Distribution of the Timing, Trigger and Control Signals in the Endcap Cathode Strip Chamber System at CMS

This paper presents the implementation of the Timing, Trigger and Control (TTC) signal distribution tree in the Cathode Strip Chamber (CSC) sub-detector of the CMS Experiment at CERN. The key electronic component, the Clock and Control Board (CCB) is described in detail, as well as the transmission of TTC signals from the top of the system down to the front-end boards

Huge decreases in the risk of breast cancer relapse over the last three decades

Introduction The aim of this study was to evaluate local and systemic breast cancer control by comparing the risk of relapse in breast cancer patients in 2003–2004 with that in 1972–1979 and in 1980–1986. Methods About 8,570 women diagnosed with invasive breast cancer in 2003–2004 were selected from the population-based Netherlands Cancer Registry and compared with 133 patients treated in 1972–1979 and 174 in 1980–1986. Five-year risk of relapse was calculated by the Kaplan–Meier method. Cox-proportional hazard models were applied to adjust for tumour size, nodal status and age at diagnosis. Results Patients diagnosed in 2003–2004 had smaller tumours and a less advanced nodal stage than patients diagnosed in 1972–1986. In 1972–1979, 1980–1986 and 2003–2004, treatment included mastectomy in 94%, 72% and 47%; postmastectomy radiotherapy in 75%, 70% and 30%; chemotherapy in 9%, 14% and 37% and hormonal therapy in 3%, 3% and 42% of patients, respectively. Five-year risk of locoregional and distant recurrence decreased from 37% and 34% to 15%, respectively. The 5-year risk of second primary breast cancer did not differ and was 1%, 4% and 2%, respectively. The improved relapse-free survival in patients diagnosed in 2003–2004 as compared with 1972–1979 hardly changed after adjustment (HR = 0.38, 95% CI = 0.28–0.52). Conclusion Over the last decades, local breast cancer therapies have become less rigorous, whereas systemic therapy use has increased. Simultaneously, the risk of breast cancer relapse has tremendously decreased. Future novel therapies may lead to such small additional decreases in relapse rates, while the long-term side effects in breast cancer survivors will increas

Leray and LANS-α\alpha modeling of turbulent mixing

Mathematical regularisation of the nonlinear terms in the Navier-Stokes equations provides a systematic approach to deriving subgrid closures for numerical simulations of turbulent flow. By construction, these subgrid closures imply existence and uniqueness of strong solutions to the corresponding modelled system of equations. We will consider the large eddy interpretation of two such mathematical regularisation principles, i.e., Leray and LANS$-\alpha$ regularisation. The Leray principle introduces a {\bfi smoothed transport velocity} as part of the regularised convective nonlinearity. The LANS$-\alpha$ principle extends the Leray formulation in a natural way in which a {\bfi filtered Kelvin circulation theorem}, incorporating the smoothed transport velocity, is explicitly satisfied. These regularisation principles give rise to implied subgrid closures which will be applied in large eddy simulation of turbulent mixing. Comparison with filtered direct numerical simulation data, and with predictions obtained from popular dynamic eddy-viscosity modelling, shows that these mathematical regularisation models are considerably more accurate, at a lower computational cost.Comment: 42 pages, 12 figure

Second generation of vortex-antivortex states in mesoscopic superconductors: stabilization by artificial pinning

Antagonistic symmetries of superconducting polygons and their induced multi-vortex states in a homogeneous magnetic field may lead to appearance of antivortices in the vicinity of the superconducting/normal state boundary (where mesoscopic confinement is particularly strong). Resulting vortex-antivortex (V-Av) molecules match the sample symmetry, but are extremely sensitive to defects and fluctuations and remain undetected experimentally. Here we show that V-Av states can re-appear deep in the superconducting state due to an array of perforations in a polygonal setting, surrounding a central hole. Such states are no longer caused by the symmetry of the sample but rather by pinning itself, which prevents the vortex-antivortex annihilation. As a result, even micron-size, clearly spaced V-Av molecules can be stabilized in large mesoscopic samples.Comment: 5 pages, 6 figure

Stabilization of vortex-antivortex configurations in mesoscopic superconductors by engineered pinning

Symmetry-induced vortex-antivortex configurations in superconducting squares and triangles were predicted earlier; yet, they have not been resolved in experiment up to date. Namely, with vortex-antivortex states being highly unstable with respect to defects and temperature fluctuations, it is very unlikely that samples can be fabricated with the needed quality. Here we show how these drawbacks can be overcome by strategically placed nanoholes in the sample. As a result, (i) the actual shape of the sample becomes far less important, (ii) the stability of the vortex-antivortex configurations in general is substantially enhanced, and (iii) states comprising novel giant-antivortices (with higher winding numbers) become energetically favorable in perforated disks. In the analysis, we stress the potent of strong screening to destabilize the vortex-antivortex states. In turn, the screening-symmetry competition favors stabilization of new asymmetric ground states, which arise for small values of the effective Ginzburg-Landau parameter kappa.Comment: 12 pages, 20 figure

Random forests with random projections of the output space for high dimensional multi-label classification

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage

Distributed utterances

I propose an apparatus for handling intrasentential change in context. The standard approach has problems with sentences with multiple occurrences of the same demonstrative or indexical. My proposal involves the idea that contexts can be complex. Complex contexts are built out of (“simple”) Kaplanian contexts by ordered n-tupling. With these we can revise the clauses of Kaplan’s Logic of Demonstratives so that each part of a sentence is taken in a different component of a complex context. I consider other applications of the framework: to agentially distributed utterances (ones made partly by one speaker and partly by another); to an account of scare-quoting; and to an account of a binding-like phenomenon that avoids what Kit Fine calls “the antinomy of the variable.

Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. In addition, the proposed StackNet also achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub.Comment: 8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge 2019; Added ND