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.

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.

This letter proposes a novel single-fingered reconfigurable robotic gripper for grasping objects in narrow working spaces. The finger of the developed gripper realizes two configurations, namely, the insertion and grasping modes, using only a single motor. In the insertion mode, the finger assumes a thin shape such that it can insert its tip into a narrow space. The grasping mode of the finger is activated through a folding mechanism. Mode switching can be achieved in two ways: switching the mode actively by a motor, or combining passive rotation of the fingertip through contact with the support surface and active motorized construction of the claw. The latter approach is effective when it is unclear how much finger insertion is required for a specific task. The structure provides a simple control scheme. The performance of the proposed robotic gripper design and control methodology was experimentally evaluated. The minimum width of the insertion space required to grasp an object is 4 mm (1 mm, when using a strategy).

如今，世界各地的城市推出了电动公共汽车以优化城市交通，减少当地碳排放量。为了减少碳排放并最大化电动公共汽车的效用，重要的是为它们选择合适的路线很重要。传统上，路线选择是在专用调查的基础上，这在时间和劳动力成本高昂。在本文中，我们主要关注智能规划电动公交线路，具体取决于整个城市各地区的独特需求。我们提出了一种铺张山庄，一个路线规划系统，利用深度神经网络和多层的感知者，以预测未来人民的旅行和整个城市的未来运输碳排放。鉴于人们旅行和运输碳排放的未来信息，我们利用了一种贪婪的机制来推荐将以理想状态离开的电动公交车的公交线路。此外，从异构城市数据集中提取两个神经网络的代表特征。我们通过对珠海省珠海真实世界资源的大量实验来评估我们的方法。结果表明，我们设计的基于神经网络的算法始终如一地优于典型的基线。此外，电动公交车的建议路线有助于降低碳排放的峰值，并充分利用城市的电动公共汽车。

安全保证是自动驾驶（AD）系统发展和社会接受（AD）系统的核心问题。感知是广告的关键方面，严重依赖机器学习（ML）。尽管基于ML的组件的安全性有已知的挑战，但最近已经提出针对解决这些组件的单位安全案例的建议。不幸的是，AD安全案例在系统级别上表示安全要求，这些努力缺少将安全性要求与单位级别的组件性能要求整合在一起所需的关键链接参数。在本文中，我们提出了感知的集成安全案例（ISCAP），这是针对专门针对感知组件量身定制的这种链接安全参数的通用模板。该模板采用演绎且形式上的方法来定义级别之间强大的可追溯性。我们通过详细的案例研究证明了ISCAP的适用性，并讨论了其作为支持感知成分增量发展的工具的使用。