The search for D0→e±μ∓D^0\to{}e^{\pm}\mu^{\mp}

An overview is presented of a method to search for $D^0\to{}e^{\pm}\mu^{\mp}$ with LHCb data. In order to reduce combinatorial backgrounds, tagged $D^0$ candidates from the decay $D^{\ast+}\to{}D^0\pi^+$ are used. This measurement is performed with respect to $\mathcal{B}\left(D^0\to{}\pi^+\pi^-\right)$, which cancels uncertainties in the luminosity and $D^{\ast+}$ production cross-section. It is estimated that using $3\,\mathrm{fb}^{-1}$ of LHCb data an upper limit can be attained of $\mathcal{O}\left(10^{-7}\right)$ at a $90\%$ confidence level.Comment: To appear in the proceedings of The 6th International Workshop on Charm Physics (CHARM 2013

Controlling Tokamak Geometry with 3D Magnetic Perturbations

It is shown that small externally applied magnetic perturbations can significantly alter important geometric properties of magnetic flux surfaces in tokamaks. Through 3D shaping, experimentally relevant perturbation levels are large enough to influence turbulent transport and MHD stability in the pedestal region. It is shown that the dominant pitch-resonant flux surface deformations are primarily induced by non-resonant 3D fields, particularly in the presence of significant axisymmetric shaping. The spectral content of the applied 3D field can be used to control these effects

A PARAMETRIC INVESTIGATION OF THE LUNAR-ORBIT-RENDEZVOUS SCHEME

Lunar orbit rendezvous scheme - mission analysi

Data compression and computational efficiency

In this thesis we seek to make advances towards the goal of effective learned compression. This entails using machine learning models as the core constituent of compression algorithms, rather than hand-crafted components. To that end, we first describe a new method for lossless compression. This method allows a class of existing machine learning models – latent variable models – to be turned into lossless compressors. Thus many future advancements in the field of latent variable modelling can be leveraged in the field of lossless compression. We demonstrate a proof-of-concept of this method on image compression. Further, we show that it can scale to very large models, and image compression problems which closely resemble the real-world use cases that we seek to tackle. The use of the above compression method relies on executing a latent variable model. Since these models can be large in size and slow to run, we consider how to mitigate these computational costs. We show that by implementing much of the models using binary precision parameters, rather than floating-point precision, we can still achieve reasonable modelling performance but requiring a fraction of the storage space and execution time. Lastly, we consider how learned compression can be applied to 3D scene data - a data medium increasing in prevalence, and which can require a significant amount of space. A recently developed class of machine learning models - scene representation functions - has demonstrated good results on modelling such 3D scene data. We show that by compressing these representation functions themselves we can achieve good scene reconstruction with a very small model size

Regular and First Order List Functions

We define two classes of functions, called regular (respectively, first-order) list functions, which manipulate objects such as lists, lists of lists, pairs of lists, lists of pairs of lists, etc. The definition is in the style of regular expressions: the functions are constructed by starting with some basic functions (e.g. projections from pairs, or head and tail operations on lists) and putting them together using four combinators (most importantly, composition of functions). Our main results are that first-order list functions are exactly the same as first-order transductions, under a suitable encoding of the inputs; and the regular list functions are exactly the same as MSO-transductions

The George Arents Library Award: A Recollection

Thomas E. Bird relates his submission for the George Arents Library Award in 1955, which recognized the student with the best personal library. Bird had a personal library containing over 500 Russian history titles. He received a medal designed by famous Syracuse University professor and scultor Ivan Mestrovic, and which was inscribed by professor and poet A.E. Johnson

The Mosher Books, Season of MDCCCCIII

A catalog of offerings of specialty printer T. B. Mosher, Portland, Maine, for 1903