Variability of the Indian summer monsoon (ISM) profoundly affects the large population of South Asia, exerting substantial socioeconomic impacts. However, its prediction remains challenging, partly due to our incomplete understanding of its physical drivers. As the dominant interannual climate variability mode in the tropical Atlantic, Atlantic Niño can suppress the ISM rainfall (ISMR) through atmospheric teleconnections. Furthermore, a recent study has identified two types of Atlantic Niño with warming centered in the central and eastern basins, respectively. Combining observational analysis and numerical experiments, we investigate their distinct influences on the ISMR. Results show that the Eastern Atlantic Niño (EAN) excites extratropical Rossby waves, inducing a west-east rainfall dipole anomaly over North India. In contrast, the Central Atlantic Niño (CAN) primarily induces easterly wind anomalies in the tropics and weakens the Indian monsoon trough, resulting in a meridional tripole rainfall pattern. The recent marked weakening of the EAN and changing mean state have allowed the CAN to emerge as the dominant driver of the Atlantic Niño’s remote influence on ISMR. Furthermore, since the CAN is closely linked to El Niño-Southern Oscillation (ENSO), the emergence of the CAN-ISMR connection may partly explain the recovery in the ENSO-ISMR correlation since around 2000. These findings underscore the importance of distinguishing between the two types of Atlantic Niño in order to improve predictions of the ISM.
Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine-learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving a balanced accuracy of 74%. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions.
We present practical and formal methods for gauging non-invertible symmetries in (2+1)d topological quantum field theories. Along the way, we generalize various aspects of invertible 0-form gauging, including symmetry fractionalization, discrete torsion, and the fixed point theorem for symmetry action on lines. Our approach involves two complementary strands: the fusion of topological interfaces and Morita theory of fusion 2-categories. We use these methods to derive constraints on gaugeable symmetries and their duals while unifying the prescription for gauging non-invertible 0-form and 1-form symmetries and various higher structures. With a view toward recent advances in creating non-Abelian topological orders from Abelian ones, we give a simple recipe for non-invertible 0-form gauging that takes large classes of the latter to the former. We also describe conditions under which iterated gauging of invertible 0-form symmetries is equivalent to a single-step gauging of a non-invertible symmetry. We conclude with a set of concrete examples illustrating these various phenomena involving gauging symmetries of the infrared limit of the toric code.
For four decades, statistical physics has been providing a framework to analyze neural networks. A long-standing question remained on its capacity to tackle deep-learning models capturing rich feature-learning effects, thus going beyond the narrow networks or kernel methods analyzed until now. We positively answer through the study of the supervised learning of a multilayer perceptron. Importantly, (i) its width scales as the input dimension, making it more prone to feature learning than ultrawide networks and more expressive than narrow ones or ones with fixed embedding layers, and (ii) we focus on the challenging interpolation regime where the number of trainable parameters and data are comparable, which forces the model to adapt to the task. We consider the matched teacher-student setting. Therefore, we provide the fundamental limits of learning random deep neural-network targets and identify the sufficient statistics describing what is learned by an optimally trained network as the data budget increases. A rich phenomenology emerges with various learning transitions. With enough data, optimal performance is attained through the model’s “specialization” toward the target, but it can be hard to reach for training algorithms, which get attracted by suboptimal solutions predicted by the theory. Specialization occurs inhomogeneously across layers, propagating from shallow toward deep ones but also across neurons in each layer. Furthermore, deeper targets are harder to learn. Despite its simplicity, the Bayes-optimal setting provides insights into how the depth, nonlinearity, and finite (proportional) width influence neural networks in the feature-learning regime that are potentially relevant in much more general settings.
We investigate the evolution of density perturbations in dark matter, including the new combined effects of finite number density and non-zero velocity dispersion. Using a truncated BBGKY hierarchy, we derive analytical expressions for the dark matter power spectrum during radiation and matter domination. A component of warm white noise emerges in our analysis, which arises due to the finite number density and undergoes scale-dependent evolution because of the velocity dispersion. Although free streaming erases adiabatic initial perturbations on small scales, warm white noise persists below the free-streaming length and grows during matter domination, with growth suppressed below the dark matter Jeans length. Our calculated power spectra agree with N-body simulations in the linear regime and accurately predict halo mass functions in the nonlinear regime. Effects of warm white noise can emerge on observable quasi-linear scales for ultralight dark matter produced after inflation with a subhorizon correlation length. Our formalism is applicable to these scenarios (with de Broglie-scale quasi-particles), to cases in which dark matter includes macroscopic structures (such as primordial black holes), and to traditional warm and cold dark matter scenarios.
