Detection and Localization of Disconnections in a Large-Scale String of Photovoltaics Using SSTDR
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Here, we present a fast, asymptotically linear-scaling implementation of the perturbative quadruples energy correction in coupled-cluster theory using local natural orbitals. Our work follows the domain-based local pair natural orbital (DLPNO) approach previously applied to lower levels of excitations in coupled-cluster theory. Our DLPNO-CCSDT(Q) algorithm uses converged doubles and triples amplitudes from a preceding DLPNO-CCSDT computation to compute the quadruples amplitude and energy in the quadruples natural orbital (QNO) basis. We demonstrate the compactness of the QNO space, showing that more than 95% of the (Q) correction can be recovered using relatively loose natural orbital cutoffs, compared to the tighter cutoffs used in pair and triples natural orbitals at lower levels of coupled-cluster theory. We also highlight the accuracy of our algorithm in the computation of relative energies, which yields deviations of sub-kJ mol −1 in relative energy compared to the canonical CCSDT(Q). Timings are conducted on a series of growing linear alkanes (up to 10 carbons and 608 basis functions) and water clusters (up to 49 water molecules and 2842 basis functions) to establish the asymptotic linear-scaling of our DLPNO-(Q) algorithm.
The coexistence of competing species is a long-lasting puzzle in evolutionary ecology research. Despite abundant experimental evidence showing that the opportunity for coexistence decreases as niche overlap increases between species, bacterial species and strains competing for the same resources are commonly found across diverse spatially heterogeneous habitats. We thus hypothesized that the spatial scale of competition may play a key role in determining bacterial coexistence, and interact with other mechanisms that promote coexistence, including a growth–motility trade-off. To test this hypothesis, we let two Pseudomonas putida strains compete at local and regional scales by inoculating them either in a mixed droplet or in separate droplets in the same Petri dish, respectively. We also created conditions that allow the bacterial strains to disperse across abiotic or fungal hyphae networks. We found that competition at the local scale led to competitive exclusion while regional competition promoted coexistence. When competing in the presence of dispersal networks, the growth–motility trade-off promoted coexistence only when the strains were inoculated in separate droplets. Our results provide a mechanism by which existing laboratory data suggesting competitive exclusion at a local scale is reconciled with the widespread coexistence of competing bacterial strains in complex natural environments with dispersal.
Locally Purified Density Operators (LPDOs) are state-of-the-art tensor network ansatze candidates that efficiently represent mixed quantum states at scale. However, given their non-uniqueness, their representational complexity is generally sub-optimal in practical computations. Here, in this work we perform a comprehensive numerical and analytical analysis and resolve this issue in the experimentally relevant limit where noise depolarizes the density operator into a maximally mixed state. To resolve the sub-optimality issue, we analyze two numerical tools, one analytic method, and detail the relations between them. The numerical tools used are fidelity-preserving truncations and isometric gauge transformations leveraging Riemannian optimizations over entropic objective functions. In addition, by invoking the injectivity and symmetry constraints of the maximally mixed LPDO, we also present analytical closed-form expressions for the disentangler and discuss their relation to numerical optimizers. Further, away from the maximally mixed state, our simulations highlight how the truncation threshold smoothly interpolate, as a function of depolarization, between established matrix product results and our new results. Our work shows how, by minimizing the resources required to represent key states of practical interest in experiment, the efficiency of tensor network algorithms can be substantially increased. This paves the path for uncovering tensor network’s fundamental scalability limits and latent potential in representing the wide locus of mixed quantum states that are accessible on near-term quantum devices.
Abstract Major interactions are known to trigger star formation in galaxies and alter their color. We study the major interactions in filaments and sheets using SDSS data to understand the influence of large-scale environments on galaxy interactions. We identify the galaxies in filaments and sheets using the local dimension and also find the major pairs residing in these environments. The star formation rate (SFR) and color of the interacting galaxies as a function of pair separation are separately analyzed in filaments and sheets. The analysis is repeated for three volume limited samples covering different magnitude ranges. The major pairs residing in the filaments show a significantly higher SFR and bluer color than those residing in the sheets up to the projected pair separation of ∼50 kpc. We observe a complete reversal of this behavior for both the SFR and color of the galaxy pairs having a projected separation larger than 50 kpc. Some earlier studies report that the galaxy pairs align with the filament axis. Such alignment inside filaments indicates anisotropic accretion that may cause these differences. We do not observe these trends in the brighter galaxy samples. The pairs in filaments and sheets from the brighter galaxy samples trace relatively denser regions in these environments. The absence of these trends in the brighter samples may be explained by the dominant effect of the local density over the effects of the large-scale environment.
Many applications of physics and engineering involve wide ranges of time and spatial scales. The numerical simulation of localized small scales such as shock waves and material interfaces requires a large number of computational cells in these regions. For these applications, Lagrangian and Arbitrary-Lagrangian-Eulerian (ALE) related methods are engaging since the moving mesh feature naturally brings mesh cells on shock discontinuities and material interfaces are carefully captured. In addition, Adaptive-Mesh-Refinement (AMR) strategies aim to optimize computational resources by concentrating finer mesh cells only in areas of interest while using coarser cells elsewhere. A key but challenging AMR requirement consists in efficiently distributing the computational effort to achieve high accuracy without the prohibitive computational costs associated with uniformly fine grids. Here, in this document, the coupling of the p4est AMR library with a cell-centered Lagrangian scheme is presented with the goal to perform reliable 3D Lagrangian-AMR and indirect Euler-AMR multi-material simulations. In particular, it is shown that starting from a 3D indirect ALE code, the memory management and load balancing requirements can be delegated to an external library (here the p4est library) to unlock ALE-AMR capabilities. First, we present a strategy to transcribe the octant-based connectivity of the 3D AMR framework with that of an unstructured mesh of polygonal cells used in Lagrangian hydrodynamics. Then, we show how refinement and coarsening operations must be adapted to the particular Lagrangian framework to ensure the conservation of volume during those steps. Finally, several numerical test cases are presented that demonstrate the capabilities of the Lagrangian-AMR and indirect Euler-AMR algorithms.
In the standard picture of fully developed turbulence, highly intermittent hydrodynamic fields are nonlinearly coupled across scales, where local energy cascades from large scales into dissipative vortices and large density gradients. Microscopically, however, constituent fluid molecules are in constant thermal (Brownian) motion, but the role of molecular fluctuations in large-scale turbulence is largely unknown, and with rare exceptions, it has historically been considered irrelevant at scales larger than the molecular mean free path. Recent theoretical and computational investigations have shown that molecular fluctuations can impact energy cascade at Kolmogorov length scales. Here, we show that molecular fluctuations not only modify energy spectrum at wavelengths larger than the Kolmogorov length in compressible turbulence, but also significantly inhibit spatio-temporal intermittency across the entire dissipation range. Using large-scale direct numerical simulations of computational fluctuating hydrodynamics, we demonstrate that the extreme intermittency characteristic of turbulence models is replaced by nearly Gaussian statistics in the dissipation range. These results demonstrate that the compressible Navier–Stokes equations should be augmented with molecular fluctuations to accurately predict turbulence statistics across the dissipation range. Our findings have significant consequences for turbulence modelling in applications such as astrophysics, reactive flows and hypersonic aerodynamics, where dissipation-range turbulence is approximated by closure models.
Abstract. Boundary layer moisture variability at the Eastern North Atlantic (ENA) site during marine conditions is examined at monthly and daily timescales using 5 years of ground-based observations and output from the European Center for Medium range Weather Forecast (ECMWF) reanalysis model. The annual cycle of the mixed-layer total water budgets is presented to estimate the relative contribution of large-scale advection, local moisture tendency, entrainment, and precipitation to balance the moistening due to surface latent heat flux on monthly timescales. When marine conditions prevail, advection of colder and dry air from the north acts as an important moisture sink (∼ 50 % of the overall budget) during fall and winter driving the seasonality of the budget. Entrainment and precipitation contribute to the drying of the boundary layer (∼ 25 % and ∼ 15 % respectively), and the local change in moisture contributes to a small residual part. On a daily temporal scale, moist and dry mesoscale columns of vapor (∼ 10 km) are analyzed during 10 selected days of precipitating stratocumulus clouds. Adjacent moist and dry columns present distinct mesoscale features that are strongly correlated with clouds and precipitation. Dry columns adjacent to moist columns have more frequent and stronger downdrafts immediately below the cloud base. Moist columns have more frequent updrafts, stronger cloud-top cooling, and higher liquid water path and precipitation compared to the dry columns. This study highlights the complex interaction between large-scale and local processes controlling the boundary layer moisture and the importance of spatial distribution of vapor to support convection and precipitation.
Managing carbon stocks in the land, ocean, and atmosphere under changing climate requires a globally‐integrated view of carbon cycle processes at local and regional scales. The growing Earth Observation (EO) record is the backbone of this multi‐scale system, providing local information with discrete coverage from surface measurements and regional information at global scale from satellites. Carbon flux information, anchored by inverse estimates from spaceborne Greenhouse Gas (GHG) concentrations, provides an important top‐down view of carbon emissions and sinks, but currently lacks global continuity at assessment and management scales (<100 km). Partial‐column data can help separate signals in the boundary layer from the overlying atmosphere, providing an opportunity to enhance surface sensitivity and bring flux resolution down from that of column‐integrated data (100–500 km). Based on a workshop held in September 2024, the carbon cycle community envisions a carbon observation system leveraging GHG partial columns in the lower and upper troposphere to weave together information across scales from surface and satellite EO data, and integration of top‐down/bottom‐up analyses to link process understanding to global assessment.
ABSTRACT Galaxies in cosmic voids have been reported with properties related to a delayed evolution compared to the rest of the Universe. These characteristics reflect the interaction of galaxies with the environment. However, it is not clear the degree of influence of the large-scale structure on the properties of void galaxies or if these are only influenced by the low local density around them typical of these regions. In this article, we identified cosmic voids in the SDSS-DR16 and studied the g–r colour, star formation rate, and concentration of galaxies. We identified galaxy groups to characterize the local environment and studied the properties of galaxies as a function of total and stellar masses, separately analysing those in voids and the general sample. Our results show that galaxies that inhabit haloes of a given mass (below ${\sim}10^{13.5}{\rm M}_{\odot }$), are bluer, have a higher star formation rate and are less concentrated when the host halo is inside voids compared to other regions. For larger halo masses, the trend disappears. We also analyse whether the properties of galaxies are sensitive to the type of voids that inhabit. This is done by separating voids embedded in overdense regions (S-type) from those that asymptotically converge to the average density of the Universe (R-type). We found that galaxies in R-type voids are bluer, with higher SFR and less concentration than in S-type voids. Our results indicate some degree of correlation of galaxy properties with the large-scale environment provided by voids, suggesting possible second-order mechanisms in galaxy evolution.
The Perdew–Zunger self-interaction correction (PZ-SIC) removes unphysical electron self-interaction from calculations employing standard density functional approximations. Doing so improves many computed properties, bringing them into better agreement with experimental observations or with results from high-level quantum chemistry calculations. However, while PZ-SIC generally corrects in the right direction relative to the corresponding reference values, in many cases, it over-corrects. For this reason, scaled-down versions of PZ-SIC have been proposed and investigated. These approaches have mostly employed exterior scaling in which SIC correction terms are scaled in the same way at every point in space. Recently, a new local, or interior, scaling SIC method was proposed on non-empirical grounds to restore a property of the exact, but unknown, density functional that is broken in PZ-SIC. Here, the scaling at each point depends on the character of the charge density at that point. However, the local scaling can be done in various ways while still restoring the behavior of the exact functional. In this work, we compare and contrast the performance of various interior scaling approaches for addressing over-corrections of calculated molecular dipole moments and atomic polarizabilities and properties that reflect the nature of the electronic charge density.
ABSTRACT Recent cosmological analyses with large-scale structure and weak lensing measurements, usually referred to as 3 × 2pt, had to discard a lot of signal to noise from small scales due to our inability to accurately model non-linearities and baryonic effects. Galaxy–galaxy lensing, or the position–shear correlation between lens and source galaxies, is one of the three two-point correlation functions that are included in such analyses, usually estimated with the mean tangential shear. However, tangential shear measurements at a given angular scale θ or physical scale R carry information from all scales below that, forcing the scale cuts applied in real data to be significantly larger than the scale at which theoretical uncertainties become problematic. Recently, there have been a few independent efforts that aim to mitigate the non-locality of the galaxy–galaxy lensing signal. Here, we perform a comparison of the different methods, including the Y-transformation, the point-mass marginalization methodology, and the annular differential surface density statistic. We do the comparison at the cosmological constraints level in a combined galaxy clustering and galaxy–galaxy lensing analysis. We find that all the estimators yield equivalent cosmological results assuming a simulated Rubin Observatory Legacy Survey of Space and Time (LSST) Year 1 like set-up and also when applied to DES Y3 data. With the LSST Y1 set-up, we find that the mitigation schemes yield ∼1.3 times more constraining S8 results than applying larger scale cuts without using any mitigation scheme.
The localized corrosion of Li-containing nm hardening precipitates in the 3rd generation of Al-Cu-Li alloys was investigated based on a quasi in situ approach by sequentially exposing the material to NaCl solution and characterizing the structural, chemical, and electrochemical evolution at atomic scale using electron microscopy, spectroscopy, 3D tomography, electrochemical measurements, and DFT calculations. Localized corrosion of Al 7.5 Cu 4 Li (TB phase) initiated along {001} family of planes through the dealloying of Al and Li due to a low surface work function. Cu was enriched along the Cu (110) // T B (011) // Al (100) orientations on and around corroded T B precipitates. No strong galvanic interactions were observed at the T B and Al matrix interface due to the formation of a Li-C-O rich passivation layer during electrolyte exposure. Similarities and differences between T B and other common Al-Cu-Li precipitates (Al 2 CuLi, Al 6 CuLi 3 , and Al 3 Li) with respect to corrosion are discussed. Overall, the reported corrosion mechanism can assist in the assessment of the localized corrosion susceptibility of precipitation-hardened Al alloys and assist in the design of new alloys.
The next generation of spectroscopic surveys is expected to achieve an unprecedented level of accuracy in the measurement of cosmological parameters. To avoid confirmation bias and thereby improve the reliability of these results, blinding procedures become a standard practice in the cosmological analyses of such surveys. Blinding is especially crucial when the impact of observational systematics is important relative to the cosmological signal, and a detection of that signal would have significant implications. This is the case for local primordial non-gaussianity, as probed by the scale-dependent bias of the galaxy power spectrum at large scales that are heavily sensitive to the dependence of the target selection on the imaging quality, known as imaging systematics. We propose a blinding method for the scale-dependent bias signature of local primordial non-gaussianity at the density field level which consists in generating a set of weights for the data that replicate the scale-dependent bias. The applied blinding is predictable, and can be straightforwardly combined with other catalog-level blinding procedures that have been designed for the baryon acoustic oscillation and redshift space distortion signals. The procedure is validated through simulations that replicate data from the first year of observation of the Dark Energy Spectroscopic Instrument, but may find applications to other upcoming spectroscopic surveys.
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Abstract Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for spiking neural networks that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve 24.56% higher accuracy and 3.5 × faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to a k -means clustering approach.