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Mesoscale sea surface temperature (SST) variability influences the marine atmosphere boundary layer (MABL), affecting near‐surface winds and turbulent heat fluxes. This study examines precipitation response to mesoscale SST forcing using satellite observations, ERA5 reanalysis, and high‐ and low‐resolution climate models. The results show that high‐resolution models produce a precipitation response to mesoscale SST consistent with satellite observations and ERA5. However, partitioning ERA5 and model precipitation into resolved and parameterized convective components reveals that even in high‐resolution models, the simulated mesoscale SST‐precipitation relationship is shaped by the characteristics of convective parameterization. Further, the precipitation response to SST is strongly dependent on the background SST and SST variability in coupled models. Further analysis of ERA5 and high‐resolution simulations shows a vertical velocity response extending to 500 hPa. However, the reliance on convective parameterizations introduces uncertainties about whether high‐resolution models accurately capture these effects.

The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales—of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure—irrespective of species, imaging modality, or spatial resolution. •The human brain can be represented as a multi-scale network.•Characterizing the architecture of multi-scale networks requires new tools.•We review promising multilayer tools from network science.

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self‐supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image‐wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi‐resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self‐supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high‐resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state‐of‐the‐art image registration algorithms. We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks under a self‐supervised learning setting. Experimental results have demonstrated our method could obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state‐of‐the‐art image registration algorithms.

Passive high‐resolution attenuation tomography is used here to image the geological structure in the first upper 4 km of the shallow crust beneath the Campi Flegrei caldera, southern Italy. The inverse Q was estimated for each source‐receiver path using the coda‐normalization method (S‐waves) and the slope decay method (P‐waves and S‐waves). Inversion was performed using a multi‐resolution method, which ensures a minimum cell‐size resolution of 500 m. The study of the resolution matrix as well as the synthetic tests guarantee an optimal reproduction of the input anomalies in the center of the caldera, between 0 and 3.5 km in depth. High attenuation vertical structures are connected at the surface with the main volcanological features (e.g., the Solfatara and Mofete fumarole fields), and depict vertical Q contrast imaging important geological structures, such as the La Starza fault. These high attenuation volumes extend between the surface and a depth of about 3 km, where a hard rock layer is imaged by the sharp contrast of the quality factors. The retrieved image of the Campi Flegrei has been jointly interpreted taking into account evidence from seismological, geological, volcanological and geochemical investigations. This analysis has allowed an unprecedented view of the feeding systems in this area, and in particular it recognizes the vertically extending, high attenuation structures that correspond to gas or fluid reservoirs beneath Pozzuoli‐Solfatara, Solfatara, Mofete‐Mt. Nuovo and Agnano. This high‐attenuation system is possibly connected with the magma sill revealed at about 7 km in depth by passive travel‐time tomography.

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional “soft” functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results. •We contribute finely-resolved high-dimensional functional modes for fMRI analysis.•Those are trained on millions of varied fMRI functional brain volumes, using a sparse matrix factorisation algorithm. The total training size is 2.4TB.•These Dictionaries of Functional Modes (DiFuMo) are multi-scale, with a number of functional networks ranging from 64 to 1024.•Our benchmarks reveal the importance of using high-dimensional “soft” continuous-valued functional atlases when extracting image-derived phenotypes.•We provide an anatomical name to each of the modes of the DiFuMo atlases. Those are available at https://parietal-inria.github.io/DiFuMo/.

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

Partial discharge (PD) is not only an important symptom for monitoring the imperfections in the insulation system of a gas-insulated switchgear (GIS), but also the factor that accelerates the degradation. At present, monitoring ultra-high-frequency (UHF) signals induced by PDs is regarded as one of the most effective approaches for assessing the insulation severity and classifying the PDs. Therefore, in this paper, a deep learning-based PD classification algorithm is proposed and realized with a multi-column convolutional neural network (CNN) that incorporates UHF spectra of multiple resolutions. First, three subnetworks, as characterized by their specified designed temporal filters, frequency filters, and texture filters, are organized and then intergraded by a fully-connected neural network. Then, a long short-term memory (LSTM) network is utilized for fusing the embedded multi-sensor information. Furthermore, to alleviate the risk of overfitting, a transfer learning approach inspired by manifold learning is also present for model training. To demonstrate, 13 modes of defects considering both the defect types and their relative positions were well designed for a simulated GIS tank. A detailed analysis of the performance reveals the clear superiority of the proposed method, compared to18 typical baselines. Several advanced visualization techniques are also implemented to explore the possible qualitative interpretations of the learned features. Finally, a unified framework based on matrix projection is discussed to provide a possible explanation for the effectiveness of the architecture.

Heavy ice coating of high–voltage overhead transmission lines may lead to conductor breakage and tower collapse causing the unexpected interrupt of power supply. The optical load cell applied in ice monitoring systems is immune to electromagnetic interference and has no need of a power supply on site. Therefore, it has become a hot research topic in China and other countries. In this paper, to solve the problem of eccentric load in measurement, we adopt the shearing structure with additional grooves to improve the strain distribution and acquire good repeatability. Then, the fiber Bragg grating (FBG) with a permanent weldable package are mounted onto the front/rear groove of the elastic element by spot welding, the direction deviation of FBGs is 90° from each other to achieve temperature compensation without an extra FBG. After that, protection parts are designed to guarantee high sensitivity for a light load condition and industrial safety under a heavy load up to 65 kN. The results of tension experiments indicate that the sensitivity and resolution of the load cell is 0.1285 pm/N and 7.782 N in the conventional measuring range (0–10 kN). Heavy load tension experiments prove that the protection structure works and the sensitivity and resolution are not changed after several high load (65 kN) cycles. In addition, the experiment shows that the resolution of the sensor is 87.79 N in the large load range, allowing the parameter to be used in heavy icing monitoring.

Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser or larger spatial units—rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi-resolution spatio-temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between-ROIs). The model is used in a motor-task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.

A new climate model has been developed that employs a multi-resolution dynamical core for the sea ice-ocean component. In principle, the multi-resolution approach allows one to use enhanced horizontal resolution in dynamically active regions while keeping a coarse-resolution setup otherwise. The coupled model consists of the atmospheric model ECHAM6 and the finite element sea ice-ocean model (FESOM). In this study only moderate refinement of the unstructured ocean grid is applied and the resolution varies from about 25 km in the northern North Atlantic and in the tropics to about 150 km in parts of the open ocean; the results serve as a benchmark upon which future versions that exploit the potential of variable resolution can be built. Details of the formulation of the model are given and its performance in simulating observed aspects of the mean climate is described. Overall, it is found that ECHAM6–FESOM realistically simulates many aspects of the observed climate. More specifically it is found that ECHAM6–FESOM performs at least as well as some of the most sophisticated climate models participating in the fifth phase of the Coupled Model Intercomparison Project. ECHAM6–FESOM shares substantial shortcomings with other climate models when it comes to simulating the North Atlantic circulation.