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We consider reversible ergodic Markov chains with finite state space, and we introduce a new notion of quasi-stationary distribution that does not require the presence of any absorbing state. In our setting, the hitting time of the absorbing set is replaced by an optimal strong stationary time, representing the ``hitting time of the stationary distribution''. On the one hand, we show that our notion of quasi-stationary distribution corresponds to the natural generalization of the \emph{Yaglom limit}. On the other hand, similarly to the classical quasi-stationary distribution, we show that it can be written in terms of the eigenvectors of the underlying Markov kernel, and it is therefore amenable of a geometric interpretation. Moreover, we recover the usual exponential behavior that characterizes quasi-stationary distributions and metastable systems. We also provide some toy examples, which show that the phenomenology is richer compared to the absorbing case. Finally, we present some counterexamples, showing that the assumption on the reversibility cannot be weakened in general., Comment: 20 pages, 7 figures

We revisit a subexponential bound for the $abc$ conjecture due to the first author, and we establish a variation of it using linear forms in logarithms. As an application, we prove an unconditional subexponential bound towards the $4$-terms $abc$ conjecture under a suitable hypothesis on the size of the variables.

We developed a grid of stellar rotation models for low-mass and solar-type Classical T Tauri stars (CTTS) ($0.3M_{\odot}

The worldline formalism is a useful scheme in Quantum Field Theory which has also become a powerful tool for numerical computations. It is based on the first quantisation of a point-particle whose transition amplitudes correspond to the heat-kernel of the operator of quantum fluctuations of the field theory. However, to study a quantum field theory in a bounded manifold one needs to restrict the path integration domain of the point-particle to a specific subset of worldlines enclosed by those boundaries. In the present article it is shown how to implement this restriction for the case of a spinor field in a two-dimensional curved half-plane under MIT bag boundary conditions, and compute the first few heat-kernel coefficients as a verification of the proposed construction. This construction admits several generalisations., Comment: 34 pages, 1 figure. New version matches published version

The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell components. These characteristics are seen as motion changes in the particle trajectories. Despite the existence of multiple approaches to carry out this type of analysis, no objective assessment of these methods has been performed so far. Here, we have designed a competition to characterize and rank the performance of these methods when analyzing the dynamic behavior of single molecules. To run this competition, we have implemented a software library to simulate realistic data corresponding to widespread diffusion and interaction models, both in the form of trajectories and videos obtained in typical experimental conditions. The competition will constitute the first assessment of these methods, provide insights into the current limits of the field, foster the development of new approaches, and guide researchers to identify optimal tools for analyzing their experiments., Comment: 19 pages, 4 figure, 2 tables. Stage 1 registered report, accepted in principle in Nature Communications (https://springernature.figshare.com/articles/journal_contribution/Quantitative_evaluation_of_methods_to_analyze_motion_changes_in_single-particle_experiments_Registered_Report_Stage_1_Protocol_/24771687)

Controlling false positives (Type I errors) through statistical hypothesis testing is a foundation of modern scientific data analysis. Existing causal structure discovery algorithms either do not provide Type I error control or cannot scale to the size of modern scientific datasets. We consider a variant of the causal discovery problem with two sets of nodes, where the only edges of interest form a bipartite causal subgraph between the sets. We develop Scalable Causal Structure Learning (SCSL), a method for causal structure discovery on bipartite subgraphs that provides Type I error control. SCSL recasts the discovery problem as a simultaneous hypothesis testing problem and uses discrete optimization over the set of possible confounders to obtain an upper bound on the test statistic for each edge. Semi-synthetic simulations demonstrate that SCSL scales to handle graphs with hundreds of nodes while maintaining error control and good power. We demonstrate the practical applicability of the method by applying it to a cancer dataset to reveal connections between somatic gene mutations and metastases to different tissues., Comment: 10 figures, 24 pages

Stochastic processes have found numerous applications in science, as they are broadly used to model a variety of natural phenomena. Due to their intrinsic randomness and uncertainty, they are however difficult to characterize. Here, we introduce an unsupervised machine learning approach to determine the minimal set of parameters required to effectively describe the dynamics of a stochastic process. Our method builds upon an extended $\beta$-variational autoencoder architecture. By means of simulated datasets corresponding to paradigmatic diffusion models, we showcase its effectiveness in extracting the minimal relevant parameters that accurately describe these dynamics. Furthermore, the method enables the generation of new trajectories that faithfully replicate the expected stochastic behavior. Overall, our approach enables for the autonomous discovery of unknown parameters describing stochastic processes, hence enhancing our comprehension of complex phenomena across various fields., Comment: 9 pages, 5 figures, 1 table. Code available at https://github.com/GabrielFernandezFernandez/SPIVAE . Corrected a reference, a typographical error in the appendix, and acknowledgments

Spatial distributions of electrons ionized and scattered from ultra-low pressure gases are proposed and experimentally demonstrated as a method to directly measure the intensity of an ultra-high intensity laser pulse. Analytic models relating the peak scattered electron energy to the peak laser intensity are derived and compared to paraxial Runge-Kutta simulations highlighting two models suitable for describing electrons scattered from weakly paraxial beams ($f_{\#}>5$) for intensities in the range of $10^{18}-10^{21}$Wcm$^{-2}$. Scattering energies are shown to be dependant on gas species emphasizing the need for specific gases for given intensity ranges. Direct measurements of the laser intensity at full power of two laser systems is demonstrated both showing a good agreement between indirect methods of intensity measurement and the proposed method. One experiment exhibited the role of spatial aberrations in the scattered electron distribution motivating a qualitative study on the effect. We propose the use of convolutional neural networks as a method for extracting quantitative information of the spatial structure of the laser at full power. We believe the presented technique to be a powerful tool that can be immediately implemented in many high-power laser facilities worldwide.

Over recent years, super-resolution and single-molecule imaging methods have delivered unprecedented details on the nanoscale organization and dynamics of individual molecules in different contexts. Yet, visualizing single-molecule processes in living cells with the required spatial and temporal resolution remains highly challenging. Here, we report on an analytical approach that extracts such information from live-cell single-molecule imaging at high-labeling densities using standard fluorescence probes. Our high-density-mapping (HiDenMap) methodology provides single-molecule nanometric localization accuracy together with millisecond temporal resolution over extended observation times, delivering multi-scale spatiotemporal data that report on the interaction of individual molecules with their dynamic environment. We validated HiDenMaps by simulations of Brownian trajectories in the presence of patterns that restrict free diffusion with different probabilities. We further generated and analyzed HiDenMaps from single-molecule images of transmembrane proteins having different interaction strengths to cortical actin, including the transmembrane receptor CD44. HiDenMaps uncovered a highly heterogenous and multi-scale spatiotemporal organization for all the proteins that interact with the actin cytoskeleton. Notably, CD44 alternated between periods of random diffusion and transient trapping, likely resulting from actin-dependent CD44 nanoclustering. Whereas receptor trapping was dynamic and lasted for hundreds of milliseconds, actin remodeling occurred at the timescale of tens of seconds, coordinating the assembly and disassembly of CD44 nanoclusters rich regions. Together, our data demonstrate the power of HiDenMaps to explore how individual molecules interact with and are organized by their environment in a dynamic fashion., Comment: 33 pages, 5 figures

Understanding the sticking coefficient $\sigma$, i.e., the probability of an adatom sticking to a surface, is essential for controlling the stoichiometry during epitaxial film growth. However, $\sigma$ on monolayer graphene-covered surfaces and its impact on remote epitaxy are not understood. Here, using molecular-beam epitaxial (MBE) growth of the magnetic shape memory alloy Ni$_2$MnGa, we show that the sticking coefficients for metals on graphene-covered MgO (001) are less than one and are temperature and element dependent, as revealed by ion backscattering spectrometry (IBS) and energy dispersive x-ray spectroscopy (EDS). This lies in stark contrast with most transition metals sticking on semiconductor and oxide substrates, for which $\sigma$ is near unity at typical growth temperatures ($T<800\degree$C). By initiating growth below $400 \degree$ C, where the sticking coefficients are closer to unity and wetting on the graphene surface is improved, we demonstrate epitaxy of Ni$_2$MnGa films with controlled stoichiometry that can be exfoliated to produce freestanding membranes. Straining these membranes tunes the magnetic coercive field. Our results provide a route to synthesize membranes with complex stoichiometries whose properties can be manipulated via strain.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

In order to characterize the mechanisms governing the diffusion of particles in biological scenarios, it is essential to accurately determine their diffusive properties. To do so, we propose a machine learning method to characterize diffusion processes with time-dependent properties at the experimental time resolution. Our approach operates at the single-trajectory level predicting the properties of interest, such as the diffusion coefficient or the anomalous diffusion exponent, at every time step of the trajectory. In this way, changes in the diffusive properties occurring along the trajectory emerge naturally in the prediction, and thus allow the characterization without any prior knowledge or assumption about the system. We first benchmark the method on synthetic trajectories simulated under several conditions. We show that our approach can successfully characterize both abrupt and continuous changes in the diffusion coefficient or the anomalous diffusion exponent. Finally, we leverage the method to analyze experiments of single-molecule diffusion of two membrane proteins in living cells: the pathogen-recognition receptor DC-SIGN and the integrin $\alpha5\beta1$. The analysis allows us to characterize physical parameters and diffusive states with unprecedented accuracy, shedding new light on the underlying mechanisms., Comment: 17 pages, 9 figures, 1 table. Code is found in https://github.com/BorjaRequena/step