In cities, air pollution can vary sharply from one street to the next. Fumes may linger between tall buildings, collect at busy junctions or drift towards a school gate. However, cities often lack the detailed forecasts needed to act before levels become dangerous. For Professor Soledad Le Clainche, an applied mathematician at the Technical University of Madrid and leader of the ModelFLOWs research group, better understanding how air moves through cities could help tackle one of Europe's biggest environmental health challenges. As part of an EU-funded research project called MODELAIR, Le Clainche is working with researchers in Belgium, the Netherlands, Sweden and the UK to make the invisible, highly local patterns of urban pollution easier for cities to understand.

Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Selects Say More Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net Versus Trending Now All Series How to watch France vs. Belgium online for free Live stream select fixtures from the UEFA Nations League without spending anything. Joseph Green is the Global Shopping Editor for Mashable. He covers VPNs, headphones, fitness gear, dating sites, streaming, and shopping events like Black Friday and Prime Day. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

Mashable Selects Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Look Up Creator Playbook Say More AI at School Safety Net Versus Trending Now Back to School Good Connection: Uplifting stories for a digital age All Series How to watch Belgium vs. France online for free Live stream select fixtures from the UEFA Nations League without spending anything. Joseph Green is the Global Shopping Editor for Mashable. He covers VPNs, headphones, fitness gear, dating sites, streaming, and shopping events like Black Friday and Prime Day. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

Archeology and gaming technology join forces to preserve battlefields of the past. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The rolling, green hills of West Flanders, Belgium, were once the bitter, brutal staging ground for some of the bloodiest fighting of World War I . An estimated 600,000 soldiers were killed, wounded, or went missing in that region between 1914 and 1918, often fighting along intricate but claustrophobically cramped trench lines that were perpetually bombarded by artillery fire and sinking into thick, waterlogged mud. However, a visitor walking across many of those old battlegrounds today might never know they were treading across a graveyard.

When some parameters of a constrained optimization problem (COP) are uncertain, this gives rise to a predict-then-optimize (PtO) problem, comprising two stages: the prediction of the unknown parameters from contextual information and the subsequent optimization using those predicted parameters. Decision-focused learning (DFL) implements the first stage by training a machine learning (ML) model to optimize the quality of the decisions made using the predicted parameters. When the predicted parameters occur in the constraints, they can lead to infeasible solutions. Therefore, it is important to simultaneously manage both feasibility and decision quality. We develop a DFL framework for predicting constraint parameters in a generic COP.

A.1 Compilation and Standardization of Datasets We compile ESCAPE from 27 peptide databases by systematically extracting experimentally validated antimicrobial peptides annotated for antibacterial, antifungal, antiparasitic, or antiviral activity. Databases exclusively focusing on a single category, such as AVPdb [1] (antiviral), are directly mapped to one of the four target classes. Additionally, we follow the methodology outlined in TransImbAMP[6], selecting non-antimicrobial peptides from UniProt [7] by applying strict exclusion criteria. Specifically, we discard sequences containing keywords such as "membrane," "toxic," "secretory," "defensive," "antibiotic," "anticancer," "antiviral," or "antifungal" to enhance the quality of the negative class. For large and hierarchically structured databases such as DBAASP[8], DRAMP[9], dbAMP (with species-level annotations)[10], and SATPdb (which lists 38 functional categories)[11], we retain all peptides with annotations that map either directly or through hierarchical or taxonomic relationships to one of our four defined antimicrobial classes (antibacterial, antifungal, antiparasitic, antiviral).

The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of Large Language Models (LLMs). Along with this, the potential retention of sensitive data of LLMs has spurred increasing research into machine unlearning. However, existing unlearning approaches face a critical dilemma: Aggressive unlearning compromises model utility, while conservative strategies preserve utility but risk hallucinated responses. This significantly limits LLMs' reliability in knowledge-intensive applications. To address this, we introduce a novel Attention-Shifting (AS) framework for selective unlearning.

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Motivated by the recently shown connection between self-attention and (kernel) principal component analysis (PCA), we revisit the fundamentals of PCA. Using the difference-of-convex (DC) framework, we present several novel formulations and provide new theoretical insights. In particular, we show the kernelizability and outof-sample applicability for a PCA-like family of problems. Moreover, we uncover that simultaneous iteration, which is connected to the classical QR algorithm, is an instance of the difference-of-convex algorithm (DCA), offering an optimization perspective on this longstanding method. Further, we describe new algorithms for PCA and empirically compare them with state-of-the-art methods. Lastly, we introduce a kernelizable dual formulation for a robust variant of PCA that minimizes the l1-deviation of the reconstruction errors.

Plus: Autonomous drones may have killed soldiers for the first time. Imagine tuning in to the opening kickoff of a World Cup match and seeing a player intentionally kick the ball out of bounds. You may question the logic of surrendering possession seconds into a game. If you were Jesse Davis, though, you'd know that this play could be a prime setup to score. Davis is a professor of computer science at KU Leuven in Belgium and head of its Sports Analytics Lab, which has been at the vanguard of a data awakening in soccer. Using AI and data analytics, his team has uncovered hidden tactical patterns and challenged long-held assumptions about how the game should be played.