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The spatial organization of a passive scalar plume originating from a point source in a boundary layer is studied to understand its meandering characteristics. We focus shortly downstream of the isokinetic injection ($1.5\le x/\delta \le 3$, $\delta$ being boundary layer thickness) where the scalar concentration is highly intermittent, the plume rapidly meanders, and breaks-up into concentrated scalar pockets due to the action of turbulent structures. Two injection locations were considered: the center of logarithmic-region and the wake-region of the boundary layer. Simultaneous quantitative planar laser-induced fluorescence (PLIF) and particle-image velocimetry (PIV) were performed in a wind-tunnel, using acetone vapors to measure scalar mixture fraction and velocity fields. Single- and multi-point statistics were compared to established works to validate the diagnostic novelties. Additionally, the spatial characteristics of the plume intermittency were quantified using `blob' size, shape, orientation and mean concentration. It was observed that straining and break up were the primary plume-evolution modes in this region, with little small-scale homogenization. Further, the dominant role of coherent vortex motions in meandering and break-up of the plume was evident. Their action is found to be the primary mechanism by which the scalar injected within the log-region is transported away from the wall (`large meander events'). Strong spatial correlation was observed in both instantaneous and conditional fields between the high scalar concentration regions and the individual vortex heads. This coherent transport was weaker for wake-injection case, where the plume only interacts with outer vortex motions. A coherent-structure based mechanism is suggested to explain these transport mechanisms.

We present Quantum Monte Carlo calculations of magnetic moments, form factors, and densities of $A\le 10$ nuclei within a chiral effective field theory approach. We use the Norfolk two- and three-body chiral potentials and their consistent electromagnetic one- and two-nucleon current operators. We find that two-body contributions to the magnetic moment can be large (up to $\sim33\%$ in $A=9$ systems). We study the model dependence of these observables and place particular emphasis on investigating their sensitivity to using different cutoffs to regulate the many-nucleon operators. Calculations of elastic magnetic form factors for $A\leq 10$ nuclei show excellent agreement with the data out to momentum transfers $q\approx 3$ fm$^{-1}$., Comment: 25 pages, 14 figures, 6 tables

Understanding how light modifies long-range order is key to improve our ability to control material functionality on an ultrafast timescale. Transient spatial heterogeneity has been proposed in many materials, but isolating the dynamics of different regions experimentally has been challenging. Here we address this issue and measure the dynamics of orbital order melting in the layered manganite, La0.5Sr1.5MnO4, and isolate the surface dynamics from the bulk for the first time. Bulk measurements show orbital order is rapidly suppressed, but the correlation length surprisingly increases. However, the surface dynamics, show a stronger suppression and a significant decrease in correlation length. By isolating the surface changes, we find that light preferentially melts a less ordered surface and the loss of long-range order is likely driven by the formation of local and disordered polarons. Melting the disordered surface effectively increases the average correlation of the bulk probed volume, resolving the contradictory response. These results show that surface scattering methods are necessary to understand both surface and bulk dynamics in heterogeneous materials., Comment: 22 pages, 8 figures

We present Quantum Monte Carlo calculations of magnetic form factors in $A=6-10$ nuclei, based on Norfolk two- and three-nucleon interactions, and associated one- and two-body electromagnetic currents. Agreement with the available experimental data for $^6$Li, $^7$Li, $^9$Be and $^{10}$B up to values of momentum transfer $q\sim 3$ fm$^{-1}$ is achieved when two-nucleon currents are accounted for. We present a set of predictions for the magnetic form factors of $^7$Be, $^8$Li, $^9$Li, and $^9$C. In these systems, two-body currents account for $\sim40-60\%$ of the total magnetic strength. Measurements in any of these radioactive systems would provide valuable insights on the nuclear magnetic structure emerging from the underlying many-nucleon dynamics. A particularly interesting case is that of $^7$Be, as it would enable investigations of the magnetic structure of mirror nuclei., Comment: 5 pages, 3 figures, 1 table

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Changes to motion patterns in these cells are one of the important features to be characterized to understand how an introduced drug or disease may alter the human heart beat. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently lacking. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

This study uses the 2021 Census to describe the educational attainment and earnings of the Canadian-born Black population, focusing on three groups: (1) those with at least one African-born parent (African-origin); (2) those with at least one Caribbean-born parent (Caribbean-origin) and (3) those whose parents were both born in Canada (Canadian-origin). Comparisons are drawn with the non-racialized, non-Indigenous population, both second generation and third generation or more. The study provides a descriptive analysis of the demographic and educational characteristics of the three Canadian-born Black populations, followed by a regression analysis examining factors affecting earnings, including educational attainment, job characteristics, and other factors.

Electrostatically stabilized nanocrystals (NCs) and, in particular, quantum dots (QDs) hold promise for forming strongly coupled superlattices due to their compact and electronically conductive surface ligands. However, studies on the colloidal dispersion and interparticle interactions of electrostatically stabilized sub-10 nm NCs have been limited, hindering the optimization of colloidal stability and self-assembly. In this study, we employed small-angle X ray scattering (SAXS) experiments to investigate the interparticle interactions and arrangement of PbS QDs with thiostannate ligands (PbS-Sn2S64-) in polar solvents. The study reveals significant deviations from ideal solution behavior in electrostatically stabilized QD dispersions. Our results demonstrate that PbS-Sn2S64- QDs exhibit long-range interactions within the solvent, in contrast to the short-range steric repulsion characteristic of PbS QDs with oleate ligands (PbS-OA). Introducing highly charged multivalent electrolytes screens electrostatic interactions between charged QDs, reducing the length scale of the repulsive interactions. Furthermore, we calculate the second virial (B2) coefficients from SAXS data, providing insights into how surface chemistry, solvent, and size influence pair potentials. Finally, we explore the influence of long-range interparticle interactions of PbS-Sn2S64- QDs on the morphology of films produced by drying or spin-coating colloidal solutions. The long-range repulsive term of PbS-Sn2S64- QDs promotes the formation of amorphous films, and screening the electrostatic repulsion by addition of an electrolyte enables the formation of a crystalline film. These findings highlight the critical role of NC-NC interactions in tailoring the properties of functional nanomaterials.

Data visualizations help extract insights from datasets, but reaching these insights requires decomposing high level goals into low-level analytic tasks that can be complex due to varying degrees of data literacy and visualization experience. Recent advancements in large language models (LLMs) have shown promise for lowering barriers for users to achieve tasks such as writing code and may likewise facilitate visualization insight. Scalable Vector Graphics (SVG), a text-based image format common in data visualizations, matches well with the text sequence processing of transformer-based LLMs. In this paper, we explore the capability of LLMs to perform 10 low-level visual analytic tasks defined by Amar, Eagan, and Stasko directly on SVG-based visualizations. Using zero-shot prompts, we instruct the models to provide responses or modify the SVG code based on given visualizations. Our findings demonstrate that LLMs can effectively modify existing SVG visualizations for some tasks like Cluster but perform poorly on tasks requiring mathematical operations like Compute Derived Value. We also discovered that LLM performance can vary based on factors such as the number of data points, the presence of value labels, and the chart type. Our findings contribute to gauging the general capabilities of LLMs and highlight the need for further exploration and development to fully harness their potential in supporting visual analytic tasks.

The ability to understand and ultimately control the transformations and properties of various nanoscale systems, from proteins to synthetic nanomaterial assemblies, hinges on the ability to directly elucidate their dynamics on their characteristic length and time scales. Here, we use MHz X-ray photon correlation spectroscopy (XPCS) to directly elucidate the characteristic microsecond-dynamics of density fluctuations of semiconductor nanocrystals (NCs), not only in a colloidal dispersion but also in a liquid phase consisting of densely packed, yet mobile, NCs with no long-range order. By carefully disentangling X-ray induced effects, we find the wavevector-dependent fluctuation rates in the liquid phase are suppressed relative to those in the colloidal phase and to those in experiments and hydrodynamic theories of densely packed repulsive particles. We show that the suppressed rates are due to a substantial decrease in the self-diffusion of NCs in the liquid phase, which we attribute to explicit attractive interactions. Via comparison with simulations, we find that the extracted strength of the attractions explains the stability of the liquid phase, in contrast to the gelation observed via XPCS in many other charged colloidal systems. This work opens the door to elucidating fast, condensed phase dynamics in a variety of complex fluids and other nanoscale soft matter systems, such as densely packed proteins and non-equilibrium self-assembly processes., Comment: 15 pages, 4 figures

Bottom-up assembly of nanocrystals (NCs) into ordered arrays, or superlattices (SLs), is a promising route to design materials with new functionalities, but the degree of control over assembly into functional structures remains challenging. Using electrostatics, rather than density, to tune the interactions between semiconductor NCs, we watch self-assembly proceeding through a metastable liquid phase. We systematically investigate the phase behavior as a function of quench conditions in situ and in real time using small angle X-ray scattering (SAXS). Through quantitative fitting to colloid, liquid, and SL models, we extract the time evolution of each phase and the system phase diagram, which we find to be consistent with short-range attractive interactions. Using the phase diagram's predictive power, we establish control of the self-assembly rate over three orders of magnitude, and identify one- and two-step self-assembly regimes, with only the latter implicating the metastable liquid as an intermediate. Importantly, the presence of the metastable liquid increases SL formation rates relative to the equivalent one-step pathway, and SL order counterintuitively increases with the rate, revealing a highly desirable and generalizable kinetic strategy to promote and enhance ordered assembly., Comment: 16 pages, 4 figures

Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibration approaches have successfully quantified the uncertainties inherent in identified parameters. Yet, routinely estimating the posterior distribution for all BC parameters in 3D simulations has been unattainable due to the infeasible computational demand. We propose an efficient method to identify Windkessel parameter posteriors using results from a single high-fidelity three-dimensional (3D) model evaluation. We only evaluate the 3D model once for an initial choice of BCs and use the result to create a highly accurate zero-dimensional (0D) surrogate. We then perform Sequential Monte Carlo (SMC) using the optimized 0D model to derive the high-dimensional Windkessel BC posterior distribution. We validate this approach in a publicly available dataset of N=72 subject-specific vascular models. We found that optimizing 0D models to match 3D data a priori lowered their median approximation error by nearly one order of magnitude. In a subset of models, we confirm that the optimized 0D models still generalize to a wide range of BCs. Finally, we present the high-dimensional Windkessel parameter posterior for different measured signal-to-noise ratios in a vascular model using SMC. We further validate that the 0D-derived posterior is a good approximation of the 3D posterior. The minimal computational demand of our method using a single 3D simulation, combined with the open-source nature of all software and data used in this work, will increase access and efficiency of Bayesian Windkessel calibration in cardiovascular fluid dynamics simulations.

Pair-breaking edges of $d$-wave superconductors feature Andreev bound states at the Fermi energy. Since these states are energetically highly unfavorable they are susceptible to effects that shift them to finite energy. We investigate the free energy of two different mechanisms: spontaneous phase gradients in the superconducting order parameter and surface ferromagnetism caused by Fermi liquid interaction effects. We find that the surface magnetization appears at lower temperatures than the spontaneous current flow of the phase-crystal state. The magnetic state can, however, be energetically favorable at lower temperatures for sufficiently strong Fermi liquid effects. As a result, first-order transitions between the two states are possible, suggesting a rich low-temperature phase diagram in $d$-wave superconductors., Comment: 10 pages, 6 figures

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.

Unfitted finite element methods, like CutFEM, have traditionally been implemented in a matrix-based fashion, where a sparse matrix is assembled and later applied to vectors while solving the resulting linear system. With the goal of increasing performance and enabling algorithms with polynomial spaces of higher degrees, this contribution chooses a more abstract approach by matrix-free evaluation of the operator action on vectors instead. The proposed method loops over cells and locally evaluates the cell, face, and interface integrals, including the contributions from cut cells and the different means of stabilization. The main challenge is the efficient numerical evaluation of terms in the weak form with unstructured quadrature points arising from the unfitted discretization in cells cut by the interface. We present design choices and performance optimizations for tensor-product elements and demonstrate the performance by means of benchmarks and application examples. We demonstrate a speedup of more than one order of magnitude for the operator evaluation of a discontinuous Galerkin discretization with polynomial degree three compared to a sparse matrix-vector product and develop performance models to quantify the performance properties over a wide range of polynomial degrees.

In this paper, we prove interior Hessian estimates for shrinkers, expanders, translators, and rotators of the Lagrangian mean curvature flow under the assumption that the Lagrangian phase is hypercritical. We further extend our results to a broader class of Lagrangian mean curvature type equations., Comment: 28 pages

Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers, Transcription Factor Binding Site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, TAD boundaries) and analyze their pros and cons. We also point out obstacles of computational prediction of 3D interactions and suggest future research directions., Comment: Systematic review, one figure, three tables, 29 pages

Surface ion traps with two-dimensional layouts of trapping regions are natural architectures for storing large numbers of ions and supporting the connectivity needed to implement quantum algorithms. Many of the components and operations needed to fully exploit this architecture have already been demonstrated, including operation at cryogenic temperatures with low heating, low excitation transport, and ion control and detection with integrated photonics. Here we demonstrate a trap that addresses the scaling challenge of increasing power dissipation as the RF electrode increases in size. By raising the RF electrode and removing most of the insulating dielectric layer below it we reduce both ohmic and dielectric power dissipation. We also measure heating rates across a range of motional frequencies and for different voltage sources in a trap with a raised RF electrode but solid dielectric., Comment: 7 pages, 4 figures

Fast and efficient simulations of metal additive manufacturing (AM) processes are highly relevant to exploring the full potential of this promising manufacturing technique. The microstructure composition plays an important role in characterizing the part quality and deriving mechanical properties. When complete parts are simulated, one often needs to resort to strong simplifications such as layer-wise heating due to the large number of simulated time steps compared to the small time step sizes. This article proposes a scan-resolved approach to the coupled thermo-microstructural problem. Building on a highly efficient thermal model, we discuss the implementation of a phenomenological microstructure model for the evolution of the three main constituents of Ti-6Al-4V: stable $\alpha_s$-phase, martensite $\alpha_m$-phase and $\beta$-phase. The implementation is tailored to modern hardware features using vectorization and fast approximations of transcendental functions. A performance model and numerical examples verify the high degree of optimization. We demonstrate the applicability and predictive power of the approach and the influence of scan strategy and geometry. Depending on the specific example, results can be obtained with moderate computational resources in a few hours to days. The numerical examples include a prediction of the microstructure on the full NIST AM Benchmark cantilever specimen.

In the prevalent picture of ultrafast structural phase transitions, the atomic motion occurs in a slowly varying potential energy surface determined adiabatically by the fast electrons. However, this ignores non-conservative forces caused by electron-lattice collisions, which can significantly influence atomic motion. Most ultrafast techniques only probe the average structure and are less sensitive to random displacements, and therefore do not detect the role played by non-conservative forces in phase transitions. Here we show that the lattice dynamics of the prototypical insulator-to-metal transition of VO2 cannot be described by a potential energy alone. We use the sample temperature to control the preexisting lattice disorder before ultrafast photoexcitation across the phase transition and our ultrafast diffuse scattering experiments show that the fluctuations characteristic of the rutile metal develop equally fast (120 fs) at initial temperatures of 100 K and 300 K. This indicates that additional non-conservative forces are responsible for the increased lattice disorder. These results highlight the need for more sophisticated descriptions of ultrafast phenomena beyond the Born-Oppenheimer approximation as well as ultrafast probes of spatial fluctuations beyond the average unit cell measured by diffraction.

The increasing variety and quantity of tagged multimedia content on platforms such as TikTok provides an opportunity to advance computer vision modeling. We have curated a distinctive dataset of 283,582 unique video clips categorized under 386 hashtags relating to modern human actions. We release this dataset as a valuable resource for building domain-specific foundation models for human movement modeling tasks such as action recognition. To validate this dataset, which we name TikTokActions, we perform two sets of experiments. First, we pretrain the state-of-the-art VideoMAEv2 with a ViT-base backbone on TikTokActions subset, and then fine-tune and evaluate on popular datasets such as UCF101 and the HMDB51. We find that the performance of the model pre-trained using our Tik-Tok dataset is comparable to models trained on larger action recognition datasets (95.3% on UCF101 and 53.24% on HMDB51). Furthermore, our investigation into the relationship between pre-training dataset size and fine-tuning performance reveals that beyond a certain threshold, the incremental benefit of larger training sets diminishes. This work introduces a useful TikTok video dataset that is available for public use and provides insights into the marginal benefit of increasing pre-training dataset sizes for video-based foundation models., Comment: 10 pages

Expanding the work of arXiv:1504.08040, we show that black holes obey a second law for linear perturbations to bifurcate Killing horizons, in any covariant higher curvature gravity coupled to scalar and vector fields. The vector fields do not need to be gauged, and (like the scalars) can have arbitrary non-minimal couplings to the metric. The increasing entropy has a natural expression in covariant phase space language, which makes it manifestly invariant under JKM ambiguities. An explicit entropy formula is given for f(Riemann) gravity coupled to vectors, where at most one derivative acts on each vector. Besides the previously known curvature terms, there are three extra terms involving differentiating the Lagrangian by the symmetric vector derivative (which therefore vanish for gauge fields)., Comment: 38 pages, 2 figures, 2 tables; references and comments added in v2

To address the challenge of performance portability, and facilitate the implementation of electronic structure solvers, we developed the Basic Matrix Library (BML) and Parallel, Rapid O(N) and Graph-based Recursive Electronic Structure Solver (PROGRESS) libraries. BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense, sparse) and architectures (CPUs, GPUs). Focusing on Density Functional Theory (DFT) and Tight-Binding (TB) models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. We demonstrate the portability of this approach and its performance on benchmark problems.

Computational modeling of the melt pool dynamics in laser-based powder bed fusion metal additive manufacturing (PBF-LB/M) promises to shed light on fundamental mechanisms of defect generation. These processes are accompanied by rapid evaporation so that the evaporation-induced recoil pressure and cooling arise as major driving forces for fluid dynamics and temperature evolution. The magnitude of these interface fluxes depends exponentially on the melt pool surface temperature, which, therefore, has to be predicted with high accuracy. The present work utilizes a diffuse interface finite element model based on a continuum surface flux (CSF) description of interface fluxes to study dimensionally reduced thermal two-phase problems representative for PBF-LB/M in a finite element framework. It is demonstrated that the extreme temperature gradients combined with the high ratios of material properties between metal and ambient gas lead to significant errors in the interface temperatures and fluxes when classical CSF approaches, along with typical interface thicknesses and discretizations, are applied. It is expected that this finding is also relevant for other types of diffuse interface PBF-LB/M melt pool models. A novel parameter-scaled CSF approach is proposed, which is constructed to yield a smoother temperature field in the diffuse interface region, significantly increasing the solution accuracy. The interface thickness required to predict the temperature field with a given level of accuracy is less restrictive by at least one order of magnitude for the proposed parameter-scaled approach compared to classical CSF, drastically reducing computational costs. Finally, we showcase the general applicability of the parameter-scaled CSF to a 3D simulation of stationary laser melting of PBF-LB/M considering the fully coupled thermo-hydrodynamic multi-phase problem, including phase change.

A formulation is presented to efficiently model ionic conduction inside, i.e. across and along, grain boundaries. Efficiency and accuracy is achieved by reducing it to a two-dimensional manifold while guaranteeing the conservation of mass and charge at the intersection of multiple grain boundaries. The formulation treats the electric field and the electric current as independent solution variables. We elaborate on the numerical challenges this formulation implies and compare the computed solution with results from an analytical solution by quantifying the convergence towards the exact solution. Towards the end of this work, the model is firstly applied to setups with extreme values of crucial parameters of grain boundaries to study the influence of the ionic conduction in the grain boundary on the overall battery cell voltage and, secondly, to a realistic microstructure to show the capabilities of the formulation.

We present a new algorithm for finding isolated zeros of a system of real-valued functions in a bounded interval in $\mathbb{R}^n$. It uses the Chebyshev proxy method combined with a mixture of subdivision, reduction methods, and elimination checks that leverage special properties of Chebyshev polynomials. We prove the method has R-quadratic convergence locally near simple zeros of the system. We also analyze the temporal complexity and the numerical stability of the algorithm and provide numerical evidence in dimensions up to three that the method is both fast and accurate on a wide range of problems. The algorithm should also work well in higher dimensions. Our tests show that the algorithm outperforms other standard methods on this problem of finding all real zeros in a bounded domain. Our Python implementation of the algorithm is publicly available.

Improving the productivity of the agricultural sector is part of one of the Sustainable Development Goals set by the United Nations. To this end, many international organizations have funded training and technology transfer programs that aim to promote productivity and income growth, fight poverty and enhance food security among smallholder farmers in developing countries. Stochastic production frontier analysis can be a useful tool when evaluating the effectiveness of these programs. However, accounting for treatment endogeneity, often intrinsic to these interventions, only recently has received any attention in the stochastic frontier literature. In this work, we extend the classical maximum likelihood estimation of stochastic production frontier models by allowing both the production frontier and inefficiency to depend on a potentially endogenous binary treatment. We use instrumental variables to define an assignment mechanism for the treatment, and we explicitly model the density of the first and second-stage composite error terms. We provide empirical evidence of the importance of controlling for endogeneity in this setting using farm-level data from a soil conservation program in El Salvador.

A bar-joint framework $(G,p)$ is the combination of a finite simple graph $G=(V,E)$ and a placement $p:V\rightarrow \mathbb{R}^d$. The framework is rigid if the only edge-length preserving continuous motions of the vertices arise from isometries of the space. Motivated by applications where boundary conditions play a significant role, one may generalise and consider linearly constrained frameworks where some vertices are constrained to move on fixed affine subspaces. Streinu and Theran characterised exactly which linearly constrained frameworks are generically rigid in 2-dimensional space. In this article we extend their characterisation to symmetric frameworks. In particular necessary combinatorial conditions are given for a symmetric linearly constrained framework in the plane to be isostatic (i.e. minimally infinitesimally rigid) under any finite point group symmetry. In the case of rotation symmetry groups whose order is either 2 or odd, these conditions are then shown to be sufficient under suitable genericity assumptions, giving precise combinatorial descriptions of symmetric isostatic graphs in these contexts., Comment: 26 pages, 9 figures. arXiv admin note: text overlap with arXiv:2210.06060

Generative artificial intelligence has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access, but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI., Comment: PNAS Nexus, in press

Solving inverse problems using Bayesian methods can become prohibitively expensive when likelihood evaluations involve complex and large scale numerical models. A common approach to circumvent this issue is to approximate the forward model or the likelihood function with a surrogate model. But also there, due to limited computational resources, only a few training points are available in many practically relevant cases. Thus, it can be advantageous to model the additional uncertainties of the surrogate in order to incorporate the epistemic uncertainty due to limited data. In this paper, we develop a novel approach to approximate the log likelihood by a constrained Gaussian process based on prior knowledge about its boundedness. This improves the accuracy of the surrogate approximation without increasing the number of training samples. Additionally, we introduce a formulation to integrate the epistemic uncertainty due to limited training points into the posterior density approximation. This is combined with a state of the art active learning strategy for selecting training points, which allows to approximate posterior densities in higher dimensions very efficiently. We demonstrate the fast convergence of our approach for a benchmark problem and infer a random field that is discretized by 30 parameters using only about 1000 model evaluations. In a practically relevant example, the parameters of a reduced lung model are calibrated based on flow observations over time and voltage measurements from a coupled electrical impedance tomography simulation., Comment: 21 pages, 15 figures

Heterogeneous mechanical dyskinesis during acute myocardial ischaemia is thought to contribute to arrhythmogenic alterations to cardiac electrophysiology. Various forms of mechano-electric coupling (MEC) mechanisms have been suggested to contribute to these changes, with two primary mechanisms being: (1) myofilament-dependent calcium release events, and (2) the activation of stretch-activated currents (SACs). In this computational investigation, we assessed the collective impact of these processes on mechanically-induced alternans that create an arrhythmogenic substrate during acute ischaemia. To appraise the potential involvement of MEC in ischaemia-induced arrhythmias, we developed a coupled model of ventricular myocyte electrophysiology and contraction including SACs and stretch-dependent calcium buffering and release. The model, reflecting observed electrophysiological changes during ischaemia, was exposed to a series of stretch protocols that replicated both physiological and pathological mechanical conditions. Pathologically realistic myofiber stretch variations revealed calcium sensitivity changes dependent on myofilament, leading to alterations in cytosolic calcium concentrations. Under calcium overload conditions, these changes resulted in electrical alternans. The study implies that strain impacts cellular electrophysiology through myofilament calcium release and SAC opening in ventricular mechano-electrical models, parameterised to available data. This supports experimental evidence suggesting that both calcium-driven instability via MEC and SAC-induced effects contribute to electrical alternans in acute ischaemia., Comment: 20 pages; 8 figures; original paper

Measuring the spectral phase of a pulse is key for performing wavelength resolved ultrafast measurements in the few femtosecond regime. However, accurate measurements in real experimental conditions can be challenging. We show that the reflectivity change induced by coherent phonons in a quantum material can be used to infer the spectral phase of an optical probe pulse with few-femtosecond accuracy., Comment: 5 figures