In this work, Yoshikawa's manipulability index is used to investigate reinforcement learning (RL) as a framework for morphology optimization in planar robotic manipulators. A 2R manipulator tracking a circular end-effector path is first examined because this case has a known analytical optimum: equal link lengths and the second joint orthogonal to the first. This serves as a validation step to test whether RL can rediscover the optimum using reward feedback alone, without access to the manipulability expression or the Jacobian. Three RL algorithms (SAC, DDPG, and PPO) are compared with grid search and black-box optimizers, with morphology represented by a single action parameter phi that maps to the link lengths. All methods converge to the analytical solution, showing that numerical recovery of the optimum is possible without supplying analytical structure. Most morphology design tasks have no closed-form solutions, and grid or heuristic search becomes expensive as dimensionality increases. RL is therefore explored as a scalable alternative. The formulation used for the circular path is extended to elliptical and rectangular paths by expanding the action space to the full morphology vector (L1, L2, theta2). In these non-analytical settings, RL continues to converge reliably, whereas grid and black-box methods require far larger evaluation budgets. These results indicate that RL is effective for both recovering known optima and solving morphology optimization problems without analytical solutions.

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic `0' and `1', AQFP devices serve as excellent carriers for binary neural network (BNN) computations. Although recent research has made initial strides toward developing an AQFP-based BNN accelerator, several critical challenges remain, preventing the design from being a comprehensive solution. In this paper, we propose SupeRBNN, an AQFP-based randomized BNN acceleration framework that leverages software-hardware co-optimization to eventually make the AQFP devices a feasible solution for BNN acceleration. Specifically, we investigate the randomized behavior of the AQFP devices and analyze the impact of crossbar size on current attenuation, subsequently formulating the current amplitude into the values suitable for use in BNN computation. To tackle the accumulation problem and improve overall hardware performance, we propose a stochastic computing-based accumulation module and a clocking scheme adjustment-based circuit optimization method. We validate our SupeRBNN framework across various datasets and network architectures, comparing it with implementations based on different technologies, including CMOS, ReRAM, and superconducting RSFQ/ERSFQ. Experimental results demonstrate that our design achieves an energy efficiency of approximately 7.8x10^4 times higher than that of the ReRAM-based BNN framework while maintaining a similar level of model accuracy. Furthermore, when compared with superconductor-based counterparts, our framework demonstrates at least two orders of magnitude higher energy efficiency.

In this position paper, we address potential merits of a novel conversational system using the group form of mul-tiple robots that provides users with a stronger sense of conversation, with which a person can feel as if he or she is participating in a conversation. The merits can be per-formed by implementing the group behavior of multiple robots so that appropriate turn-taking is inserted to en-hance the sense of conversation against potential conver-sational break-down. Through introducing the preliminary analysis of three experiments, how the sense of conversa-tion can be enhanced and evaluated is exemplified and its limitations and potentials are argued.

What's happening in Japan is written all over our faces -- our blank, expressionless, masked faces. Never before, it seems safe to say, have so many people gone about masked. Thus we confront the microbes that assault us. "As self-protection, your mask is practically useless," says Shukan Gendai magazine this month. Commercial face masks, medical authorities say, can block particles measuring 3 to 5 micrometers.

NIIGATA/KYOTO - Self-driving tractors, tomato-picking robots, camera-mounted drones to survey fields and spot crop damage, and satellite data from the Japan Aerospace Exploration Agency (JAXA) to help farms keep track of climate and weather data. At over a dozen booths beside the G20 farm ministers' meeting venue earlier this month in the Sea of Japan city of Niigata, agricultural organizations and technology firms touted products and services they see as necessary tools to ensure a prosperous future for agriculture. "In today's Japan, the aging of farmers has become an issue, and the overall population of the country is decreasing. Collaboration between agriculture and nonagricultural sectors, such as satellite technology, IoT ("internet of things," internet connectivity into physical devices like tractors) and artificial intelligence has a key role to play in fostering agricultural innovation," said Susumu Hamamura, parliamentary vice minister at the Ministry of Agriculture, Forestry and Fisheries. The increased use of easily accessible data on tablet computers and smartphones to provide farmers with a wide range of agricultural data was a key message at the Niigata conference.

This article discusses building a computable design process model, which is a prerequisite for realizing intelligent computer-aided design systems. First, we introduce general design theory, from which a descriptive model of design processes is derived. In this model, the concept of metamodels plays a crucial role in describing the evolutionary nature of design. Second, we show a cognitive design process model obtained by observing design processes using a protocol analysis method. We then discuss a computable model that can explain most parts of the cognitive model and also interpret the descriptive model. In the computable model, a design process is regarded as an iterative logical process realized by abduction, deduction, and circumscription. We implemented a design simulator that can trace design processes in which design specifications and design solutions are gradually revised as the design proceeds.