We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.

智能杂草系统为了执行植物特定的运营，可以有助于农业和环境的可持续性。尽管近年来对精密杂草管理的自主机器人技术造成巨大进展，但尚未实现在领域的底盖内的工作。这种系统的先决条件是可靠的检测和杂草的分类，以避免错误地喷涂，从而损坏周围的植物。实时多级杂草鉴定使特异性的杂草治疗能够显着降低除草剂的使用量。在这里，我们的第一个贡献是第一个充分的大型现实图像数据集\ texit {aiweeds}（一个图像中的一个/多种杂草），一个约10,000个亚麻的注释图像，以及在田间和花园中最常见的14个杂草从北达科他州，加利福尼亚州和中国中部的20个不同的地方取自20个不同的地方。其次，我们提供了一个完整的管道，从模型培训，最大效率将规则解优化模型部署到单板计算机上。基于\ Texit {Aiweeds}和管道，我们使用五个基准CNN模型提出了一种分类性能的基线。其中，MobileNetv2具有最短的推理时间和最低记忆消耗，是实时应用程序的合格候选者。最后，我们将MobileNetv2部署到我们自己的紧凑型自主机器人\ Textit {Sambot}以进行实时杂草检测。在亚麻领域的先前看不见的场景中实现了90 \％测试精度（具有0.2-0.3米的行间距，杂草和杂草，失真，模糊和阴影，是真实世界中精确杂草控制的里程碑。我们公开发布了DataSet和代码以生成\ URL {https://github.com/structurescomp/multi-class-weed-classification}。

我们探索粒状介质（GM）中软机器的运动，由细长杆的弹性变形产生。提出了由细菌的生理结构的低成本，迅速制造的机器人。它由刚性头部，带有电动机和电池的嵌入式和电池，以及多个弹性杆（我们的灯泡模型）来调查通用汽车的运动。弹性鞭毛在电机一端旋转，它们由于从GM的拖动而变形，推动机器人。外部拖动由鞭毛形状决定，而后者由于外部负载和弹力之间的竞争而改变。在该耦合的流体结构相互作用问题中，我们观察到增加鞭毛的数量可以减小或增加机器人的推进速度，这取决于系统的物理参数。这种简单机器人之间的功能关系中的这种非线性激励我们利用理论，数值模拟和实验来从根本上分析其力学。我们提出了一个简单的欧拉伯努利光束理论的分析框架，其能够定性地捕获这两种情况。当鞭毛变形小时，理论预测定量匹配实验。为了考虑经常在软机器人和微生物中遇到的几何非线性变形，我们实施了一种仿真框架，该框架包括弹性杆的离散微分几何形状模拟，这是一种基于电阻理论的拖曳模型，以及用于流体动力学的改进的斯托克斯法机器人头。与实验数据的比较表明模拟可以定量地预测机器人运动。总的来说，本文中提出的理论和数值工具可以在粒状或流体介质中的这类清晰的机器人的设计和控制来阐明。

可变形物体的力学实验分析，特别是其稳定性，需要重复测试，并且根据物体形状的复杂性，可以在对象的边界处操纵多程度的自由度的测试设置。通过最近的可变形物体操纵机器人操纵的最新进步，本文通过构建用于细长弹性棒的自动稳定性测试的方法来解决这些挑战 - 使用机器人系统的可变形物体的规范示例 - 我们专注于带螺旋中心线的杆配置，因为只有三个参数可以描述螺杆的稳定性，但是通过实验确定稳定性需要操纵杆的一端的位置和取向，这是使用传统实验不可能的仅致力于有限数量的自由度的方法。使用最近的螺旋杆的稳定性的几何表征，我们构建和实施操作方案以探索稳定螺旋的空间，我们使用视觉系统来检测该空间内的不稳定性。我们的自动化测试系统获得的实验结果表明，螺旋配置中弹性杆的数值模拟良好。本文中描述的方法为自动化进行了基础，以在实验力学领域内生长。

The BLOOM model is a large open-source multilingual language model capable of zero-shot learning, but its pretraining was limited to 46 languages. To improve its zero-shot performance on unseen languages, it is desirable to adapt BLOOM, but previous works have only explored adapting small language models. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at \url{https://github.com/bigscience-workshop/multilingual-modeling/}.

Speed estimation of an ego vehicle is crucial to enable autonomous driving and advanced driver assistance technologies. Due to functional and legacy issues, conventional methods depend on in-car sensors to extract vehicle speed through the Controller Area Network bus. However, it is desirable to have modular systems that are not susceptible to external sensors to execute perception tasks. In this paper, we propose a novel 3D-CNN with masked-attention architecture to estimate ego vehicle speed using a single front-facing monocular camera. To demonstrate the effectiveness of our method, we conduct experiments on two publicly available datasets, nuImages and KITTI. We also demonstrate the efficacy of masked-attention by comparing our method with a traditional 3D-CNN.

In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class-imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, over-sampling, and under-sampling, yet these ad-hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the Generative Adversarial Network (GAN), named the balanced semi-supervised GAN (BSS-GAN). It adopts the semi-supervised learning concept and applies balanced-batch sampling in training to resolve low-data and imbalanced-class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low-data imbalanced-class regime with limited computing power. The results show that the BSS-GAN is able to achieve better damage detection in terms of recall and $F_\beta$ score than other conventional methods, indicating its state-of-the-art performance.

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.

在农业环境中的现代除草剂应用通常依赖于将除草剂分配给作物和杂草相似的或便携式喷雾器的大型喷雾器，这些喷雾器需要劳动密集型手动操作。前一种方法导致过度使用除草剂并减少作物产量，而后者在大规模操作中经常站立。本文介绍了能够基于计算机视觉的导航，杂草检测，完整的现场覆盖以及\ $ 400下的计算机视觉的行作物的杂草管理的第一个完全自主机器人。目标应用程序是在裁剪领域中的自主行行杂草控制，例如，亚麻和油菜，在农作物之间的间距像一只脚一样小。所提出的机器人足够小，可以在植物生长的所有阶段之间通过植物生长的阶段，同时检测杂草和喷洒除草剂。充电系统包括新设计的机器人硬件，斜坡，机器人充电臂和移动充电站。采用集成视觉算法，有效地帮助充电器对齐。结合，它们使机器人能够在现场中连续工作而不获得电力。此外，将与预处理技术相结合的基于颜色的轮廓算法用于依赖于从车载单手套摄像机的输入上的鲁棒导航。将这种紧凑的机器人纳入农场可以帮助自动化杂草控制，即使在增长的后期阶段，并通过精确定位杂草减少除草剂。机器人平台在北达科他州的亚麻籽领域进行了现场测试。

微生物，特别是微型游泳者，对生物学和流体动力学的领域感兴趣的运动效率和机械效率。设计鞭打的微型和宏观机器人的挑战是从弹性和流体动力学的相互作用中随后的细长结构（例如棒状鞭毛）的几何非线性变形。某些类型的细菌如大肠杆菌通过在低雷诺流中旋转多个丝状结构来推动自己。这种多鞭状的推进机制与其他类型的细菌（如富轴霍乱）呈现的单鞭状机制定性不同。差异包括鞭毛形成束，以提高细胞运动性的方向稳定性，为细胞移动提供冗余，并提供鞭毛成为递送材料本身的能力。最重要的是，多鞭状的生物系统可以激发新型软机器，用于在人体内施用药物运输和递送。我们提出了一种宏观软机械硬件平台和用于多鞭状机器人的物理合理的仿真模型的计算框架。流体结构相互作用仿真将离散弹性棒算法与正则化的阶段段的方法耦合。由于Spillmann和Teschner，两个鞭毛之间的联系由基于惩罚的方法处理。我们在我们的实验和仿真结果之间显示比较，并验证模拟工具是否可以捕获此问题的基本物理。将多抹布机器人的稳定性和效率与单鞭状的对应物进行比较。