On the Perturbations of Viscous Rotating Newtonian Fluids

The perturbations of weakly-viscous, barotropic, non-self-gravitating, Newtonian rotating fluids are analyzed via a single partial differential equation. The results are then used to find an expression for the viscosity-induced normal-mode complex eigenfrequency shift, with respect to the case of adiabatic perturbations. However, the effects of viscosity are assumed to have been incorporated in the unperturbed (equilibrium) model. This paper is an extension of the normal-mode formalism developed by Ipser & Lindblom for adiabatic pulsations of purely-rotating perfect fluids. The formulas derived are readily applicable to the perturbations of thin and thick accretion disks. We provide explicit expressions for thin disks, employing results from previous relativistic analyses of adiabatic normal modes of oscillation. In this case, we find that viscosity causes the fundamental p- and g- modes to grow while the fundamental c-mode could have either sign of the damping rate.Comment: Accepted for publication by The Astrophysical Journal. 11 pages, no figure

Design Principles for Sparse Matrix Multiplication on the GPU

We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion. While previous SpMM work concentrates on thread-level parallelism, we additionally focus on latency hiding with instruction-level parallelism and load-balancing. We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access pattern that allows efficient access into both input and output matrices that is crucial to getting excellent performance on SpMM. By combining these two ingredients---(i) merge-based load-balancing and (ii) row-major coalesced memory access---we demonstrate a 4.1x peak speedup and a 31.7% geomean speedup over state-of-the-art SpMM implementations on real-world datasets.Comment: 16 pages, 7 figures, International European Conference on Parallel and Distributed Computing (Euro-Par) 201

On the common origin of the AB Dor moving group and the Pleiades cluster

AB Dor is the nearest identified moving group. As with other such groups, the age is important for understanding of several key questions. It is important, for example, in establishing the origin of the group and also in comparative studies of the properties of planetary systems, eventually surrounding some of the AB Dor group members, with those existing in other groups. For AB Dor two rather different estimates for its age have been proposed: a first one, of the order of 50 Myr, by Zuckerman and coworkers from a comparison with Tucana/Horologium moving group and a second one of about 100-125 Myr by Luhman and coworkers from color-magnitude diagrams (CMD). Using this last value and the closeness in velocity space of AB Dor and the Pleiades galactic cluster, Luhman and coworkers suggested coevality for these systems. Because strictly speaking such a closeness does not still guarantee coevality, here we address this problem by computing and comparing the full 3D orbits of AB Dor, Pleiades, alpha Persei and IC 2602. The latter two open clusters have estimated ages of about 85-90 Myr and 50 Myr. The resulting age 119 $\pm$ 20 Myr is consistent with AB Dor and Pleiades being coeval. Our solution and the scenario of open cluster formation proposed by Kroupa and collaborators suggest that the AB Dor moving group may be identified with the expanding subpopulation (Group I) present in this scenario. We also discuss other related aspects as iron and lithium abundances, eventual stellar mass segregation during the formation of the systems and possible fraction of debris discs in AB Dor group.Comment: 11 pages, 5 figures and 2 table

Extracutaneous involvement of pyoderma gangrenosum

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Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches

Trigonometry of 'complex Hermitian' type homogeneous symmetric spaces

This paper contains a thorough study of the trigonometry of the homogeneous symmetric spaces in the Cayley-Klein-Dickson family of spaces of 'complex Hermitian' type and rank-one. The complex Hermitian elliptic CP^N and hyperbolic CH^N spaces, their analogues with indefinite Hermitian metric and some non-compact symmetric spaces associated to SL(N+1,R) are the generic members in this family. The method encapsulates trigonometry for this whole family of spaces into a single "basic trigonometric group equation", and has 'universality' and '(self)-duality' as its distinctive traits. All previously known results on the trigonometry of CP^N and CH^N follow as particular cases of our general equations. The physical Quantum Space of States of any quantum system belongs, as the complex Hermitian space member, to this parametrised family; hence its trigonometry appears as a rather particular case of the equations we obtain.Comment: 46 pages, LaTe

Estimation of cellularity in tumours treated with Neoadjuvant therapy: A comparison of Machine Learning algorithms

This paper describes a method for residual tumour cellularity (TC) estimation in Neoadjuvant treatment (NAT) of advanced breast cancer. This is determined manually by visual inspection by a radiologist, then an automated computation will contribute to reduce time workload and increase precision and accuracy. TC is estimated as the ratio of tumour area by total image area estimated after the NAT. The method proposed computes TC by using machine learning techniques trained with information on morphological parameters of segmented nuclei in order to classify regions of the image as tumour or normal. The data is provided by the 2019 SPIE Breast challenge, which was proposed to develop automated TC computation algorithms. Three algorithms were implemented: Support Vector Machines, Nearest K-means and Adaptive Boosting (AdaBoost) decision trees. Performance based on accuracy is compared and evaluated and the best result was obtained with Support Vector Machines. Results obtained by the methods implemented were submitted during ongoing challenge with a maximum of 0.76 of prediction probability of success