An Universal Image Attractiveness Ranking Framework

We propose a new framework to rank image attractiveness using a novel pairwise deep network trained with a large set of side-by-side multi-labeled image pairs from a web image index. The judges only provide relative ranking between two images without the need to directly assign an absolute score, or rate any predefined image attribute, thus making the rating more intuitive and accurate. We investigate a deep attractiveness rank net (DARN), a combination of deep convolutional neural network and rank net, to directly learn an attractiveness score mean and variance for each image and the underlying criteria the judges use to label each pair. The extension of this model (DARN-V2) is able to adapt to individual judge's personal preference. We also show the attractiveness of search results are significantly improved by using this attractiveness information in a real commercial search engine. We evaluate our model against other state-of-the-art models on our side-by-side web test data and another public aesthetic data set. With much less judgments (1M vs 50M), our model outperforms on side-by-side labeled data, and is comparable on data labeled by absolute score.Comment: Accepted by 2019 Winter Conference on Application of Computer Vision (WACV

Molecular subtyping of bladder cancer using Kohonen self-organizing maps

Kohonen self-organizing maps (SOMs) are unsupervised Artificial Neural Networks (ANNs) that are good for low-density data visualization. They easily deal with complex and nonlinear relationships between variables. We evaluated molecular events that characterize high- and low-grade BC pathways in the tumors from 104 patients. We compared the ability of statistical clustering with a SOM to stratify tumors according to the risk of progression to more advanced disease. In univariable analysis, tumor stage (log rank P = 0.006) and grade (P < 0.001), HPV DNA (P < 0.004), Chromosome 9 loss (P = 0.04) and the A148T polymorphism (rs 3731249) in CDKN2A (P = 0.02) were associated with progression. Multivariable analysis of these parameters identified that tumor grade (Cox regression, P = 0.001, OR.2.9 (95% CI 1.6–5.2)) and the presence of HPV DNA (P = 0.017, OR 3.8 (95% CI 1.3–11.4)) were the only independent predictors of progression. Unsupervised hierarchical clustering grouped the tumors into discreet branches but did not stratify according to progression free survival (log rank P = 0.39). These genetic variables were presented to SOM input neurons. SOMs are suitable for complex data integration, allow easy visualization of outcomes, and may stratify BC progression more robustly than hierarchical clustering

First measurements of the performance of the Barrel RPC system in CMS

During the summer 2006, a first integrated test of a part of the CMS experiment was performed at CERN collecting a data sample of several millions of cosmic rays events. A fraction of the Resistive Plate Chambers system was successfully operated. Results on the RPC performance are reported

Electronic system of the RPC Muon Trigger in CMS experiment at LHC accelerator (Elektroniczny system trygera mionowego RPC w eksperymencie CMS akceleratora LHC

This paper presents implementation of distributed, multichannel electronic measurement system for RPC - based Muon Trigger in the CMS experiment at LHC. The introduction shortly describes the research aims of LHC and shows the metrological requirements for CMS - good spatial and time resolution, and possibility to estimate multiple physical parameters from registered collisions of particles. Further the paper describes RPC Muon Trigger consisting of 200 000 independent channels for position measurement. The first part of the paper presents the functional structure of the system in the context of requirements put by the CMS experiment, like global triggering system and data acquisition. The second part describes the hardware solutions used in particular parts of the RPC detector measuremnt system and shows some test results. The paper has a digest and overview nature

Resistive Plate Chambers performance with Cosmic Rays in the CMS experiment

The Resistive Plate Chambers are used in the CMS experiment as a dedicated muon trigger both in barrel and endcap system. About 4000 square meter of double gap RPCs have been produced and have been installed in the experiment since more than one year and half. The full barrel system and a fraction of the endcaps have been monitored to study dark current behaviour and system stability, and have been extensively commissioned with Cosmic Rays collected by the full CMS experiment

The CMS High Level Trigger

At the Large Hadron Collider at CERN the proton bunches cross at a rate of 40MHz. At the Compact Muon Solenoid experiment the original collision rate is reduced by a factor of O (1000) using a Level-1 hardware trigger. A subsequent factor of O(1000) data reduction is obtained by a software-implemented High Level Trigger (HLT) selection that is executed on a multi-processor farm. In this review we present in detail prototype CMS HLT physics selection algorithms, expected trigger rates and trigger performance in terms of both physics efficiency and timing.Comment: accepted by EPJ Nov 200

CMS physics technical design report : Addendum on high density QCD with heavy ions

Peer reviewe

Les orfèvres français à Cracovie au XVIe siècle

Pietrusinski Jerzy. Les orfèvres français à Cracovie au XVIe siècle. In: Iconographica. Mélanges offerts à Piotr Skubiszewski. Poitiers : Centre d'études supérieures de civilisation médiévale, 1999. pp. 187-192. (Civilisation Médiévale, 7