If manganese nodules can be mined in an environmentally friendly way, the critical metals needed for the energy transition could be produced with low CO2 emissions

A recent study explores how Japanese learners of French produce ambiguous speech errors. Using a specialized assessment tool, researchers found that many mistakes once considered purely “phonetic” may actually be rooted in morpholexical misunderstanding, and vice-versa. The findings offer teachers valuable guidance for improving pronunciation pedagogy and error correction. This work opens new possibilities for assessing ambiguity in second-language speech and better understanding the interplay between sound and meaning in learning French.

Magnetic resonance imaging (MRI) is invaluable in the medical world. But despite all the good it does, there is room for improvement. One way to enhance the sensitivity of MRI is called dynamic nuclear polarization (DNP), where target molecules for imaging are modified so they form clearer images when scanned with an MRI machine. But this technique requires some special crystalline materials mixed with polarizing agents which are difficult to create. For the first time, researchers including those from the University of Tokyo demonstrate the use of molecules called fullerenes as polarizing agents. Their new method can make DNP targets sufficient to yield far greater clarity when imaged with an MRI machine, with potential benefits in various medical applications.

The Quantum Education Summit event, hosted at CosmoCaixa Science Museum of Barcelona, will gather more than 300 participants from five different continents.

The topics of the conference will cover the full spectrum of quantum education:  from foundational curriculum design, workforce training and inclusive global participation to policy strategies, educational innovations, and outreach efforts that advance accessibility and broaden the impact of quantum learning worldwide.

Keynote speakers include Nobel Laureate Carl Wieman, from Stanford University, Maité Depuis, from Perimeter Institute of Canada, and Eboney Hearn, from MIT Introduction to Technology, Engineering and Science.

New review in iOptics demonstrates how AI is overcoming key challenges in metasurface design, enabling advanced applications in compact optics and computational imaging.

Reactive planning and control capacity for collaborative robots is essential when the tasks change online in an unstructured environment. This is more difficult for collaborative mobile manipulators (CMM) due to high redundancies. To this end, this paper proposed a reactive whole-body locomotion-integrated manipulation approach based on combined learning and optimization. First, human demonstrations are collected, where the wrist and pelvis movements are treated as whole-body trajectories, mapping to the end-effector (EE) and the mobile base (MB) of CMM, respectively. A time-input kernelized movement primitive (T-KMP) learns the whole-body trajectory, and a multi-dimensional kernelized movement primitive (M-KMP) learns the spatial relationship between the MB and EE pose. According to task changes, the T-KMP adapts the learned trajectories online by inserting the new desired point predicted by M-KMP. Then, the updated reference trajectories are sent to a hierarchical quadratic programming (HQP) controller, where the EE and the MB trajectories tracking are set as the first and second priority tasks, generating the feasible and optimal joint level commands. An ablation simulation experiment with CMM of the HQP is conducted to show the necessity of MB trajectory tracking in mimicking human whole-body motion behavior. Finally, the tasks of the reactive pick-and-place and reactive reaching were undertaken, where the target object was randomly moved, even out of the region of demonstrations. The results showed that the proposed approach can successfully transfer and adapt the human whole-body loco-manipulation skills to CMM online with task changes.

The development of deep learning has motivated the advancement of unconventional computing that leverages analog physical systems such as analog electronics, spintronics, and photonics. These technologies have also led to the development of unique computational paradigms harnessing the features of analog devices, including compute-in-memory for nonvolatile devices and compute-in-sensor for analog electronics. What, then, are the computational paradigms that can exploit the characteristics of photonics? Optical computing has emerged as a promising candidate as it offers low-latency and low-power computation by utilizing the inherent parallelism of light. Additionally, the low-loss medium of optical fibers allows for the transmission of information over long distances. In this study, a remotely driven optical neural network that combines these advantageous features is demonstrated. Namely, computations can be executed with data transfer over a photonic network, which provides a computational paradigm named photonic compute-in-wire. As a proof-of-concept, an optoelectronic benchtop with a 20-km fiber access line are constructed, confirming good classification accuracy for image recognition tasks. The reported approach broadens the opportunities to utilize optical computation from local edge computing to in-network computing for low-latency and low-energy computation.