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Solar radiation is made up of three components of electromagnetic waves: infrared, visible and ultraviolet. The infrared component is the cause of thermal energy, the visible spectrum allows to see through the eyes and the ultraviolet component is the most energetic and damaging. Solar radiation has several benefits, such as helping to synthesize vitamin D in the skin, favors blood circulation, among others benefits for the human body. In the Earth, it is the main source of energy for agriculture, also used as an alternative source of energy to hydrocarbons, through solar cells. The solar irradiance represents the surface power density with units W/m$^2$ in SI. Too much exposure can cause damage and an increase in value over the time can be can be also damaging. In this work it was used an open data base provided by Secretar\'ia del Medio Ambiente, from which a statistical analysis was performed of the solar irradiance values measured at various meteorological stations in Mexico City and the so-called metropolitan area, from 2018 to 2021. This analysis was carried out per years, months and days. From the solar irradiance values distributions, it was obtained the averages, maximums and means were it was found there was no variation in the solar irradiance values over this period of years.

Statistical downscaling is a technique used in climate modeling to increase the resolution of climate simulations. High-resolution climate information is essential for various high-impact applications, including natural hazard risk assessment. However, simulating climate at high resolution is intractable. Thus, climate simulations are often conducted at a coarse scale and then downscaled to the desired resolution. Existing downscaling techniques are either simulation-based methods with high computational costs, or statistical approaches with limitations in accuracy or application specificity. We introduce Generative Bias Correction and Super-Resolution (GenBCSR), a two-stage probabilistic framework for statistical downscaling that overcomes the limitations of previous methods. GenBCSR employs two transformations to match high-dimensional distributions at different resolutions: (i) the first stage, bias correction, aligns the distributions at coarse scale, (ii) the second stage, statistical super-resolution, lifts the corrected coarse distribution by introducing fine-grained details. Each stage is instantiated by a state-of-the-art generative model, resulting in an efficient and effective computational pipeline for the well-studied distribution matching problem. By framing the downscaling problem as distribution matching, GenBCSR relaxes the constraints of supervised learning, which requires samples to be aligned. Despite not requiring such correspondence, we show that GenBCSR surpasses standard approaches in predictive accuracy of critical impact variables, particularly in predicting the tails (99% percentile) of composite indexes composed of interacting variables, achieving up to 4-5 folds of error reduction.

The five SEERNet digital learning platforms (DLPs) present unique opportunities for researchers by offering tools, processes, and infrastructure to make research more efficient, scalable, and relevant. However, conducting research within a DLP may require a shift in a researcher's orientation or mindset in how they think about potential research questions, study samples, available data, and planning study logistics. We encourage researchers to think flexibly about how they might adapt their ideas to the specific learning platform and consider iterating toward a more comprehensive understanding of their hypotheses. We illustrate some of the potential mindset shifts and offer key considerations at stages of the research process.

This paper presents a conceptual exploration of how Digital Learning Platforms (DLPs) can be utilized to investigate the impact of language clarity, precision, engagement, and contextual relevance on mathematics learning from word problems. Focusing on three distinct DLPs--ASSISTments/E-TRIALS, MATHia/UpGrade, and Canvas/Terracotta--we propose hypothetical studies aimed at uncovering how nuanced language modifications can enhance mathematical understanding and engagement. While these studies are illustrative in nature, they provide a blueprint for researchers interested in leveraging DLPs for empirical investigation so that future investigators gain a better understanding of the emerging infrastructure for research in DLPs and the opportunities provided by them. In highlighting three distinct implementations of the same core research question, we reveal both commonalities as well as differences in how different educational technologies might build evidence, offering a unique opportunity to advance the field of math education and other education research fields.

Superconducting sensors doped with rare isotopes have recently demonstrated powerful sensing performance for sub-keV radiation from nuclear decay. Here, we report the first high-resolution recoil spectroscopy of a single, selected nuclear state using superconducting tunnel junction (STJ) sensors. The STJ sensors were used to measure the eV-scale nuclear recoils produced in $^7$Be electron capture decay in coincidence with a 478 keV $\gamma$-ray emitted in decays to the lowest-lying excited nuclear state in $^7$Li. Details of the Doppler broadened recoil spectrum depend on the slow-down dynamics of the recoil ion. The measured spectral broadening is compared to empirical stopping power models as well as modern molecular dynamics simulations at low energy. The results have implications in several areas from nuclear structure and stopping powers at eV-scale energies to direct searches for dark matter, neutrino mass measurements, and other physics beyond the standard model.

Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the state-of-the-art method to produce localized future climate information, involves running a regional climate model (RCM) driven by an Earth System Model (ESM), but it is too computationally expensive to apply to large climate projection ensembles. We propose a novel approach combining dynamical downscaling with generative artificial intelligence to reduce the cost and improve the uncertainty estimates of downscaled climate projections. In our framework, an RCM dynamically downscales ESM output to an intermediate resolution, followed by a generative diffusion model that further refines the resolution to the target scale. This approach leverages the generalizability of physics-based models and the sampling efficiency of diffusion models, enabling the downscaling of large multi-model ensembles. We evaluate our method against dynamically-downscaled climate projections from the CMIP6 ensemble. Our results demonstrate its ability to provide more accurate uncertainty bounds on future regional climate than alternatives such as dynamical downscaling of smaller ensembles, or traditional empirical statistical downscaling methods. We also show that dynamical-generative downscaling results in significantly lower errors than bias correction and spatial disaggregation (BCSD), and captures more accurately the spectra and multivariate correlations of meteorological fields. These characteristics make the dynamical-generative framework a flexible, accurate, and efficient way to downscale large ensembles of climate projections, currently out of reach for pure dynamical downscaling.

We present a generative AI algorithm for addressing the challenging task of fast, accurate and robust statistical computation of three-dimensional turbulent fluid flows. Our algorithm, termed as GenCFD, is based on a conditional score-based diffusion model. Through extensive numerical experimentation with both incompressible and compressible fluid flows, we demonstrate that GenCFD provides very accurate approximation of statistical quantities of interest such as mean, variance, point pdfs, higher-order moments, while also generating high quality realistic samples of turbulent fluid flows and ensuring excellent spectral resolution. In contrast, ensembles of operator learning baselines which are trained to minimize mean (absolute) square errors regress to the mean flow. We present rigorous theoretical results uncovering the surprising mechanisms through which diffusion models accurately generate fluid flows. These mechanisms are illustrated with solvable toy models that exhibit the relevant features of turbulent fluid flows while being amenable to explicit analytical formulas., Comment: 71 pages, 30 figures

Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime. In this paper, we employ a rational neural network to accurately estimate the local smoothness of the solution, dynamically adapting the stencil weights based on local solution features. As rational neural networks can represent fast transitions between smooth and sharp regimes, this approach achieves a granular reconstruction with significantly reduced dissipation, improving the accuracy of the simulation. The network is trained offline on a carefully chosen dataset of analytical functions, bypassing the need for differentiable solvers. We also propose a robust model selection criterion based on estimates of the interpolation's convergence order on a set of test functions, which correlates better with the model performance in downstream tasks. We demonstrate the effectiveness of our approach on several one-, two-, and three-dimensional fluid flow problems: our scheme generalizes across grid resolutions while handling smooth and discontinuous solutions. In most cases, our rational network-based scheme achieves higher accuracy than conventional WENO3 with the same stencil size, and in a few of them, it achieves accuracy comparable to WENO5, which uses a larger stencil.

The design and optimization of laser-Compton x-ray systems based on compact distributed charge accelerator structures can enable micron-scale imaging of disease and the concomitant production of beams of Very High Energy Electrons (VHEEs) capable of producing FLASH-relevant dose rates. The physics of laser-Compton x-ray scattering ensures that the scattered x-rays follow exactly the trajectory of the incident electrons, thus providing a route to image-guided, VHEE FLASH radiotherapy. The keys to a compact architecture capable of producing both laser-Compton x-rays and VHEEs are the use of X-band RF accelerator structures which have been demonstrated to operate with over 100 MeV/m acceleration gradients. The operation of these structures in a distributed charge mode in which each radiofrequency (RF) cycle of the drive RF pulse is filled with a low-charge, high-brightness electron bunch is enabled by the illumination of a high-brightness photogun with a train of UV laser pulses synchronized to the frequency of the underlying accelerator system. The UV pulse trains are created by a patented pulse synthesis approach which utilizes the RF clock of the accelerator to phase and amplitude modulate a narrow band continuous wave (CW) seed laser. In this way it is possible to produce up to 10 $\mu$A of average beam current from the accelerator. Such high current from a compact accelerator enables production of sufficient x-rays via laser-Compton scattering for clinical imaging and does so from a machine of "clinical" footprint. At the same time, the production of 1000 or greater individual micro-bunches per RF pulse enables > 10 nC of charge to be produced in a macrobunch of < 100 ns. The design, construction, and test of the 100-MeV class prototype system in Irvine, CA is also presented., Comment: Accepted by Frontiers in Physics; Changes: expanded section 6, added numerical spectrum plot to Figure 10, expanded table 2 info, added citations; 29 pages, 13 figures, 2 tables

We present Wideband back-projection diffusion, an end-to-end probabilistic framework for approximating the posterior distribution induced by the inverse scattering map from wideband scattering data. This framework leverages conditional diffusion models coupled with the underlying physics of wave-propagation and symmetries in the problem, to produce highly accurate reconstructions. The framework introduces a factorization of the score function into a physics-based latent representation inspired by the filtered back-propagation formula and a conditional score function conditioned on this latent representation. These two steps are also constrained to obey symmetries in the formulation while being amenable to compression by imposing the rank structure found in the filtered back-projection formula. As a result, empirically, our framework is able to provide sharp reconstructions effortlessly, even recovering sub-Nyquist features in the multiple-scattering regime. It has low-sample and computational complexity, its number of parameters scales sub-linearly with the target resolution, and it has stable training dynamics.

Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering. However, their study remains a challenge due to the large range scales, and the strong interaction with other, often not fully understood, physics. As a consequence, the spatiotemporal resolution required for accurate simulation of these systems is typically computationally infeasible, particularly for applications of long-term risk assessment, such as the quantification of extreme weather risk due to climate change. While data-driven modeling offers some promise of alleviating these obstacles, the scarcity of high-quality simulations results in limited available data to train such models, which is often compounded by the lack of stability for long-horizon simulations. As such, the computational, algorithmic, and data restrictions generally imply that the probability of rare extreme events is not accurately captured. In this work we present a general strategy for training neural network models to non-intrusively correct under-resolved long-time simulations of chaotic systems. The approach is based on training a post-processing correction operator on under-resolved simulations nudged towards a high-fidelity reference. This enables us to learn the dynamics of the underlying system directly, which allows us to use very little training data, even when the statistics thereof are far from converged. Additionally, through the use of probabilistic network architectures we are able to leverage the uncertainty due to the limited training data to further improve extrapolation capabilities. We apply our framework to severely under-resolved simulations of quasi-geostrophic flow and demonstrate its ability to accurately predict the anisotropic statistics over time horizons more than 30 times longer than the data seen in training.

The DArk Matter In CCDs at Modane (DAMIC-M) experiment is designed to search for light dark matter (m$_{\chi}$<10\,GeV/c$^2$) at the Laboratoire Souterrain de Modane (LSM) in France. DAMIC-M will use skipper charge-coupled devices (CCDs) as a kg-scale active detector target. Its single-electron resolution will enable eV-scale energy thresholds and thus world-leading sensitivity to a range of hidden sector dark matter candidates. A DAMIC-M prototype, the Low Background Chamber (LBC), has been taking data at LSM since 2022. The LBC provides a low-background environment, which has been used to characterize skipper CCDs, study dark current, and measure radiopurity of materials planned for DAMIC-M. It also allows testing of various subsystems like readout electronics, data acquisition software, and slow control. This paper describes the technical design and performance of the LBC.

This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model for VRPs should be able to represent variants by treating each as a subset of a generalized problem equipped with different attributes. We propose a unified VRP environment capable of efficiently handling any attribute combination. The RouteFinder model leverages a modern transformer-based encoder and global attribute embeddings to improve task representation. Additionally, we introduce two reinforcement learning techniques to enhance multi-task performance: mixed batch training, which enables training on different variants at once, and multi-variant reward normalization to balance different reward scales. Finally, we propose efficient adapter layers that enable fine-tuning for new variants with unseen attributes. Extensive experiments on 24 VRP variants show RouteFinder achieves competitive results. Our code is openly available at https://github.com/ai4co/routefinder., Comment: A version of this work has been presented as an Oral at the ICML 2024 FM-Wild Workshop

Knowledge management (KM) involves collecting, organizing, storing, and disseminating information to improve decision-making, innovation, and performance. Implementing KM at scale has become essential for organizations to effectively leverage vast accessible data. This paper is a compilation of concepts that emerged from KM workshops hosted by EMBL-EBI, attended by SMEs and industry. We provide guidance on envisioning, executing, evaluating, and evolving knowledge management platforms. We emphasize essential considerations such as setting knowledge domain boundaries and measuring success, as well as the importance of making knowledge accessible for downstream applications and non-computational users and highlights necessary personal and organizational skills for success. We stress the importance of collaboration and the need for convergence on shared principles and commitment to provide or seek resources to advance KM. The community is invited to join the journey of KM and contribute to the advancement of the field by applying and improving on the guidelines described., Comment: 18 pages, 1 figure

The solar radiation are electromagnetic waves, composed of infrared, visible spectrum and ultraviolet. The infrared component is the cause of thermal energy, the visible spectrum allows to see and the ultraviolet component is the most energetic part and dangerous for the human body (skin and eyes). The ultraviolet rays are divided in a wavelength range, in three parts; called UVA (100-280 nm), UVB (280-315 nm) and UVC (315-400 nm). The UVC are the most energetic (followed by the UVB rays ), however are stopped in the ozone layer. In this work it shown a statistical analysis of the UVB index measured by five station in Mexico City, from the years 2000 to 2022. Through a Gaussian fit distribution, it was found that the range when the IUVB value exceeds the value of seven, which is from 11:00 to 16:00, having an average mean value of 13:09 hrs. $\pm$ 4 min. i.e. the time at which the UVB index reaches its maximum value. This occurs in the months from February to October. More than 80% of radiation is for UVB values less than 7 and less than the remaining 20% is for IUVB values greater than 7. Finally, it was possible to observe that the mean value per year, reaches its maximum when the solar cycles occurs, which was in the years 2003 and 2013.

Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponentially amplify errors in the learned dynamics. However, many of these systems exhibit ergodicity and an attractor: a compact and highly complex manifold, to which trajectories converge in finite-time, that supports an invariant measure, i.e., a probability distribution that is invariant under the action of the dynamics, which dictates the long-term statistical behavior of the system. In this work, we leverage this structure to propose a new framework that targets learning the invariant measure as well as the dynamics, in contrast with typical methods that only target the misfit between trajectories, which often leads to divergence as the trajectories' length increases. We use our framework to propose a tractable and sample efficient objective that can be used with any existing learning objectives. Our Dynamics Stable Learning by Invariant Measure (DySLIM) objective enables model training that achieves better point-wise tracking and long-term statistical accuracy relative to other learning objectives. By targeting the distribution with a scalable regularization term, we hope that this approach can be extended to more complex systems exhibiting slowly-variant distributions, such as weather and climate models., Comment: ICML 2024; Code to reproduce our experiments is available at https://github.com/google-research/swirl-dynamics/tree/main/swirl_dynamics/projects/ergodic

Light penetration depth in biological tissue is limited by tissue scattering. There is an urgent need for scattering compensation in vivo focusing and imaging, particularly challenging in photon-starved scenarios, without access to the transmission side of the scattering tissue. Here, we introduce a two-photon microscopy system with Fourier-domain open-channel coupling for scattering correction (2P-FOCUS). 2P-FOCUS corrects scattering by utilizing the non-linearity of multiple-beam interference and two-photon excitation, eliminating the need for a guide star, iterative optimization, or measuring transmission or reflection matrices. We demonstrate that 2P-FOCUS significantly enhances two-photon fluorescence signals by several tens of folds when focusing through a bone sample, compared to cases without scattering compensation at equivalent excitation power. We also show that 2P-FOCUS can correct scattering over large volumes by imaging neurons and cerebral blood vessels within a 230x230x500 um\textsuperscript{3} volume in the mouse brain in vitro. 2P-FOCUS could serve as a powerful tool for deep tissue imaging in bulky organisms or live animals.

Diamond possesses exceptional physical properties due to its remarkably strong carbon-carbon bonding, leading to significant resilience to structural transformations at very high pressures and temperatures. Despite several experimental attempts, synthesis and recovery of the theoretically predicted post-diamond BC8 phase remains elusive. Through quantum accurate, multi-million atom molecular dynamics (MD) simulations, we have uncovered the extreme metastability of diamond at very high pressures, significantly exceeding its range of thermodynamic stability. We predict the post-diamond BC8 phase to be experimentally accessible only within a narrow high pressure-temperature region of the carbon phase diagram. The diamond to BC8 transformation proceeds through pre-melting followed by BC8 nucleation and growth in the metastable carbon liquid. We propose a double-shock compression pathway to achieve BC8 synthesis, which is currently being explored in theory-inspired experiments at the National Ignition Facility.