Accelerating, hyperaccelerating, and decelerating networks

Many growing networks possess accelerating statistics where the number of links added with each new node is an increasing function of network size so the total number of links increases faster than linearly with network size. In particular, biological networks can display a quadratic growth in regulator number with genome size even while remaining sparsely connected. These features are mutually incompatible in standard treatments of network theory which typically require that every new network node possesses at least one connection. To model sparsely connected networks, we generalize existing approaches and add each new node with a probabilistic number of links to generate either accelerating, hyperaccelerating, or even decelerating network statistics in different regimes. Under preferential attachment for example, slowly accelerating networks display stationary scale-free statistics relatively independent of network size while more rapidly accelerating networks display a transition from scale-free to exponential statistics with network growth. Such transitions explain, for instance, the evolutionary record of single-celled organisms which display strict size and complexity limits.

Inherent size constraints on prokaryote gene networks due to "accelerating" growth

Networks exhibiting accelerating growth have total link numbers growing faster than linearly with network size and either reach a limit or exhibit graduated transitions from nonstationary-to-stationary statistics and from random to scale-free to regular statistics as the network size grows. However, if for any reason the network cannot tolerate such gross structural changes then accelerating networks are constrained to have sizes below some critical value. This is of interest as the regulatory gene networks of single-celled prokaryotes are characterized by an accelerating quadratic growth and are size constrained to be less than about 10,000 genes encoded in DNA sequence of less than about 10 megabases. This paper presents a probabilistic accelerating network model for prokaryotic gene regulation which closely matches observed statistics by employing two classes of network nodes (regulatory and non-regulatory) and directed links whose inbound heads are exponentially distributed over all nodes and whose outbound tails are preferentially attached to regulatory nodes and described by a scale-free distribution. This model explains the observed quadratic growth in regulator number with gene number and predicts an upper prokaryote size limit closely approximating the observed value. (c) 2005 Elsevier GmbH. All rights reserved.

An FPGA network architecture for accelerating 3DES - CBC

This paper presents a DES/3DES core that will support cipher block chaining (CBC) and also has a built in keygen that together take up about 10% of the resources in a Xilinx Virtex II 1000-4. The core will achieve up to 200Mbit/s of encryption or decryption. Also presented is a network architecture that will allow these CBC capable 3DES cores to perform their processing in parallel.

An Empirical Evaluation of Preconditioning Data for Accelerating Convex Hull Computations

The convex hull describes the extent or shape of a set of data and is used ubiquitously in computational geometry. Common algorithms to construct the convex hull on a finite set of n points (x,y) range from O(nlogn) time to O(n) time. However, it is often the case that a heuristic procedure is applied to reduce the original set of n points to a set of s < n points which contains the hull and so accelerates the final hull finding procedure. We present an algorithm to precondition data before building a 2D convex hull with integer coordinates, with three distinct advantages. First, for all practical purposes, it is linear; second, no explicit sorting of data is required and third, the reduced set of s points is constructed such that it forms an ordered set that can be directly pipelined into an O(n) time convex hull algorithm. Under these criteria a fast (or O(n)) pre-conditioner in principle creates a fast convex hull (approximately O(n)) for an arbitrary set of points. The paper empirically evaluates and quantifies the acceleration generated by the method against the most common convex hull algorithms. An extra acceleration of at least four times when compared to previous existing preconditioning methods is found from experiments on a dataset.

An Empirical Evaluation of Preconditioning Data for Accelerating Convex Hull Computations

The convex hull describes the extent or shape of a set of data and is used ubiquitously in computational geometry. Common algorithms to construct the convex hull on a finite set of n points (x,y) range from O(nlogn) time to O(n) time. However, it is often the case that a heuristic procedure is applied to reduce the original set of n points to a set of s < n points which contains the hull and so accelerates the final hull finding procedure. We present an algorithm to precondition data before building a 2D convex hull with integer coordinates, with three distinct advantages. First, for all practical purposes, it is linear; second, no explicit sorting of data is required and third, the reduced set of s points is constructed such that it forms an ordered set that can be directly pipelined into an O(n) time convex hull algorithm. Under these criteria a fast (or O(n)) pre-conditioner in principle creates a fast convex hull (approximately O(n)) for an arbitrary set of points. The paper empirically evaluates and quantifies the acceleration generated by the method against the most common convex hull algorithms. An extra acceleration of at least four times when compared to previous existing preconditioning methods is found from experiments on a dataset.

Introduction: Home Fronts, Gender War and Conflict

Introduction to special edition of Womens History Review special edition on Gender War and Conflict

Detection of mesoscale thermal fronts from 4km data using smoothing techniques: Gradient-based fronts classification and basin scale application

In order to optimize frontal detection in sea surface temperature fields at 4 km resolution, a combined statistical and expert-based approach is applied to test different spatial smoothing of the data prior to the detection process. Fronts are usually detected at 1 km resolution using the histogram-based, single image edge detection (SIED) algorithm developed by Cayula and Cornillon in 1992, with a standard preliminary smoothing using a median filter and a 3 × 3 pixel kernel. Here, detections are performed in three study regions (off Morocco, the Mozambique Channel, and north-western Australia) and across the Indian Ocean basin using the combination of multiple windows (CMW) method developed by Nieto, Demarcq and McClatchie in 2012 which improves on the original Cayula and Cornillon algorithm. Detections at 4 km and 1 km of resolution are compared. Fronts are divided in two intensity classes (“weak” and “strong”) according to their thermal gradient. A preliminary smoothing is applied prior to the detection using different convolutions: three type of filters (median, average and Gaussian) combined with four kernel sizes (3 × 3, 5 × 5, 7 × 7, and 9 × 9 pixels) and three detection window sizes (16 × 16, 24 × 24 and 32 × 32 pixels) to test the effect of these smoothing combinations on reducing the background noise of the data and therefore on improving the frontal detection. The performance of the combinations on 4 km data are evaluated using two criteria: detection efficiency and front length. We find that the optimal combination of preliminary smoothing parameters in enhancing detection efficiency and preserving front length includes a median filter, a 16 × 16 pixel window size, and a 5 × 5 pixel kernel for strong fronts and a 7 × 7 pixel kernel for weak fronts. Results show an improvement in detection performance (from largest to smallest window size) of 71% for strong fronts and 120% for weak fronts. Despite the small window used (16 × 16 pixels), the length of the fronts has been preserved relative to that found with 1 km data. This optimal preliminary smoothing and the CMW detection algorithm on 4 km sea surface temperature data are then used to describe the spatial distribution of the monthly frequencies of occurrence for both strong and weak fronts across the Indian Ocean basin. In general strong fronts are observed in coastal areas whereas weak fronts, with some seasonal exceptions, are mainly located in the open ocean. This study shows that adequate noise reduction done by a preliminary smoothing of the data considerably improves the frontal detection efficiency as well as the global quality of the results. Consequently, the use of 4 km data enables frontal detections similar to 1 km data (using a standard median 3 × 3 convolution) in terms of detectability, length and location. This method, using 4 km data is easily applicable to large regions or at the global scale with far less constraints of data manipulation and processing time relative to 1 km data.

Accelerating retreat and high-elevation thinning of glaciers in central Spitsbergen

Svalbard is a heavily glacier-covered archipelago in the Arctic. Dickson Land (DL), in the central part of the largest island, Spitsbergen, is relatively arid, and as a result, glaciers there are relatively small and restricted mostly to valleys and cirques. This study presents a comprehensive analysis of glacier changes in DL based on inventories compiled from topographic maps and digital elevation models for the Little Ice Age maximum (LIA), the 1960s, 1990 and 2009/11. Total glacier area decreased by ~38 % since the LIA maximum, and front retreat has increased over the study period. Recently, most of the local glaciers have been consistently thinning in all elevation bands, in contrast to larger Svalbard ice masses which remain closer to balance. The mean 1990–2009/11 geodetic mass balance of glaciers in DL is among the most negative from the Svalbard regional means known from the literature.

The battle fronts of Europe /

Shows the area between the European western and eastern fronts overlaid over a map of the United States.

Accelerating wide-angle converging waves in the near field

We show that a wide-angle converging wave may be transformed into a shape-preserving accelerating beam having a beam-width near the diffraction limit. For that purpose, we followed a strategy that is particularly conceived for the acceleration of nonparaxial laser beams, in contrast to the well-known method by Siviloglou et al (2007 Phys. Rev. Lett. 99 213901). The concept of optical near-field shaping is applied to the design of non-flat ultra-narrow diffractive optical elements. The engineered curvilinear caustic can be set up by the beam emerging from a dynamic assembly of elementary gratings, the latter enabling to modify the effective refractive index of the metamaterial as it is arranged in controlled orientations. This light shaping process, besides being of theoretical interest, is expected to open up a wide range of broadband application possibilities.

Highly localized accelerating beams using nano-scale metallic gratings

Spatially accelerating beams are non-diffracting beams whose intensity is localized along curvilinear trajectories, also incomplete circular trajectories, before diffraction broadening governs their propagation. In this paper we report on numerical simulations showing the conversion of a high-numerical-aperture focused beam into a nonparaxial shape-preserving accelerating beam having a beam-width near the diffraction limit. Beam shaping is induced near the focal region by a diffractive optical element that consists of a non-planar subwavelength grating enabling a Bessel signature.

Vortex evolution in the wake of accelerating low-aspect-ratio plates and three-dimensional bodies of revolution

To find examples of effecient locomotion and manoeuvrability, one need only turn to the elegant solutions natural flyers and swimmers have converged upon. This dissertation is specifically motivated by processes of evolutionary convergence, which have led to the propulsors and body shapes in nature that exhibit strong geometric collapse over diverse scales. These body features are abstracted in the studies presented herein using low-aspect-ratio at plates and a three-dimensional body of revolution (a sphere). The highly-separated vortical wakes that develop during accelerations are systematically characterized as a function of planform shape, aspect ratio, Reynolds number, and initial boundary conditions. To this end, force measurements and time-resolved (planar) particle image velocimetry have been used throughout to quantify the instantaneous forces and vortex evolution in the wake of the bluff bodies. During rectilinear motions, the wake development for the flat plates is primarily dependent on plate aspect ratio, with edge discontinuities and curvature playing only a secondary role. Furthermore, the axisymmetric case, i.e. the circular plate, shows strong sensitivity to Reynolds number, while this sensitivity quickly diminishes with increasing aspect ratio. For rotational motions, global insensitivity to plate aspect ratio has been observed. For the sphere, it has been shown that accelerations play an important role in the mitigation of flow separation. These results - expounded upon in this dissertation - have begun to shed light on the specific vortex dynamics that may be coopted by flying and swimming species of all shapes and sizes towards efficient locomotion.