Support detection in super-resolution

We study the problem of super-resolving a superposition of point sources from noisy low-pass data with a cut-off frequency f. Solving a tractable convex program is shown to locate the elements of the support with high precision as long as they are separated by 2/f and the noise level is small with respect to the amplitude of the signal

From seismography to compressed sensing and back: a brief history of optimization-based signal processing

In this talk we provide an overview of the history of l1-norm minimization applied to underdetermined inverse problems. In the 70s and 80s geophysicists proposed using l1-norm minimization for deconvolution from bandpass data in reflection seismography. In the 2000s, inspired by this approach and by magnetic resonance imaging, a method to provably recover sparse signals from random projections, known as compressed sensing, was developed. Theoretical insights used to analyze compressed sensing have recently been adapted to understand the potential and limitations of l1-norm minimization for deterministic problems. These include super-resolution from low-pass data and the deconvolution problem that originally motivated the geophysicists.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Aspects of selection in an interbred flock based on Perendales crossed with Merino X Romney ewes : a thesis presented in partial fulfilment of the requirements for the degree of Master of Agricultural Science in Animal Science at Massey University

Genetic, phenotypic and environmental parameters were estimated from hogget traits recorded on 237 Perendale x (Merino x Romney) ewe hoggets between 1977 and 1984. Non-genetic effects on weaning weight (WW) for 634 ewe and ram lambs were analysed. A selection objective and criteria was defined and appraised for the flock. The traits examined were hogget liveweight (HLW), greasy fleece weight (GFW), clean fleece weight (CFW), quality number (QN), character grade (CHG), handle grade (HG), cotting grade (CG), soundness grade (SG), greasy colour grade (GCG), scoured colour grade (SCG), staple length (SL), total crimp number (TCN), clean scoured yield (Y), mean fibre diameter (MFD) and crimps per centimetre (CPC). The least squares method of fitting constants was used to estimate the major environmental factors influencing the traits studied. Heritabilities (h2) were obtained by the daughter-dam regression (DDR) and daughter-dam correlation (DDC) methods. The genetic (rG), phenotypic (rP) and environmental (rE) correlations were calculated by the daughter-dam method. The estimates of environmental effects agree in most cases with the published estimates. Between year differences were important sources of variation and had a highly significant effect on all traits except SCG. Rearing rank effect was found to be the most important source of variation for WW and HLW. Age of dam and sex had a highly significant effect on WW. Neither rearing rank nor age of dam exerted any significant influence on wool traits. The estimates of heritability calculated by daughter-dam regression method were: HLW (0.16), GFW (0.17), CFW (0.24), QN (0.42), CHG (0.38), SG (0.02), GCG (0.38), SCG (0.09), SL (0.12), TCN (0.08), Y (0.41) and MFD (0.29). Genetic and phenotypic correlations calculated among some hogget traits were respectively: HLW x GFW (0.67 and 0.66); HLW x CFW (0.62 and 0.56); HLW x SL (0.79 and 0.44); HLW x MFD (-0.45 and 0.24); GFW x CFW (0.87 and 0.94); GFW x SL (0.37 and 0.60); GFW x MFD (-0.98 and 0.38); CFW x GCG (0.52 and 0.02); QN x MFD (-0.79 and -0.30); SG x MFD (0.73 and -0.21); GCG x SCG (0.87 and 0.38); GCG x Y (0.96 and 0.04) and SCG x Y (0.77 and 0.00). Lifetime economic weights derived using the marginal profit method were calculated to define a selection objective for the flock studied. The traits included in the objective were number of lambs weaned (NLW (dam)), WW, CFW, MFD and SCG. Besides the traits in the objective, HLW, GFW, QN and GCG were included as selection criteria. The appropriate selection indices for ram hoggets (I1), ewe hoggets (I2) and lambs (I3 ) were respectively: I1 =4.66 NLW (dam) + 0.62 HLW + 0.10 WW + 3.91 GFW - 1.70 MFD + 0.50 GCG. I2 =4.79 NLW (dam) + 0.61 HLW + 0.04 WW + 1.99 GFW + 0.23 QN + 1.60 GCG. I3 = 4.87 NLW (dam) + 0.48 WW

Endogenous and exogenous constraints in the population changes of wild boar (sus scrofa Linnaeus, 1758)

The population dynamics of wild boar (Sus scrofa) was studied in a time series over 26 years using data from the Regional Hunting Reserve of Somiedo (northern Spain). This population is controlled by a complex negative feedback system that acts with one (main) and two (secondary) years of delay (lags). The primary feedback might be explained by intraspecific competition for food resulting from fluctuations in mast production (acorns and beech), and the secondary feedback might be explained by the influence of weather conditions or the delay of a cohort to reach reproductive status. We used a stochastic model that takes environmental variability into consideration when testing the demographical analysis that’s obtains simulations from real data