is large. A one-standard-deviation increase in the other four centrality measures is associated with a 6.6 to 8.2 percentage point increase from the unconditional survival probability of 66.8%, holding all other covariates at their sample means. C. Portfolio Company Exit Finally, we equate good performance

. A third candidate is an underestimation of the climate sensitivity to CO 2 (i.e., model error). Note that the dashed scenarios shown are for a medium climate sensitivity of 3 ºC for a doubling of CO 2 concentration, while the grey band surrounding the scenarios shows the effect of uncertainty

. The head and shoulder mask system was used as immobilization device for cervical (C) spine lesions. Initial patient setup used the infrared positioning system with body markers. Stereotactic X-ray imaging was then performed and correc-tion was made if the initial setup error exceeded predetermined

This article gives theoretical insights into the performance of K-SVD, a dictionary learning algorithm that has gained significant popularity in practical applications. The particular question studied here is when a dictionary Φ ∈ Rd×K can be recovered as local minimum of the minimisation criterion underlying K-SVD from a set of N training signals yn = Φxn. A theoretical analysis of the problem leads to two types of identifiability results assuming the training signals are generated from a tight frame with coefficients drawn from a random symmetric distribution. First asymptotic results showing, that in expectation the generating dictionary can be recovered exactly as a local minimum of the K-SVD criterion if the coefficient distribution exhibits sufficient decay. This decay can be characterised by the coherence of the dictionary and the `1-norm of the coefficients. Based on the asymptotic results it is further demonstrated that given a finite number of training samples N, such that N / logN = O(K3d), except with probability O(N−Kd) there is a local minimum of the K-SVD criterion within distance O(KN−1/4) to the generating dictionary. Index Terms dictionary learning, sparse coding, K-SVD, finite sample size, sampling complexity, dictionary identification, minimisation criterion, sparse representation 1

Due to an error during the production stages, Figure 2C in the article incorrectly displayed Figure 2C from our publication below: FEBS Open Bio. 2013 May 30;3:237-42. doi: 10.1016/ j.fob.2013.05.001. Identification of distinct nisin leader peptide Khusainov R, Moll GN, Kuipers OP We are providing

in the original work remain unaffected by this error and correspond to the updated supplementary data in this correction. Supporting Information S1 File. Supplementary data tables: In-vitro results. Excel table showing for each line the IC50, Amax, experimental sensitivity call, MAS5 gene expression value, 2-gene

is provided here. This new Fig 2 also adds stereochemistry to that moiety. 2. The authors identified some errors within the manuscript text in referencing Fig 5 (unal-tered original figure is provided here for reference). These have now been corrected in the paragraph below. A corrected version

Abstract. Eective recombination coecients are given for C ii transitions between doublet states. The cal-culations are carried out in the temperature range 500 − 20 000 K and for an electron density of 104 cm−3. The eects of electron collisions on the excited states are included. The necessary

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