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.

The quest for cognitive immortality continues. Have you ever thought about what makes you? Science assures us it has everything to do with the three-pound organ between our ears, with its billions of neurons and trillions of delicate connections. But it's a lot trickier to explain why we have a sense of self-awareness or to pin down precisely where that first memory of Mommy actually resides. As we age, we forget most of what we've experienced, yet there's still undeniable continuity: At 30, or 50, or 70, you will still be you. To preserve that elusive continuity of self, some far-thinking technologists are working on ways to keep your brain going--even beyond death.

Solar geoengineering could buy humanity time against climate change--or trigger a dangerous new arms race. A first-of-its-kind simulation offered a glimpse of what happens when geopolitics takes the wheel. When it comes to deciding the fate of the planet, Colleen Golja always thought science and reason would have a seat at the table. That was until one morning last September in London, when the climate researcher took part in the first in a series of war games. The goal: to prevent an uncontrolled global race to develop a technology aimed at cooling the planet but that comes with vast, unknown risks that could reshape life on Earth. "I thought that would be such an easy thing. Like, we can all agree to do ethical research and make sure it's for the better of all humans," she told WIRED.

What makes'The Pitt' so horrifyingly realistic? We asked the show's makeup and prosthetic pros. 'Depending on the procedure, it can vary between two and nine weeks to build.' 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 Pitt' features a level of realism not previously seen on screen.

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When a fish holds its position against a current in a river, its brain must figure out how fast to swim and how to steer to offset the water flow. Most fish use vision to register the world sliding past, detect optic flow speed and direction, and their brains turn these signals into compensatory swimming. The retina captures signals of optic-flow direction, central pretectal neurons interpret direction, and spinal nerves drive muscle contractions. The catch is that one cannot easily change the living brain to test how these circuits work. Although advances in imaging now allow detailed recording, and even manipulation, of neurons alongside behavior, rewiring connections to ask what a particular link actually does remains almost impossible in the living, complex animal.

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In such settings, the inverse scattering transform (IST) of Ablowitz, Kaup, Newell, and Segur [2] has enjoyed a rich and successful history, and is now the standard theoretical framework for deriving reduced-order evolution equations for soliton dynamics. Although these derivations are traditionally of an analytical - rather than data-driven - nature, recent work has employed the IST formalism as a tool for experimental data analysis, using the technique to analyze soliton content from empirical measurements [8, 15, 24]. Moreover, recent approaches using alternative parameterization techniques have demonstrated that the learning of reduced-order, interpretable equations of motion for solitons is tenable in a data-driven setting [6, 26, 27]. Despite the success of this recent work, however, little effort has been devoted to developing a data-driven modeling approach based on the IST itself, most likely due to the fact that the framework is fundamentally problem-specific. In this paper, we address the question of whether effective soliton dynamics can be inferred directly from observed scattering data (as opposed to being derived or approximated analytically).

A growing number of techniques leverage the spatial structures that underlie many real-world datasets. Despite these advances, the complementary task of estimating spatial structures and understanding their role within these techniques has often been overlooked. In neurophysiological data analysis specifically, numerous methods exist to estimate brain connectivity, but most are not explicitly model-based, dynamic, multivariate, or directed. To address these limitations, we previously introduced noise-driven heat modelling on graphs for neurophysiological connectivity estimation. In this study, we extend this framework by relaxing earlier noise assumptions and adding regularisation to improve robustness. We also develop a simulation procedure to characterise and evaluate our technique in a controlled setting. Finally, we demonstrate that the technique is able to capture meaningful spatial structure across two experiments, each using two real-world datasets. The explicit model formulation of our connectivity estimator has the potential to improve the interpretability of graph-based techniques across a wide range of applications. The code implementing our method is available at https://github.com/sgoerttler/Heat_Connectivity.

Normalizing Flows excel at modeling a single fixed density, yet many problems across the sciences, such as high energy physics, instead require modeling how that density deforms as a function of continuous parameters: the strength of a physical effect, a calibration constant, or a source of systematic uncertainty. Learning a separate flow for every parameter configuration quickly becomes intractable, since the number of joint settings grows exponentially with the number of parameters. We introduce Factorizable Normalizing Flows (FNFs), which represent the parameter-dependent density as a fixed, high-fidelity flow for a reference configuration composed with a learnable transformation that is polynomial in the parameters and factorized over them. This structure has a practical consequence: each parameter's effect is learned in isolation, from samples in which that parameter alone is varied. The combined response of many parameters is then recovered by summation at inference, without ever sampling their combinatorially large joint space. On a controlled problem with two interpretable deformations applied jointly to the data, the learned transformation reproduces the true deformations and matches the optimal likelihood, while optional interaction terms capture residual correlations when several parameters vary strongly at once. The resulting model is interpretable, scales linearly with the number of parameters, and keeps the likelihood tractable. This provides a general tool for any inference workflow requiring continuous density morphing, and directly enables the next generation of unbinned likelihood fits in high energy physics.