Online Bin Covering with Limited Migration

Semi-online models where decisions may be revoked in a limited way have been studied extensively in the last years. This is motivated by the fact that the pure online model is often too restrictive to model real-world applications, where some changes might be allowed. A well-studied measure of the amount of decisions that can be revoked is the migration factor beta: When an object o of size s(o) arrives, the decisions for objects of total size at most beta * s(o) may be revoked. Usually beta should be a constant. This means that a small object only leads to small changes. This measure has been successfully investigated for different, classical problems such as bin packing or makespan minimization. The dual of makespan minimization - the Santa Claus or machine covering problem - has also been studied, whereas the dual of bin packing - the bin covering problem - has not been looked at from such a perspective. In this work, we extensively study the bin covering problem with migration in different scenarios. We develop algorithms both for the static case - where only insertions are allowed - and for the dynamic case, where items may also depart. We also develop lower bounds for these scenarios both for amortized migration and for worst-case migration showing that our algorithms have nearly optimal migration factor and asymptotic competitive ratio (up to an arbitrary small epsilon). We therefore resolve the competitiveness of the bin covering problem with migration

Combining and Steganography of 3D Face Textures

One of the serious issues in communication between people is hiding information from others, and the best way for this, is deceiving them. Since nowadays face images are mostly used in three dimensional format, in this paper we are going to steganography 3D face images, detecting which by curious people will be impossible. As in detecting face only its texture is important, we separate texture from shape matrices, for eliminating half of the extra information, steganography is done only for face texture, and for reconstructing 3D face, we can use any other shape. Moreover, we will indicate that, by using two textures, how two 3D faces can be combined. For a complete description of the process, first, 2D faces are used as an input for building 3D faces, and then 3D textures are hidden within other images.Comment: 6 pages, 10 figures, 16 equations, 5 section

Fully Dynamic Bin Packing Revisited

We consider the fully dynamic bin packing problem, where items arrive and depart in an online fashion and repacking of previously packed items is allowed. The goal is, of course, to minimize both the number of bins used as well as the amount of repacking. A recently introduced way of measuring the repacking costs at each timestep is the migration factor, defined as the total size of repacked items divided by the size of an arriving or departing item. Concerning the trade-off between number of bins and migration factor, if we wish to achieve an asymptotic competitive ration of $1 + \epsilon$ for the number of bins, a relatively simple argument proves a lower bound of $\Omega(\frac{1}{\epsilon})$ for the migration factor. We establish a nearly matching upper bound of $O(\frac{1}{\epsilon}^4 \log \frac{1}{\epsilon})$ using a new dynamic rounding technique and new ideas to handle small items in a dynamic setting such that no amortization is needed. The running time of our algorithm is polynomial in the number of items $n$ and in $\frac{1}{\epsilon}$. The previous best trade-off was for an asymptotic competitive ratio of $\frac{5}{4}$ for the bins (rather than $1+\epsilon$) and needed an amortized number of $O(\log n)$ repackings (while in our scheme the number of repackings is independent of $n$ and non-amortized)

Parameter Synthesis in Markov Models: A Gentle Survey

This paper surveys the analysis of parametric Markov models whose transitions are labelled with functions over a finite set of parameters. These models are symbolic representations of uncountable many concrete probabilistic models, each obtained by instantiating the parameters. We consider various analysis problems for a given logical specification $\varphi$: do all parameter instantiations within a given region of parameter values satisfy $\varphi$?, which instantiations satisfy $\varphi$ and which ones do not?, and how can all such instantiations be characterised, either exactly or approximately? We address theoretical complexity results and describe the main ideas underlying state-of-the-art algorithms that established an impressive leap over the last decade enabling the fully automated analysis of models with millions of states and thousands of parameters

Testing market imperfections via genetic programming

The thesis checks the validity of the efficient markets hypothesis focusing on stock markets. Technical trading rules are generated by using an evolutionary optimization algorithm (Genetic Programming) based on training samples. The trading rules are subsequently applied to data samples unknown to the algorithm beforehand. The benchmark strategy consists of a classic buy-and-hold strategy in the DAX and the Hang Seng. The trading rules generally fail at consistently beating the benchmark thus indicating that market efficiency holds.Gegenstand der Dissertation ist die Überprüfung von Markteffizienz auf Aktienmärkten. Hierzu werden technische Handelsregeln mit Hilfe eines evolutionären Optimierungsalgorithmus (Genetic Programming) anhand von Trainingsdaten erlernt und anschließend auf eine unbekannte Zeitreihe angewandt. Als Benchmark dient eine klassische buy-and-hold Strategie im DAX und Hang Seng. Es zeigt sich, dass die mittels Genetic Programming generierten Handelsstrategien den Benchmark auf risikoadjustierter Basis nicht durchgängig schlagen können und somit die These effizienter Märkte für den DAX und den Hang Seng gültig ist