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For many machine learning methods, creating a model requires setting a parameter that controls the model's capacity before training, e.g.~number of neurons in DNNs, or inducing points in GPs. Increasing capacity improves performance until all the information from the dataset is captured. After this point, computational cost keeps increasing, without improved performance. This leads to the question ``How big is big enough?'' We investigate this problem for Gaussian processes (single-layer neural networks) in continual learning. Here, data becomes available incrementally, and the final dataset size will therefore not be known before training, preventing the use of heuristics for setting the model size. We provide a method that automatically adjusts this, while maintaining near-optimal performance, and show that a single hyperparameter setting for our method performs well across datasets with a wide range of properties., Comment: 9 pages main, 19 pages total, 9 figures, 3 tables, preprint

Living systems rely on coordinated molecular interactions, especially those related to gene expression and protein activity. The Unfolded Protein Response is a crucial mechanism in eukaryotic cells, activated when unfolded proteins exceed a critical threshold. It maintains cell homeostasis by enhancing protein folding, initiating quality control, and activating degradation pathways when damage is irreversible. This response functions as a dynamic signaling network, with proteins as nodes and their interactions as edges. We analyze these protein-protein networks across different organisms to understand their intricate intra-cellular interactions and behaviors. In this work, analyzing twelve organisms, we assess how fundamental measures in network theory can individuate seed-proteins and specific pathways across organisms. We employ network robustness to evaluate and compare the strength of the investigated PPI networks, and the structural controllability of complex networks to find and compare the sets of driver nodes necessary to control the overall networks. We find that network measures are related to phylogenetics, and advanced network methods can identify main pathways of significance in the complete Unfolded Protein Response mechanism.

Developing new methods for predicting electromagnetic instabilities in cardiac activity is of primary importance. However, we still need a comprehensive view of the heart's magnetic activity at the tissue scale. To fill this gap, we present a model of soft active matter, including thermo-electric coupling, suitably modified to reproduce cardiac magnetic field. Our theoretical framework shows that periodic stimulations of cardiac cells create an external magnetic field evidencing restitution features of nonlinear cardiac dynamics and magnetic restitution curves better discriminate instabilities and bifurcations in cardiac activity. This new framework lays the foundation for innovative, non-invasive diagnostic tools for cardiac arrhythmias.

Measuring Deeply Virtual Compton Scattering on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of Generalized Parton Distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD $E$. This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks' angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the GPDs $H$ and $E$., Comment: 7 pages, 6 figures

Variational logistic regression is a popular method for approximate Bayesian inference seeing wide-spread use in many areas of machine learning including: Bayesian optimization, reinforcement learning and multi-instance learning to name a few. However, due to the intractability of the Evidence Lower Bound, authors have turned to the use of Monte Carlo, quadrature or bounds to perform inference, methods which are costly or give poor approximations to the true posterior. In this paper we introduce a new bound for the expectation of softplus function and subsequently show how this can be applied to variational logistic regression and Gaussian process classification. Unlike other bounds, our proposal does not rely on extending the variational family, or introducing additional parameters to ensure the bound is tight. In fact, we show that this bound is tighter than the state-of-the-art, and that the resulting variational posterior achieves state-of-the-art performance, whilst being significantly faster to compute than Monte-Carlo methods., Comment: Accepted at the 41st International Conference on Machine Learning

Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either multi-determinant Jastrow-Slater wave functions in variational Monte Carlo or just a single determinant in diffusion Monte Carlo. The excellent performance of our protocols is assessed against high-level coupled cluster calculations on a diverse set of representative configurations of the system. Finally, we train machine-learning force fields on the QMC forces and compare them to models trained on coupled cluster reference data, showing that a force field based on the diffusion Monte Carlo forces with a single determinant can faithfully reproduce coupled cluster power spectra in molecular dynamics simulations., Comment: 9 pages, 3 figures

Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervised diffusion model, obtaining a 0.83(p<0.01) correlation between chronological and predicted age on low quality T1w MR images. This was competitive with state-of-the-art non-generative methods. Furthermore, the predictions produced by our model were significantly associated with survival length (r=0.24, p<0.05) in Amyotrophic Lateral Sclerosis. Thus, our approach demonstrates the value of diffusion-based architectures for the task of brain age prediction.

This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Process Latent Variable Models (GPLVMs) to incorporate the case where each data point comprises the weighted sum of a known number of pure component signals, observed across several input locations. Our framework allows the use of a range of priors for the weights of each observation. This flexibility enables us to represent use cases including sum-to-one constraints for estimating fractional makeup, and binary weights for classification. Our contributions are particularly relevant to spectroscopy, where changing conditions may cause the underlying pure component signals to vary from sample to sample. To demonstrate the applicability to both spectroscopy and other domains, we consider several applications: a near-infrared spectroscopy data set with varying temperatures, a simulated data set for identifying flow configuration through a pipe, and a data set for determining the type of rock from its reflectance.

Understanding the nature of the photoexcitation and ultrafast charge dynamics pathways in organic halide perovskite nanocubes and their aggregation into superlattices is key for the potential applications as tunable light emitters, photon harvesting materials and light-amplification systems. In this work, we apply two-dimensional coherent electronic spectroscopy (2DES) to track in real time the formation of near-infrared optical excitons and their ultrafast relaxation in CH(NH2)2PbI3 nanocube superlattices. Our results unveil that the coherent ultrafast dynamics is limited by the combination of the inherent short exciton decay time ~40 fs and the dephasing due to the coupling with selective optical phonon modes at higher temperatures. On the picosecond timescale, we observe the progressive formation of long-lived localized trap states. The analysis of the temperature dependence of the excitonic intrinsic linewidth, as extracted by the anti-diagonal components of the 2D spectra, unveils a dramatic change of the excitonic coherence time across the cubic to tetragonal structural transition. Our results offer a new way to control and enhance the ultrafast coherent dynamics of photocarrier generation in hybrid halide perovskite synthetic solids.

The flow of emulsions in confined microfluidic channels is affected by surface roughness. Directional roughness effects have recently been reported in channels with asymmetric boundary conditions featuring a flat wall, and a wall textured with a directional roughness, the latter promoting a change in the velocity profiles when the flow direction of emulsions is inverted [D. Filippi et al., Adv. Mater. Technol. 8, 2201748 (2023)]. An operative protocol is needed to reconstruct the stress profile inside the channel from velocity data to shed light on the trigger of the directional response. To this aim, we performed lattice Boltzmann numerical simulations of the flow of model emulsions with a minimalist model of directional roughness in two dimensions: a confined microfluidic channel with one flat wall and the other patterned by right-angle triangular-shaped posts. Simulations are essential to develop the protocol based on mechanical arguments to reconstruct stress profiles. Hence, one can analyze data to relate directional effects in velocity profiles to different rheological responses close to the rough walls associated with opposite flow directions. We finally show the universality of this protocol by applying it to other realizations of directional roughness by considering experimental data on emulsions in a microfluidic channel featured with a flat wall and a wall textured by herringbone-shaped roughness., Comment: 9 pages, 6 figures

Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, especially as the number of observed events grows. This paper addresses this issue by introducing the Deep Relational Event Additive Model (DREAM) as a solution to the computational challenges presented by modeling non-linear effects in Relational Event Models (REMs). DREAM relies on Neural Additive Models to model non-linear effects, allowing each effect to be captured by an independent neural network. By strategically trading computational complexity for improved memory management and leveraging the computational capabilities of Graphic Processor Units (GPUs), DREAM efficiently captures complex non-linear relationships within data. This approach demonstrates the capability of DREAM in modeling dynamic networks and scaling to larger networks. Comparisons with traditional REM approaches showcase DREAM superior computational efficiency. The model potential is further demonstrated by an examination of the patent citation network, which contains nearly 8 million nodes and 100 million events.

Laser technologies improved after the understanding of the Chirped Pulse Amplification (CPA) which allows energetic laser beams to be compressed to tens of femtosecond (fs) pulse durations and focused to few $\mu$m. Protons of tens of MeV can be accelerated using for instance the Target Normal Sheath Acceleration (TNSA) method and focused on secondary targets. In such conditions, nuclear reactions can occur and radioisotopes relevant for medical purposes be produced. High repetition lasers can be used to produce enough isotopes for medical applications. This route is competitive to conventional methods mostly based on accelerators. In this paper we study the production of $^{67}$Cu, $^{63}$Zn, $^{18}$F and $^{11}$C currently used in positron emission tomography (PET) and other applications. At the same time, we study the reaction $^{10}$B(p,$\alpha$)$^{7}$Be and $^{70}$Zn(p,4n)$^{67}$Ga to put further constraints to the proton distributions at different angles and to the reaction $^{11}$B(p,$\alpha$)$^{8}$Be relevant for energy production. The experiment was performed at the 1 petawatt (PW) laser facility at Vega III located in Salamanca-Spain. Angular distributions of radioisotopes in the forward (with respect to the laser direction) and backward directions were measured using a High Purity Germanium Detector (HPGE). Our results are reasonably reproduced by the numerical estimates following the approach of Kimura et al. (NIMA637(2011)167)

Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations., Comment: includes major coordination problems

The present study contributes to an increased understanding of pyro-convection phenomena by using a fire-atmosphere coupled simulation, and investigates in detail the large-scale meteorological conditions affecting Portugal during the occurrence of multiple mega-fires events on 15 October 2017. Two numerical simulations were performed using the MesoNH atmospheric model. The first simulation, was run for a large single domain (300 x 250 grid points) with a 15 km resolution. In the second one, the MesoNH was coupled to a fire propagation model (ForeFire) to study in detail the Quiaios's fire. To optimize both high resolution in the proximity of the fire region and computational efficiency, the simulation is set up using 3 nested domains (300 x 300 grid points) with horizontal resolution of 2000 m, 400 m, and 80 m respectively. The emission into the atmosphere of the heat and the water vapour fluxes caused by the evolving fire is managed by the ForeFire code. The fire spatio-temporal evolution is based on an assigned map, which follows what reported by public authorities. At the large scale, the simulation shows the evolution of the hurricane Ophelia, pointing out the influence of south/southwest winds on the rapid spread of active fires, as well as the subtropical moisture transport toward mainland Portugal in the early evening, when violent pyro-convective activity was observed in Central Portugal. The coupled simulation allowed to reproduce the formation of a PyroCu cloud inside the smoke plume. The convective updraughts caused by the fire led to the vertical transport of water vapour to higher levels and enhanced the development of a high-based cloud over a dry atmospheric layer within the smoke plume.