Mixture-of-Experts (MoE) is a flexible framework that combines multiple specialized submodels (``experts''), by assigning covariate-dependent weights (``gating functions'') to each expert, and have been commonly used for analyzing heterogeneous data. Existing statistical MoE formulations typically assume constant coefficients, for covariate effects within the expert or gating models, which can be inadequate for longitudinal, spatial, or other dynamic settings where covariate influences and latent subpopulation structure evolve across a known dimension. We propose a Varying-Coefficient Mixture of Experts (VCMoE) model that allows all coefficient effects in both the gating functions and expert models to vary along an indexing variable. We establish identifiability and consistency of the proposed model, and develop an estimation procedure, label-consistent EM algorithm, for both fully functional and hybrid specifications, along with the corresponding asymptotic distributions of the resulting estimators. For inference, simultaneous confidence bands are constructed using both asymptotic theory for the maximum discrepancy between the estimated functional coefficients and their true counterparts, and with bootstrap methods. In addition, a generalized likelihood ratio test is developed to examine whether a coefficient function is genuinely varying across the index variable. Simulation studies demonstrate good finite-sample performance, with acceptable bias and satisfactory coverage rates. We illustrate the proposed VCMoE model using a dataset of single nucleus gene expression in embryonic mice to characterize the temporal dynamics of the associations between the expression levels of genes Satb2 and Bcl11b across two latent cell subpopulations of neurons, yielding results that are consistent with prior findings.

On a commercial thoroughfare in old town Pasadena, California, a stone's throw from NASA's Jet Propulsion Laboratory (JPL), you'll find the Neon Retro Arcade. Among its collection of vintage video games is the 1979 Atari classic Asteroids, in which a pixelated spaceship shoots down a barrage of space rocks to stave off fatal collisions. After long days of work at JPL, Amy Mainzer used to rack up high scores on that console. "It was a hoot," she says. It was also apt, considering she oversees a space mission designed to spot dangerous asteroids before they crash into Earth. That mission, the Near-Earth Object (NEO) Surveyor, was conceived in the early 2000s and finally got the green light in 2022. Its components are now being built, tested, and assembled in clean rooms across the United States ahead of its planned launch in September 2027. "We're in the thick of building everything," says Mainzer, NEO Surveyor's principal investigator and now an astronomer at the University of California, Los Angeles (UCLA).

Background: Accurate proton resonance frequency (PRF) MR thermometry is essential for monitoring temperature rise during thermal ablation with high intensity focused ultrasound (FUS). Conventional referenceless methods such as complex field estimation (CFE) and phase finite difference (PFD) tend to exhibit errors when susceptibility-induced phase discontinuities occur at tissue interfaces.

This review explores the integration of Artificial Intelligence into Horizon Scanning, focusing on identifying and responding to emerging threats and opportunities linked to Infectious Diseases. We examine how AI tools can enhance signal detection, data monitoring, scenario analysis, and decision support. We also address the risks associated with AI adoption and propose strategies for effective implementation and governance. The findings contribute to the growing body of Foresight literature by demonstrating the potential and limitations of AI in Public Health preparedness.

A bear warning sign is displayed in Shirakawa-go, a popular tourist spot in Gifu Prefecture. A town in Miyagi Prefecture has retracted its social media post warning of a bear sighting after discovering an image submitted to it had been generated using artificial intelligence. A Japanese town has deleted a social media post warning of a bear sighting after discovering that a picture it had received showing the fearsome creature was generated using artificial intelligence. Similar fake images have been circulating online as fear of bears runs high in the country, where the animals have killed a record 13 people this year. "The town prioritized informing residents to avoid danger, but we apologize for causing any anxiety or confusion," the town of Onagawa, Miyagi Prefecture, said on its official X social media account on Wednesday.

SoftBank shares have plunged around 40% since late October as it sits at the forefront of a global AI selloff. Growing unease over frothy artificial intelligence valuations is weighing on shares of SoftBank Group, which traders increasingly view as a proxy for privately held OpenAI. The Japanese tech investor sits at the forefront of a global AI selloff amid worries about new pressure on OpenAI following Alphabet's Gemini 3.0 debut. SoftBank shares have plunged around 40% since late October, erasing over ¥16 trillion ($102 billion) in market value, as its founder Masayoshi Son prepares to double down on OpenAI and the infrastructure that supports it. SoftBank has ridden the global AI investment boom faster than any other Japanese company.

In large part, rodents "see" the world through their whiskers, a powerful tactile

Limbed climbing robots are designed to explore challenging vertical walls, such as the skylights of the Moon and Mars. In such robots, the primary role of a hand-eye camera is to accurately estimate 3D positions of graspable points (i.e., convex terrain surfaces) thanks to its close-up views. While conventional climbing robots often employ RGB-D cameras as hand-eye cameras to facilitate straightforward 3D terrain mapping and graspable point detection, RGB-D cameras are large and consume considerable power. This work presents a 3D terrain mapping system designed for space exploration using limbed climbing robots equipped with a monocular hand-eye camera. Compared to RGB-D cameras, monocular cameras are more lightweight, compact structures, and have lower power consumption. Although monocular SLAM can be used to construct 3D maps, it suffers from scale ambiguity. To address this limitation, we propose a SLAM method that fuses monocular visual constraints with limb forward kinematics. The proposed method jointly estimates time-series gripper poses and the global metric scale of the 3D map based on factor graph optimization. We validate the proposed framework through both physics-based simulations and real-world experiments. The results demonstrate that our framework constructs a metrically scaled 3D terrain map in real-time and enables autonomous grasping of convex terrain surfaces using a monocular hand-eye camera, without relying on RGB-D cameras. Our method contributes to scalable and energy-efficient perception for future space missions involving limbed climbing robots. See the video summary here: https://youtu.be/fMBrrVNKJfc

Breakthroughs, discoveries, and DIY tips sent every weekday. Humans don't need to blast off into space to find some truly alien-looking wonders. The deepest depths of our ocean are like another planet, teeming with the charismatic "Casper" octopus, the carnivorous (aka the flying spaghetti monster), and even some sharks . A team from Kumamoto University in Japan recently uncovered a deep-sea anemone that has a tight bond with hermit crabs. These wispy pink invertebrates build shell-like "homes" for the crabs.

Despite well-reported instances of robots being used in disaster response, there is scant published data on the internal composition of the void spaces within structural collapse incidents. Data collected during these incidents is mired in legal constraints, as ownership is often tied to the responding agencies, with little hope of public release for research. While engineered rubble piles are used for training, these sites are also reluctant to release information about their proprietary training grounds. To overcome this access challenge, we present RubbleSim -- an open-source, reconfigurable simulator for photorealistic void space exploration. The design of the simulation assets is directly informed by visits to numerous training rubble sites at differing levels of complexity. The simulator is implemented in Unity with multi-operating system support. The simulation uses a physics-based approach to build stochastic rubble piles, allowing for rapid iteration between simulation worlds while retaining absolute knowledge of the ground truth. Using RubbleSim, we apply a state-of-the-art structure-from-motion algorithm to illustrate how perception performance degrades under challenging visual conditions inside the emulated void spaces. Pre-built binaries and source code to implement are available online: https://github.com/mit-ll/rubble_pile_simulator.