Iterative adaption of the bidimensional wall of the French T2 wind tunnel around a C5 axisymmetrical model: Infinite variation of the Mach number at zero incidence and a test at increased incidence

The top and bottom two-dimensional walls of the T2 wind tunnel are adapted through an iterative process. The adaptation calculation takes into account the flow three-dimensionally. This method makes it possible to start with any shape of walls. The tests were performed with a C5 axisymmetric model at ambient temperature. Comparisons are made with the results of a true three-dimensional adaptation

Bilateral nephrectomy for adult polycystic kidney disease does not affect the graft function of transplant patients and does not result in sensitisation

Background. Native nephrectomy in Adult Polycystic Kidney Disease (ADPKD) patients is a major operation with controversy related to timing and indications. We present our single centre experience in transplanted patients and future candidates for transplantation. Methods. Retrospective analysis from an anonymised database of bilateral nephrectomies for ADPKD patients. Results were reported as median, range, and percentage. Differences between groups were tested using ANOVA and t-test. Surgery was performed between January 2012 and July 2018. Results. Thirty-three patients underwent bilateral native nephrectomy for APKD. 18 had a functioning kidney transplant (transplant group, 55%) while 15 patients were on dialysis (dialysis group, 45%) at the time of surgery; 8 patients of the latter group (24% of the whole cohort) were eventually transplanted. 53% were males, with median age of 55 years (27-71). Indications to surgery were the following: space (symptoms related to the size of the native kidneys or need to create space for transplantation) (59%), recurrent cyst infection (36%), haematuria (15%), pain (24%), and weight loss associated with cystic alteration on imaging (3%). In the transplant group, postoperative kidney function was not affected; haemoglobin serum levels significantly dropped in the whole cohort: 121 (82-150) g/L, versus 108 (58-154) g/L (p<0.001), with 14 patients being transfused perioperatively. Elevation of anti-HLA antibodies was noted in one female patient on dialysis, with no change in DSA levels and no rejection after transplant for all 26 transplanted patients. Median postoperative length of hospital stay was 9 days (6-71). One patient died (3%) after six months. Median follow-up for the whole cohort was 282 days (13-1834). Histopathological examination revealed incidental renal neoplasms in five cases (15%): 1 pT1a papillary renal cell carcinoma and 4 papillary adenomas. Conclusions. Native nephrectomy for ADPKD could be safely performed in case of refractory symptoms, suspect of cancer or to create space for transplantation. It does not affect graft function or DSA status of transplanted patients or the prospect of transplantation of those on the waiting list

Parallel Repetition of k-Player Projection Games

We study parallel repetition of k-player games where the constraints satisfy the projection property. We prove exponential decay in the value of a parallel repetition of projection games with value less than 1.Comment: 17 page

Learning and generation of long-range correlated sequences

We study the capability to learn and to generate long-range, power-law correlated sequences by a fully connected asymmetric network. The focus is set on the ability of neural networks to extract statistical features from a sequence. We demonstrate that the average power-law behavior is learnable, namely, the sequence generated by the trained network obeys the same statistical behavior. The interplay between a correlated weight matrix and the sequence generated by such a network is explored. A weight matrix with a power-law correlation function along the vertical direction, gives rise to a sequence with a similar statistical behavior.Comment: 5 pages, 3 figures, accepted for publication in Physical Review

Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields

Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-aliasing has previously been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone rather than points along a ray, but this approach is not natively compatible with current grid-based techniques. We show how ideas from rendering and signal processing can be used to construct a technique that combines mip-NeRF 360 and grid-based models such as Instant NGP to yield error rates that are 8% - 76% lower than either prior technique, and that trains 22x faster than mip-NeRF 360.Comment: Project page: https://jonbarron.info/zipnerf

Epigenetic modification of the oxytocin and glucocorticoid receptor genes is linked to attachment avoidance in young adults

Attachment in the context of intimate pair bonds is most frequently studied in terms of the universal strategy to draw near, or away, from significant others at moments of personal distress. However, important interindividual differences in the quality of attachment exist, usually captured through secure versus insecure – anxious and/or avoidant – attachment orientations. Since Bowlby’s pioneering writings on the theory of attachment, it has been assumed that attachment orientations are influenced by both genetic and social factors – what we would today describe and measure as gene by environment interaction mediated by epigenetic DNA modification – but research in humans on this topic remains extremely limited. We for the first time examined relations between intra-individual differences in attachment and epigenetic modification of the oxytocin receptor (OXTR) and glucocorticoid receptor (NR3C1) gene promoter in 109 young adult human participants. Our results revealed that attachment avoidance was significantly and specifically associated with increased OXTR and NR3C1 promoter methylation. These findings offer first tentative clues on the possible etiology of attachment avoidance in humans by showing epigenetic modification in genes related to both social stress regulation and HPA axis functioning

Local Guarantees in Graph Cuts and Clustering

Correlation Clustering is an elegant model that captures fundamental graph cut problems such as Min $s-t$ Cut, Multiway Cut, and Multicut, extensively studied in combinatorial optimization. Here, we are given a graph with edges labeled $+$ or $-$ and the goal is to produce a clustering that agrees with the labels as much as possible: $+$ edges within clusters and $-$ edges across clusters. The classical approach towards Correlation Clustering (and other graph cut problems) is to optimize a global objective. We depart from this and study local objectives: minimizing the maximum number of disagreements for edges incident on a single node, and the analogous max min agreements objective. This naturally gives rise to a family of basic min-max graph cut problems. A prototypical representative is Min Max $s-t$ Cut: find an $s-t$ cut minimizing the largest number of cut edges incident on any node. We present the following results: $(1)$ an $O(\sqrt{n})$-approximation for the problem of minimizing the maximum total weight of disagreement edges incident on any node (thus providing the first known approximation for the above family of min-max graph cut problems), $(2)$ a remarkably simple $7$-approximation for minimizing local disagreements in complete graphs (improving upon the previous best known approximation of $48$), and $(3)$ a $1/(2+\varepsilon)$-approximation for maximizing the minimum total weight of agreement edges incident on any node, hence improving upon the $1/(4+\varepsilon)$-approximation that follows from the study of approximate pure Nash equilibria in cut and party affiliation games

Field Guide to Exhumed Major Faults in Southern California

This field guide provides an overview of exposures and provides a field trip guide to localities of exhumed faults in southern California. We focus on exposures of faults that are documented or inferred to be exhumed from seismogenic depths. The goal of this guidebook is to provide geoscientists who are interested in fault zone mechanics and earthquake processes a summary of the results of the work on these sites

SubspaceNet:Deep Learning-Aided Subspace Methods for DoA Estimation

Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the measurements into distinct signal and noise subspaces. Subspace methods, such as Multiple Signal Classification (MUSIC) and Root-MUSIC, rely on several restrictive assumptions, including narrowband non-coherent sources and fully calibrated arrays, and their performance is considerably degraded when these do not hold. In this work we propose SubspaceNet; a data-driven DoA estimator which learns how to divide the observations into distinguishable subspaces. This is achieved by utilizing a dedicated deep neural network to learn the empirical autocorrelation of the input, by training it as part of the Root-MUSIC method, leveraging the inherent differentiability of this specific DoA estimator, while removing the need to provide a ground-truth decomposable autocorrelation matrix. Once trained, the resulting SubspaceNet serves as a universal surrogate covariance estimator that can be applied in combination with any subspace-based DoA estimation method, allowing its successful application in challenging setups. SubspaceNet is shown to enable various DoA estimation algorithms to cope with coherent sources, wideband signals, low SNR, array mismatches, and limited snapshots, while preserving the interpretability and the suitability of classic subspace methods