In new research from the University of Chicago Pritzker School of Molecular Engineering, Argonne National Laboratory and Purdue University, the AI-driven robotic lab Polybot designed and built new polymers that hit precise, targeted shades of orange and green – a task that takes researchers decades of experience to learn – in just 72 hours.

Korean researchers confirmed that underground wireless communication is possible, moving beyond the terrestrial wireless communication they have primarily focused on until now. This opened up a new wireless channel for confirming the survival of buried people in the event of a collapse of an underground facility such as a mine, conducting underground rescue operations, or conducting underground military operations.

Neuroimaging analysis in brain disorders faces a persistent challenge: brain signals are complex and high-dimensional, while high-quality labeled datasets remain limited. This review article systematically examines how self-supervised learning can help address that gap by learning meaningful representations directly from unlabeled neuroimaging data. It covers major methodological families, including contrastive, generative, and hybrid generative-contrastive approaches, and discusses their applications in functional MRI, EEG, and multimodal brain network analysis.

The review argues that self-supervised learning offers more than annotation efficiency. It may enable more transferable and clinically useful representations for disease screening, diagnosis, and prognosis across heterogeneous datasets and disorders. At the same time, interpretability, data heterogeneity, missing modalities, and clinical validation remain major barriers. Future work will likely focus on stronger multimodal fusion, better cross-site generalization, and more clinically adaptable model design.

In an era where electric vehicles (EVs) are accelerating toward mainstream adoption, the global push for sustainable transportation is undeniable. With fossil fuels dwindling and climate concerns mounting, EVs promise cleaner roads and reduced emissions. However, this surge in EV popularity is straining our existing power grids, especially at charging stations where unpredictable fleets of vehicles plug in and out randomly. This creates imbalances in power demand, leading to issues like voltage drops, harmonic distortions, and overall poor power quality that could hinder widespread EV integration. Enter the innovative solution explored in this research: using a device called D-STATCOM (Distribution Static Compensator) to dynamically balance loads and supply reactive power right at the charging station. By addressing these local challenges, the study paves the way for more reliable, efficient EV infrastructure, making electric mobility not just viable but truly attractive for everyday users.

Vehicle re-identification (Re-ID) stands as a cornerstone technology in intelligent transportation systems, enabling the tracking of individual vehicles across non-overlapping surveillance cameras in urban environments. Despite substantial progress in deep learning approaches, real-world deployment faces persistent obstacles from diverse vehicle poses caused by varying camera angles, viewpoints, and driving directions. These pose variations scatter feature representations of the same vehicle in the embedding space, leading to reduced discriminative power and lower identification accuracy. Traditional methods relying on deep metric learning struggle to bridge these gaps, as pose differences create discrete clusters even for identical vehicles, complicating reliable matching in practical traffic scenarios.

A recent study introduces an innovative strategy to mitigate this challenge by projecting vehicle images from diverse poses into a unified target pose, generating synthetic images that serve as pose-invariant auxiliary information to strengthen Re-ID models. Recognizing the high costs and logistical difficulties of acquiring paired images of the same vehicle from different cameras, researchers developed VehicleGAN, the first pair-flexible pose-guided image synthesis framework tailored for vehicle Re-ID. This end-to-end Generative Adversarial Network accepts a source vehicle image and a target pose as inputs, synthesizing the vehicle in the desired pose without depending on detailed 3D geometric models. VehicleGAN operates effectively in both supervised settings, using paired data when available, and unsupervised scenarios through a novel AutoReconstruction mechanism. In this self-supervised approach, the model transfers an image to the target pose and back to the original, reconstructing the input to learn robust transformations without requiring expensive paired annotations. This flexibility addresses key limitations of prior 3D-based methods, which demand precise camera parameters often unavailable in real surveillance setups, and supervised 2D methods burdened by labor-intensive labeling.