sentiment, though still inversely proportional, differs greatly across query types. We find this relationship to be the most clear within the individual queries and the least prevalent within the event-based queries. This paper provides a sociological progression in our understanding of opinion and networks, while providing a methodological advancement for future studies on similar subjects.

We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to "act-or-not" at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.

Data deprivation, or the lack of easily available and actionable information on the well-being of individuals, is a significant challenge for the developing world and an impediment to the design and operationalization of policies intended to alleviate poverty. In this paper we explore the suitability of data derived from OpenStreetMap to proxy for the location of two crucial public services: schools and health clinics. Thanks to the efforts of thousands of digital humanitarians, online mapping repositories such as OpenStreetMap contain millions of records on buildings and other structures, delineating both their location and often their use. Unfortunately much of this data is locked in complex, unstructured text rendering it seemingly unsuitable for classifying schools or clinics. We apply a scalable, unsupervised learning method to unlabeled OpenStreetMap building data to extract the location of schools and health clinics in ten countries in Africa. We find the topic modeling approach greatly improves performance versus reliance on structured keys alone. We validate our results by comparing schools and clinics identified by our OSM method versus those identified by the WHO, and describe OSM coverage gaps more broadly.