High Temperature QCD and Dimensional Reduction

In this talk I will first give a short discussion of some lattice results for QCD at finite temperature. I will then describe in some detail the technique of dimensional reduction, which in principle is a powerful technique to obtain results on the long distance properties of the quark-gluon plasma. Finally I will describe some new results, which test the technique in a simpler model, namely three dimensional gauge theory.Comment: talk presented at the International Workshop on Non-Perturbative Methods and Lattice QCD, Guangzhou, May 2000, 9 pp. LaTeX2e, uses ws-p8-50x6-00.cls (enclosed), 2 eps fig

Milk progesterone as a tool to improve fertility in dairy cows

Milk progesterone offers an opportunity to objectively study fertility in dairy cows, in contrast to traditional measures of dairy cow fertility, which in general are highly influenced by on-farm management decisions. The aim of this thesis was to study how milk progesterone could be used as a genetic and management tool to improve fertility in dairy cows. Progesterone-based measures were influenced by different systematic factors, e.g. breed, parity, season, housing and lameness, studied in a dataset from a Swedish experimental herd. The repeatabilities were higher for progesterone-based measures compared with traditional measures of fertility based on insemination data. If a cow had an atypical progesterone profile in one lactation, the risk of an atypical profile in the next lactation was increased. Genetic parameters for progesterone measures based on different milk sampling intervals were estimated in a British dataset. Heritability estimates were moderate, but decreased with increased sampling intervals. It was shown that progesterone analysis of monthly milk samples, resembling milk sampling as in the current Swedish milk recording system, could be used to increase the accuracy of genetic evaluation for an earlier start of cyclical ovarian activity after calving. Inclusion of monthly milk sampling for progesterone analysis in predictive models could also be used to identify cows with delayed ovarian cyclicity with a high accuracy already two months after calving. This enables an earlier treatment of ovarian dysfunction and therefore, probably, a shorter calving interval. In conclusion, this thesis shows that milk progesterone may be used for improved management and genetic evaluation of dairy cow fertility

ATLAS diboson excess from low scale supersymmetry breaking

We provide an interpretation of the recent ATLAS diboson excess in terms of a class of supersymmetric models in which the scale of supersymmetry (SUSY) breaking is in the few TeV range. The particle responsible for the excess is the scalar superpartner of the Goldstone fermion associated with SUSY breaking, the sgoldstino. This scalar couples strongly to the Standard Model vector bosons and weakly to the fermions, with all coupling strengths determined by ratios of soft SUSY breaking parameters over the SUSY breaking scale. Explaining the ATLAS excess selects particular relations and ranges for the gaugino masses, while imposing no constraints on the other superpartner masses. Moreover, this signal hypothesis predicts a rate in the $Z\gamma$ final state that is expected to be observable at the LHC Run II already with a few fb$^{-1}$ of integrated luminosity.Comment: 6 pages, 1 figure, 1 table; v2 bibtex compilation problem fixe

Beyond the c=1 Barrier in Two-Dimensional Quantum Gravity

We introduce a simple model of touching random surfaces, by adding a chemical potential rho for ``minimal necks'', and study this model numerically coupled to a Gaussian model in d-dimensions (for central charge c = d = 0, 1 and 2). For c <= 1, this model has a phase transition to branched polymers, for sufficiently large rho. For c = 2, however, the extensive simulations indicate that this transition is replaced by a cross-over behavior on finite lattices --- the model is always in the branched polymer phase. This supports recent speculations that, in 2d-gravity, the behavior observe in simulations for $c \leq 1$, is dominated by finite size effects, which are exponentially enhanced as c -> 1+.Comment: 5 pages, 6 eps-figure

Model Reduction using a Frequency-Limited H2-Cost

We propose a method for model reduction on a given frequency range, without the use of input and output filter weights. The method uses a nonlinear optimization approach to minimize a frequency limited H2 like cost function. An important contribution in the paper is the derivation of the gradient of the proposed cost function. The fact that we have a closed form expression for the gradient and that considerations have been taken to make the gradient computationally efficient to compute enables us to efficiently use off-the-shelf optimization software to solve the optimization problem.Comment: Submitted to Systems and Control Letter

GOGMA: Globally-Optimal Gaussian Mixture Alignment

Gaussian mixture alignment is a family of approaches that are frequently used for robustly solving the point-set registration problem. However, since they use local optimisation, they are susceptible to local minima and can only guarantee local optimality. Consequently, their accuracy is strongly dependent on the quality of the initialisation. This paper presents the first globally-optimal solution to the 3D rigid Gaussian mixture alignment problem under the L2 distance between mixtures. The algorithm, named GOGMA, employs a branch-and-bound approach to search the space of 3D rigid motions SE(3), guaranteeing global optimality regardless of the initialisation. The geometry of SE(3) was used to find novel upper and lower bounds for the objective function and local optimisation was integrated into the scheme to accelerate convergence without voiding the optimality guarantee. The evaluation empirically supported the optimality proof and showed that the method performed much more robustly on two challenging datasets than an existing globally-optimal registration solution.Comment: Manuscript in press 2016 IEEE Conference on Computer Vision and Pattern Recognitio

Monte Carlo versus multilevel Monte Carlo in weak error simulations of SPDE approximations

The simulation of the expectation of a stochastic quantity E[Y] by Monte Carlo methods is known to be computationally expensive especially if the stochastic quantity or its approximation Y_n is expensive to simulate, e.g., the solution of a stochastic partial differential equation. If the convergence of Y_n to Y in terms of the error |E[Y - Y_n]| is to be simulated, this will typically be done by a Monte Carlo method, i.e., |E[Y] - E_N[Y_n]| is computed. In this article upper and lower bounds for the additional error caused by this are determined and compared to those of |E_N[Y - Y_n]|, which are found to be smaller. Furthermore, the corresponding results for multilevel Monte Carlo estimators, for which the additional sampling error converges with the same rate as |E[Y - Y_n]|, are presented. Simulations of a stochastic heat equation driven by multiplicative Wiener noise and a geometric Brownian motion are performed which confirm the theoretical results and show the consequences of the presented theory for weak error simulations.Comment: 16 pages, 5 figures; formulated Section 2 independently of SPDEs, shortened Section 3, added example of geometric Brownian motion in Section

DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling

We define the object detection from imagery problem as estimating a very large but extremely sparse bounding box dependent probability distribution. Subsequently we identify a sparse distribution estimation scheme, Directed Sparse Sampling, and employ it in a single end-to-end CNN based detection model. This methodology extends and formalizes previous state-of-the-art detection models with an additional emphasis on high evaluation rates and reduced manual engineering. We introduce two novelties, a corner based region-of-interest estimator and a deconvolution based CNN model. The resulting model is scene adaptive, does not require manually defined reference bounding boxes and produces highly competitive results on MSCOCO, Pascal VOC 2007 and Pascal VOC 2012 with real-time evaluation rates. Further analysis suggests our model performs particularly well when finegrained object localization is desirable. We argue that this advantage stems from the significantly larger set of available regions-of-interest relative to other methods. Source-code is available from: https://github.com/lachlants/denetComment: 8 pages, ICCV2017 (poster