Homogenization based two-scale modelling of fluid-saturated porous media with self-contact and flow in micropores

The contribution is devoted to the numerical modelling of the homogenized fluid-saturated porous media subject to loads which, at the pore level, induce unilateral self-contact. The initial microstructure is periodic, being generated by a representative cell consisting of elastic skeleton and a rigid inclusion which is anchored in the skeleton on a part of its pore surface. The unilateral frictionless contact interaction is considered between the inclusion and the elastic skeleton on matching surfaces. Depending on the deformation due to applied macroscopic loads, the self-contact interaction alters the one between the solid and fluid phases. Both the disconnected and connected porosities are treated; in the latter case, quasistatic fluid flow is described by the Stokes model. A homogenized model is derived using the periodic unfolding and the method of oscillating test functions. The macroscopic model attains the form of a nonlinear Biot continuum, whereby the Darcy flow model governs the fluid redistribution. We propose an efficient algorithm for two-scale computational analysis with the numerical model obtained using the FE discretization of the homogenized model. For this, a sequential linearization is used which leads to consistent stiffness matrices of the macroscopic elasticity problem. At the local level, the contact problem attains the form of a nonsmooth equation which which is solved using the semi-smooth Newton method without any regularization, or a problem relaxation. Numerical examples of 2D deforming structures are presented

TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank

Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applications such as large-scale search, recommender systems, document summarization and question answering. While there is widespread support for classification and regression based learning, support for learning-to-rank in deep learning has been limited. We propose TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework. It is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning-to-rank setting. Our library is developed on top of TensorFlow and can thus fully leverage the advantages of this platform. For example, it is highly scalable, both in training and in inference, and can be used to learn ranking models over massive amounts of user activity data, which can include heterogeneous dense and sparse features. We empirically demonstrate the effectiveness of our library in learning ranking functions for large-scale search and recommendation applications in Gmail and Google Drive. We also show that ranking models built using our model scale well for distributed training, without significant impact on metrics. The proposed library is available to the open source community, with the hope that it facilitates further academic research and industrial applications in the field of learning-to-rank.Comment: KDD 201

A database of the healthy human spinal cord morphometry in the PAM50 template space

ABSTRACT: Measures of spinal cord morphometry computed from magnetic resonance images serve as relevant prognostic biomarkers for a range of spinal cord pathologies, including traumatic and non-traumatic spinal cord injury and neurodegenerative diseases. However, interpreting these imaging biomarkers is difficult due to considerable intra- and inter-subject variability. Yet, there is no clear consensus on a normalization method that would help reduce this variability and more insights into the distribution of these morphometrics are needed. In this study, we computed a database of normative values for six commonly used measures of spinal cord morphometry: cross-sectional area, anteroposterior diameter, transverse diameter, compression ratio, eccentricity, and solidity. Normative values were computed from a large open-access dataset of healthy adult volunteers (N = 203) and were brought to the common space of the PAM50 spinal cord template using a newly proposed normalization method based on linear interpolation. Compared to traditional image-based registration, the proposed normalization approach does not involve image transformations and, therefore, does not introduce distortions of spinal cord anatomy. This is a crucial consideration in preserving the integrity of the spinal cord anatomy in conditions such as spinal cord injury. This new morphometric database allows researchers to normalize based on sex and age, thereby minimizing inter-subject variability associated with demographic and biological factors. The proposed methodology is open-source and accessible through the Spinal Cord Toolbox (SCT) v6.0 and higher

Diffusion magnetic resonance imaging reveals tract‐specific microstructural correlates of electrophysiological impairments in non‐myelopathic and myelopathic spinal cord compression

ABSTRACT: Background and purpose: Non- myelopathic degenerative cervical spinal cord compres-sion (NMDC) frequently occurs throughout aging and may progress to potentially irre-versible degenerative cervical myelopathy (DCM). Whereas standard clinical magnetic resonance imaging (MRI) and electrophysiological measures assess compression sever-ity and neurological dysfunction, respectively, underlying microstructural deficits still have to be established in NMDC and DCM patients. The study aims to establish tract- specific diffusion MRI markers of electrophysiological deficits to predict the progression of asymptomatic NMDC to symptomatic DCM. Methods: High-resolution 3 T diffusion MRI was acquired for 103 NMDC and 21 DCM patients compared to 60 healthy controls to reveal diffusion alterations and relation-ships between tract-specific diffusion metrics and corresponding electrophysiological measures and compression severity. Relationship between the degree of DCM disability, assessed by the modified Japanese Orthopaedic Association scale, and tract-specific mi-crostructural changes in DCM patients was also explored. Results: The study identified diffusion-derived abnormalities in the gray matter, dor-sal and lateral tracts congruent with trans-synaptic degeneration and demyelination in chronic degenerative spinal cord compression with more profound alterations in DCM than NMDC. Diffusion metrics were affected in the C3-6 area as well as above the com-pression level at C3 with more profound rostral deficits in DCM than NMDC. Alterations in lateral motor and dorsal sensory tracts correlated with motor and sensory evoked po-tentials, respectively, whereas electromyography outcomes corresponded with gray mat-ter microstructure. DCM disability corresponded with microstructure alteration in lateral columns. Conclusions: Outcomes imply the necessity of high- resolution tract-specific diffusion MRI for monitoring degenerative spinal pathology in longitudinal studies

The fastest stars in the Galaxy

We report a spectroscopic search for hypervelocity white dwarfs (WDs) that are runaways from Type Ia supernovae (SNe Ia) and related thermonuclear explosions. Candidates are selected from Gaia data with high tangential velocities and blue colors. We find six new runaways, including four stars with radial velocities (RVs) $>1000\,\rm km\,s^{-1}$ and total space velocities $\gtrsim 1300\,\rm km\,s^{-1}$. These are most likely the surviving donors from double-degenerate binaries in which the other WD exploded. The other two objects have lower minimum velocities, $\gtrsim 600\,\rm km\,s^{-1}$, and may have formed through a different mechanism, such as pure deflagration of a WD in a Type Iax supernova. The four fastest stars are hotter and smaller than the previously known "D$^6$ stars," with effective temperatures ranging from $\sim$20,000 to $\sim$130,000 K and radii of $\sim 0.02-0.10\,R_{\odot}$. Three of these have carbon-dominated atmospheres, and one has a helium-dominated atmosphere. Two stars have RVs of $-1694$ and $-2285\rm \,km\,s^{-1}$ -- the fastest systemic stellar RVs ever measured. Their inferred birth velocities, $\sim 2200-2500\,\rm km\,s^{-1}$, imply that both WDs in the progenitor binary had masses $>1.0\,M_{\odot}$. The high observed velocities suggest that a dominant fraction of the observed hypervelocity WD population comes from double-degenerate binaries whose total mass significantly exceeds the Chandrasekhar limit. However, the two nearest and faintest D$^6$ stars have the lowest velocities and masses, suggesting that observational selection effects favor rarer, higher-mass stars. A significant population of fainter low-mass runaways may still await discovery. We infer a birth rate of D$^6$ stars that is consistent with the SN Ia rate. The birth rate is poorly constrained, however, because the luminosities and lifetimes of $\rm D^6$ stars are uncertain.Comment: 26 pages, 17 figures. Accepted to OJ

The Astropy Problem

The Astropy Project (http://astropy.org) is, in its own words, "a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages." For five years this project has been managed, written, and operated as a grassroots, self-organized, almost entirely volunteer effort while the software is used by the majority of the astronomical community. Despite this, the project has always been and remains to this day effectively unfunded. Further, contributors receive little or no formal recognition for creating and supporting what is now critical software. This paper explores the problem in detail, outlines possible solutions to correct this, and presents a few suggestions on how to address the sustainability of general purpose astronomical software