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Resistive magnetohydrodynamics is thought to play a key role in transient astrophysical phenomena such as black hole flares and neutron star magnetospheres. When performing numerical simulations of resistive magnetohydrodynamics, one is faced with the issue that Amperes law becomes stiff in the high conductivity limit which poses challenges to the numerical evolution. We show that using a description of resistive magnetohydrodynamics based on higher form symmetry, one can perform simulations with a generalized dual Faraday tensor without having to use Amperes Law, thereby avoiding the stiffness problem. We also explain the relation of this dual model to a traditional description of resistive magnetohydrodynamics and how causality is guaranteed by introducing second order corrections.

As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability.

We present a new additive method, nicknamed sage for Simplified Additive Gaussian processes Emulator, to emulate climate model Perturbed Parameter Ensembles (PPEs). It estimates the value of a climate model output as the sum of additive terms. Each additive term is the mean of a Gaussian Process, and corresponds to the impact of a parameter or parameter group on the variable of interest. This design caters to the sparsity of PPEs which are characterized by limited ensemble members and high dimensionality of the parameter space. sage quantifies the variability explained by different parameters and parameter groups, providing additional insights on the parameter-climate model output relationship. We apply the method to two climate model PPEs and compare it to a fully connected Neural Network. The two methods have comparable performance with both PPEs, but sage provides insights on parameter and parameter group importance as well as diagnostics useful for optimizing PPE design. Insights gained are valid regardless of the emulator method used, and have not been previously addressed. Our work highlights that analyzing the PPE used to train an emulator is different from analyzing data generated from an emulator trained on the PPE, as the former provides more insights on the data structure in the PPE which could help inform the emulator design., Comment: Manuscript to be submitted to Journal of Advances in Modeling Earth Systems (JAMES)

Graph Signal Processing deals with the problem of analyzing and processing signals defined on graphs. In this paper, we introduce a novel filtering method for graph-based signals by employing ideas from topological data analysis. We begin by working with signals over general graphs and then extend our approach to what we term signals over graphs with faces. To construct the filter, we introduce a new structure called the Basin Hierarchy Tree, which encodes the persistent homology. We provide an efficient algorithm and demonstrate the effectiveness of our approach through examples with synthetic and real datasets. This work bridges topological data analysis and signal processing, presenting a new application of persistent homology as a topological data processing tool., Comment: 14 pages, 14 figures, software available at https://github.com/mvlier/topapprox

The arrival of Large Language Models (LLMs) has stirred up philosophical debates about the possibility of realizing agency in an artificial manner. In this work we contribute to the debate by presenting a theoretical model that can be used as a threshold conception for artificial agents. The model defines agents as systems whose actions and goals are always influenced by a dynamic framework of factors that consists of the agent's accessible history, its adaptive repertoire and its external environment. This framework, in turn, is influenced by the actions that the agent takes and the goals that it forms. We show with the help of the model that state-of-the-art LLMs are not agents yet, but that there are elements to them that suggest a way forward. The paper argues that a combination of the agent architecture presented in Park et al. (2023) together with the use of modules like the Coscientist in Boiko et al. (2023) could potentially be a way to realize agency in an artificial manner. We end the paper by reflecting on the obstacles one might face in building such an artificial agent and by presenting possible directions for future research., Comment: Accepted for publication in journal Intellectica, special issue "Philosophies of AI: thinking and writing with LLMs" (Intellectica, issue 81)

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases., Comment: 28 pages, 5 figures. To appear in Data & Policy journal

Measuring lift force on symmetrically shaped obstacles immersed in laminar flow is the quintessential way of signalling odd viscosity. For flow past cylinders, such a lift force does not arise when incompressibility and no-slip boundary conditions hold, whereas for spheres, a lift force was found in Stokes flow, applying to cases where the Reynolds number is negligible and convection can be ignored. When considering the role of convection at low but nonzero Reynolds numbers, two arising hurdles are the Whitehead paradox and the breaking of axial symmetry, which are overcome by asymptotic matching and the Lorentz reciprocal theorem respectively. We also consider the case where axial symmetry is retained because the translation of the sphere is aligned with the anisotropy vector of odd viscosity. We find that while lift vanishes, the interplay between odd viscosity and convection gives rise to a stream-induced non-vanishing torque.

Active matter concerns many-body systems comprised of living or self-driven agents that collectively exhibit macroscopic phenomena distinct from conventional passive matter. Using Schwinger-Keldysh effective field theory, we develop a novel hydrodynamic framework for thermal active matter that accounts for local temperature variations and the ensuing stochastic effects. This framework provides a deeper understanding of energy balance, second law of thermodynamics, and thermostated steady states in active matter, while also addressing the systematic violations of fluctuation-dissipation theorem and detailed balance. We use our framework of active hydrodynamics to develop effective field theory actions for active superfluids and active nematics that offer a first-principle derivation of various active transport coefficients and feature activity-induced phase transitions.

We study energy-momentum and charge transport in strongly interacting holographic quantum field theories in an anisotropic thermal state by contrasting three different holographic methods to compute transport coefficients: standard holographic calculation of retarded Greens functions, a method based on the null-focusing equation near horizon and the novel method based on background variations. Employing these methods we compute anisotropic shear and bulk viscosities and conductivities with anisotropy induced externally, for example by an external magnetic field. We show that all three methods yield consistent results. The novel method allows us to read off the transport coefficients from the horizon data and express them in analytic form from which we derive universal relations among them. Furthermore we extend the method based on the null-focusing equation to Gauss-Bonnet theory to compute higher derivative corrections to the aforementioned transport coefficients.

Odd viscosity is a transport coefficient that can occur when fluids experience breaking of parity and time-reversal symmetry. Previous knowledge indicates that cylinders in incompressible odd viscous fluids, under no-slip boundary conditions, do not exhibit lift force, a phenomenon that poses challenges for the experimental detection of odd viscosity. This study investigates the impact of slip in Stokes flow, employing the odd generalization of the Lorentz reciprocal theorem. Our findings reveal that, at linear order in slip length, lift does not manifest. Subsequently, we explore the scenario involving a thin sheet with momentum decay as well as that of a finite system size, demonstrating that for Stokes flow lift does occur for the second order slip length contribution. We address cylinder flow beyond the Stokes approximation by solving the Oseen equation to obtain a fluid profile that shows an interplay between odd viscosity and inertia, and acquire an explicit expression for the leading order slip length contribution to Oseen lift at low Reynolds number.

Thorough analysis of local droplet-level interactions is crucial to better understand the microphysical processes in clouds and their effect on the global climate. High-accuracy simulations of relevant droplet size distributions from Large Eddy Simulations (LES) of bin microphysics challenge current analysis techniques due to their high dimensionality involving three spatial dimensions, time, and a continuous range of droplet sizes. Utilizing the compact latent representations from Variational Autoencoders (VAEs), we produce novel and intuitive visualizations for the organization of droplet sizes and their evolution over time beyond what is possible with clustering techniques. This greatly improves interpretation and allows us to examine aerosol-cloud interactions by contrasting simulations with different aerosol concentrations. We find that the evolution of the droplet spectrum is similar across aerosol levels but occurs at different paces. This similarity suggests that precipitation initiation processes are alike despite variations in onset times., Comment: 4 pages, 3 figures, accepted at NeurIPS 2023 (Machine Learning and the Physical Sciences Workshop)

We introduce a novel and effective method to compute transport coefficients in strongly interacting plasma states in holographic QFTs. Our method is based on relating the IR limit of fluctuations on a gravitational background to its variations providing a previously overlooked connection between boundary and near horizon data. We use this method to derive analytic formulas for the viscosities of an ansiotropic plasma state in the presence of an external magnetic field or another isotropy breaking external source. We then apply our findings to holographic QCD., Comment: v1: 7 pages + 1 figure, v2: References updated, Minor typos fixed, v3: References updated, Minor typos fixed

We consider an active, stochastic microscopic model of particles suspended in a fluid and show that the coarse-grained description of this model renders odd viscoelasticity. The particles are odd dumbbells, each featuring a robotic device as the bead, which exhibits a particular torque response. We show that this model can be macroscopically treated as a viscoelastic fluid, analytically calculate the coefficients of the corresponding viscoelastic model, and corroborate the results using molecular dynamics simulations. This work provides a unified analytical framework for several experimental and numerical setups designed to elucidate odd effects in fluids., Comment: 17 pages, 8 figures

Odd viscoelastic materials are constrained by fewer symmetries than their even counterparts. The breaking of these symmetries allow these materials to exhibit different features, which have attracted considerable attention in recent years. Immersing a bead in such complex fluids allows for probing their physical properties, highlighting signatures of their oddity and exploring consequences of these broken symmetries. We present the conditions under which the activity of an odd viscoelastic fluid can give rise to linear instabilities in the motion of the probe particle and unveil how the features of the probe particle dynamics depend on the oddity and activity of the viscoelastic medium in which it is immersed.