East Japan Railway has said it will launch a trial in April of a system that uses artificial intelligence to monitor pantographs on trains running on its busy Yamanote Line in Tokyo to detect defects at an early stage. The railway operator, known as JR East, also plans to use drones to inspect overhead wires and other infrastructure, aiming to reduce the time required to resume operations by 30% when transport service disruptions occur due to equipment problems. Cameras to monitor pantographs, which are located on the roof of a train car and connect the carriage to overheard electrical wires, will be installed near Shimbashi, Ebisu, Mejiro and Uguisudani stations in the capital, the company said Tuesday. The AI system will analyze the images in real time, and if damage is detected, it will notify the control room or other relevant sections. Drones will be dispatched later to inspect overhead wires and other equipment, facilitating faster restoration work.

Machine-assisted theorem proving refers to the process of conducting structured reasoning to automatically generate proofs for mathematical theorems. Recently, there has been a surge of interest in using machine learning models in conjunction with proof assistants to perform this task. In this paper, we introduce Pantograph, a tool that provides a versatile interface to the Lean 4 proof assistant and enables efficient proof search via powerful search algorithms such as Monte Carlo Tree Search. In addition, Pantograph enables high-level reasoning by enabling a more robust handling of Lean 4's inference steps. We provide an overview of Pantograph's architecture and features. We also report on an illustrative use case: using machine learning models and proof sketches to prove Lean 4 theorems. Pantograph's innovative features pave the way for more advanced machine learning models to perform complex proof searches and high-level reasoning, equipping future researchers to design more versatile and powerful theorem provers.

When designing a robot to interact with people, the decision to incorporate a robot arm may arise. In this paper, we investigate adding an inexpensive, functional arm to our mobile CoBot service robots. Specifically, we examine two-dimensional extendable pantograph arms for CoBot. Pantograph arms have intuitive kinematics and inverse kinematics. Pantograph arms are modular and adding additional linkages corresponds to simple changes in the kinematic calculations. These arms have several advantages (and disadvantages) compared to traditional robot arms. A prototype pantograph arm is currently in development and our goal is to attach a modular pantograph arm to CoBot to perform simple needed tasks, such as knocking on doors and pressing elevator buttons.