本文提出了一种在机器人操纵运动中处理物体滑移的新型控制方法。滑移是许多机器人抓握和操纵任务中失败的主要原因。现有工程增加了抓地力以避免/控制滑移。但是，当（i）机器人无法增加抓地力时，这可能是不可行的 - 最大抓地力已被施加或（ii）增加的力损坏了抓地物物体，例如软果。此外，机器人在物体表面形成稳定的掌握时固定了握力，并且在实时操作过程中更改握紧力可能不是有效的控制政策。我们提出了一种新颖的控制方法，以避免滑移，包括学到的动作条件的滑移预测指标和受约束的优化器，避免了预测的机器人动作。我们通过一系列真实机器人测试案例显示了拟议的轨迹适应方法的有效性。我们的实验结果表明，我们提出的数据驱动的预测控制器可以控制训练中看不见的物体的滑动。

本文提出了一种从示威（LFD）中进行深度机器人学习的新型概率方法。深度运动原语（DMP）是确定性的LFD模型，可直接将视觉信息映射到机器人轨迹中。本文扩展了DMP，并提出了一个深层概率模型，该模型将视觉信息映射到有效的机器人轨迹的分布中。提出了导致轨迹精度最高水平的结构，并与现有方法进行了比较。此外，本文介绍了一种用于学习域特异性潜在特征的新型培训方法。我们展示了在实验室的草莓收集任务中提出的概率方法和新颖的潜在空间学习的优势。实验结果表明，潜在空间学习可以显着改善模型预测性能。提出的方法允许从分布中采样轨迹并优化机器人轨迹以满足次级目标，例如避免碰撞。

A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean \textit{k}-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines.

为了更好地优化全球食品供应链，需要机器人解决方案来自动化人类目前完成的任务。也就是说，表型，质量分析和收获都是农业机器人技术领域的空旷问题。机器人感知是自治解决方案的关键挑战，例如场景理解和对象检测是机器人可能承担的任何掌握任务的重要先决条件。这项工作对现代机器人感知模型进行了简要审查，并讨论了它们在农业食品领域内的功效。

在最近的过去，草莓的机器人收获引起了很多兴趣。尽管有很多创新，但它们尚未达到与人类采摘专家相当的水平。末端效应单元在定义这种机器人收割系统的效率方面起着重要作用。即使有关于草莓收集的各种最终效应子的报道，但是在某些情况下，研究人员可以依靠某些参数来开发新的最终效应子。这些参数包括可以在花梗上应用的抓地力极限，以有效地抓握，切割草莓花梗所需的力等。这些估计将对目标的最终效应器的设计周期有所帮助，以握住和切割在收获动作期间，草莓花梗。本文通过实验研究了这些参数的估计和分析。据估计，花梗的握力可以限制为10N。这使最终效应器能够抓住高达50克的草莓，而操纵加速度为50 m/s $^2 $，而不会挤压花梗。关于花梗切割力的研究表明，15 n的力足以在30度方向上使用楔形角度为16.6度的刀片切出草莓花梗。

科学研究的基本目标是了解因果关系。然而，尽管因果关系在生活和社会科学中的重要作用，但在自然语言处理（NLP）中并不具有相同的重要性，而自然语言处理（NLP）传统上更加重视预测任务。这种区别开始逐渐消失，随着因果推理和语言处理的融合，跨学科研究的新兴领域。尽管如此，关于NLP因果关系的研究仍然散布在没有统一的定义，基准数据集的情况下，并清楚地表达了将因果推论应用于文本领域的挑战和机遇，并具有其独特的属性。在这项调查中，我们巩固了整个学术领域的研究，并将其置于更广泛的NLP景观中。我们介绍了用文本估算因果效应的统计挑战，其中包含文本用作结果，治疗或解决混杂问题的设置。此外，我们探讨了因果推理的潜在用途，以提高NLP模型的鲁棒性，公平性和解释性。因此，我们提供了NLP社区因果推断的统一概述。

With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, for a computer to learn from data accurately and efficiently, some auxiliary information about the data distribution and target function should be provided to it through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. In this paper, we propose a learning model that exploits this multiscale data structure and discuss its statistical and computational benefits. The hierarchical learning model is inspired by the logical and progressive easy-to-hard learning mechanism of human beings and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.

This paper proposes a novel observer-based controller for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive measurements from a Vision-Aided Inertial Navigation System (VA-INS) and produce the required thrust and rotational torque inputs. The VA-INS is composed of a vision unit (monocular or stereo camera) and a typical low-cost 6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a gyroscope. A major benefit of this approach is its applicability for environments where the Global Positioning System (GPS) is inaccessible. The proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately estimate attitude (orientation), gyroscope bias, position, and linear velocity. Ability to use VA-INS measurements directly makes the proposed observer design more computationally efficient as it obviates the need for attitude and position reconstruction. Once the motion components are estimated, the observer-based controller is used to control the VTOL-UAV attitude, angular velocity, position, and linear velocity guiding the vehicle along the desired trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and the control errors of the observer-based controller are proven to be exponentially stable starting from almost any initial condition. To achieve global and unique VTOL-UAV representation in 6 DoF, the proposed approach is posed on the Lie Group and the design in unit-quaternion is presented. Although the proposed approach is described in a continuous form, the discrete version is provided and tested. Keywords: Vision-aided inertial navigation system, unmanned aerial vehicle, vertical take-off and landing, stochastic, noise, Robotics, control systems, air mobility, observer-based controller algorithm, landmark measurement, exponential stability.