A 2-Approximation Algorithm for the Complementary Maximal Strip Recovery Problem

The Maximal Strip Recovery problem (MSR) and its complementary (CMSR) are well-studied NP-hard problems in computational genomics. The input of these dual problems are two signed permutations. The goal is to delete some gene markers from both permutations, such that, in the remaining permutations, each gene marker has at least one common neighbor. Equivalently, the resulting permutations could be partitioned into common strips of length at least two. Then MSR is to maximize the number of remaining genes, while the objective of CMSR is to delete the minimum number of gene markers. In this paper, we present a new approximation algorithm for the Complementary Maximal Strip Recovery (CMSR) problem. Our approximation factor is 2, improving the currently best 7/3-approximation algorithm. Although the improvement on the factor is not huge, the analysis is greatly simplified by a compensating method, commonly referred to as the non-oblivious local search technique. In such a method a substitution may not always increase the value of the current solution (it sometimes may even decrease the solution value), though it always improves the value of another function seemingly unrelated to the objective function

lp-Recovery of the Most Significant Subspace among Multiple Subspaces with Outliers

We assume data sampled from a mixture of d-dimensional linear subspaces with spherically symmetric distributions within each subspace and an additional outlier component with spherically symmetric distribution within the ambient space (for simplicity we may assume that all distributions are uniform on their corresponding unit spheres). We also assume mixture weights for the different components. We say that one of the underlying subspaces of the model is most significant if its mixture weight is higher than the sum of the mixture weights of all other subspaces. We study the recovery of the most significant subspace by minimizing the lp-averaged distances of data points from d-dimensional subspaces, where p>0. Unlike other lp minimization problems, this minimization is non-convex for all p>0 and thus requires different methods for its analysis. We show that if 0<p<=1, then for any fraction of outliers the most significant subspace can be recovered by lp minimization with overwhelming probability (which depends on the generating distribution and its parameters). We show that when adding small noise around the underlying subspaces the most significant subspace can be nearly recovered by lp minimization for any 0<p<=1 with an error proportional to the noise level. On the other hand, if p>1 and there is more than one underlying subspace, then with overwhelming probability the most significant subspace cannot be recovered or nearly recovered. This last result does not require spherically symmetric outliers.Comment: This is a revised version of the part of 1002.1994 that deals with single subspace recovery. V3: Improved estimates (in particular for Lemma 3.1 and for estimates relying on it), asymptotic dependence of probabilities and constants on D and d and further clarifications; for simplicity it assumes uniform distributions on spheres. V4: minor revision for the published versio

On U-Statistics and Compressed Sensing II: Non-Asymptotic Worst-Case Analysis

In another related work, U-statistics were used for non-asymptotic "average-case" analysis of random compressed sensing matrices. In this companion paper the same analytical tool is adopted differently - here we perform non-asymptotic "worst-case" analysis. Simple union bounds are a natural choice for "worst-case" analyses, however their tightness is an issue (and questioned in previous works). Here we focus on a theoretical U-statistical result, which potentially allows us to prove that these union bounds are tight. To our knowledge, this kind of (powerful) result is completely new in the context of CS. This general result applies to a wide variety of parameters, and is related to (Stein-Chen) Poisson approximation. In this paper, we consider i) restricted isometries, and ii) mutual coherence. For the bounded case, we show that k-th order restricted isometry constants have tight union bounds, when the measurements m = \mathcal{O}(k (1 + \log(n/k))). Here we require the restricted isometries to grow linearly in k, however we conjecture that this result can be improved to allow them to be fixed. Also, we show that mutual coherence (with the standard estimate \sqrt{(4\log n)/m}) have very tight union bounds. For coherence, the normalization complicates general discussion, and we consider only Gaussian and Bernoulli cases here.Comment: 12 pages. Submitted to IEEE Transactions on Signal Processin

Asymptotically isometric codes for holography

The holographic principle suggests that the low energy effective field theory of gravity, as used to describe perturbative quantum fields about some background has far too many states. It is then natural that any quantum error correcting code with such a quantum field theory as the code subspace is not isometric. We discuss how this framework can naturally arise in an algebraic QFT treatment of a family of CFT with a large-$N$ limit described by the single trace sector. We show that an isometric code can be recovered in the $N \rightarrow \infty$ limit when acting on fixed states in the code Hilbert space. Asymptotically isometric codes come equipped with the notion of simple operators and nets of causal wedges. While the causal wedges are additive, they need not satisfy Haag duality, thus leading to the possibility of non-trivial entanglement wedge reconstructions. Codes with complementary recovery are defined as having extensions to Haag dual nets, where entanglement wedges are well defined for all causal boundary regions. We prove an asymptotic version of the information disturbance trade-off theorem and use this to show that boundary theory causality is maintained by net extensions. We give a characterization of the existence of an entanglement wedge extension via the asymptotic equality of bulk and boundary relative entropy or modular flow. While these codes are asymptotically exact, at fixed $N$ they can have large errors on states that do not survive the large-$N$ limit. This allows us to fix well known issues that arise when modeling gravity as an exact codes, while maintaining the nice features expected of gravity, including, among other things, the emergence of non-trivial von Neumann algebras of various types.Comment: 74 pages plus appendices, 7 figure

Glosarium Matematika

273 p.; 24 cm

Assessment of the CORONA series of satellite imagery for landscape archaeology: a case study from the Orontes valley, Syria

In 1995, a large database of satellite imagery with worldwide coverage taken from 1960 until 1972 was declassified. The main advantages of this imagery known as CORONA that made it attractive for archaeology were its moderate cost and its historical value. The main disadvantages were its unknown quality, format, geometry and the limited base of known applications. This thesis has sought to explore the properties and potential of CORONA imagery and thus enhance its value for applications in landscape archaeology. In order to ground these investigations in a real dataset, the properties and characteristics of CORONA imagery were explored through the case study of a landscape archaeology project working in the Orontes Valley, Syria. Present-day high-resolution IKONOS imagery was integrated within the study and assessed alongside CORONA imagery. The combination of these two image datasets was shown to provide a powerful set of tools for investigating past archaeological landscape in the Middle East. The imagery was assessed qualitatively through photointerpretation for its ability to detect archaeological remains, and quantitatively through the extraction of height information after the creation of stereomodels. The imagery was also assessed spectrally through fieldwork and spectroradiometric analysis, and for its Multiple View Angle (MVA) capability through visual and statistical analysis. Landscape archaeology requires a variety of data to be gathered from a large area, in an effective and inexpensive way. This study demonstrates an effective methodology for the deployment of CORONA and IKONOS imagery and raises a number of technical points of which the archaeological researcher community need to be aware of. Simultaneously, it identified certain limitations of the data and suggested solutions for the more effective exploitation of the strengths of CORONA imagery