我们介绍RealityTalk，该系统通过语音驱动的互动虚拟元素来增强实时实时演示。增强演示文稿利用嵌入式视觉效果和动画来吸引和表现力。但是，现有的实时演示工具通常缺乏互动性和即兴创作，同时在视频编辑工具中产生这种效果需要大量的时间和专业知识。RealityTalk使用户能够通过实时语音驱动的交互创建实时增强演示文稿。用户可以通过实时语音和支持方式进行交互提示，移动和操纵图形元素。根据我们对177个现有视频编辑的增强演示文稿的分析，我们提出了一套新颖的互动技术，然后将它们纳入真人秀。我们从主持人的角度评估我们的工具，以证明系统的有效性。

本文介绍了素描的现实，这种方法结合了AR素描和驱动的有形用户界面（TUI），用于双向素描交互。双向草图使虚拟草图和物理对象通过物理驱动和数字计算相互影响。在现有的AR素描中，虚拟世界和物理世界之间的关系只是一个方向 - 虽然物理互动会影响虚拟草图，但虚拟草图对物理对象或环境没有返回效果。相反，双向素描相互作用允许草图和驱动的tuis之间的无缝耦合。在本文中，我们采用桌面大小的小型机器人（Sony Toio）和基于iPad的AR素描工具来演示该概念。在我们的系统中，在iPad上绘制和模拟的虚拟草图（例如，线，墙壁，摆和弹簧）可以移动，动画，碰撞和约束物理Toio机器人，就像虚拟草图和物理对象存在于同一空间中一样通过AR和机器人运动之间的无缝耦合。本文贡献了一组新型的互动和双向AR素描的设计空间。我们展示了一系列潜在的应用，例如有形的物理教育，可探索的机制，儿童有形游戏以及通过素描的原位机器人编程。

本文介绍了一种生成高度选择性编码的方法，这些编码可以在物理模块上磁性地“编程”，以使其能够以所选的配置自组装。我们基于Hadamard矩阵生成这些编码，并展示如何设计模块的面孔，以对其预期的伴侣具有最大吸引力，同时对其他面孔保持最大不可知论。我们得出这些界限的保证，并通过实验验证它们的吸引力和不可知论。使用其面部已被软磁性材料覆盖的立方模块，我们显示了如何使用带有平面面的廉价的被动模块来选择性地自组装成目标形状，而无需几何指南。我们表明，这些模块可以使用基于CNC的磁性绘图仪轻松地重新编程，以用于新的目标形状，并证明水箱中8个立方体的自组装。

本文介绍了基于立方体的可重构机器人，该机器人利用基于电磁体的驱动框架通过枢轴在三个维度重新配置。尽管已经探索了各种可自我配置机器人的驱动机制，但它们通常会遭受成本，复杂性，组装和尺寸要求，以阻止这种机器人的生产规模。为了应对这一挑战，我们使用基于嵌入每个立方体边缘的电磁体的驱动机制来互换创建相同或相反极化的电磁体对，分别产生排斥或吸引力。通过利用铰链形成的吸引力，并以驱动旋转操作的排斥力，我们可以通过电磁磁性磁极磁极的旋转旋转来重新配置机器人，并使其构成模块（称为电动员）。为了证明这一点，我们开发了完全不受限制的三维自我恢复的机器人，并在抛物线能飞行中使用枢轴和横向动作展示了2D和3D自我调查的方法。本文介绍了我们机器人的硬件设计，其旋转框架，我们的重新配置计划软件以及对系统的动态和电气特性的评估，以告知可扩展的自我可控机器人的设计。

Hopfield networks and Boltzmann machines (BMs) are fundamental energy-based neural network models. Recent studies on modern Hopfield networks have broaden the class of energy functions and led to a unified perspective on general Hopfield networks including an attention module. In this letter, we consider the BM counterparts of modern Hopfield networks using the associated energy functions, and study their salient properties from a trainability perspective. In particular, the energy function corresponding to the attention module naturally introduces a novel BM, which we refer to as attentional BM (AttnBM). We verify that AttnBM has a tractable likelihood function and gradient for a special case and is easy to train. Moreover, we reveal the hidden connections between AttnBM and some single-layer models, namely the Gaussian--Bernoulli restricted BM and denoising autoencoder with softmax units. We also investigate BMs introduced by other energy functions, and in particular, observe that the energy function of dense associative memory models gives BMs belonging to Exponential Family Harmoniums.

Generative models, particularly GANs, have been utilized for image editing. Although GAN-based methods perform well on generating reasonable contents aligned with the user's intentions, they struggle to strictly preserve the contents outside the editing region. To address this issue, we use diffusion models instead of GANs and propose a novel image-editing method, based on pixel-wise guidance. Specifically, we first train pixel-classifiers with few annotated data and then estimate the semantic segmentation map of a target image. Users then manipulate the map to instruct how the image is to be edited. The diffusion model generates an edited image via guidance by pixel-wise classifiers, such that the resultant image aligns with the manipulated map. As the guidance is conducted pixel-wise, the proposed method can create reasonable contents in the editing region while preserving the contents outside this region. The experimental results validate the advantages of the proposed method both quantitatively and qualitatively.

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theoretical, synthetic and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas and contributions from a wide range of subjects. Hybrid Life is an attempt to bring attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. In particular, Hybrid Life focuses on three complementary themes: 1) theories of systems and agents, 2) hybrid augmentation, with augmented architectures combining living and artificial systems, and 3) hybrid interactions among artificial and biological systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022.

Vehicle routing problems and other combinatorial optimization problems have been approximately solved by reinforcement learning agents with policies based on encoder-decoder models with attention mechanisms. These techniques are of substantial interest but still cannot solve the complex routing problems that arise in a realistic setting which can have many trucks and complex requirements. With the aim of making reinforcement learning a viable technique for supply chain optimization, we develop new extensions to encoder-decoder models for vehicle routing that allow for complex supply chains using classical computing today and quantum computing in the future. We make two major generalizations. First, our model allows for routing problems with multiple trucks. Second, we move away from the simple requirement of having a truck deliver items from nodes to one special depot node, and instead allow for a complex tensor demand structure. We show how our model, even if trained only for a small number of trucks, can be embedded into a large supply chain to yield viable solutions.

Problem instances of a size suitable for practical applications are not likely to be addressed during the noisy intermediate-scale quantum (NISQ) period with (almost) pure quantum algorithms. Hybrid classical-quantum algorithms have potential, however, to achieve good performance on much larger problem instances. We investigate one such hybrid algorithm on a problem of substantial importance: vehicle routing for supply chain logistics with multiple trucks and complex demand structure. We use reinforcement learning with neural networks with embedded quantum circuits. In such neural networks, projecting high-dimensional feature vectors down to smaller vectors is necessary to accommodate restrictions on the number of qubits of NISQ hardware. However, we use a multi-head attention mechanism where, even in classical machine learning, such projections are natural and desirable. We consider data from the truck routing logistics of a company in the automotive sector, and apply our methodology by decomposing into small teams of trucks, and we find results comparable to human truck assignment.

This study proposed a novel robotic gripper that can achieve grasping and infinite wrist twisting motions using a single actuator. The gripper is equipped with a differential gear mechanism that allows switching between the grasping and twisting motions according to the magnitude of the tip force applied to the finger. The grasping motion is activated when the tip force is below a set value, and the wrist twisting motion is activated when the tip force exceeds this value. "Twist grasping," a special grasping mode that allows the wrapping of a flexible thin object around the fingers of the gripper, can be achieved by the twisting motion. Twist grasping is effective for handling objects with flexible thin parts, such as laminated packaging pouches, that are difficult to grasp using conventional antipodal grasping. In this study, the gripper design is presented, and twist grasping is analyzed. The gripper performance is experimentally validated.