The slimming effect of advection on black-hole accretion flows

At super-Eddington rates accretion flows onto black holes have been described as slim (aspect ratio $H/R \lesssim 1$) or thick (H/R >1) discs, also known as tori or (Polish) doughnuts. The relation between the two descriptions has never been established, but it was commonly believed that at sufficiently high accretion rates slim discs inflate, becoming thick. We wish to establish under what conditions slim accretion flows become thick. We use analytical equations, numerical 1+1 schemes, and numerical radiative MHD codes to describe and compare various accretion flow models at very high accretion rates.We find that the dominant effect of advection at high accretion rates precludes slim discs becoming thick. At super-Eddington rates accretion flows around black holes can always be considered slim rather than thick.Comment: 8 pages, 5 figures. Astronomy & Astrophysics, in pres

Slim SUSY

The new SM-like Higgs boson discovered recently at the LHC, with mass $m_h \simeq$ 125 GeV, as well as the direct LHC bounds on the mass of superpartners, which are entering into the TeV range, suggest that the minimal surviving supersymmetric extension of the SM (MSSM), should be characterized by a heavy SUSY-breaking scale. Several variants of the MSSM have been proposed to account for this result, which vary according to the accepted degree of fine-tuning. We propose an alternative scenario here, Slim SUSY, which contains sfermions with multi-TeV masses and gauginos/higgsinos near the EW scale, but it includes the heavy MSSM Higgs bosons ($H^0$, $A^0$, $H^\pm$) near the EW scale too. We discuss first the formulation and constraints of the Slim SUSY scenario, and then identify distinctive heavy Higgs signals that could be searched at the LHC, within scenarios with the minimal number of superpartners with masses near the EW scale.Comment: 16 pages, 6 figures. Section 2 has been restructured, with a new subsection and some comments added. This version matches the manuscript accepted in Physics Letters

Supersparse Linear Integer Models for Optimized Medical Scoring Systems

Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction. These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and satisfy multiple operational constraints. We present a new method for creating data-driven scoring systems called a Supersparse Linear Integer Model (SLIM). SLIM scoring systems are built by solving an integer program that directly encodes measures of accuracy (the 0-1 loss) and sparsity (the $\ell_0$-seminorm) while restricting coefficients to coprime integers. SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce highly tailored models without parameter tuning. We provide bounds on the testing and training accuracy of SLIM scoring systems, and present a new data reduction technique that can improve scalability by eliminating a portion of the training data beforehand. Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM was used to create a highly tailored scoring system for sleep apnea screeningComment: This version reflects our findings on SLIM as of January 2016 (arXiv:1306.5860 and arXiv:1405.4047 are out-of-date). The final published version of this articled is available at http://www.springerlink.co

The SLIM (Social learning for the integrated management and sustainable use of water at catchment scale) Final Report

Background: SLIM stands for 'Socuak Learning for the Integrated Management and Sustainable Use of Water at Catchment Scale'. It is a multi-country research project funded by the European Commission (DG RESEARCH - 5th Framework Programme for research and technological development, 1998-2002). Its main theme is the investigation of the socio-economic aspects of the sustainable use of water. Within this theme, its main focus of interest lies in understanding the application of social learning as a conceptual framework, an operational principle, a policy instrument and a process of systemic change

Sparse Linear Identifiable Multivariate Modeling

In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully Bayesian hierarchy for sparse models using slab and spike priors (two-component delta-function and continuous mixtures), non-Gaussian latent factors and a stochastic search over the ordering of the variables. The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable computational complexity. We attribute this mainly to the stochastic search strategy used, and to parsimony (sparsity and identifiability), which is an explicit part of the model. We propose two extensions to the basic i.i.d. linear framework: non-linear dependence on observed variables, called SNIM (Sparse Non-linear Identifiable Multivariate modeling) and allowing for correlations between latent variables, called CSLIM (Correlated SLIM), for the temporal and/or spatial data. The source code and scripts are available from http://cogsys.imm.dtu.dk/slim/.Comment: 45 pages, 17 figure

Search for strange quark matter and Q-balls with the SLIM experiment

We report on the search for Strange Quark Matter (SQM) and charged Q-balls with the SLIM experiment at the Chacaltaya High Altitude Laboratory (5230 m a.s.l.) from 2001 to 2005. The SLIM experiment was a 427 m$^{2}$ array of Nuclear Track Detectors (NTDs) arranged in modules of $24 \times 24$ cm$^{2}$ area. SLIM NTDs were exposed to the cosmic radiation for 4.22 years after which they were brought back to the Bologna Laboratory where they were etched and analyzed. We estimate the properties and energy losses in matter of nuclearites (large SQM nuggets), strangelets (small charged SQM nuggets) and Q-balls; and discuss their detection with the SLIM experiment. The flux upper limits in the CR of such downgoing particles are at the level of $1.3 10^{-15}$/cm$^{2}$/s/sr (90% CL).Comment: 4 pages, 7 eps figures. Talk given at the 24th International Conference on Nuclear Tracks in Solids, Bologna, Italy, 1-5 September 200

Analisis Pengaruh Brand Equity Terhadap Keputusan Pembelian Konsumen Pada Produk Gula Tropicana Slim Di Palembang

This research aims to test the influence of brand equity towards decision purchase consumers on product Tropicana Slim in Palembang. Variable brand equity is divided into four, namely brand awareness, brand loyalty, brand association, and perceive quality. Analysis techniques were used, namely the validity of the test, test, test the reliability of classical assumptions (test test, multicollinearity, normality and test heterokedastisitas), multiple linear regression test, t-test and F-test statistics, test results showed variable. brand awareness, brand loyalty, and brand association does not affect consumer purchasing decisions at Tropicana products Slim in Palembang. Variable perceive quality shows that there is an impact on consumer purchasing decisions at Tropicana products Slim in Palembang

New constructions of two slim dense near hexagons

We provide a geometrical construction of the slim dense near hexagon with parameters $(s,t,t_{2})=(2,5,\{1,2\})$. Using this construction, we construct the rank 3 symplectic dual polar space $DSp(6,2)$ which is the slim dense near hexagon with parameters $(s,t,t_{2})=(2,6,2)$. Both the near hexagons are constructed from two copies of a generalized quadrangle with parameters (2,2)