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This paper presents the CMB angular power spectrum obtained using the \texttt{CAMB} code for three different models of inflation: the Starobinsky inflationary model, the generalized Starobinsky inflationary model, and the chaotic inflationary model with a step. The results are compared with the most recent data reported for the Planck mission. An analysis of the large ($\ell \lesssim 90$), intermediate ($90 \lesssim \ell \lesssim 900$), and small ($\ell \gtrsim 900 $) angular scales is performed. We report the position of the peaks in the intermediate region so as the cosmological parameters obtained in each of the models: age of the universe, $\Omega_m$, $\Omega_b$, $\Omega_{\Lambda}$, $\Omega_K$ and $n_\sca$. We also perform a Bayesian analysis using the \texttt{Cobaya} code to evaluate our three best-fitting models. Additionally, we generated contour plots $(n_\sca,r)$ for our inflationary models, taking into account the number of e-folds between the end of inflation and the completion of reheating., Comment: 40 pages, 18 Figures, 10 tables

InvertibleNetworks.jl is a Julia package designed for the scalable implementation of normalizing flows, a method for density estimation and sampling in high-dimensional distributions. This package excels in memory efficiency by leveraging the inherent invertibility of normalizing flows, which significantly reduces memory requirements during backpropagation compared to existing normalizing flow packages that rely on automatic differentiation frameworks. InvertibleNetworks.jl has been adapted for diverse applications, including seismic imaging, medical imaging, and CO2 monitoring, demonstrating its effectiveness in learning high-dimensional distributions., Comment: Submitted to Journal of Open Source Software (JOSS)

We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.

The classical arguments employed when obtaining error estimates of Finite Element (FE) discretisations of elliptic problems lead to more restrictive assumptions on the regularity of the exact solution when applied to non-conforming methods. The so-called minimal regularity estimates available in the literature relax some of these assumptions, but are not truly of -minimal regularity-, since a data oscillation term appears in the error estimate. Employing an approach based on a smoothing operator, we derive for the first time error estimates for Discontinuous Galerkin (DG) type discretisations of non-linear problems with $(p,\delta)$-structure that only assume the natural $W^{1,p}$-regularity of the exact solution, and which do not contain any oscillation terms.

The ability to control porosity in oxide thin films is one of the key factors that determine their properties. Despite the abundance of dry processes for the synthesis of oxide porous layers, the high porosity range is typically achieved by spin-coating-based wet chemical methods. Besides, special techniques such as supercritical drying are required to replace the pore liquid with air while maintaining the porous network. In this study, we propose a new method for the fabrication of ultra-porous titanium dioxide thin films at room or mild temperatures (T lower or equal to 120 degrees Celsius) by the sequential process involving plasma deposition and etching. These films are conformal to the substrate topography even for high-aspect-ratio substrates and show percolated porosity values above 85 percent that are comparable to advanced aerogels. The films deposited at room temperature are amorphous. However, they become partly crystalline at slightly higher temperatures presenting a distribution of anatase clusters embedded in the sponge-like structure. Surprisingly, the porous structure remains after annealing the films at 450 degrees Celsius in air, which increases the fraction of the embedded anatase nanocrystals. The films are antireflective, omniphobic, and photoactive becoming super-hydrophilic subjected to UV light irradiation The supported percolated nanoporous structure can be used as an electron-conducting electrode in perovskite solar cells. The properties of the cells depend on the aerogel film thickness reaching efficiencies close to those of commercial mesoporous anatase electrodes. This generic solvent-free synthesis is scalable and is applicable to ultra-high porous conformal oxides of different compositions with potential applications in photonics, optoelectronics, energy storage, and controlled wetting., Comment: 31 pages, 10 Figs. plus Supporting Information 7 pags, 6 figs. Full Paper

Great progress has been made in the field of quantum inertial sensors, from the laboratory to the real-world use, which will ultimately require sensor miniaturization and ruggedization for dynamic motion. However, lateral atomic movement with respect to the sensing axis limits inertial sensing, which inspires the atom guides to ensure transverse atomic confinement. Compared to a relatively large free-space optical mode, an evanescent field (EF) mode with an advantageously small mode area allows for stronger atom-light interactions and lower optical power requirements for the atom guides. We study EF atom guides in nanofibers and waveguides with traveling evanescent waves, rather than EF optical lattices with standing evanescent waves. Our preliminary demonstration of an EF atom guide takes place on a nanofiber using 685 nm and 937 nm lights, and we experimentally show that the atomic coherence of the EF atom guide (685/937 nm) is similar to the EF optical lattice. By replacing the 685 nm light with 793 nm light, we further demonstrate the sub-5mW EF atom guide (793/937 nm) on nanofibers and assess relative power reduction of 61$\%$ on nanofibers and 78$\%$ on membrane waveguides, compared to nanofibers (685/937 nm), for the same optical potential depth. Power reduction is beneficial for reducing total optical power requirements and improving heat dissipation in vacuum for atom guiding on chip-based membrane-photonic devices. To connect this work with EF-guided atom interferometry, we evaluate the viability and potential benefits of using the low-power light configuration on membrane waveguides, presenting fabricated linear and circular waveguides for future demonstrations. This is a promising step towards a chip-scale quantum inertial sensor array with reduced size, weight, and power., Comment: 12 pages, 9 figures

As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robust monitoring and detection mechanisms for potential underground CO2 leakage, particularly through pre-existing or induced faults in the storage reservoir's seals. While techniques such as history matching and time-lapse seismic monitoring of CO2 storage have been used successfully in tracking the evolution of CO2 plumes in the subsurface, these methods lack principled approaches to characterize uncertainties related to the CO2 plumes' behavior. Inclusion of systematic assessment of uncertainties is essential for risk mitigation for the following reasons: (i) CO2 plume-induced changes are small and seismic data is noisy; (ii) changes between regular and irregular (e.g., caused by leakage) flow patterns are small; and (iii) the reservoir properties that control the flow are strongly heterogeneous and typically only available as distributions. To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments. While the inferences presented are preliminary in the context of an early CO2 leakage detection system, the results do indicate that inferences with conditional normalizing flows can produce high-fidelity estimates for CO2 plumes with or without leakage. We are also confident that the inferred uncertainty is reasonable because it correlates well with the observed errors. This uncertainty stems from noise in the seismic data and from the lack of precise knowledge of the reservoir's fluid flow properties., Comment: Accepted in NeurIPS 2023 Workshop - Tackling Climate Change with Machine Learning (Spotlight)

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. By leveraging DeepSpeed's current technology pillars (training, inference and compression) as base technology enablers, DeepSpeed4Science will create a new set of AI system technologies tailored for accelerating scientific discoveries by addressing their unique complexity beyond the common technical approaches used for accelerating generic large language models (LLMs). In this paper, we showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.

The superbubbles (SBs) carved in the interstellar medium by stellar winds and supernovae (SNe) are filled with hot ($T>$10$^{6}$~K) gas that produces soft X-ray emission (0.3-2.0\,keV). Models that assume a constant density medium and central SNe events fail to reproduce the soft X-ray luminosity that is observed in some SBs. We address this problem by generating models that trace the history of SNe in the SB and that produce off-centre SNe, that account for the missing soft X-ray emission. We test the models against archival, radio, optical, and X-ray observations of the SB DEM\,L50 located in the Large Magellanic Cloud. The soft X-ray properties of DEM L50, including its high luminosity, makes it a perfect candidate to test our models. Furthermore, the multiple wave-band observations of this object will help us to assess how well our models can reproduce other SB properties beside its soft X-ray properties. We find that a configuration where DEM L50 forms at the edge of a filament reproduces the observed soft X-ray luminosity, optical morphology, shell velocity, and swept-up mass of neutral gas. This configuration is supported by IR observations of the LMC. In addition, we find that off-centre SNe, which enhance soft X-ray emission, naturally occur for all of the initial ambient conditions we tested in our models. Finally, we show that an off-centre SN can explain the observed soft X-ray luminosity of DEM L50, and that the resulting luminosity is consistent with a plasma in non-equilibrium ionisation.