Space-efficient dynamic orthogonal point location, segment intersection, and range reporting

Given a set of geometric objects each associated with a time value, we wish to determine whether a given property is true for a subset of those objects whose time values fall within a query time window. We call such problems time-windowed decision problems, and they have been the subject of much recent attention, for instance studied by Bokal, Cabello, and Eppstein [SoCG 2015]. In this paper, we present new approaches to this class of problems that are conceptually simpler than Bokal et al.'s, and also lead to faster algorithms. For instance, we present algorithms for preprocessing for the time-windowed 2D diameter decision problem in O(n log n) time and the time-windowed 2D convex hull area decision problem in O(n alpha(n) log n) time (where alpha is the inverse Ackermann function), improving Bokal et al.'s O(n log^2 n) and O(n log n loglog n) solutions respectively. Our first approach is to reduce time-windowed decision problems to a generalized range successor problem, which we solve using a novel way to search range trees. Our other approach is to use dynamic data structures directly, taking advantage of a new observation that the total number of combinatorial changes to a planar convex hull is near linear for any FIFO update sequence, in which deletions occur in the same order as insertions. We also apply these approaches to obtain the first O(n polylog n) algorithms for the time-windowed 3D diameter decision and 2D orthogonal segment intersection detection problems.

We present a deterministic fully-dynamic data structure for maintaining information about the bridges in a graph. We support updates in O((log n)2) amortized time, and can find a bridge in the component of any given vertex, or a bridge separating any two given vertices, in O(log n/ log log n) worst case time. Our bounds match the current best for bounds for deterministic fully-dynamic connectivity up to log log n factors. The previous best dynamic bridge finding was an O((log n)3) amortized time algorithm by Thorup [STOC2000], which was a bittrick-based improvement on the O((log n)4) amortized time algorithm by Holm et al.[STOC98, JACM2001]. Our approach is based on a different and purely combinatorial improvement of the algorithim of Holm et al., which by itself gives a new combinatorial O((log n)3) amortized time algorithm. Combining it with Thorup's bittrick, we get down to the claimed O((log n)2) amortized time. Essentially the same new trick can be applied to the biconnectivity data structure from [STOC98, JACM2001], improving the amortized update time to O((log n)3). We also offer improvements in space. We describe a general trick which applies to both of our new algorithms, and to the old ones, to get down to linear space, where the previous best use O(m + n log n log log n). Our result yields an improved running time for deciding whether a unique perfect matching exists in a static graph.

We describe a powerful framework for designing efficient batch algorithms for certain large-scale dynamic problems that must be solved using external memory. The class of problems we consider, which we call colorable eztemaldecomposable problems, include rectangle intersection, orthogonal line segment intersection, range searching, and point location. We are particularly interested in these problems in two and higher dimensions. They have numerous applications in geographic information systems (GIS), spatial databases, and VLSI and CAD design. We present simplied algorithms for problems previously solved by more complicated approaches (such as rectangle intersection), and we present efficient algorithms for problems not previously solved in an efficient way (such as point location and higherdimensional versions of range searching and rectangle intersection). communication is the bottleneck in many large-scale applications such as those arising in VLSI and CAD design, spatial databases, and geographic information systems (GIS). In this paper we consider I/O-efficient algorithms for batched searching problems. We consider both batched static and batched dynamic problems, and use the correspondence which often exists between a ddimensional static problem and a (dl)-dimensional dynamic problem to obtain a number of new d-dimensional algorithms. We give experimental results concerning the running time for our approach applied to the red-blue rectangle intersection problem, which is a key component of the extremely important database operation spatial join. Our algorithm scales well with the problem size, and for large problems sizes it greatly outperforms the well-known sweepline approach. One prominent example of the problems we consider is the rectangle intersection problem, which is a key component in VLSI design rule checking [31] and in the extremely important database operation spatial join [34]. We illustrate the practical significance of our algorithms by comparing the empirical performance of our algorithm for this problem with the well-known sweepline algorithm developed for internal memory. 1.1 Problem definition and memory model

In this paper we present new space efficient dynamic data structures for orthogonal range reporting. The described data structures support planar range reporting queries in time O(log n+klog log (4n/(k+1))) and space O(nlog log n), or in time O(log n+k) and space O(nlog en) for any e>0. Both data structures can be constructed in O(nlog n) time and support insert and delete operations in amortized time O(log 2n) and O(log nlog log n) respectively. These results match the corresponding upper space bounds of Chazelle (SIAM J. Comput. 17, 427–462, 1988) for the static case. We also present a dynamic data structure for d-dimensional range reporting with search time O(log d−1n+k), update time O(log dn), and space O(nlog d−2+en) for any e>0. The model of computation used in our paper is a unit cost RAM with word size log n.

Algorithms for Computing Motorcycle Graphs Lie Yan We present a new algorithm for computing motorcycle graphs that runs inO(n) time for any e > 0, improving on all previously known algorithms. If the motorcycles follow only C different directions, our algorithm can be made to run in O(Cn log n) time. The main application of this result is to computing the straight skeleton of a polygon. It allows us to compute the straight skeleton of a non-degenerate polygon with h holes in O(n √ h+ 1 log n+n) expected time. If all input coordinates are O(log n)-bit rational numbers, we can compute the straight skeleton of a (possibly degenerate) polygon with h holes in O(n √ h+ 1 log n) expected time. In particular, it means that we can compute the straight skeleton of a simple polygon in O(n log n) expected time if all input coordinates areO(log n)-bit rationals, while all previously known algorithms have worst-case running time ω(n).

Atom lithography is a technique to structure layers of atoms during deposition, using interactions of near-resonant light fields with neutral atoms. The basic scheme uses a standing wave light field, aligned just above a substrate, while a beam of atoms impinges perpendicularly on the standing wave light field. The interaction of the light field and the atoms focuses the atoms towards the crests or troughs of the standing wave intensity, dependent on the tuning of the light field. The focused atoms are subsequently deposited on the substrate, resulting in an array of lines, whose periodicity is determined by the wavelength of the light field. The standing wave light field can therefore be seen as an array of lenses, focusing the atom beam into an array of lines. The first experiments on atom lithography were performed in the 1990s with sodium and chromium. In this thesis experiments with iron are described, which is a magnetic element and thus allows for the creation of magnetic nanostructures. To focus atom beams into structures using atom lithography, well-colimated atom beams are required. The most common technique to create collimated atom beams is transverse laser cooling, but iron has no closed optical transition suitable for laser cooling. Fortunately Smeets and Te Sligte found that even then atom lithography of iron is possible. If the standing wave light field is considered to be an array of lenses, the focusing of the atom beam on a single lens is determined by the local collimation of the atom beam at that position. This local collimation is determined by the geometry of the beam source and the distance of the beam source to the standing wave lens. Using a small beam source (e.g. 1 mm diameter) positioned far from the standing wave (e.g. 1 m), the geometrically collimated atom beam is suitable for atom lithography. Chapter 3 shows what the implications of geometric collimation are on the structure formation in atom lithography. The geometric effects that locally collimate the atom beam on the sample also introduce local offset angles of the atom beam with respect to the focusing light field. Structure formation has been observed with lines of up to 7 nm high on a background layer of 15 nm average height, over areas of up to 400 µm × 6 mm. This indicates that the offset angles can vary over an 8 mrad range, which is more than an order of magnitude larger than the local angle of collimation. The offset angle influence the local geometry of the deposited structures: an increasing offset angle decreases the local line height, increases the width and creates a skewness in the local line shape. The local geometry of the lines has been modelled with a Monte Carlo particle tracking model and the results were compared to experiments. It was found that the highest and narrowest lines suffer from significant broadening due to surface diffusion in experiments, limiting the achievable structure width of iron on SiOx samples to about 80 nm. We have also found that in atom lithography without laser cooling, each standing wave lens is effectively imaging the source geometry onto the substrate. We therefore propose using structured atom beam sources to image more complex patterns on sub-wavelength scales in a parallel way. The reason to use iron in atom lithography, is the option to create magnetic nanostructures. However, in previous experiments no clear magnetic signature of the atom lithographic structuring of iron was found. Therefore in Chapter 4 firstly the magnetic properties of unstructured layers of iron (Fe), nickel (Ni) and FexNi1-x are investigated. These layers are all ferromagnetic, but with a reduced magnetic moment compared to bulk values due to contaminations and crystal growth effects. As a reference for periodic atom lithography structures, periodic line structures are created with interference lithography in a polymer layer and onto these structures thin layers of FexNi1-x are shadow deposited, thereby creating a layer periodically modulated in both height and elemental composition. This periodic modulation in composition has been observed with SEM-EDX on sub-micron scales. The structures showed a clear magnetic anisotropy with an easy axis along the direction of the lines, in accordance with expectations. In Chapter 5, the magnetic properties of atom lithographic iron line structures are presented. Using MOKE microscopy, direct comparison of structured and unstructured parts of a sample is possible, which allows for the observation of clear magnetic signatures due to the atom lithographic structuring. In layers of an average thickness of about 15 nm, no anisotropy is induced by the line structures, while a magnetic easy axis along the direction of the lines is expected. The line structures introduce an isotropic increase in the coercivity of the layers, indicating that the line structures can be considered as isotropic corrugations instead of directional line structures. For layers of 30 nm average thickness and thus higher line structures, a clear magnetic anisotropy is observed for the highest line structures. This anisotropy can be seen as a magnetic easy axis along the direction of the lines, as expected for line structures. However, we also observe a sharp increase in coercivity for applied fields perpendicular to the lines. This phenomenon is intriguing and may be explained by a pinning of head-to-head domain walls along the direction of the lines. In Chapter 5, we also report on the first results of co-deposition of FexNi1-x, where the Fe is structured into lines using atom lithography, while the Ni is deposited uniformly, thereby creating an alloy of modulating composition. Magnetic analysis of these structures indicates that these structures are anisotropic, both the coercivity and the shape of domain walls is dependent on the angle of the applied field relative to the line structures. Finally in Chapter 6, we investigate the possibilities to focus a thermal beam of atoms into a single 100 nm spot using light fields, thereby creating a nanopencil suitable for deposition of nanostructures. A Monte Carlo particle tracking program was developed to model the all optical focusing or funneling of 10 µm sized atom beams to a 100 nm spot. This model included effects of initial beam divergence, magnetic substructure, laser cooling, and spontaneous as well as stimulated diffusion and it was applied to a number of promising light field configurations. The best results obtained for experimentally realistic settings are a beam focused to a full width at half maximum of FWHM = 0.55 µm in a blue detuned hollow beam where atoms are focused towards the dark center, or a FWHM = 0.31 µm in a red detuned axicon light field, where atoms are focused towards the maximum intensity at the heart line. However, in the latter case the focused beam has a flux only 25 times larger than the background flux, which is unpractical for applications. The limiting factors of all schemes are heating due to stimulated diffusion and the limited interaction time available for a thermal beam of atoms. A nanopencil could be possible if it were based on the focussing of a slow and / or monochromatic atom beam, but this would seriously complicate the practical application of such a device.

In this paper we consider the dynamic vertical ray shooting problem, that is the task of maintaining a dynamic set S of n non intersecting horizontal line segments in the plane subject to a query that reports the first segment in S intersecting a vertical ray from a query point. We develop a linear-size structure that supports queries, insertions and deletions in O(log n) worst-case time. Our structure works in the comparison model and uses a RAM.

The present thesis studies some important random walk-based algorithms, which are randomized rumor spreading and balanced allocation protocols on networks. In the rst part of the thesis, we study the Push and the Push-Pull protocols introduced by [DGG+87], which are basic randomized protocols for information dissemination on networks. In Chapter 2, we propose a new model where the number of calls of each node in every round is chosen independently according to a probability distribution R with bounded mean determined at the beginning of the process. In addition to the model being a natural extension of the standard protocols, it also serves as a more realistic model for rumor spreading in a network whose entities are not completely uniform and may have di erent levels of power. We provide both lower and upper bounds on the rumor spreading time depending on statistical properties of R such as the mean or the variance. While it is well-known that the standard protocols need Θ(log n) rounds to spread a rumor on a complete network with n nodes, we show that, if R follows a power law distribution with exponent β ∈ (2, 3), then the Push-Pull protocol spreads a rumor in Θ(log log n) rounds. Moreover, when β = 3, we show a runtime of Θ ( logn log logn ) . In Chapter 3, we analyze the behavior of the standard Push-Pull protocol on a class of random graphs, called random k-trees for every integer k > 2, that are suitable to model poorly connected, small-world and scale free networks. Here, we show that the Push-Pull protocol propagates a rumor from a randomly chosen informed node to almost all nodes of a random k-tree with n nodes in O((log n)1+ck) rounds with high probability, where 0 3 can be any integer. Let l be a given positive integer. In each round t, 1 6 t 6 n, ball t picks a node of G uniformly at random and performs a nonbacktracking random walk of length l from the chosen node. Then it deterministically selects a subset of the visited nodes as the potential choices and allocates itself on one of the choices with minimum load (ties are broken uniformly at random). Provided G has a su ciently large girth, we establish an upper bound for the maximum number of balls at any bin after allocating n balls by the algorithm. We also show that the upper bound is tight up to a O(log log n) factor. In particular, we show that if we set l = b(log n) 1+ 2 c, for any constant ∈ (0, 1], and G has girth at least ω(l), then the maximum load is bounded by O(1/ ) with high probability.

In this paper, we extend Rothemund and Winfree's examination of the tile complexity of tile self-assembly [6]. They provided a lower bound of Ω(log N/log log N) on the tile complexity of assembling an N × N square for almost all N. Adleman et al. [1] gave a construction which achieves this bound. We consider whether the tile complexity for self-assembly can be reduced through several natural generalizations of the model. One of our results is a tile set of size O(√log N) which assembles an N × N square in a model which allows flexible glue strength between non-equal glues (This was independently discovered in [3]). This result is matched by a lower bound dictated by Kolmogorov complexity. For three other generalizations, we show that the Ω(log N/log log N) lower bound applies to N × N squares. At the same time, we demonstrate that there are some other shapes for which these generalizations allow reduced tile sets. Specifically, for thin rectangles with length N and width k, we provide a tighter lower bound of Ω(N(1/k)/k) for the standard model, yet we also give a construction which achieves O(log N/log log N) complexity in a model in which the temperature of the tile system is adjusted during assembly. We also investigate the problem of verifying whether a given tile system uniquely assembles into a given shape, and show that this problem is NP-hard.

OF THE DISSERTATION Random matrices and random boxes by Linh V. Tran Dissertation Director: Prof. Van Vu This thesis concerns two questions on random structures: the semi-circular law for adjacency matrix of regular random graph and the piercing number for random boxes. • Random matrices We proved in full generality the semi-circular law for random d-regular graph model in the case d tends to infinity as n does. Our result complements the McKay law [19], which applied for the case d is an absolute constant. • Random boxes. Take n random boxes with axis-parallel edges inside the unit cube [0, 1]d, the piercing number τd(n) is the minimum number of points needed to pierce all boxes. Using hypergraph setting, we was able to prove a near sharp estimation for τd(n): for any d ≥ 2 Ωd( √ n(log n)d/2−1) = τd(n) = Od( √ n(log n)d/2−1 log log n). This thesis is based on two papers by the author [31] and [30] (joint work with Van Vu and Ke Wang).

Formalized music is usually based on the asynchronous creation of musical structures by the composer, that are later expressed in the form of music notation. This can also be considered as an algorithmic structure that, once executed by the musical performer, reaches the state of sonic art; the music per se, formed by the organization of sounds along time. This is perceived by the listener whose cognition attributes to it a personal meaning. This article introduces a computational process that aims to artistically explore the inversion of its natural order, respectively given by: the composer, the performer and the listener. Throughout a computational model, listener perception data is retrieved and used to control the dynamic creation of musical notation in real-time. After this process is over, the final result is a structured musical notation, generated by a self-organizing process given by the systemic interaction of independent agents, that are: the computer, the composer, the performer and the listeners. Keywords: Musical notation; Computer music; Interactive music.

In this thesis, we investigate the behavior of Anderson Localization in high dimension. In the first part we study Levy Matrices (LMs), a Random Matrix model with long-range hopping presenting strong analogy with the problem of Anderson Localization on tree-like structure, representative of the limit of infinite dimensionality. We establish the equation determining the localization transition and obtain the phase diagram. We investigate then the unusual behavior of the delocalized phase. Using arguments based on supersymmetric field theory and Dyson Brownian motion we show that the eigenvalue statistics is the same one as of the Gaussian orthogonal ensemble in the whole delocalized phase and is Poisson-like in the localized phase. Our numerical analysis confirms this result, valid in the limit of infinitely large LMs, and provides information on the behavior of other observables like the wave-functions statistics. At the same time, numerical results also reveal that the characteristic scale governing finite size effects diverges much faster than a power law approaching the transition and is already very large far from it. This leads to a very wide crossover region in which the system looks as if it were in a mixed phase. In the second part we study numerically the behavior of the Anderson Model in dimension from 3 to 6 through exact diagonalization, Transfer Matrix method and an approximate Strong Disorder Renormalization Group technique. The results we find suggest that the upper critical dimension of Anderson Localization is infinite. Finally, we discuss the possible implications of this scenario on the anomalous behavior of the delocalized phase of models representative of the limit of infinite dimension, like Levy Matrices and the Anderson model on tree-like structures.

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent structures that characterise and identify the signals. Motivated by the highly non-linear structure of most datasets, we firstly investigate the geometry of manifolds. Manifolds are low dimensional, non-linear structures that are naturally employed to describe sets of strongly related signals such as the images of a 3-D object captured from different viewpoints. However, the use of manifolds in applications is not straightforward due to their usually non-analytic and non-linear form. We propose here a way to `disassemble' a manifold into simpler components by approximating it with affine subspaces. Our objective is to discover a set of low dimensional affine subspaces that can represent manifold data accurately while preserving the manifold's structure. To this end, we employ a greedy technique that iteratively merges manifold samples into groups based on the difference of local tangents. We use our algorithm to approximate synthetic and real manifolds and to demonstrate that it is competitive to state-of-the-art techniques. Then, we consider structured sparse representations of signals and we propose a new sparsity model, where signals are essentially composed of a small number of structured {\it molecules }. We define the molecules to be linear combinations of a small number of atoms in a redundant dictionary. Our multi-level model takes into account the energy distribution of the significant signal components in addition to their support. It permits to define typical visual patterns and recognise them in prototypical or deformed form. We define a new structural difference measure between molecules and their deformed versions, which is based on their sparse codes and we create an algorithm for decomposing signals into molecules that can account for different deviations in the internal molecule structure. Our experiments verify the benefits of the new image model in various restoration tasks and they confirm that the development of proper models that extend the mere notion of sparsity can be very useful for various inverse problems in imaging. Finally, we investigate the problem of learning molecule representations directly in the sparse code domain. We constrain sparse codes to be linear combinations of a few, possibly deformed, molecules and we design an algorithm that can learn the structure from the codes without transforming them back into the signal domain. To this end, we take advantage of our structural difference which is based on the sparse codes and we devise a scheme for representing the codes with molecules and learn the molecules at the same time. To illustrate the effectiveness of our proposed algorithm we apply it to various synthetic and real datasets and we compare the results with traditional sparse coding and dictionary learning techniques. From the experiments, we verify the superior performance of our scheme in interpreting and recognising correctly the underlying structure. In short, in this thesis we are interested in low-dimensional, structured models. Among the various choices, we focus on manifolds and sparse representations and we propose schemes that enhance their structural properties and highlight their effectiveness in signal representations.

We investigate the problem of computing in a reliable fashion in the presence of faults that may arbitrarily corrupt memory locations. In this framework, we focus on the design of resilient data structures, i.e., data structures that, despite the corruption of some memory values during their lifetime, are nevertheless able to operate correctly (at least) on the set of uncorrupted values. In particular, we present resilient search trees which achieve optimal time and space bounds while tolerating up to O(√log n) memory faults, where n is the current number of items in the search tree. In more detail, our resilient search trees are able to insert, delete and search for a key in O(log n + δ2) amortized time, where δ is an upper bound on the total number of faults. The space required is O(n + δ).

Structural Health Monitoring (SHM) systems are widely regarded as capable of significantly reducing inspection costs of safety-critical structures in industries such as aerospace, nuclear, and oil and gas, among others. Successful SHM systems can be considered those which combine good sensitivity to defects, preferably with the capability of localization and identification, with a low sensor density. Techniques based on sparse arrays of sensors which generate and receive guided waves are among the most promising candidates. Guided waves propagate over large distances and certain modes have the ability to transmit through a variety of structural features leading to a relatively small number of distributed sensors being able to cover the structure. In complex structures, which contain high densities of structural elements, the timetraces obtained are often too complex to be directly interpreted due to the large number of overlapping reflections. In this case, the Baseline Subtraction technique becomes attractive. In this method a current signal from the structure is subtracted from a signal which has been acquired during the initial stages of operation of the structure. This eliminates the need for interpretation of the complex raw time signal and any defects will be clearly seen provided the amplitude of the residual signal obtained after subtraction of the baseline signal is sufficiently low when the structure is undamaged. However, it is well known that environmental effects such as stress, ambient temperature variations and liquid loading affect the velocity of guided waves; this modifies the time-traces and leads to high levels of residual signal if a single baseline, taken under different conditions, is used. Of these effects, temperature variations are the most commonly encountered and are critical since they affect not only the wave propagation but also the response of transducers. The present work aims to demonstrate the potential of guided wave health monitoring of large area complex structures. It starts with a general literature review on inspection and monitoring of large area structures, in which the advantages and disadvantages of this technique compared to other well-established SHM techniques 2 are presented. The design and behaviour of two different temperature-stable transducers generating high A0 or S0 mode purity in the sub-200kHz frequency region are described. The efficiency of different signal processing techniques aimed at reducing or eliminating the influence of temperature on wave propagation is evaluated and a temperature compensation signal processing strategy is proposed. Finally, a large metallic structure is used to demonstrate a sparse-array SHM system based on this signal processing strategy, and imaging algorithms are used to combine the information from a large number of sensor combinations, ultimately leading to the localization of defects artificially introduced in the structure.