In recent years, autonomous driving has garnered escalating attention for its potential to relieve drivers' burdens and improve driving safety. Vision-based 3D occupancy prediction, which predicts the spatial occupancy status and semantics of 3D voxel grids around the autonomous vehicle from image inputs, is an emerging perception task suitable for cost-effective perception system of autonomous driving. Although numerous studies have demonstrated the greater advantages of 3D occupancy prediction over object-centric perception tasks, there is still a lack of a dedicated review focusing on this rapidly developing field.

With the increasing frequency of natural disasters and health emergencies, wearable infrared thermal imaging devices have gained widespread use in the firefighting and medical fields. However, such devices tend to have poor imaging performance and often suffer from low contrast, dark areas, high noise and blurred boundaries, which greatly hinder practical applications.

The Blavatnik Awards for Young Scientists today announced the Finalists for the 2026 Blavatnik Awards for Young Scientists in the United Kingdom. The Awards recognise scientific advances by UK researchers across Life Sciences, Chemical Sciences, and Physical Sciences & Engineering. Now in its ninth year, each Blavatnik Awards Laureate will receive an unrestricted £100,000 (US$135,000) prize, while the remaining six Finalists will be awarded £30,000 (US$40,400) each. 

This year’s Finalists include:

Chemical Sciences Finalists

Michael J. Booth, PhD – University College London (UCL)

Mathew H. Horrocks, PhD – The University of Edinburgh

Maxie M. Roessler, DPhil – Imperial College London

Life Sciences Finalists

Nicholas R. Casewell, PhD – Liverpool School of Tropical Medicine

Thi Hoang Duong (Kelly) Nguyen, PhD – MRC Laboratory of Molecular Biology

Pontus Skoglund, PhD – The Francis Crick Institute

Physical Sciences & Engineering Finalists

Radha Boya, PhD – The University of Manchester

Paola Pinilla, PhD – University College London (UCL)

Iestyn Woolway, PhD – Bangor University

Argument mining (AM), aiming to extract and identify argumentative structures from natural language text, has become an established field in the NLP community. The main challenge in this task comes three-fold: the insufficiency of contextual information on targets, cross-domain adaptation across varying targets, and implicit argumentative information within the argument. Current approaches primarily address the first two challenges by improving the integration of target-related semantic information with arguments, while there has been little work on modeling all three aspects.

Estimating the number of triangles in a graph is a fundamental problem and has found applications in many fields. This problem has been widely studied in the context of graph stream processing. However, most of these algorithms are not robust or are limited to unweighted graphs. The reason why they do not robust is because most algorithms assume that the entire stream is predetermined before algorithm execution, rendering them vulnerable to adaptive inputs.

Real-world data typically exhibits long-tailed class distribution and contains label noise. Previous long-tail learning methods overlooked the prevalence of noisy labels in training. Moreover, the commonly used small-loss noisy label detection criterion fails in long-tail data.

Researchers at Qingdao University have developed a novel algorithm, Microbiome Elastic Feature Extraction (MEFE), that significantly improves the identification of microbiome biomarkers by incorporating phylogenetic, taxonomic, and functional relationships among microbes. This advancement addresses longstanding challenges in microbiome research, such as data sparsity and sequencing errors, potentially leading to more accurate disease diagnostics and personalized medicine. The findings were published on 15 January 2026 in Frontiers of Computer Science.