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I've been trying to read books about AI lately to get a firmer grasp on this important topic, and the best book I've read for both a basic grounding of the history and the state of play of AI, but also looking at potential frameworks for the technology, both ethically and socially is A Human Algorithm: How Artificial Intelligence is Redefining Who We Are by Flynn Coleman.Sponsors:PixelUnionMealime

I've been trying to read books about AI lately to get a firmer grasp on this important topic, and the best book I've read for both a basic grounding of the history and the state of play of AI, but also looking at potential frameworks for the technology, both ethically and socially is A Human Algorithm: How Artificial Intelligence is Redefining Who We Are by Flynn Coleman.Sponsors:PixelUnionMealime

Today's edition of Ben's Story of the Day features:1) People were protesting outside of Facebook's offices in London in the hopes of changing the way they moderate pictures of nipples vs tattoos of nipples on breast cancer survivors. Though some protestors dressed up as giant breasts, hopefully no one acted like one.2) Gender reveals have caused much damage and devastation around the world. An expectant father from Tennessee got the good news and simply had to fire off a few celebratory rounds from his shotgun into the air prompting 3 nearby schools to go into lockdown.3) Some collectors have extremely detailed and expensive collections of just about everything you can think of. One such collection was tragically thrown out by a man's parents after he moved out. A lawsuit was filed and won by the 43-year-old man whose parents now have to pay him $30,0441 to cover for the collection of pornography and sex toys.Which one will be crowned Ben's Story of the Day? Which one is your story of the day?

With the increasing reliance on digital communication and data storage, the need for secure cryptographic algorithms has become paramount. However, the rise of encrypted malware and unauthorized encrypted communications poses a significant challenge to cybersecurity. This paper presents an Encrypted Algorithm Detector using Cryptography, a system designed to verifying and identify encryption algorithms embedded within data transmissions or software applications. The proposed system leverages cryptographic fingerprinting techniques, statistical analysis, and machine learning models to detect and classify encryption methods such as AES, RSA, DES, and other cryptographic schemes. The detector functions by analyzing entropy levels, byte distribution, and frequency patterns to distinguish between encrypted and non-encrypted data. This approach can be applied in cybersecurity for malware detection, forensic investigations, and preventing unauthorized encrypted data transmission in secure environments. The implementation of this system contributes to strengthening cybersecurity measures by enabling early detection of encrypted threats and ensuring compliance with security protocols.

Advancements in wireless communication technology have empowered the researchers to develop large scale wireless networks with huge number of sensor nodes. In these networks localization is very active field of research. Localization is a way to determine the physical position of sensor nodes which is useful in many aspects such as to find the origin of events, routing and network coverage. Locating nodes with GPS systems is expensive, power consuming and not applicable to indoor environments. Localization in three dimensional space and accuracy of the estimated location are two factors of major concern. In this paper, a new three dimensional Distributed range-free algorithm which is known as CP-NR is proposed. This algorithm has high localization accuracy and resolved the problem of existing NR algorithm. CP-NR (Coplanar and Projected Node Reproduction) algorithm makes use of co-planarity and projection of point on plane concepts to reduce the localization error. Results have shown that CP-NR algorithm is superior to NR algorithm and comparison is done for the localization accuracy with respect to variations in range, anchor density and node density.

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The advancement of computer technology has raised significant issues with digital content piracy and copyright law. A popular method of protecting copyright and related uses is digital watermarking. Various algorithms have been developed to address the need for invisible performance and robustness in digital watermarking schemes. Here, we proposed a novel evolutionary algorithm, the hybrid Grass Hopper Optimization algorithm, and the Genetic algorithm (HGOAGA) for optimizing multiple scaling factors in the digital watermarking scheme in the frequency domain of hybrid Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD) method (hybrid DWT-SVD). The subcomponents of the image are determined by calculating the DWT of the cover image. The problem is determining the best scaling factor for watermarking after converting the subcomponent to the frequency domain using SVD. In HGOAGA, an optimal solution of multiple scaling factors is found after several iterations, starting with a set of randomly generated solutions. The advantages of GOA and GA are combined in the HGOAGA to balance exploration and exploitation functionalities. Furthermore, HGOAGA can converge quickly and escape local optima well. Some standard images were used in the MATLAB environment to test the proposed algorithm. The evaluation of the experiment was carried out using various metrics such as the Structural Similarity Index (SSIM), the Normalized Cross-Correlation (NCC), and the Peak Signal-to-Noise Ratio (PSNR). The experimental results of the tests showed a PSNR value of 51db for the proposed method compared to existing methods, and they are best suited to solving conflict problems between robustness and quality.

The advancement of computer technology has raised significant issues with digital content piracy and copyright law. A popular method of protecting copyright and related uses is digital watermarking. Various algorithms have been developed to address the need for invisible performance and robustness in digital watermarking schemes. Here, we proposed a novel evolutionary algorithm, the hybrid Grass Hopper Optimization algorithm, and the Genetic algorithm (HGOAGA) for optimizing multiple scaling factors in the digital watermarking scheme in the frequency domain of hybrid Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD) method (hybrid DWT-SVD). The subcomponents of the image are determined by calculating the DWT of the cover image. The problem is determining the best scaling factor for watermarking after converting the subcomponent to the frequency domain using SVD. In HGOAGA, an optimal solution of multiple scaling factors is found after several iterations, starting with a set of randomly generated solutions. The advantages of GOA and GA are combined in the HGOAGA to balance exploration and exploitation functionalities. Furthermore, HGOAGA can converge quickly and escape local optima well. Some standard images were used in the MATLAB environment to test the proposed algorithm. The evaluation of the experiment was carried out using various metrics such as the Structural Similarity Index (SSIM), the Normalized Cross-Correlation (NCC), and the Peak Signal-to-Noise Ratio (PSNR). The experimental results of the tests showed a PSNR value of 51db for the proposed method compared to existing methods, and they are best suited to solving conflict problems between robustness and quality.

DokuWiki: Chair 11: ALGORITHM ENGINEERING — Lehrstuhl 11 Algorithm Engineering Dumped with DokuWiki-Dumper v0.1.44, and uploaded with dokuWikiUploader v0.1.44.

thank you @GlaceGwyneth for algorithm help https://t.co/1So0BlS594 Source: https://twitter.com/narcissawright/status/1252788704265060354 Uploader: Narcissa

Twitter's Algorithm, the oldest uploaded version: taken from https://github.com/twitter/the-algorithm

An algorithm analyzes rain-gauge data to identify statistical outliers that could be deemed to be erroneous readings. Heretofore, analyses of this type have been performed in burdensome manual procedures that have involved subjective judgements. Sometimes, the analyses have included computational assistance for detecting values falling outside of arbitrary limits. The analyses have been performed without statistically valid knowledge of the spatial and temporal variations of precipitation within rain events. In contrast, the present algorithm makes it possible to automate such an analysis, makes the analysis objective, takes account of the spatial distribution of rain gauges in conjunction with the statistical nature of spatial variations in rainfall readings, and minimizes the use of arbitrary criteria. The algorithm implements an iterative process that involves nonparametric statistics.

The Geoscience Laser Altimeter System (GLAS) was the sole instrument for NASAs Ice, Cloud and land Elevation Satellite (ICESat) laser altimetry mission. The primary purpose of the ICESat mission was to make ice sheet elevation measurements of the polar regions. Additional goals were to measure the global distribution of clouds and aerosols and to map sea ice, land topography and vegetation. ICESat was the benchmark Earth Observing System (EOS) mission to be used to determine the mass balance of the ice sheets, as well as for providing cloud property information, especially for stratospheric clouds common over polar areas.

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 3 details the advanced CERES methods for performing scene identification and inverting each CERES scanner radiance to a top-of-the-atmosphere (TOA) flux. CERES determines cloud fraction, height, phase, effective particle size, layering, and thickness from high-resolution, multispectral imager data. CERES derives cloud properties for each pixel of the Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer. Cloud properties for each imager pixel are convolved with the CERES footprint point spread function to produce average cloud properties for each CERES scanner radiance. The mean cloud properties are used to determine an angular distribution model (ADM) to convert each CERES radiance to a TOA flux. The TOA fluxes are used in simple parameterization to derive surface radiative fluxes. This state-of-the-art cloud-radiation product will be used to substantially improve our understanding of the complex relationship between clouds and the radiation budget of the Earth-atmosphere system.

In this paper we have proposed a semi-heuristic optimization algorithm for designing optimal plant layouts in process-focused manufacturing/service facilities. Our proposed algorithm marries the well-known CRAFT (Computerized Relative Allocation of Facilities Technique) with the Hungarian assignment algorithm. Being a semi-heuristic search, our algorithm is likely to be more efficient in terms of computer CPU engagement time as it tends to converge on the global optimum faster than the traditional CRAFT algorithm - a pure heuristic. We also present a numerical illustration of our algorithm.

OhjyA is an ASCII-text file encryption program. OhjyA carries a unique encryption algrithm. This e-booklet explains OhjyA and its algorithm. This is the Arabic version. Ghasan Al-Sakkaf

Skip Graph, a member of the distributed hash table (DHT) family, has several benefits as an underlying structure in peer-to-peer (P2P) storage systems. In such systems, replication plays a key role on the system's performance. The traditional decentralized replication algorithms do not consider the locations of Skip Graph nodes in the network. Negligence of node locations in the placement of the replicas results in high access delays between the nodes and their closest replicas. This negatively affects the performance of the whole storage system. In this paper, with the aim of making Skip Graph's replication locality aware, we propose dynamic fully decentralized LARAS approach, where the data owner can replicate itself based on the system size, possible data requester nodes' set and using local information of the storage system. Our extensive performance results show that LARAS improves replication access delay of the Skip Graph based storage system about 20% and 38% in comparison to the best known decentralized counterpart in the public and private replication scenarios, respectively.

Algorithm for computing sequential least squares estimates with process noise

If a set of N points or nodes with a nonnegative cost associated with each ordered pair is known, it is desired to find a path from one given node to another given node which minimizes the cost sum. An algorithm is presented which yields a global minimum solution after at most N - 1 iterations or on a typical large third-generation computer, after 1 hour of computation time for a 10,000-node problem. The rapid-access data storage capacity demanded by the algorithm is approximately 3N words for costs read in from slow-access storage or 2N words for calculable costs. The time-storage requirements of the algorithm known to the authors. When the problem is viewed as a discretized optimal control problem, after N-1 iterations, an optimal control or node transition is established for each of the N nodes or states; thus, the algorithm can be applied to situations were there may be errors in the control that necessitate a closed loop control that necessitate a closed loop control philosophy.

The iterative convergence properties of an approximate-factorization implicit finite-difference algorithm are analyzed both theoretically and numerically. Modifications to the base algorithm were made to remove the inconsistency in the original implementation of artificial dissipation. In this way, the steady-state solution became independent of the time-step, and much larger time-steps can be used stably. To accelerate the iterative convergence, large time-steps and a cyclic sequence of time-steps were used. For a model transonic flow problem governed by the Euler equations, convergence was achieved with 10 times fewer time-steps using the modified differencing scheme. A particular form of instability due to variable coefficients is also analyzed.

An iterative method for numerically solving the time independent Navier-Stokes equations for viscous compressible flows is presented. The method is based upon partial application of the Gauss-Seidel principle in block form to the systems of nonlinear algebraic equations which arise in construction of finite element (Galerkin) models approximating solutions of fluid dynamic problems. The C deg-cubic element on triangles is employed for function approximation. Computational results for a free shear flow at Re = 1,000 indicate significant achievement of economy in iterative convergence rate over finite element and finite difference models which employ the customary time dependent equations and asymptotic time marching procedure to steady solution. Numerical results are in excellent agreement with those obtained for the same test problem employing time marching finite element and finite difference solution techniques.

Algorithm for computing acceleration and partial derivatives in satellite equations of motion, using recurrences in spherical coordinates

For any homogeneous identity between $q$-minors, we provide an identity between $P,Q$-minors.

Simple algorithm of dynamics of Ising magnetic is described. The algorithm can be implemented on conventional digital computer and can be used for construction of specialized processor for simulation of ferromagnetic systems. The algorithm gives a simple way to calculate 1D correlation functions for 1D Ising magnetic.

We introduce a new tractable temporal constraint language, which strictly contains the Ord-Horn language of Buerkert and Nebel and the class of AND/OR precedence constraints. The algorithm we present for this language decides whether a given set of constraints is consistent in time that is quadratic in the input size. We also prove that (unlike Ord-Horn) this language cannot be solved by Datalog or by establishing local consistency.

We provide a real algebraic symbolic-numeric algorithm for computing the real variety $V_R(I)$ of an ideal $I$, assuming it is finite while $V_C(I)$ may not be. Our approach uses sets of linear functionals on $R[X]$, vanishing on a given set of polynomials generating $I$ and their prolongations up to a given degree, as well as on polynomials of the real radical ideal of $I$, obtained from the kernel of a suitably defined moment matrix assumed to be positive semidefinite and of maximum rank. We formulate a condition on the dimensions of projections of these sets of linear functionals, which serves as stopping criterion for our algorithm. This algorithm, based on standard numerical linear algebra routines and semidefinite optimization, combines techniques from previous work of the authors together with an existing algorithm for the complex variety. This results in a unified methodology for the real and complex cases.

We apply analytically Niedermayer's algorithm to the Edwards-Anderson model on random graphs with arbitary degree distributions. The results for the multicritical point on the Nishimori line are shown. The results are shown by applying a criterion for spin models on the random graphs with arbitary degree distributions. The application of Niedermayer's algorithm makes the size of the Fortuin-Kasteleyn cluster small and shifts the percolation threshold. The results for the $\pm J$ model and the Gaussian model are respectively shown. In the present article, it is respectively shown for the $\pm J$ model and the Gaussian model that, by adjusting an introduced parameter for Niedermayer's algorithm, the percolation threshold obtained in the present article agrees with the location of the multicritical point. We naively estimate the locations of the multicritical points for the $\pm J$ model and the Gaussian model on the randam graphs with arbitary degree distributions.

One of the strongest techniques available for showing lower bounds on quantum communication complexity is the logarithm of the approximation rank of the communication matrix--the minimum rank of a matrix which is entrywise close to the communication matrix. This technique has two main drawbacks: it is difficult to compute, and it is not known to lower bound quantum communication complexity with entanglement. Linial and Shraibman recently introduced a norm, called gamma_2^{alpha}, to quantum communication complexity, showing that it can be used to lower bound communication with entanglement. Here the parameter alpha is a measure of approximation which is related to the allowable error probability of the protocol. This bound can be written as a semidefinite program and gives bounds at least as large as many techniques in the literature, although it is smaller than the corresponding alpha-approximation rank, rk_alpha. We show that in fact log gamma_2^{alpha}(A)$ and log rk_{alpha}(A)$ agree up to small factors. As corollaries we obtain a constant factor polynomial time approximation algorithm to the logarithm of approximate rank, and that the logarithm of approximation rank is a lower bound for quantum communication complexity with entanglement.

In this paper we present a linear programming solution for sign pattern recovery of a sparse signal from noisy random projections of the signal. We consider two types of noise models, input noise, where noise enters before the random projection; and output noise, where noise enters after the random projection. Sign pattern recovery involves the estimation of sign pattern of a sparse signal. Our idea is to pretend that no noise exists and solve the noiseless $\ell_1$ problem, namely, $\min \|\beta\|_1 ~ s.t. ~ y=G \beta$ and quantizing the resulting solution. We show that the quantized solution perfectly reconstructs the sign pattern of a sufficiently sparse signal. Specifically, we show that the sign pattern of an arbitrary k-sparse, n-dimensional signal $x$ can be recovered with $SNR=\Omega(\log n)$ and measurements scaling as $m= \Omega(k \log{n/k})$ for all sparsity levels $k$ satisfying $0 < k \leq \alpha n$, where $\alpha$ is a sufficiently small positive constant. Surprisingly, this bound matches the optimal \emph{Max-Likelihood} performance bounds in terms of $SNR$, required number of measurements, and admissible sparsity level in an order-wise sense. In contrast to our results, previous results based on LASSO and Max-Correlation techniques either assume significantly larger $SNR$, sublinear sparsity levels or restrictive assumptions on signal sets. Our proof technique is based on noisy perturbation of the noiseless $\ell_1$ problem, in that, we estimate the maximum admissible noise level before sign pattern recovery fails.

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

In this note, following suggestions by Tao, we extend the randomized algorithm for linear equations over prime fields by Raghavendra to a randomized algorithm for linear equations over the reals. We also show that the algorithm can be parallelized to solve a system of linear equations $A x = b$ with a regular $n \times n$ matrix $A$ in time $O(n^2)$, with probability one. Note that we do not assume that $A$ is symmetric.

We consider Markov decision processes (MDPs) with $\omega$-regular specifications given as parity objectives. We consider the problem of computing the set of almost-sure winning vertices from where the objective can be ensured with probability 1. The algorithms for the computation of the almost-sure winning set for parity objectives iteratively use the solutions for the almost-sure winning set for B\"uchi objectives (a special case of parity objectives). We study for the first time the average case complexity of the classical algorithm for computing almost-sure winning vertices for MDPs with B\"uchi objectives. Our contributions are as follows: First, we show that for MDPs with constant out-degree the expected number of iterations is at most logarithmic and the average case running time is linear (as compared to the worst case linear number of iterations and quadratic time complexity). Second, we show that for general MDPs the expected number of iterations is constant and the average case running time is linear (again as compared to the worst case linear number of iterations and quadratic time complexity). Finally we also show that given all graphs are equally likely, the probability that the classical algorithm requires more than constant number of iterations is exponentially small.

We construct an efficient quantum algorithm to compute the quantum Schur-Weyl transform for any value of the quantum parameter $q \in [0,\infty]$. Our algorithm is a $q$-deformation of the Bacon-Chuang-Harrow algorithm, in the sense that it has the same structure and is identically equal when $q=1$. When $q=0$, our algorithm is the unitary realization of the Robinson-Schensted-Knuth (or RSK) algorithm, while when $q=\infty$ it is the dual RSK algorithm together with phase signs. Thus, we interpret a well-motivated quantum algorithm as a generalization of a well-known classical algorithm.

In this work we consider the problem of maximizing the PageRank of a given target node in a graph by adding $k$ new links. We consider the case that the new links must point to the given target node (backlinks). Previous work shows that this problem has no fully polynomial time approximation schemes unless $P=NP$. We present a polynomial time algorithm yielding a PageRank value within a constant factor from the optimal. We also consider the naive algorithm where we choose backlinks from nodes with high PageRank values compared to the outdegree and show that the naive algorithm performs much worse on certain graphs compared to the constant factor approximation scheme.

The ocean has been traditionally viewed as a 2 class system. Morel and Prieur (1977) classified ocean water according to the dominant absorbent particle suspended in the water column. Case 1 is described as having a high concentration of phytoplankton (and detritus) relative to other particles. Conversely, case 2 is described as having inorganic particles such as suspended sediments in high concentrations. Little work has gone into the problem of mixing bio-optical models for these different water types. An approach is put forth here to blend bio-optical algorithms based on a fuzzy classification scheme. This scheme involves two procedures. First, a clustering procedure identifies classes and builds class statistics from in-situ optical measurements. Next, a classification procedure assigns satellite pixels partial memberships to these classes based on their ocean color reflectance signature. These membership assignments can be used as the basis for a weighting retrievals from class-specific bio-optical algorithms. This technique is demonstrated with in-situ optical measurements and an image from the SeaWiFS ocean color satellite.

This report details the ongoing efforts by GATS, Inc., in conjunction with Hampton University and University of Wyoming, in NASA's Mission to Planet Earth UARS Science Investigator Program entitled "HALOE Algorithm Improvements for Upper Tropospheric Soundings." The goal of this effort is to develop and implement major inversion and processing improvements that will extend HALOE measurements further into the troposphere. In particular, O3, H2O, and CH4 retrievals may be extended into the middle troposphere, and NO, HCl and possibly HF into the upper troposphere. Key areas of research being carried out to accomplish this include: pointing/tracking analysis; cloud identification and modeling; simultaneous multichannel retrieval capability; forward model improvements; high vertical-resolution gas filter channel retrievals; a refined temperature retrieval; robust error analyses; long-term trend reliability studies; and data validation. The current (first-year) effort concentrates on the pointer/tracker correction algorithms, cloud filtering and validation, and multi-channel retrieval development. However, these areas are all highly coupled, so progress in one area benefits from and sometimes depends on work in others.

A low-energy neutron transport algorithm for use in space radiation protection is developed. The algorithm is based upon a multigroup analysis of the straight-ahead Boltzmann equation by using a mean value theorem for integrals. This analysis is accomplished by solving a realistic but simplified neutron transport test problem. The test problem is analyzed by using numerical and analytical procedures to obtain an accurate solution within specified error bounds. Results from the test problem are then used for determining mean values associated with rescattering terms that are associated with a multigroup solution of the straight-ahead Boltzmann equation. The algorithm is then coupled to the Langley HZETRN code through the evaporation source term. Evaluation of the neutron fluence generated by the solar particle event of February 23, 1956, for a water and an aluminum-water shield-target configuration is then compared with LAHET and MCNPX Monte Carlo code calculations for the same shield-target configuration. The algorithm developed showed a great improvement in results over the unmodified HZETRN solution. In addition, a two-directional solution of the evaporation source showed even further improvement of the fluence near the front of the water target where diffusion from the front surface is important.

Fronts in the coastal ocean describe areas of strong horizontal gradients in both physical and biological properties associated with tidal mixing and freshwater estuarine output (e.g. Simpson, 1981 and O Donnell, 1993). Related gradients in optically important constituents mean that fronts can be observed from space as changes in ocean color as well as sea surface temperature (e.g., Dupouy et al., 1986). This research program is designed to determine which processes and optically important constituents must be considered to explain ocean color variations associated with coastal fronts on the New England continental shelf, in particular the National Ocean Partnership Program (NOPP) Front Resolving Observational Network with Telemetry (FRONT) site. This site is located at the mouth of Long Island sound and was selected after the analysis of 12 years of AVHRR data showed the region to be an area of strong frontal activity (Ullman and Cornillon, 1999). FRONT consists of a network of modem nodes that link bottom mounted Acoustic Doppler Current Profilers (ADCPs) and profiling arrays. At the center of the network is the Autonomous Vertically Profiling Plankton Observatory (AVPPO) (Thwaites et al. 1998). The AVPPO consists of buoyant sampling vehicle and a trawl-resistant bottom-mounted enclosure, which holds a winch, the vehicle (when not sampling), batteries, and controller. Three sampling systems are present on the vehicle, a video plankton recorder, a CTD with accessory sensors, and a suite of bio-optical sensors including Satlantic OCI-200 and OCR-200 spectral radiometers and a WetLabs ac-9 dual path absorption and attenuation meter. At preprogrammed times the vehicle is released, floats to the surface, and is then winched back into the enclosure with power and data connection maintained through the winch cable. Communication to shore is possible through a bottom cable and nearby surface telemetry buoy, equipped with a mobile modem, giving the capability for near-real time data transmission and interactive sampling control.

Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space A dministration Dryden Flight Research Center to automate analysis and design process by leveraging existing tools such as NASTRAN, ZAERO a nd CFD codes to enable true multidisciplinary optimization in the pr eliminary design stage of subsonic, transonic, supersonic, and hypers onic aircraft. This is a promising technology, but faces many challe nges in large-scale, real-world application. This paper describes cur rent approaches, recent results, and challenges for MDAO as demonstr ated by our experience with the Ikhana fire pod design.

This poster details a technique of bright point identification that is used to find sources in Chandra X-ray data. The algorithm, part of a program called LEXTRCT, searches for regions of a given size that are above a minimum signal to noise ratio. The algorithm allows selected pixels to be excluded from the source-finding, thus allowing exclusion of saturated pixels (from flares and/or active regions). For Chandra data the noise is determined by photon counting statistics, whereas solar telescopes typically integrate a flux. Thus the calculated signal-to-noise ratio is incorrect, but we find we can scale the number to get reasonable results. For example, Nakakubo and Hara (1998) find 297 bright points in a September 11, 1996 Yohkoh image; with judicious selection of signal-to-noise ratio, our algorithm finds 300 sources. To further assess the efficacy of the algorithm, we analyze a SOHO/EIT image (195 Angstroms) and compare results with those published in the literature (McIntosh and Gurman, 2005). Finally, we analyze three sets of data from Hinode, representing different parts of the decline to minimum of the solar cycle.

A physical-based simulation has been developed for the Mars Exploration Rover (MER) mission that applies a slope-induced wheel-slippage to the rover location estimator. Using the digital elevation map from the stereo images, the computational method resolves the quasi-dynamic equations of motion that incorporate the actual wheel-terrain speed to estimate the gross velocity of the vehicle. Based on the empirical slippage measured by the Visual Odometry software of the rover, this algorithm computes two factors for the slip model by minimizing the distance of the predicted and actual vehicle location, and then uses the model to predict the next drives. This technique, which has been deployed to operate the MER rovers in the extended mission periods, can accurately predict the rover position and attitude, mitigating the risk and uncertainties in the path planning on high-slope areas.

We present a hierarchically blocked one-sided Jacobi algorithm for the singular value decomposition (SVD), targeting both single and multiple graphics processing units (GPUs). The blocking structure reflects the levels of GPU's memory hierarchy. The algorithm may outperform MAGMA's dgesvd, while retaining high relative accuracy. To this end, we developed a family of parallel pivot strategies on GPU's shared address space, but applicable also to inter-GPU communication. Unlike common hybrid approaches, our algorithm in a single GPU setting needs a CPU for the controlling purposes only, while utilizing GPU's resources to the fullest extent permitted by the hardware. When required by the problem size, the algorithm, in principle, scales to an arbitrary number of GPU nodes. The scalability is demonstrated by more than twofold speedup for sufficiently large matrices on a Tesla S2050 system with four GPUs vs. a single Fermi card.

No abstract available

A central element of air traffic management is the safe merging and spacing of aircraft during the terminal area flight phase. This paper derives and examines an algorithm for the merging and interval managing problem for Standard Terminal Arrival Routes. It describes a factor analysis for performance based on the distribution of arrivals, the operating period of the terminal, and the topology of the arrival routes; then presents results from a performance analysis and from a safety analysis for a realistic topology based on typical routes for a runway at Phoenix International Airport. The heart of the safety analysis is a statistical derivation on how to conduct a safety analysis for a local simulation when the safety requirement is given for the entire airspace.

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (tau), effective radius (r(sub eff)), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.

We address the problem of solving systems of two bivariate polynomials of total degree at most $d$ with integer coefficients of maximum bitsize $\tau$. It is known that a linear separating form, that is a linear combination of the variables that takes different values at distinct solutions of the system, can be computed in $\sOB(d^{8}+d^7\tau)$ bit operations (where $O_B$ refers to bit complexities and $\sO$ to complexities where polylogarithmic factors are omitted) and we focus here on the computation of a Rational Univariate Representation (RUR) given a linear separating form. We present an algorithm for computing a RUR with worst-case bit complexity in $\sOB(d^7+d^6\tau)$ and bound the bitsize of its coefficients by $\sO(d^2+d\tau)$. We show in addition that isolating boxes of the solutions of the system can be computed from the RUR with $\sOB(d^{8}+d^7\tau)$ bit operations. Finally, we show how a RUR can be used to evaluate the sign of a bivariate polynomial (of degree at most $d$ and bitsize at most $\tau$) at one real solution of the system in $\sOB(d^{8}+d^7\tau)$ bit operations and at all the $\Theta(d^2)$ {real} solutions in only $O(d)$ times that for one solution.

In this paper we present a new efficient algorithm for factoring the RSA and the Rabin moduli in the particular case when the difference between their two prime factors is bounded. As an extension, we also give some theoretical results on factoring integers.

The application of the Reformulation Linearization Technique (RLT) to the Quadratic Assignment Problem (QAP) leads to a tight linear relaxation with huge dimensions that is hard to solve. Previous works found in the literature show that these relaxations combined with branch-and-bound algorithms belong to the state-of-the-art of exact methods for the QAP. For the level 3 RLT (RLT3), using this relaxation is prohibitive in conventional machines for instances with more than 22 locations due to memory limitations. This paper presents a distributed version of a dual ascent algorithm for the RLT3 QAP relaxation that approximately solves it for instances with up to 30 locations for the first time. Although, basically, the distributed algorithm has been implemented on top of its sequential conterpart, some changes, which improved not only the parallel performance but also the quality of solutions, were proposed here. When compared to other lower bounding methods found in the literature, our algorithm generates the best known lower bounds for 26 out of the 28 tested instances, reaching the optimal solution in 18 of them.

Charge-Coupled Device (CCD) detectors, widely used to obtain digital imaging, can be damaged by high energy radiation. Degraded images appear blurred, because of an effect known as Charge Transfer Inefficiency (CTI), which trails bright objects as the image is read out. It is often possible to correct most of the trailing during post-processing, by moving flux back to where it belongs. We compare several popular algorithms for this: quantifying the effect of their physical assumptions and tradeoffs between speed and accuracy. We combine their best elements to construct a more accurate model of damaged CCDs in the Hubble Space Telescope's Advanced Camera for Surveys/Wide Field Channel, and update it using data up to early 2013. Our algorithm now corrects 98% of CTI trailing in science exposures, a substantial improvement over previous work. Further progress will be fundamentally limited by the presence of read noise. Read noise is added after charge transfer so does not get trailed - but it is incorrectly untrailed during post-processing.