Scientific Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fermilab

2017-09-01

Scientists, engineers and programmers at Fermilab are tackling today’s most challenging computational problems. Their solutions, motivated by the needs of worldwide research in particle physics and accelerators, help America stay at the forefront of innovation.

A cross-disciplinary introduction to quantum annealing-based algorithms

NASA Astrophysics Data System (ADS)

Venegas-Andraca, Salvador E.; Cruz-Santos, William; McGeoch, Catherine; Lanzagorta, Marco

2018-04-01

A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science; hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model. We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.

Robotic Challenges: Robots Bring New Life to Gifted Classes, Teach Students Hands-On Problem Solving, Computer Skills.

ERIC Educational Resources Information Center

Smith, Ruth Baynard

1994-01-01

Intermediate level academically talented students learn essential elements of computer programming by working with robots at enrichment workshops at Dwight-Englewood School in Englewood, New Jersey. The children combine creative thinking and problem-solving skills to program the robots' microcomputers to perform a variety of movements. (JDD)

A Problem Posing-Based Practicing Strategy for Facilitating Students' Computer Programming Skills in the Team-Based Learning Mode

ERIC Educational Resources Information Center

Wang, Xiao-Ming; Hwang, Gwo-Jen

2017-01-01

Computer programming is a subject that requires problem-solving strategies and involves a great number of programming logic activities which pose challenges for learners. Therefore, providing learning support and guidance is important. Collaborative learning is widely believed to be an effective teaching approach; it can enhance learners' social…

Computer aided reliability, availability, and safety modeling for fault-tolerant computer systems with commentary on the HARP program

NASA Technical Reports Server (NTRS)

Shooman, Martin L.

1991-01-01

Many of the most challenging reliability problems of our present decade involve complex distributed systems such as interconnected telephone switching computers, air traffic control centers, aircraft and space vehicles, and local area and wide area computer networks. In addition to the challenge of complexity, modern fault-tolerant computer systems require very high levels of reliability, e.g., avionic computers with MTTF goals of one billion hours. Most analysts find that it is too difficult to model such complex systems without computer aided design programs. In response to this need, NASA has developed a suite of computer aided reliability modeling programs beginning with CARE 3 and including a group of new programs such as: HARP, HARP-PC, Reliability Analysts Workbench (Combination of model solvers SURE, STEM, PAWS, and common front-end model ASSIST), and the Fault Tree Compiler. The HARP program is studied and how well the user can model systems using this program is investigated. One of the important objectives will be to study how user friendly this program is, e.g., how easy it is to model the system, provide the input information, and interpret the results. The experiences of the author and his graduate students who used HARP in two graduate courses are described. Some brief comparisons were made with the ARIES program which the students also used. Theoretical studies of the modeling techniques used in HARP are also included. Of course no answer can be any more accurate than the fidelity of the model, thus an Appendix is included which discusses modeling accuracy. A broad viewpoint is taken and all problems which occurred in the use of HARP are discussed. Such problems include: computer system problems, installation manual problems, user manual problems, program inconsistencies, program limitations, confusing notation, long run times, accuracy problems, etc.

The implementation of AI technologies in computer wargames

NASA Astrophysics Data System (ADS)

Tiller, John A.

2004-08-01

Computer wargames involve the most in-depth analysis of general game theory. The enumerated turns of a game like chess are dwarfed by the exponentially larger possibilities of even a simple computer wargame. Implementing challenging AI is computer wargames is an important goal in both the commercial and military environments. In the commercial marketplace, customers demand a challenging AI opponent when they play a computer wargame and are frustrated by a lack of competence on the part of the AI. In the military environment, challenging AI opponents are important for several reasons. A challenging AI opponent will force the military professional to avoid routine or set-piece approaches to situations and cause them to think much deeper about military situations before taking action. A good AI opponent would also include national characteristics of the opponent being simulated, thus providing the military professional with even more of a challenge in planning and approach. Implementing current AI technologies in computer wargames is a technological challenge. The goal is to join the needs of AI in computer wargames with the solutions of current AI technologies. This talk will address several of those issues, possible solutions, and currently unsolved problems.

A comparison of approaches for finding minimum identifying codes on graphs

NASA Astrophysics Data System (ADS)

Horan, Victoria; Adachi, Steve; Bak, Stanley

2016-05-01

In order to formulate mathematical conjectures likely to be true, a number of base cases must be determined. However, many combinatorial problems are NP-hard and the computational complexity makes this research approach difficult using a standard brute force approach on a typical computer. One sample problem explored is that of finding a minimum identifying code. To work around the computational issues, a variety of methods are explored and consist of a parallel computing approach using MATLAB, an adiabatic quantum optimization approach using a D-Wave quantum annealing processor, and lastly using satisfiability modulo theory (SMT) and corresponding SMT solvers. Each of these methods requires the problem to be formulated in a unique manner. In this paper, we address the challenges of computing solutions to this NP-hard problem with respect to each of these methods.

Computing Nash equilibria through computational intelligence methods

NASA Astrophysics Data System (ADS)

Pavlidis, N. G.; Parsopoulos, K. E.; Vrahatis, M. N.

2005-03-01

Nash equilibrium constitutes a central solution concept in game theory. The task of detecting the Nash equilibria of a finite strategic game remains a challenging problem up-to-date. This paper investigates the effectiveness of three computational intelligence techniques, namely, covariance matrix adaptation evolution strategies, particle swarm optimization, as well as, differential evolution, to compute Nash equilibria of finite strategic games, as global minima of a real-valued, nonnegative function. An issue of particular interest is to detect more than one Nash equilibria of a game. The performance of the considered computational intelligence methods on this problem is investigated using multistart and deflection.

Joint Model and Parameter Dimension Reduction for Bayesian Inversion Applied to an Ice Sheet Flow Problem

NASA Astrophysics Data System (ADS)

Ghattas, O.; Petra, N.; Cui, T.; Marzouk, Y.; Benjamin, P.; Willcox, K.

2016-12-01

Model-based projections of the dynamics of the polar ice sheets play a central role in anticipating future sea level rise. However, a number of mathematical and computational challenges place significant barriers on improving predictability of these models. One such challenge is caused by the unknown model parameters (e.g., in the basal boundary conditions) that must be inferred from heterogeneous observational data, leading to an ill-posed inverse problem and the need to quantify uncertainties in its solution. In this talk we discuss the problem of estimating the uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem--i.e., the posterior probability density--is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal sliding coefficient field). To overcome these twin computational challenges, it is essential to exploit problem structure (e.g., sensitivity of the data to parameters, the smoothing property of the forward model, and correlations in the prior). To this end, we present a data-informed approach that identifies low-dimensional structure in both parameter space and the forward model state space. This approach exploits the fact that the observations inform only a low-dimensional parameter space and allows us to construct a parameter-reduced posterior. Sampling this parameter-reduced posterior still requires multiple evaluations of the forward problem, therefore we also aim to identify a low dimensional state space to reduce the computational cost. To this end, we apply a proper orthogonal decomposition (POD) approach to approximate the state using a low-dimensional manifold constructed using ``snapshots'' from the parameter reduced posterior, and the discrete empirical interpolation method (DEIM) to approximate the nonlinearity in the forward problem. We show that using only a limited number of forward solves, the resulting subspaces lead to an efficient method to explore the high-dimensional posterior.

Parallel Computational Fluid Dynamics: Current Status and Future Requirements

NASA Technical Reports Server (NTRS)

Simon, Horst D.; VanDalsem, William R.; Dagum, Leonardo; Kutler, Paul (Technical Monitor)

1994-01-01

One or the key objectives of the Applied Research Branch in the Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Allies Research Center is the accelerated introduction of highly parallel machines into a full operational environment. In this report we discuss the performance results obtained from the implementation of some computational fluid dynamics (CFD) applications on the Connection Machine CM-2 and the Intel iPSC/860. We summarize some of the experiences made so far with the parallel testbed machines at the NAS Applied Research Branch. Then we discuss the long term computational requirements for accomplishing some of the grand challenge problems in computational aerosciences. We argue that only massively parallel machines will be able to meet these grand challenge requirements, and we outline the computer science and algorithm research challenges ahead.

Instructional Designers' Media Selection Practices for Distributed Problem-Based Learning Environments

ERIC Educational Resources Information Center

Fells, Stephanie

2012-01-01

The design of online or distributed problem-based learning (dPBL) is a nascent, complex design problem. Instructional designers are challenged to effectively unite the constructivist principles of problem-based learning (PBL) with appropriate media in order to create quality dPBL environments. While computer-mediated communication (CMC) tools and…

Integrating autonomous distributed control into a human-centric C4ISR environment

NASA Astrophysics Data System (ADS)

Straub, Jeremy

2017-05-01

This paper considers incorporating autonomy into human-centric Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) environments. Specifically, it focuses on identifying ways that current autonomy technologies can augment human control and the challenges presented by additive autonomy. Three approaches to this challenge are considered, stemming from prior work in two converging areas. In the first, the problem is approached as augmenting what humans currently do with automation. In the alternate approach, the problem is approached as treating humans as actors within a cyber-physical system-of-systems (stemming from robotic distributed computing). A third approach, combines elements of both of the aforementioned.

The challenges of developing computational physics: the case of South Africa

NASA Astrophysics Data System (ADS)

Salagaram, T.; Chetty, N.

2013-08-01

Most modern scientific research problems are complex and interdisciplinary in nature. It is impossible to study such problems in detail without the use of computation in addition to theory and experiment. Although it is widely agreed that students should be introduced to computational methods at the undergraduate level, it remains a challenge to do this in a full traditional undergraduate curriculum. In this paper, we report on a survey that we conducted of undergraduate physics curricula in South Africa to determine the content and the approach taken in the teaching of computational physics. We also considered the pedagogy of computational physics at the postgraduate and research levels at various South African universities, research facilities and institutions. We conclude that the state of computational physics training in South Africa, especially at the undergraduate teaching level, is generally weak and needs to be given more attention at all universities. Failure to do so will impact negatively on the countrys capacity to grow its endeavours generally in the field of computational sciences, with negative impacts on research, and in commerce and industry.

Chess games: a model for RNA based computation.

PubMed

Cukras, A R; Faulhammer, D; Lipton, R J; Landweber, L F

1999-10-01

Here we develop the theory of RNA computing and a method for solving the 'knight problem' as an instance of a satisfiability (SAT) problem. Using only biological molecules and enzymes as tools, we developed an algorithm for solving the knight problem (3 x 3 chess board) using a 10-bit combinatorial pool and sequential RNase H digestions. The results of preliminary experiments presented here reveal that the protocol recovers far more correct solutions than expected at random, but the persistence of errors still presents the greatest challenge.

WE-D-303-00: Computational Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lewis, John; Brigham and Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA

2015-06-15

Modern medical physics deals with complex problems such as 4D radiation therapy and imaging quality optimization. Such problems involve a large number of radiological parameters, and anatomical and physiological breathing patterns. A major challenge is how to develop, test, evaluate and compare various new imaging and treatment techniques, which often involves testing over a large range of radiological parameters as well as varying patient anatomies and motions. It would be extremely challenging, if not impossible, both ethically and practically, to test every combination of parameters and every task on every type of patient under clinical conditions. Computer-based simulation using computationalmore » phantoms offers a practical technique with which to evaluate, optimize, and compare imaging technologies and methods. Within simulation, the computerized phantom provides a virtual model of the patient’s anatomy and physiology. Imaging data can be generated from it as if it was a live patient using accurate models of the physics of the imaging and treatment process. With sophisticated simulation algorithms, it is possible to perform virtual experiments entirely on the computer. By serving as virtual patients, computational phantoms hold great promise in solving some of the most complex problems in modern medical physics. In this proposed symposium, we will present the history and recent developments of computational phantom models, share experiences in their application to advanced imaging and radiation applications, and discuss their promises and limitations. Learning Objectives: Understand the need and requirements of computational phantoms in medical physics research Discuss the developments and applications of computational phantoms Know the promises and limitations of computational phantoms in solving complex problems.« less

Computational ecology as an emerging science

PubMed Central

Petrovskii, Sergei; Petrovskaya, Natalia

2012-01-01

It has long been recognized that numerical modelling and computer simulations can be used as a powerful research tool to understand, and sometimes to predict, the tendencies and peculiarities in the dynamics of populations and ecosystems. It has been, however, much less appreciated that the context of modelling and simulations in ecology is essentially different from those that normally exist in other natural sciences. In our paper, we review the computational challenges arising in modern ecology in the spirit of computational mathematics, i.e. with our main focus on the choice and use of adequate numerical methods. Somewhat paradoxically, the complexity of ecological problems does not always require the use of complex computational methods. This paradox, however, can be easily resolved if we recall that application of sophisticated computational methods usually requires clear and unambiguous mathematical problem statement as well as clearly defined benchmark information for model validation. At the same time, many ecological problems still do not have mathematically accurate and unambiguous description, and available field data are often very noisy, and hence it can be hard to understand how the results of computations should be interpreted from the ecological viewpoint. In this scientific context, computational ecology has to deal with a new paradigm: conventional issues of numerical modelling such as convergence and stability become less important than the qualitative analysis that can be provided with the help of computational techniques. We discuss this paradigm by considering computational challenges arising in several specific ecological applications. PMID:23565336

Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution

NASA Astrophysics Data System (ADS)

Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik

2018-05-01

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

Iterative Importance Sampling Algorithms for Parameter Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grout, Ray W; Morzfeld, Matthias; Day, Marcus S.

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is a challenging task. Several sampling algorithms have been proposed over the past years that take an iterative approach to constructing a proposal distribution. We investigate the applicabilitymore » of such algorithms by applying them to two realistic and challenging test problems, one in subsurface flow, and one in combustion modeling. More specifically, we implement importance sampling algorithms that iterate over the mean and covariance matrix of Gaussian or multivariate t-proposal distributions. Our implementation leverages massively parallel computers, and we present strategies to initialize the iterations using 'coarse' MCMC runs or Gaussian mixture models.« less

Code IN Exhibits - Supercomputing 2000

NASA Technical Reports Server (NTRS)

Yarrow, Maurice; McCann, Karen M.; Biswas, Rupak; VanderWijngaart, Rob F.; Kwak, Dochan (Technical Monitor)

2000-01-01

The creation of parameter study suites has recently become a more challenging problem as the parameter studies have become multi-tiered and the computational environment has become a supercomputer grid. The parameter spaces are vast, the individual problem sizes are getting larger, and researchers are seeking to combine several successive stages of parameterization and computation. Simultaneously, grid-based computing offers immense resource opportunities but at the expense of great difficulty of use. We present ILab, an advanced graphical user interface approach to this problem. Our novel strategy stresses intuitive visual design tools for parameter study creation and complex process specification, and also offers programming-free access to grid-based supercomputer resources and process automation.

Object-oriented Tools for Distributed Computing

NASA Technical Reports Server (NTRS)

Adler, Richard M.

1993-01-01

Distributed computing systems are proliferating, owing to the availability of powerful, affordable microcomputers and inexpensive communication networks. A critical problem in developing such systems is getting application programs to interact with one another across a computer network. Remote interprogram connectivity is particularly challenging across heterogeneous environments, where applications run on different kinds of computers and operating systems. NetWorks! (trademark) is an innovative software product that provides an object-oriented messaging solution to these problems. This paper describes the design and functionality of NetWorks! and illustrates how it is being used to build complex distributed applications for NASA and in the commercial sector.

Mistaking Identities: Challenging Representations of Language, Gender, and Race in High Tech Television Programs.

ERIC Educational Resources Information Center

Voithofer, R. J.

Television programs are increasingly featuring information technologies like computers as significant narrative devices, including the use of computer-based technologies as virtual worlds or environments in which characters interact, the use of computers as tools in problem solving and confronting conflict, and characters that are part human, part…

Automated Analysis of Composition and Style of Photographs and Paintings

ERIC Educational Resources Information Center

Yao, Lei

2013-01-01

Computational aesthetics is a newly emerging cross-disciplinary field with its core situated in traditional research areas such as image processing and computer vision. Using a computer to interpret aesthetic terms for images is very challenging. In this dissertation, I focus on solving specific problems about analyzing the composition and style…

VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication

NASA Astrophysics Data System (ADS)

Denina, Giovanni; Bhanu, Bir; Nguyen, Hoang Thanh; Ding, Chong; Kamal, Ahmed; Ravishankar, Chinya; Roy-Chowdhury, Amit; Ivers, Allen; Varda, Brenda

Human-activity recognition is one of the most challenging problems in computer vision. Researchers from around the world have tried to solve this problem and have come a long way in recognizing simple motions and atomic activities. As the computer vision community heads toward fully recognizing human activities, a challenging and labeled dataset is needed. To respond to that need, we collected a dataset of realistic scenarios in a multi-camera network environment (VideoWeb) involving multiple persons performing dozens of different repetitive and non-repetitive activities. This chapter describes the details of the dataset. We believe that this VideoWeb Activities dataset is unique and it is one of the most challenging datasets available today. The dataset is publicly available online at http://vwdata.ee.ucr.edu/ along with the data annotation.

Summary of the Tandem Cylinder Solutions from the Benchmark Problems for Airframe Noise Computations-I Workshop

NASA Technical Reports Server (NTRS)

Lockard, David P.

2011-01-01

Fifteen submissions in the tandem cylinders category of the First Workshop on Benchmark problems for Airframe Noise Computations are summarized. Although the geometry is relatively simple, the problem involves complex physics. Researchers employed various block-structured, overset, unstructured and embedded Cartesian grid techniques and considerable computational resources to simulate the flow. The solutions are compared against each other and experimental data from 2 facilities. Overall, the simulations captured the gross features of the flow, but resolving all the details which would be necessary to compute the noise remains challenging. In particular, how to best simulate the effects of the experimental transition strip, and the associated high Reynolds number effects, was unclear. Furthermore, capturing the spanwise variation proved difficult.

Mathematical and Computational Challenges in Population Biology and Ecosystems Science

NASA Technical Reports Server (NTRS)

Levin, Simon A.; Grenfell, Bryan; Hastings, Alan; Perelson, Alan S.

1997-01-01

Mathematical and computational approaches provide powerful tools in the study of problems in population biology and ecosystems science. The subject has a rich history intertwined with the development of statistics and dynamical systems theory, but recent analytical advances, coupled with the enhanced potential of high-speed computation, have opened up new vistas and presented new challenges. Key challenges involve ways to deal with the collective dynamics of heterogeneous ensembles of individuals, and to scale from small spatial regions to large ones. The central issues-understanding how detail at one scale makes its signature felt at other scales, and how to relate phenomena across scales-cut across scientific disciplines and go to the heart of algorithmic development of approaches to high-speed computation. Examples are given from ecology, genetics, epidemiology, and immunology.

Fast sweeping methods for hyperbolic systems of conservation laws at steady state II

NASA Astrophysics Data System (ADS)

Engquist, Björn; Froese, Brittany D.; Tsai, Yen-Hsi Richard

2015-04-01

The idea of using fast sweeping methods for solving stationary systems of conservation laws has previously been proposed for efficiently computing solutions with sharp shocks. We further develop these methods to allow for a more challenging class of problems including problems with sonic points, shocks originating in the interior of the domain, rarefaction waves, and two-dimensional systems. We show that fast sweeping methods can produce higher-order accuracy. Computational results validate the claims of accuracy, sharp shock curves, and optimal computational efficiency.

Speeding up parallel processing

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1988-01-01

In 1967 Amdahl expressed doubts about the ultimate utility of multiprocessors. The formulation, now called Amdahl's law, became part of the computing folklore and has inspired much skepticism about the ability of the current generation of massively parallel processors to efficiently deliver all their computing power to programs. The widely publicized recent results of a group at Sandia National Laboratory, which showed speedup on a 1024 node hypercube of over 500 for three fixed size problems and over 1000 for three scalable problems, have convincingly challenged this bit of folklore and have given new impetus to parallel scientific computing.

Next Generation Distributed Computing for Cancer Research

PubMed Central

Agarwal, Pankaj; Owzar, Kouros

2014-01-01

Advances in next generation sequencing (NGS) and mass spectrometry (MS) technologies have provided many new opportunities and angles for extending the scope of translational cancer research while creating tremendous challenges in data management and analysis. The resulting informatics challenge is invariably not amenable to the use of traditional computing models. Recent advances in scalable computing and associated infrastructure, particularly distributed computing for Big Data, can provide solutions for addressing these challenges. In this review, the next generation of distributed computing technologies that can address these informatics problems is described from the perspective of three key components of a computational platform, namely computing, data storage and management, and networking. A broad overview of scalable computing is provided to set the context for a detailed description of Hadoop, a technology that is being rapidly adopted for large-scale distributed computing. A proof-of-concept Hadoop cluster, set up for performance benchmarking of NGS read alignment, is described as an example of how to work with Hadoop. Finally, Hadoop is compared with a number of other current technologies for distributed computing. PMID:25983539

Next generation distributed computing for cancer research.

PubMed

Agarwal, Pankaj; Owzar, Kouros

2014-01-01

Advances in next generation sequencing (NGS) and mass spectrometry (MS) technologies have provided many new opportunities and angles for extending the scope of translational cancer research while creating tremendous challenges in data management and analysis. The resulting informatics challenge is invariably not amenable to the use of traditional computing models. Recent advances in scalable computing and associated infrastructure, particularly distributed computing for Big Data, can provide solutions for addressing these challenges. In this review, the next generation of distributed computing technologies that can address these informatics problems is described from the perspective of three key components of a computational platform, namely computing, data storage and management, and networking. A broad overview of scalable computing is provided to set the context for a detailed description of Hadoop, a technology that is being rapidly adopted for large-scale distributed computing. A proof-of-concept Hadoop cluster, set up for performance benchmarking of NGS read alignment, is described as an example of how to work with Hadoop. Finally, Hadoop is compared with a number of other current technologies for distributed computing.

Combining Symbolic Computation and Theorem Proving: Some Problems of Ramanujan

DTIC Science & Technology

1994-01-01

1994 CMU-CS--94- 103 ¶ DTIC MAY 0e o99 c -rnepe Combining symbolic computation and theorem proving: some problems of Ramanujan Edmund Clarke Xudong Zhao...Research and Development Center, Aeronautical Systems Division (AFSC), U.S. Air Force, Wright-Patterson AFB, Ohio 45433-6543 under Contract F33615-90- C ...Availability Codes n n = f Avail and Ior7. k= f(k) = _L k~of(nk Dist Special 8. =I f (k + c ) =_k=,+ I f (k) A .[ 3. List of problems The list of challenge

A Novel Coupling Pattern in Computational Science and Engineering Software

EPA Science Inventory

Computational science and engineering (CSE) software is written by experts of certain area(s). Due to the specialization, existing CSE software may need to integrate other CSE software systems developed by different groups of experts. The coupling problem is one of the challenges...

A Novel Coupling Pattern in Computational Science and Engineering Software

EPA Science Inventory

Computational science and engineering (CSE) software is written by experts of certain area(s). Due to the specialization,existing CSE software may need to integrate other CSE software systems developed by different groups of experts. Thecoupling problem is one of the challenges f...

A Statistician's View of Upcoming Grand Challenges

NASA Astrophysics Data System (ADS)

Meng, Xiao Li

2010-01-01

In this session we have seen some snapshots of the broad spectrum of challenges, in this age of huge, complex, computer-intensive models, data, instruments,and questions. These challenges bridge astronomy at many wavelengths; basic physics; machine learning; -- and statistics. At one end of our spectrum, we think of 'compressing' the data with non-parametric methods. This raises the question of creating 'pseudo-replicas' of the data for uncertainty estimates. What would be involved in, e.g. boot-strap and related methods? Somewhere in the middle are these non-parametric methods for encapsulating the uncertainty information. At the far end, we find more model-based approaches, with the physics model embedded in the likelihood and analysis. The other distinctive problem is really the 'black-box' problem, where one has a complicated e.g. fundamental physics-based computer code, or 'black box', and one needs to know how changing the parameters at input -- due to uncertainties of any kind -- will map to changing the output. All of these connect to challenges in complexity of data and computation speed. Dr. Meng will highlight ways to 'cut corners' with advanced computational techniques, such as Parallel Tempering and Equal Energy methods. As well, there are cautionary tales of running automated analysis with real data -- where "30 sigma" outliers due to data artifacts can be more common than the astrophysical event of interest.

Human-computer interfaces applied to numerical solution of the Plateau problem

NASA Astrophysics Data System (ADS)

Elias Fabris, Antonio; Soares Bandeira, Ivana; Ramos Batista, Valério

2015-09-01

In this work we present a code in Matlab to solve the Problem of Plateau numerically, and the code will include human-computer interface. The Problem of Plateau has applications in areas of knowledge like, for instance, Computer Graphics. The solution method will be the same one of the Surface Evolver, but the difference will be a complete graphical interface with the user. This will enable us to implement other kinds of interface like ocular mouse, voice, touch, etc. To date, Evolver does not include any graphical interface, which restricts its use by the scientific community. Specially, its use is practically impossible for most of the Physically Challenged People.

An Advanced User Interface Approach for Complex Parameter Study Process Specification in the Information Power Grid

NASA Technical Reports Server (NTRS)

Yarrow, Maurice; McCann, Karen M.; Biswas, Rupak; VanderWijngaart, Rob; Yan, Jerry C. (Technical Monitor)

2000-01-01

The creation of parameter study suites has recently become a more challenging problem as the parameter studies have now become multi-tiered and the computational environment has become a supercomputer grid. The parameter spaces are vast, the individual problem sizes are getting larger, and researchers are now seeking to combine several successive stages of parameterization and computation. Simultaneously, grid-based computing offers great resource opportunity but at the expense of great difficulty of use. We present an approach to this problem which stresses intuitive visual design tools for parameter study creation and complex process specification, and also offers programming-free access to grid-based supercomputer resources and process automation.

Benchmark Problems Used to Assess Computational Aeroacoustics Codes

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Envia, Edmane

2005-01-01

The field of computational aeroacoustics (CAA) encompasses numerical techniques for calculating all aspects of sound generation and propagation in air directly from fundamental governing equations. Aeroacoustic problems typically involve flow-generated noise, with and without the presence of a solid surface, and the propagation of the sound to a receiver far away from the noise source. It is a challenge to obtain accurate numerical solutions to these problems. The NASA Glenn Research Center has been at the forefront in developing and promoting the development of CAA techniques and methodologies for computing the noise generated by aircraft propulsion systems. To assess the technological advancement of CAA, Glenn, in cooperation with the Ohio Aerospace Institute and the AeroAcoustics Research Consortium, organized and hosted the Fourth CAA Workshop on Benchmark Problems. Participants from industry and academia from both the United States and abroad joined to present and discuss solutions to benchmark problems. These demonstrated technical progress ranging from the basic challenges to accurate CAA calculations to the solution of CAA problems of increasing complexity and difficulty. The results are documented in the proceedings of the workshop. Problems were solved in five categories. In three of the five categories, exact solutions were available for comparison with CAA results. A fourth category of problems representing sound generation from either a single airfoil or a blade row interacting with a gust (i.e., problems relevant to fan noise) had approximate analytical or completely numerical solutions. The fifth category of problems involved sound generation in a viscous flow. In this case, the CAA results were compared with experimental data.

The Development of Interactive Distance Learning in Taiwan: Challenges and Prospects.

ERIC Educational Resources Information Center

Chu, Clarence T.

1999-01-01

Describes three types of interactive distance-education systems under development in Taiwan: real-time multicast systems; virtual-classroom systems; and curriculum-on-demand systems. Discusses the use of telecommunications and computer technology in higher education, problems and challenges, and future prospects. (Author/LRW)

Test and Evaluation of Architecture-Aware Compiler Environment

DTIC Science & Technology

2011-11-01

biology, medicine, social sciences , and security applications. Challenges include extremely large graphs (the Facebook friend network has over...Operations with Temporal Binning ....................................................................... 32 4.12 Memory behavior and Energy per...five challenge problems empirically, exploring their scaling properties, computation and datatype needs, memory behavior , and temporal behavior

A Benders based rolling horizon algorithm for a dynamic facility location problem

DOE PAGES

Marufuzzaman,, Mohammad; Gedik, Ridvan; Roni, Mohammad S.

2016-06-28

This study presents a well-known capacitated dynamic facility location problem (DFLP) that satisfies the customer demand at a minimum cost by determining the time period for opening, closing, or retaining an existing facility in a given location. To solve this challenging NP-hard problem, this paper develops a unique hybrid solution algorithm that combines a rolling horizon algorithm with an accelerated Benders decomposition algorithm. Extensive computational experiments are performed on benchmark test instances to evaluate the hybrid algorithm’s efficiency and robustness in solving the DFLP problem. Computational results indicate that the hybrid Benders based rolling horizon algorithm consistently offers high qualitymore » feasible solutions in a much shorter computational time period than the standalone rolling horizon and accelerated Benders decomposition algorithms in the experimental range.« less

A pervasive parallel framework for visualization: final report for FWP 10-014707

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moreland, Kenneth D.

2014-01-01

We are on the threshold of a transformative change in the basic architecture of highperformance computing. The use of accelerator processors, characterized by large core counts, shared but asymmetrical memory, and heavy thread loading, is quickly becoming the norm in high performance computing. These accelerators represent significant challenges in updating our existing base of software. An intrinsic problem with this transition is a fundamental programming shift from message passing processes to much more fine thread scheduling with memory sharing. Another problem is the lack of stability in accelerator implementation; processor and compiler technology is currently changing rapidly. This report documentsmore » the results of our three-year ASCR project to address these challenges. Our project includes the development of the Dax toolkit, which contains the beginnings of new algorithms for a new generation of computers and the underlying infrastructure to rapidly prototype and build further algorithms as necessary.« less

WE-D-303-01: Development and Application of Digital Human Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Segars, P.

2015-06-15

Modern medical physics deals with complex problems such as 4D radiation therapy and imaging quality optimization. Such problems involve a large number of radiological parameters, and anatomical and physiological breathing patterns. A major challenge is how to develop, test, evaluate and compare various new imaging and treatment techniques, which often involves testing over a large range of radiological parameters as well as varying patient anatomies and motions. It would be extremely challenging, if not impossible, both ethically and practically, to test every combination of parameters and every task on every type of patient under clinical conditions. Computer-based simulation using computationalmore » phantoms offers a practical technique with which to evaluate, optimize, and compare imaging technologies and methods. Within simulation, the computerized phantom provides a virtual model of the patient’s anatomy and physiology. Imaging data can be generated from it as if it was a live patient using accurate models of the physics of the imaging and treatment process. With sophisticated simulation algorithms, it is possible to perform virtual experiments entirely on the computer. By serving as virtual patients, computational phantoms hold great promise in solving some of the most complex problems in modern medical physics. In this proposed symposium, we will present the history and recent developments of computational phantom models, share experiences in their application to advanced imaging and radiation applications, and discuss their promises and limitations. Learning Objectives: Understand the need and requirements of computational phantoms in medical physics research Discuss the developments and applications of computational phantoms Know the promises and limitations of computational phantoms in solving complex problems.« less

A Well-Tempered Hybrid Method for Solving Challenging Time-Dependent Density Functional Theory (TDDFT) Systems.

PubMed

Kasper, Joseph M; Williams-Young, David B; Vecharynski, Eugene; Yang, Chao; Li, Xiaosong

2018-04-10

The time-dependent Hartree-Fock (TDHF) and time-dependent density functional theory (TDDFT) equations allow one to probe electronic resonances of a system quickly and inexpensively. However, the iterative solution of the eigenvalue problem can be challenging or impossible to converge, using standard methods such as the Davidson algorithm for spectrally dense regions in the interior of the spectrum, as are common in X-ray absorption spectroscopy (XAS). More robust solvers, such as the generalized preconditioned locally harmonic residual (GPLHR) method, can alleviate this problem, but at the expense of higher average computational cost. A hybrid method is proposed which adapts to the problem in order to maximize computational performance while providing the superior convergence of GPLHR. In addition, a modification to the GPLHR algorithm is proposed to adaptively choose the shift parameter to enforce a convergence of states above a predefined energy threshold.

Numerical Boundary Conditions for Computational Aeroacoustics Benchmark Problems

NASA Technical Reports Server (NTRS)

Tam, Chritsopher K. W.; Kurbatskii, Konstantin A.; Fang, Jun

1997-01-01

Category 1, Problems 1 and 2, Category 2, Problem 2, and Category 3, Problem 2 are solved computationally using the Dispersion-Relation-Preserving (DRP) scheme. All these problems are governed by the linearized Euler equations. The resolution requirements of the DRP scheme for maintaining low numerical dispersion and dissipation as well as accurate wave speeds in solving the linearized Euler equations are now well understood. As long as 8 or more mesh points per wavelength is employed in the numerical computation, high quality results are assured. For the first three categories of benchmark problems, therefore, the real challenge is to develop high quality numerical boundary conditions. For Category 1, Problems 1 and 2, it is the curved wall boundary conditions. For Category 2, Problem 2, it is the internal radiation boundary conditions inside the duct. For Category 3, Problem 2, they are the inflow and outflow boundary conditions upstream and downstream of the blade row. These are the foci of the present investigation. Special nonhomogeneous radiation boundary conditions that generate the incoming disturbances and at the same time allow the outgoing reflected or scattered acoustic disturbances to leave the computation domain without significant reflection are developed. Numerical results based on these boundary conditions are provided.

Network gateway security method for enterprise Grid: a literature review

NASA Astrophysics Data System (ADS)

Sujarwo, A.; Tan, J.

2017-03-01

The computational Grid has brought big computational resources closer to scientists. It enables people to do a large computational job anytime and anywhere without any physical border anymore. However, the massive and spread of computer participants either as user or computational provider arise problems in security. The challenge is on how the security system, especially the one which filters data in the gateway could works in flexibility depends on the registered Grid participants. This paper surveys what people have done to approach this challenge, in order to find the better and new method for enterprise Grid. The findings of this paper is the dynamically controlled enterprise firewall to secure the Grid resources from unwanted connections with a new firewall controlling method and components.

Prediction, Error, and Adaptation during Online Sentence Comprehension

ERIC Educational Resources Information Center

Fine, Alex Brabham

2013-01-01

A fundamental challenge for human cognition is perceiving and acting in a world in which the statistics that characterize available sensory data are non-stationary. This thesis focuses on this problem specifically in the domain of sentence comprehension, where linguistic variability poses computational challenges to the processes underlying…

From Numerical Problem Solving to Model-Based Experimentation Incorporating Computer-Based Tools of Various Scales into the ChE Curriculum

ERIC Educational Resources Information Center

Shacham, Mordechai; Cutlip, Michael B.; Brauner, Neima

2009-01-01

A continuing challenge to the undergraduate chemical engineering curriculum is the time-effective incorporation and use of computer-based tools throughout the educational program. Computing skills in academia and industry require some proficiency in programming and effective use of software packages for solving 1) single-model, single-algorithm…

Educational NASA Computational and Scientific Studies (enCOMPASS)

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess

2013-01-01

Educational NASA Computational and Scientific Studies (enCOMPASS) is an educational project of NASA Goddard Space Flight Center aimed at bridging the gap between computational objectives and needs of NASA's scientific research, missions, and projects, and academia's latest advances in applied mathematics and computer science. enCOMPASS achieves this goal via bidirectional collaboration and communication between NASA and academia. Using developed NASA Computational Case Studies in university computer science/engineering and applied mathematics classes is a way of addressing NASA's goals of contributing to the Science, Technology, Education, and Math (STEM) National Objective. The enCOMPASS Web site at http://encompass.gsfc.nasa.gov provides additional information. There are currently nine enCOMPASS case studies developed in areas of earth sciences, planetary sciences, and astrophysics. Some of these case studies have been published in AIP and IEEE's Computing in Science and Engineering magazines. A few university professors have used enCOMPASS case studies in their computational classes and contributed their findings to NASA scientists. In these case studies, after introducing the science area, the specific problem, and related NASA missions, students are first asked to solve a known problem using NASA data and past approaches used and often published in a scientific/research paper. Then, after learning about the NASA application and related computational tools and approaches for solving the proposed problem, students are given a harder problem as a challenge for them to research and develop solutions for. This project provides a model for NASA scientists and engineers on one side, and university students, faculty, and researchers in computer science and applied mathematics on the other side, to learn from each other's areas of work, computational needs and solutions, and the latest advances in research and development. This innovation takes NASA science and engineering applications to computer science and applied mathematics university classes, and makes NASA objectives part of the university curricula. There is great potential for growth and return on investment of this program to the point where every major university in the U.S. would use at least one of these case studies in one of their computational courses, and where every NASA scientist and engineer facing a computational challenge (without having resources or expertise to solve it) would use enCOMPASS to formulate the problem as a case study, provide it to a university, and get back their solutions and ideas.

Lattice Boltzmann for Airframe Noise Predictions

NASA Technical Reports Server (NTRS)

Barad, Michael; Kocheemoolayil, Joseph; Kiris, Cetin

2017-01-01

Increase predictive use of High-Fidelity Computational Aero- Acoustics (CAA) capabilities for NASA's next generation aviation concepts. CFD has been utilized substantially in analysis and design for steady-state problems (RANS). Computational resources are extremely challenged for high-fidelity unsteady problems (e.g. unsteady loads, buffet boundary, jet and installation noise, fan noise, active flow control, airframe noise, etc) ü Need novel techniques for reducing the computational resources consumed by current high-fidelity CAA Need routine acoustic analysis of aircraft components at full-scale Reynolds number from first principles Need an order of magnitude reduction in wall time to solution!

ELM Meets Urban Big Data Analysis: Case Studies

PubMed Central

Chen, Huajun; Chen, Jiaoyan

2016-01-01

In the latest years, the rapid progress of urban computing has engendered big issues, which creates both opportunities and challenges. The heterogeneous and big volume of data and the big difference between physical and virtual worlds have resulted in lots of problems in quickly solving practical problems in urban computing. In this paper, we propose a general application framework of ELM for urban computing. We present several real case studies of the framework like smog-related health hazard prediction and optimal retain store placement. Experiments involving urban data in China show the efficiency, accuracy, and flexibility of our proposed framework. PMID:27656203

Segmenting root systems in xray computed tomography images using level sets

USDA-ARS?s Scientific Manuscript database

The segmentation of plant roots from soil and other growing mediums in xray computed tomography images is needed to effectively study the shapes of roots without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. ...

KAPEAN: Understanding Affective States of Children with ADHD

ERIC Educational Resources Information Center

Martínez, Fernando; Barraza, Claudia; González, Nimrod; González, Juan

2016-01-01

Affective computing seeks to create computational systems that adapt content and resources according to the affective states of the users. However, the detection of the user's affection such as motivation and emotion is challenging especially when an attention problem is present. An approach to convey learning resources to children with learning…

Reconfigurability in MDO Problem Synthesis. Part 1

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia M.; Lewis, Robert Michael

2004-01-01

Integrating autonomous disciplines into a problem amenable to solution presents a major challenge in realistic multidisciplinary design optimization (MDO). We propose a linguistic approach to MDO problem description, formulation, and solution we call reconfigurable multidisciplinary synthesis (REMS). With assistance from computer science techniques, REMS comprises an abstract language and a collection of processes that provide a means for dynamic reasoning about MDO problems in a range of contexts. The approach may be summarized as follows. Description of disciplinary data according to the rules of a grammar, followed by lexical analysis and compilation, yields basic computational components that can be assembled into various MDO problem formulations and solution algorithms, including hybrid strategies, with relative ease. The ability to re-use the computational components is due to the special structure of the MDO problem. The range of contexts for reasoning about MDO spans tasks from error checking and derivative computation to formulation and reformulation of optimization problem statements. In highly structured contexts, reconfigurability can mean a straightforward transformation among problem formulations with a single operation. We hope that REMS will enable experimentation with a variety of problem formulations in research environments, assist in the assembly of MDO test problems, and serve as a pre-processor in computational frameworks in production environments. This paper, Part 1 of two companion papers, discusses the fundamentals of REMS. Part 2 illustrates the methodology in more detail.

Reconfigurability in MDO Problem Synthesis. Part 2

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia M.; Lewis, Robert Michael

2004-01-01

Integrating autonomous disciplines into a problem amenable to solution presents a major challenge in realistic multidisciplinary design optimization (MDO). We propose a linguistic approach to MDO problem description, formulation, and solution we call reconfigurable multidisciplinary synthesis (REMS). With assistance from computer science techniques, REMS comprises an abstract language and a collection of processes that provide a means for dynamic reasoning about MDO problems in a range of contexts. The approach may be summarized as follows. Description of disciplinary data according to the rules of a grammar, followed by lexical analysis and compilation, yields basic computational components that can be assembled into various MDO problem formulations and solution algorithms, including hybrid strategies, with relative ease. The ability to re-use the computational components is due to the special structure of the MDO problem. The range of contexts for reasoning about MDO spans tasks from error checking and derivative computation to formulation and reformulation of optimization problem statements. In highly structured contexts, reconfigurability can mean a straightforward transformation among problem formulations with a single operation. We hope that REMS will enable experimentation with a variety of problem formulations in research environments, assist in the assembly of MDO test problems, and serve as a pre-processor in computational frameworks in production environments. Part 1 of two companion papers, discusses the fundamentals of REMS. This paper, Part 2 illustrates the methodology in more detail.

Challenges and opportunities of cloud computing for atmospheric sciences

NASA Astrophysics Data System (ADS)

Pérez Montes, Diego A.; Añel, Juan A.; Pena, Tomás F.; Wallom, David C. H.

2016-04-01

Cloud computing is an emerging technological solution widely used in many fields. Initially developed as a flexible way of managing peak demand it has began to make its way in scientific research. One of the greatest advantages of cloud computing for scientific research is independence of having access to a large cyberinfrastructure to fund or perform a research project. Cloud computing can avoid maintenance expenses for large supercomputers and has the potential to 'democratize' the access to high-performance computing, giving flexibility to funding bodies for allocating budgets for the computational costs associated with a project. Two of the most challenging problems in atmospheric sciences are computational cost and uncertainty in meteorological forecasting and climate projections. Both problems are closely related. Usually uncertainty can be reduced with the availability of computational resources to better reproduce a phenomenon or to perform a larger number of experiments. Here we expose results of the application of cloud computing resources for climate modeling using cloud computing infrastructures of three major vendors and two climate models. We show how the cloud infrastructure compares in performance to traditional supercomputers and how it provides the capability to complete experiments in shorter periods of time. The monetary cost associated is also analyzed. Finally we discuss the future potential of this technology for meteorological and climatological applications, both from the point of view of operational use and research.

Fundamental organometallic reactions: Applications on the CYBER 205

NASA Technical Reports Server (NTRS)

Rappe, A. K.

1984-01-01

Two of the most challenging problems of Organometallic chemistry (loosely defined) are pollution control with the large space velocities needed and nitrogen fixation, a process so capably done by nature and so relatively poorly done by man (industry). For a computational chemist these problems are on the fringe of what is possible with conventional computers (large models needed and accurate energetics required). A summary of the algorithmic modification needed to address these problems on a vector processor such as the CYBER 205 and a sketch of findings to date on deNOx catalysis and nitrogen fixation are presented.

A comparison of acceleration methods for solving the neutron transport k-eigenvalue problem

NASA Astrophysics Data System (ADS)

Willert, Jeffrey; Park, H.; Knoll, D. A.

2014-10-01

Over the past several years a number of papers have been written describing modern techniques for numerically computing the dominant eigenvalue of the neutron transport criticality problem. These methods fall into two distinct categories. The first category of methods rewrite the multi-group k-eigenvalue problem as a nonlinear system of equations and solve the resulting system using either a Jacobian-Free Newton-Krylov (JFNK) method or Nonlinear Krylov Acceleration (NKA), a variant of Anderson Acceleration. These methods are generally successful in significantly reducing the number of transport sweeps required to compute the dominant eigenvalue. The second category of methods utilize Moment-Based Acceleration (or High-Order/Low-Order (HOLO) Acceleration). These methods solve a sequence of modified diffusion eigenvalue problems whose solutions converge to the solution of the original transport eigenvalue problem. This second class of methods is, in our experience, always superior to the first, as most of the computational work is eliminated by the acceleration from the LO diffusion system. In this paper, we review each of these methods. Our computational results support our claim that the choice of which nonlinear solver to use, JFNK or NKA, should be secondary. The primary computational savings result from the implementation of a HOLO algorithm. We display computational results for a series of challenging multi-dimensional test problems.

Space-Time Conservation Element and Solution Element Method Being Developed

NASA Technical Reports Server (NTRS)

Chang, Sin-Chung; Himansu, Ananda; Jorgenson, Philip C. E.; Loh, Ching-Yuen; Wang, Xiao-Yen; Yu, Sheng-Tao

1999-01-01

The engineering research and design requirements of today pose great computer-simulation challenges to engineers and scientists who are called on to analyze phenomena in continuum mechanics. The future will bring even more daunting challenges, when increasingly complex phenomena must be analyzed with increased accuracy. Traditionally used numerical simulation methods have evolved to their present state by repeated incremental extensions to broaden their scope. They are reaching the limits of their applicability and will need to be radically revised, at the very least, to meet future simulation challenges. At the NASA Lewis Research Center, researchers have been developing a new numerical framework for solving conservation laws in continuum mechanics, namely, the Space-Time Conservation Element and Solution Element Method, or the CE/SE method. This method has been built from fundamentals and is not a modification of any previously existing method. It has been designed with generality, simplicity, robustness, and accuracy as cornerstones. The CE/SE method has thus far been applied in the fields of computational fluid dynamics, computational aeroacoustics, and computational electromagnetics. Computer programs based on the CE/SE method have been developed for calculating flows in one, two, and three spatial dimensions. Results have been obtained for numerous problems and phenomena, including various shock-tube problems, ZND detonation waves, an implosion and explosion problem, shocks over a forward-facing step, a blast wave discharging from a nozzle, various acoustic waves, and shock/acoustic-wave interactions. The method can clearly resolve shock/acoustic-wave interactions, wherein the difference of the magnitude between the acoustic wave and shock could be up to six orders. In two-dimensional flows, the reflected shock is as crisp as the leading shock. CE/SE schemes are currently being used for advanced applications to jet and fan noise prediction and to chemically reacting flows.

Integrating numerical computation into the undergraduate education physics curriculum using spreadsheet excel

NASA Astrophysics Data System (ADS)

Fauzi, Ahmad

2017-11-01

Numerical computation has many pedagogical advantages: it develops analytical skills and problem-solving skills, helps to learn through visualization, and enhances physics education. Unfortunately, numerical computation is not taught to undergraduate education physics students in Indonesia. Incorporate numerical computation into the undergraduate education physics curriculum presents many challenges. The main challenges are the dense curriculum that makes difficult to put new numerical computation course and most students have no programming experience. In this research, we used case study to review how to integrate numerical computation into undergraduate education physics curriculum. The participants of this research were 54 students of the fourth semester of physics education department. As a result, we concluded that numerical computation could be integrated into undergraduate education physics curriculum using spreadsheet excel combined with another course. The results of this research become complements of the study on how to integrate numerical computation in learning physics using spreadsheet excel.

Interaction Network Estimation: Predicting Problem-Solving Diversity in Interactive Environments

ERIC Educational Resources Information Center

Eagle, Michael; Hicks, Drew; Barnes, Tiffany

2015-01-01

Intelligent tutoring systems and computer aided learning environments aimed at developing problem solving produce large amounts of transactional data which make it a challenge for both researchers and educators to understand how students work within the environment. Researchers have modeled student-tutor interactions using complex networks in…

Rational Approximations to Rational Models: Alternative Algorithms for Category Learning

ERIC Educational Resources Information Center

Sanborn, Adam N.; Griffiths, Thomas L.; Navarro, Daniel J.

2010-01-01

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models…

Soft Computing Methods for Disulfide Connectivity Prediction.

PubMed

Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

2015-01-01

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Virtual School, Real Experience: Simulations Replicate the World of Practice for Aspiring Principals

ERIC Educational Resources Information Center

Mann, Dale; Shakeshaft, Charol

2013-01-01

A web-enabled computer simulation program presents real-world opportunities, problems, and challenges for aspiring principals. The simulation challenges areas that are not always covered in lectures, textbooks, or workshops. For example, using the simulation requires dealing--on-screen and in real time--with demanding parents, observing…

Exploring Cloud Computing Tools to Enhance Team-Based Problem Solving for Challenging Behavior

ERIC Educational Resources Information Center

Johnson, LeAnne D.

2017-01-01

Data-driven decision making is central to improving success of children. Actualizing the use of data is challenging when addressing the social, emotional, and behavioral needs of children across different types of early childhood programs (i.e., early childhood special education, early childhood family education, Head Start, and childcare).…

Challenges for Educational Technologists in the 21st Century

ERIC Educational Resources Information Center

Mayes, Robin; Natividad, Gloria; Spector, J. Michael

2015-01-01

In 1972, Edsger Dijkstra claimed that computers had only introduced the new problem of learning to use them effectively. This is especially true in 2015 with regard to powerful new educational technologies. This article describes the challenges that 21st century educational technologists are, and will be, addressing as they undertake the effective…

Tracking Student Participants from a REU Site with NAE Grand Challenges as the Common Theme

ERIC Educational Resources Information Center

Burkett, Susan; Dye, Tabatha; Johnson, Pauline

2015-01-01

The National Academy of Engineering (NAE) Grand Challenges provides the theme for this NSFfunded Research Experience for Undergraduates (REU) site. Research topics, with their broad societal impact, allow undergraduate students from multiple engineering disciplines and computer science to work together on exciting and critical problems. The…

Automatically Generated Algorithms for the Vertex Coloring Problem

PubMed Central

Contreras Bolton, Carlos; Gatica, Gustavo; Parada, Víctor

2013-01-01

The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics. Automatically revising the space constituted by combining these techniques to find the most adequate combination has received less attention. In this paper, we propose exploring the heuristics space for the vertex coloring problem using evolutionary algorithms. We automatically generate three new algorithms by combining elementary heuristics. To evaluate the new algorithms, a computational experiment was performed that allowed comparing them numerically with existing heuristics. The obtained algorithms present an average 29.97% relative error, while four other heuristics selected from the literature present a 59.73% error, considering 29 of the more difficult instances in the DIMACS benchmark. PMID:23516506

Statistical mechanics of complex neural systems and high dimensional data

NASA Astrophysics Data System (ADS)

Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

2013-03-01

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks.

Exploring the challenges faced by polytechnic students

NASA Astrophysics Data System (ADS)

Matore, Mohd Effendi @ Ewan Mohd; Khairani, Ahmad Zamri

2015-02-01

This study aims to identify other challenges besides those already faced by students, in seven polytechnics in Malaysia as a continuation to the previous research that had identified 52 main challenges faced by students using the Rasch Model. The explorative study focuses on the challenges that are not included in the Mooney Problem Checklist (MPCL). A total of 121 polytechnic students submitted 183 written responses through the open questions provided. Two hundred fifty two students had responded from a students' perspective on the dichotomous questions regarding their view on the challenges faced. The data was analysed qualitatively using the NVivo 8.0. The findings showed that students from Politeknik Seberang Perai (PSP) gave the highest response, which was 56 (30.6%) and Politeknik Metro Kuala Lumpur (PMKL) had the lowest response of 2 (1.09%). Five dominant challenges were identified, which were the English language (32, 17.5%), learning (14, 7.7%), vehicles (13, 7.1%), information technology and communication (ICT) (13, 7.1%), and peers (11, 6.0%). This article, however, focus on three apparent challenges, namely, English language, vehicles, as well as computer and ICT, as the challenges of learning and peers had been analysed in the previous MPCL. The challenge of English language that had been raised was regarding the weakness in commanding the aspects of speech and fluency. The computer and ICT challenge covered the weakness in mastering ICT and computers, as well as computer breakdowns and low-performance computers. The challenge of vehicles emphasized the unavailability of vehicles to attend lectures and go elsewhere, lack of transportation service in the polytechnic and not having a valid driving license. These challenges are very relevant and need to be discussed in an effort to prepare polytechnics in facing the transformational process of polytechnics.

Structure preserving parallel algorithms for solving the Bethe–Salpeter eigenvalue problem

DOE PAGES

Shao, Meiyue; da Jornada, Felipe H.; Yang, Chao; ...

2015-10-02

The Bethe–Salpeter eigenvalue problem is a dense structured eigenvalue problem arising from discretized Bethe–Salpeter equation in the context of computing exciton energies and states. A computational challenge is that at least half of the eigenvalues and the associated eigenvectors are desired in practice. In this paper, we establish the equivalence between Bethe–Salpeter eigenvalue problems and real Hamiltonian eigenvalue problems. Based on theoretical analysis, structure preserving algorithms for a class of Bethe–Salpeter eigenvalue problems are proposed. We also show that for this class of problems all eigenvalues obtained from the Tamm–Dancoff approximation are overestimated. In order to solve large scale problemsmore » of practical interest, we discuss parallel implementations of our algorithms targeting distributed memory systems. Finally, several numerical examples are presented to demonstrate the efficiency and accuracy of our algorithms.« less

Human-Computer Interaction Software: Lessons Learned, Challenges Ahead

DTIC Science & Technology

1989-01-01

domain communi- Iatelligent s t s s Me cation. Users familiar with problem Inteligent support systes. High-func- anddomains but inxperienced with comput...8217i. April 1987, pp. 7.3-78. His research interests include artificial intel- Creating better HCI softw-are will have a 8. S.K Catrd. I.P. Moran. arid

Algebraic Functions, Computer Programming, and the Challenge of Transfer

ERIC Educational Resources Information Center

Schanzer, Emmanuel Tanenbaum

2015-01-01

Students' struggles with algebra are well documented. Prior to the introduction of functions, mathematics is typically focused on applying a set of arithmetic operations to compute an answer. The introduction of functions, however, marks the point at which mathematics begins to focus on building up abstractions as a way to solve complex problems.…

Refocusing the Vision: The Future of Instructional Technology

ERIC Educational Resources Information Center

Pence, Harry E.; McIntosh, Steven

2011-01-01

Two decades ago, many campuses mobilized a major effort to deal with a clear problem; faculty and students needed access to desktop computing technologies. Now the situation is much more complex. Responding to the current challenges, like mobile computing and social networking, will be ore difficult but equally important. There is a clear need for…

Presentation Trainer: What Experts and Computers Can Tell about Your Nonverbal Communication

ERIC Educational Resources Information Center

Schneider, J.; Börner, D.; van Rosmalen, P.; Specht, M.

2017-01-01

The ability to present effectively is essential for professionals; therefore, oral communication courses have become part of the curricula for higher education studies. However, speaking in public is still a challenge for many graduates. To tackle this problem, driven by the recent advances in computer vision techniques and prosody analysis,…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keyes, D.; McInnes, L. C.; Woodward, C.

This report is an outcome of the workshop Multiphysics Simulations: Challenges and Opportunities, sponsored by the Institute of Computing in Science (ICiS). Additional information about the workshop, including relevant reading and presentations on multiphysics issues in applications, algorithms, and software, is available via https://sites.google.com/site/icismultiphysics2011/. We consider multiphysics applications from algorithmic and architectural perspectives, where 'algorithmic' includes both mathematical analysis and computational complexity and 'architectural' includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not alwaysmore » practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities. We also initiate a modest suite of test problems encompassing features present in many applications.« less

Programming and Tuning a Quantum Annealing Device to Solve Real World Problems

NASA Astrophysics Data System (ADS)

Perdomo-Ortiz, Alejandro; O'Gorman, Bryan; Fluegemann, Joseph; Smelyanskiy, Vadim

2015-03-01

Solving real-world applications with quantum algorithms requires overcoming several challenges, ranging from translating the computational problem at hand to the quantum-machine language to tuning parameters of the quantum algorithm that have a significant impact on the performance of the device. In this talk, we discuss these challenges, strategies developed to enhance performance, and also a more efficient implementation of several applications. Although we will focus on applications of interest to NASA's Quantum Artificial Intelligence Laboratory, the methods and concepts presented here apply to a broader family of hard discrete optimization problems, including those that occur in many machine-learning algorithms.

Zero: A "None" Number?

ERIC Educational Resources Information Center

Anthony, Glenda J.; Walshaw, Margaret A.

2004-01-01

This article discusses the challenges students face in making sense of zero as a number. A range of different student responses to a computation problem involving zero reveal students' different understandings of zero.

Technology, attributions, and emotions in post-secondary education: An application of Weiner’s attribution theory to academic computing problems

PubMed Central

Hall, Nathan C.; Goetz, Thomas; Chiarella, Andrew; Rahimi, Sonia

2018-01-01

As technology becomes increasingly integrated with education, research on the relationships between students’ computing-related emotions and motivation following technological difficulties is critical to improving learning experiences. Following from Weiner’s (2010) attribution theory of achievement motivation, the present research examined relationships between causal attributions and emotions concerning academic computing difficulties in two studies. Study samples consisted of North American university students enrolled in both traditional and online universities (total N = 559) who responded to either hypothetical scenarios or experimental manipulations involving technological challenges experienced in academic settings. Findings from Study 1 showed stable and external attributions to be emotionally maladaptive (more helplessness, boredom, guilt), particularly in response to unexpected computing problems. Additionally, Study 2 found stable attributions for unexpected problems to predict more anxiety for traditional students, with both external and personally controllable attributions for minor problems proving emotionally beneficial for students in online degree programs (more hope, less anxiety). Overall, hypothesized negative effects of stable attributions were observed across both studies, with mixed results for personally controllable attributions and unanticipated emotional benefits of external attributions for academic computing problems warranting further study. PMID:29529039

Technology, attributions, and emotions in post-secondary education: An application of Weiner's attribution theory to academic computing problems.

PubMed

Maymon, Rebecca; Hall, Nathan C; Goetz, Thomas; Chiarella, Andrew; Rahimi, Sonia

2018-01-01

As technology becomes increasingly integrated with education, research on the relationships between students' computing-related emotions and motivation following technological difficulties is critical to improving learning experiences. Following from Weiner's (2010) attribution theory of achievement motivation, the present research examined relationships between causal attributions and emotions concerning academic computing difficulties in two studies. Study samples consisted of North American university students enrolled in both traditional and online universities (total N = 559) who responded to either hypothetical scenarios or experimental manipulations involving technological challenges experienced in academic settings. Findings from Study 1 showed stable and external attributions to be emotionally maladaptive (more helplessness, boredom, guilt), particularly in response to unexpected computing problems. Additionally, Study 2 found stable attributions for unexpected problems to predict more anxiety for traditional students, with both external and personally controllable attributions for minor problems proving emotionally beneficial for students in online degree programs (more hope, less anxiety). Overall, hypothesized negative effects of stable attributions were observed across both studies, with mixed results for personally controllable attributions and unanticipated emotional benefits of external attributions for academic computing problems warranting further study.

University Internet Services: Problems and Opportunities.

ERIC Educational Resources Information Center

Phan, Dien D.; Chen, Jim Q.

This paper presents the findings of a study on the use of World Wide Web among students at St. Cloud State University, Minnesota, USA. The paper explores problems and challenges on campus Web computing and the relationships among the extent of Web usage, class level, and overall student academic performance. Specifically, the purposes of this…

Particle-based simulation of charge transport in discrete-charge nano-scale systems: the electrostatic problem

PubMed Central

2012-01-01

The fast and accurate computation of the electric forces that drive the motion of charged particles at the nanometer scale represents a computational challenge. For this kind of system, where the discrete nature of the charges cannot be neglected, boundary element methods (BEM) represent a better approach than finite differences/finite elements methods. In this article, we compare two different BEM approaches to a canonical electrostatic problem in a three-dimensional space with inhomogeneous dielectrics, emphasizing their suitability for particle-based simulations: the iterative method proposed by Hoyles et al. and the Induced Charge Computation introduced by Boda et al. PMID:22338640

Particle-based simulation of charge transport in discrete-charge nano-scale systems: the electrostatic problem.

PubMed

Berti, Claudio; Gillespie, Dirk; Eisenberg, Robert S; Fiegna, Claudio

2012-02-16

The fast and accurate computation of the electric forces that drive the motion of charged particles at the nanometer scale represents a computational challenge. For this kind of system, where the discrete nature of the charges cannot be neglected, boundary element methods (BEM) represent a better approach than finite differences/finite elements methods. In this article, we compare two different BEM approaches to a canonical electrostatic problem in a three-dimensional space with inhomogeneous dielectrics, emphasizing their suitability for particle-based simulations: the iterative method proposed by Hoyles et al. and the Induced Charge Computation introduced by Boda et al.

Advancing Cyberinfrastructure to support high resolution water resources modeling

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Ogden, F. L.; Jones, N.; Horsburgh, J. S.

2012-12-01

Addressing the problem of how the availability and quality of water resources at large scales are sensitive to climate variability, watershed alterations and management activities requires computational resources that combine data from multiple sources and support integrated modeling. Related cyberinfrastructure challenges include: 1) how can we best structure data and computer models to address this scientific problem through the use of high-performance and data-intensive computing, and 2) how can we do this in a way that discipline scientists without extensive computational and algorithmic knowledge and experience can take advantage of advances in cyberinfrastructure? This presentation will describe a new system called CI-WATER that is being developed to address these challenges and advance high resolution water resources modeling in the Western U.S. We are building on existing tools that enable collaboration to develop model and data interfaces that link integrated system models running within an HPC environment to multiple data sources. Our goal is to enhance the use of computational simulation and data-intensive modeling to better understand water resources. Addressing water resource problems in the Western U.S. requires simulation of natural and engineered systems, as well as representation of legal (water rights) and institutional constraints alongside the representation of physical processes. We are establishing data services to represent the engineered infrastructure and legal and institutional systems in a way that they can be used with high resolution multi-physics watershed modeling at high spatial resolution. These services will enable incorporation of location-specific information on water management infrastructure and systems into the assessment of regional water availability in the face of growing demands, uncertain future meteorological forcings, and existing prior-appropriations water rights. This presentation will discuss the informatics challenges involved with data management and easy-to-use access to high performance computing being tackled in this project.

Deep Learning for Computer Vision: A Brief Review

PubMed Central

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

Hybrid Quantum-Classical Approach to Quantum Optimal Control.

PubMed

Li, Jun; Yang, Xiaodong; Peng, Xinhua; Sun, Chang-Pu

2017-04-14

A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving answers from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a seven-qubit nuclear magnetic resonance system, on which we have succeeded in optimizing state preparation without involving classical computation of the large Hilbert space evolution.

Identification and addressing reduction-related misconceptions

NASA Astrophysics Data System (ADS)

Gal-Ezer, Judith; Trakhtenbrot, Mark

2016-07-01

Reduction is one of the key techniques used for problem-solving in computer science. In particular, in the theory of computation and complexity (TCC), mapping and polynomial reductions are used for analysis of decidability and computational complexity of problems, including the core concept of NP-completeness. Reduction is a highly abstract technique that involves revealing close non-trivial connections between problems that often seem to have nothing in common. As a result, proper understanding and application of reduction is a serious challenge for students and a source of numerous misconceptions. The main contribution of this paper is detection of such misconceptions, analysis of their roots, and proposing a way to address them in an undergraduate TCC course. Our observations suggest that the main source of the misconceptions is the false intuitive rule "the bigger is a set/problem, the harder it is to solve". Accordingly, we developed a series of exercises for proactive prevention of these misconceptions.

Computational path planner for product assembly in complex environments

NASA Astrophysics Data System (ADS)

Shang, Wei; Liu, Jianhua; Ning, Ruxin; Liu, Mi

2013-03-01

Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.

Automation of multi-agent control for complex dynamic systems in heterogeneous computational network

NASA Astrophysics Data System (ADS)

Oparin, Gennady; Feoktistov, Alexander; Bogdanova, Vera; Sidorov, Ivan

2017-01-01

The rapid progress of high-performance computing entails new challenges related to solving large scientific problems for various subject domains in a heterogeneous distributed computing environment (e.g., a network, Grid system, or Cloud infrastructure). The specialists in the field of parallel and distributed computing give the special attention to a scalability of applications for problem solving. An effective management of the scalable application in the heterogeneous distributed computing environment is still a non-trivial issue. Control systems that operate in networks, especially relate to this issue. We propose a new approach to the multi-agent management for the scalable applications in the heterogeneous computational network. The fundamentals of our approach are the integrated use of conceptual programming, simulation modeling, network monitoring, multi-agent management, and service-oriented programming. We developed a special framework for an automation of the problem solving. Advantages of the proposed approach are demonstrated on the parametric synthesis example of the static linear regulator for complex dynamic systems. Benefits of the scalable application for solving this problem include automation of the multi-agent control for the systems in a parallel mode with various degrees of its detailed elaboration.

Faster PET reconstruction with a stochastic primal-dual hybrid gradient method

NASA Astrophysics Data System (ADS)

Ehrhardt, Matthias J.; Markiewicz, Pawel; Chambolle, Antonin; Richtárik, Peter; Schott, Jonathan; Schönlieb, Carola-Bibiane

2017-08-01

Image reconstruction in positron emission tomography (PET) is computationally challenging due to Poisson noise, constraints and potentially non-smooth priors-let alone the sheer size of the problem. An algorithm that can cope well with the first three of the aforementioned challenges is the primal-dual hybrid gradient algorithm (PDHG) studied by Chambolle and Pock in 2011. However, PDHG updates all variables in parallel and is therefore computationally demanding on the large problem sizes encountered with modern PET scanners where the number of dual variables easily exceeds 100 million. In this work, we numerically study the usage of SPDHG-a stochastic extension of PDHG-but is still guaranteed to converge to a solution of the deterministic optimization problem with similar rates as PDHG. Numerical results on a clinical data set show that by introducing randomization into PDHG, similar results as the deterministic algorithm can be achieved using only around 10 % of operator evaluations. Thus, making significant progress towards the feasibility of sophisticated mathematical models in a clinical setting.

An element search ant colony technique for solving virtual machine placement problem

NASA Astrophysics Data System (ADS)

Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.

2017-09-01

The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

Facial Animations: Future Research Directions & Challenges

NASA Astrophysics Data System (ADS)

Alkawaz, Mohammed Hazim; Mohamad, Dzulkifli; Rehman, Amjad; Basori, Ahmad Hoirul

2014-06-01

Nowadays, computer facial animation is used in a significant multitude fields that brought human and social to study the computer games, films and interactive multimedia reality growth. Authoring the computer facial animation, complex and subtle expressions are challenging and fraught with problems. As a result, the current most authored using universal computer animation techniques often limit the production quality and quantity of facial animation. With the supplement of computer power, facial appreciative, software sophistication and new face-centric methods emerging are immature in nature. Therefore, this paper concentrates to define and managerially categorize current and emerged surveyed facial animation experts to define the recent state of the field, observed bottlenecks and developing techniques. This paper further presents a real-time simulation model of human worry and howling with detail discussion about their astonish, sorrow, annoyance and panic perception.

Exact solutions for species tree inference from discordant gene trees.

PubMed

Chang, Wen-Chieh; Górecki, Paweł; Eulenstein, Oliver

2013-10-01

Phylogenetic analysis has to overcome the grant challenge of inferring accurate species trees from evolutionary histories of gene families (gene trees) that are discordant with the species tree along whose branches they have evolved. Two well studied approaches to cope with this challenge are to solve either biologically informed gene tree parsimony (GTP) problems under gene duplication, gene loss, and deep coalescence, or the classic RF supertree problem that does not rely on any biological model. Despite the potential of these problems to infer credible species trees, they are NP-hard. Therefore, these problems are addressed by heuristics that typically lack any provable accuracy and precision. We describe fast dynamic programming algorithms that solve the GTP problems and the RF supertree problem exactly, and demonstrate that our algorithms can solve instances with data sets consisting of as many as 22 taxa. Extensions of our algorithms can also report the number of all optimal species trees, as well as the trees themselves. To better asses the quality of the resulting species trees that best fit the given gene trees, we also compute the worst case species trees, their numbers, and optimization score for each of the computational problems. Finally, we demonstrate the performance of our exact algorithms using empirical and simulated data sets, and analyze the quality of heuristic solutions for the studied problems by contrasting them with our exact solutions.

Lattice Boltzmann computation of creeping fluid flow in roll-coating applications

NASA Astrophysics Data System (ADS)

Rajan, Isac; Kesana, Balashanker; Perumal, D. Arumuga

2018-04-01

Lattice Boltzmann Method (LBM) has advanced as a class of Computational Fluid Dynamics (CFD) methods used to solve complex fluid systems and heat transfer problems. It has ever-increasingly attracted the interest of researchers in computational physics to solve challenging problems of industrial and academic importance. In this current study, LBM is applied to simulate the creeping fluid flow phenomena commonly encountered in manufacturing technologies. In particular, we apply this novel method to simulate the fluid flow phenomena associated with the "meniscus roll coating" application. This prevalent industrial problem encountered in polymer processing and thin film coating applications is modelled as standard lid-driven cavity problem to which creeping flow analysis is applied. This incompressible viscous flow problem is studied in various speed ratios, the ratio of upper to lower lid speed in two different configurations of lid movement - parallel and anti-parallel wall motion. The flow exhibits interesting patterns which will help in design of roll coaters.

Motivating Computer Engineering Freshmen through Mathematical and Logical Puzzles

ERIC Educational Resources Information Center

Parhami, B.

2009-01-01

As in many other fields of science and technology, college students in computer engineering do not come into full contact with the key ideas and challenges of their chosen discipline until the third year of their studies. This situation poses a problem in terms of keeping the students motivated as they labor through their foundational, basic…

Finally, a Good Way to Teach City Government! A Review of the Computer Simulation Game "SimCity."

ERIC Educational Resources Information Center

Pahl, Ronald H.

1991-01-01

Offers an evaluation of the computer simulation game "SimCity." Suggests possible uses for the game at different age and experience levels. Recommends the program as challenging, humorous, and an excellent aid in teaching about the problems and solutions facing city government. Explains that students serve as public officials. (DK)

Integrating IS Curriculum Knowledge through a Cluster-Computing Project--A Successful Experiment

ERIC Educational Resources Information Center

Kitchens, Fred L.; Sharma, Sushil K.; Harris, Thomas

2004-01-01

MIS curricula in business schools are challenged to provide MIS courses that give students a strong practical understanding of the basic technologies, while also providing enough hands-on experience to solve real life problems. As an experimental capstone MIS course, the authors developed a cluster-computing project to expose business students to…

Navigating Turn-Taking and Conversational Repair in an Online Synchronous Course

ERIC Educational Resources Information Center

Earnshaw, Yvonne

2017-01-01

In face-to-face conversations, speaker transitions (or hand-offs) are typically seamless. In computer-mediated communication settings, speaker hand-offs can be a bit more challenging. This paper presents the results of a study of audio communication problems that occur in an online synchronous course, and how, and by whom, those problems are…

A Framework for Representing and Jointly Reasoning over Linguistic and Non-Linguistic Knowledge

ERIC Educational Resources Information Center

Murugesan, Arthi

2009-01-01

Natural language poses several challenges to developing computational systems for modeling it. Natural language is not a precise problem but is rather ridden with a number of uncertainties in the form of either alternate words or interpretations. Furthermore, natural language is a generative system where the problem size is potentially infinite.…

When cloud computing meets bioinformatics: a review.

PubMed

Zhou, Shuigeng; Liao, Ruiqi; Guan, Jihong

2013-10-01

In the past decades, with the rapid development of high-throughput technologies, biology research has generated an unprecedented amount of data. In order to store and process such a great amount of data, cloud computing and MapReduce were applied to many fields of bioinformatics. In this paper, we first introduce the basic concepts of cloud computing and MapReduce, and their applications in bioinformatics. We then highlight some problems challenging the applications of cloud computing and MapReduce to bioinformatics. Finally, we give a brief guideline for using cloud computing in biology research.

Dynamically allocating sets of fine-grained processors to running computations

NASA Technical Reports Server (NTRS)

Middleton, David

1988-01-01

Researchers explore an approach to using general purpose parallel computers which involves mapping hardware resources onto computations instead of mapping computations onto hardware. Problems such as processor allocation, task scheduling and load balancing, which have traditionally proven to be challenging, change significantly under this approach and may become amenable to new attacks. Researchers describe the implementation of this approach used by the FFP Machine whose computation and communication resources are repeatedly partitioned into disjoint groups that match the needs of available tasks from moment to moment. Several consequences of this system are examined.

Information technology challenges of biodiversity and ecosystems informatics

USGS Publications Warehouse

Schnase, J.L.; Cushing, J.; Frame, M.; Frondorf, A.; Landis, E.; Maier, D.; Silberschatz, A.

2003-01-01

Computer scientists, biologists, and natural resource managers recently met to examine the prospects for advancing computer science and information technology research by focusing on the complex and often-unique challenges found in the biodiversity and ecosystem domain. The workshop and its final report reveal that the biodiversity and ecosystem sciences are fundamentally information sciences and often address problems having distinctive attributes of scale and socio-technical complexity. The paper provides an overview of the emerging field of biodiversity and ecosystem informatics and demonstrates how the demands of biodiversity and ecosystem research can advance our understanding and use of information technologies.

Asymptotic analysis of the narrow escape problem in dendritic spine shaped domain: three dimensions

NASA Astrophysics Data System (ADS)

Li, Xiaofei; Lee, Hyundae; Wang, Yuliang

2017-08-01

This paper deals with the three-dimensional narrow escape problem in a dendritic spine shaped domain, which is composed of a relatively big head and a thin neck. The narrow escape problem is to compute the mean first passage time of Brownian particles traveling from inside the head to the end of the neck. The original model is to solve a mixed Dirichlet-Neumann boundary value problem for the Poisson equation in the composite domain, and is computationally challenging. In this paper we seek to transfer the original problem to a mixed Robin-Neumann boundary value problem by dropping the thin neck part, and rigorously derive the asymptotic expansion of the mean first passage time with high order terms. This study is a nontrivial three-dimensional generalization of the work in Li (2014 J. Phys. A: Math. Theor. 47 505202), where a two-dimensional analogue domain is considered.

Modeling biological problems in computer science: a case study in genome assembly.

PubMed

Medvedev, Paul

2018-01-30

As computer scientists working in bioinformatics/computational biology, we often face the challenge of coming up with an algorithm to answer a biological question. This occurs in many areas, such as variant calling, alignment and assembly. In this tutorial, we use the example of the genome assembly problem to demonstrate how to go from a question in the biological realm to a solution in the computer science realm. We show the modeling process step-by-step, including all the intermediate failed attempts. Please note this is not an introduction to how genome assembly algorithms work and, if treated as such, would be incomplete and unnecessarily long-winded. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Change Detection of Mobile LIDAR Data Using Cloud Computing

NASA Astrophysics Data System (ADS)

Liu, Kun; Boehm, Jan; Alis, Christian

2016-06-01

Change detection has long been a challenging problem although a lot of research has been conducted in different fields such as remote sensing and photogrammetry, computer vision, and robotics. In this paper, we blend voxel grid and Apache Spark together to propose an efficient method to address the problem in the context of big data. Voxel grid is a regular geometry representation consisting of the voxels with the same size, which fairly suites parallel computation. Apache Spark is a popular distributed parallel computing platform which allows fault tolerance and memory cache. These features can significantly enhance the performance of Apache Spark and results in an efficient and robust implementation. In our experiments, both synthetic and real point cloud data are employed to demonstrate the quality of our method.

High End Computing Technologies for Earth Science Applications: Trends, Challenges, and Innovations

NASA Technical Reports Server (NTRS)

Parks, John (Technical Monitor); Biswas, Rupak; Yan, Jerry C.; Brooks, Walter F.; Sterling, Thomas L.

2003-01-01

Earth science applications of the future will stress the capabilities of even the highest performance supercomputers in the areas of raw compute power, mass storage management, and software environments. These NASA mission critical problems demand usable multi-petaflops and exabyte-scale systems to fully realize their science goals. With an exciting vision of the technologies needed, NASA has established a comprehensive program of advanced research in computer architecture, software tools, and device technology to ensure that, in partnership with US industry, it can meet these demanding requirements with reliable, cost effective, and usable ultra-scale systems. NASA will exploit, explore, and influence emerging high end computing architectures and technologies to accelerate the next generation of engineering, operations, and discovery processes for NASA Enterprises. This article captures this vision and describes the concepts, accomplishments, and the potential payoff of the key thrusts that will help meet the computational challenges in Earth science applications.

The services-oriented architecture: ecosystem services as a framework for diagnosing change in social ecological systems

Treesearch

Philip A. Loring; F. Stuart Chapin; S. Craig Gerlach

2008-01-01

Computational thinking (CT) is a way to solve problems and understand complex systems that draws on concepts fundamental to computer science and is well suited to the challenges that face researchers of complex, linked social-ecological systems. This paper explores CT's usefulness to sustainability science through the application of the services-oriented...

Immersive, Interactive, Web-Enabled Computer Simulation as a Trigger for Learning: The next Generation of Problem-Based Learning in Educational Leadership

ERIC Educational Resources Information Center

Mann, Dale; Reardon, R. M.; Becker, J. D.; Shakeshaft, C.; Bacon, Nicholas

2011-01-01

This paper describes the use of advanced computer technology in an innovative educational leadership program. This program integrates full-motion video scenarios that simulate the leadership challenges typically faced by principals over the course of a full school year. These scenarios require decisions that are then coupled to consequences and…

Computers in English and the Language Arts: The Challenge of Teacher Education.

ERIC Educational Resources Information Center

Selfe, Cynthia L., Ed.; And Others

This handbook combines the experience and advice of pioneers in computer-enhanced instruction in colleges and high schools across the United States and documents the scope of the problem of teacher access to training by describing the results of a survey of teacher educators conducted in November 1985. The first section of the book describes 12…

Artificial Intelligence Methods: Challenge in Computer Based Polymer Design

NASA Astrophysics Data System (ADS)

Rusu, Teodora; Pinteala, Mariana; Cartwright, Hugh

2009-08-01

This paper deals with the use of Artificial Intelligence Methods (AI) in the design of new molecules possessing desired physical, chemical and biological properties. This is an important and difficult problem in the chemical, material and pharmaceutical industries. Traditional methods involve a laborious and expensive trial-and-error procedure, but computer-assisted approaches offer many advantages in the automation of molecular design.

Cosmological neutrino simulations at extreme scale

DOE PAGES

Emberson, J. D.; Yu, Hao-Ran; Inman, Derek; ...

2017-08-01

Constraining neutrino mass remains an elusive challenge in modern physics. Precision measurements are expected from several upcoming cosmological probes of large-scale structure. Achieving this goal relies on an equal level of precision from theoretical predictions of neutrino clustering. Numerical simulations of the non-linear evolution of cold dark matter and neutrinos play a pivotal role in this process. We incorporate neutrinos into the cosmological N-body code CUBEP3M and discuss the challenges associated with pushing to the extreme scales demanded by the neutrino problem. We highlight code optimizations made to exploit modern high performance computing architectures and present a novel method ofmore » data compression that reduces the phase-space particle footprint from 24 bytes in single precision to roughly 9 bytes. We scale the neutrino problem to the Tianhe-2 supercomputer and provide details of our production run, named TianNu, which uses 86% of the machine (13,824 compute nodes). With a total of 2.97 trillion particles, TianNu is currently the world’s largest cosmological N-body simulation and improves upon previous neutrino simulations by two orders of magnitude in scale. We finish with a discussion of the unanticipated computational challenges that were encountered during the TianNu runtime.« less

An algorithmic framework for multiobjective optimization.

PubMed

Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

An Algorithmic Framework for Multiobjective Optimization

PubMed Central

Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

High Performance Biocomputation

DTIC Science & Technology

2005-03-01

in some other fields (e.g. computational hydrodynamics, lattice quantum chroniodynamics, etc.) but appears wholly inappropriate here as pointed out...restrict the overall conformational space by putting the system on a lattice . These have been used to great effect to study folding kinetics. These...many important problems to be worked on, not a single unique challenge (contrast this to QCD , for example). " almost all problems require significant

Bohnenblust-Hille inequalities: analytical and computational aspects.

PubMed

Cavalcante, Wasthenny V; Pellegrino, Daniel M

2018-02-01

The Bohnenblust-Hille polynomial and multilinear inequalities were proved in 1931 and the determination of exact values of their constants is still an open and challenging problem, pursued by various authors. The present paper briefly surveys recent attempts to attack/solve this problem; it also presents new results, like connections with classical results of the linear theory of absolutely summing operators, and new perspectives.

Experiences Teaching a Software Aided Mathematics Course for a General University Audience.

ERIC Educational Resources Information Center

McGivney, Raymond J., Jr.

1990-01-01

Described is a nonmajor mathematics course taught using computers and lab experiments. Included are the challenge, solution, description of the first class, problems, successes, the syllabus, student comments, and the conclusion. (KR)

Guidelines for Network Security in the Learning Environment.

ERIC Educational Resources Information Center

Littman, Marlyn Kemper

1996-01-01

Explores security challenges and practical approaches to safeguarding school networks against invasion. Highlights include security problems; computer viruses; privacy assaults; Internet invasions; building a security policy; authentication; passwords; encryption; firewalls; and acceptable use policies. (Author/LRW)

Rational approximations to rational models: alternative algorithms for category learning.

PubMed

Sanborn, Adam N; Griffiths, Thomas L; Navarro, Daniel J

2010-10-01

Rational models of cognition typically consider the abstract computational problems posed by the environment, assuming that people are capable of optimally solving those problems. This differs from more traditional formal models of cognition, which focus on the psychological processes responsible for behavior. A basic challenge for rational models is thus explaining how optimal solutions can be approximated by psychological processes. We outline a general strategy for answering this question, namely to explore the psychological plausibility of approximation algorithms developed in computer science and statistics. In particular, we argue that Monte Carlo methods provide a source of rational process models that connect optimal solutions to psychological processes. We support this argument through a detailed example, applying this approach to Anderson's (1990, 1991) rational model of categorization (RMC), which involves a particularly challenging computational problem. Drawing on a connection between the RMC and ideas from nonparametric Bayesian statistics, we propose 2 alternative algorithms for approximate inference in this model. The algorithms we consider include Gibbs sampling, a procedure appropriate when all stimuli are presented simultaneously, and particle filters, which sequentially approximate the posterior distribution with a small number of samples that are updated as new data become available. Applying these algorithms to several existing datasets shows that a particle filter with a single particle provides a good description of human inferences.

Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

PubMed Central

El-Baz, Ayman; Beache, Garth M.; Gimel'farb, Georgy; Suzuki, Kenji; Okada, Kazunori; Elnakib, Ahmed; Soliman, Ahmed; Abdollahi, Behnoush

2013-01-01

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems. PMID:23431282

DOE Office of Scientific and Technical Information (OSTI.GOV)

None, None

The Second SIAM Conference on Computational Science and Engineering was held in San Diego from February 10-12, 2003. Total conference attendance was 553. This is a 23% increase in attendance over the first conference. The focus of this conference was to draw attention to the tremendous range of major computational efforts on large problems in science and engineering, to promote the interdisciplinary culture required to meet these large-scale challenges, and to encourage the training of the next generation of computational scientists. Computational Science & Engineering (CS&E) is now widely accepted, along with theory and experiment, as a crucial third modemore » of scientific investigation and engineering design. Aerospace, automotive, biological, chemical, semiconductor, and other industrial sectors now rely on simulation for technical decision support. For federal agencies also, CS&E has become an essential support for decisions on resources, transportation, and defense. CS&E is, by nature, interdisciplinary. It grows out of physical applications and it depends on computer architecture, but at its heart are powerful numerical algorithms and sophisticated computer science techniques. From an applied mathematics perspective, much of CS&E has involved analysis, but the future surely includes optimization and design, especially in the presence of uncertainty. Another mathematical frontier is the assimilation of very large data sets through such techniques as adaptive multi-resolution, automated feature search, and low-dimensional parameterization. The themes of the 2003 conference included, but were not limited to: Advanced Discretization Methods; Computational Biology and Bioinformatics; Computational Chemistry and Chemical Engineering; Computational Earth and Atmospheric Sciences; Computational Electromagnetics; Computational Fluid Dynamics; Computational Medicine and Bioengineering; Computational Physics and Astrophysics; Computational Solid Mechanics and Materials; CS&E Education; Meshing and Adaptivity; Multiscale and Multiphysics Problems; Numerical Algorithms for CS&E; Discrete and Combinatorial Algorithms for CS&E; Inverse Problems; Optimal Design, Optimal Control, and Inverse Problems; Parallel and Distributed Computing; Problem-Solving Environments; Software and Wddleware Systems; Uncertainty Estimation and Sensitivity Analysis; and Visualization and Computer Graphics.« less

Solution of large nonlinear quasistatic structural mechanics problems on distributed-memory multiprocessor computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blanford, M.

1997-12-31

Most commercially-available quasistatic finite element programs assemble element stiffnesses into a global stiffness matrix, then use a direct linear equation solver to obtain nodal displacements. However, for large problems (greater than a few hundred thousand degrees of freedom), the memory size and computation time required for this approach becomes prohibitive. Moreover, direct solution does not lend itself to the parallel processing needed for today`s multiprocessor systems. This talk gives an overview of the iterative solution strategy of JAS3D, the nonlinear large-deformation quasistatic finite element program. Because its architecture is derived from an explicit transient-dynamics code, it does not ever assemblemore » a global stiffness matrix. The author describes the approach he used to implement the solver on multiprocessor computers, and shows examples of problems run on hundreds of processors and more than a million degrees of freedom. Finally, he describes some of the work he is presently doing to address the challenges of iterative convergence for ill-conditioned problems.« less

Combinatorial and Algorithmic Rigidity: Beyond Two Dimensions

DTIC Science & Technology

2012-12-01

problem. Manuscript, 2010. [35] G. Panina and I. Streinu. Flattening single-vertex origami : the non- expansive case. Computational Geometry : Theory and...in 2008, under the DARPA solicitation “Mathemat- ical Challenges, BAA 07-68”. It addressed Mathematical Challenge Ten: Al- gorithmic Origami and...a number of optimal algorithms and provided critical complexity analysis. The topic of algorithmic origami was successfully engaged from the same

GPU-based High-Performance Computing for Radiation Therapy

PubMed Central

Jia, Xun; Ziegenhein, Peter; Jiang, Steve B.

2014-01-01

Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. Graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past a few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of studies have been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this article, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented. PMID:24486639

Computational modelling of cellular level metabolism

NASA Astrophysics Data System (ADS)

Calvetti, D.; Heino, J.; Somersalo, E.

2008-07-01

The steady and stationary state inverse problems consist of estimating the reaction and transport fluxes, blood concentrations and possibly the rates of change of some of the concentrations based on data which are often scarce noisy and sampled over a population. The Bayesian framework provides a natural setting for the solution of this inverse problem, because a priori knowledge about the system itself and the unknown reaction fluxes and transport rates can compensate for the insufficiency of measured data, provided that the computational costs do not become prohibitive. This article identifies the computational challenges which have to be met when analyzing the steady and stationary states of multicompartment model for cellular metabolism and suggest stable and efficient ways to handle the computations. The outline of a computational tool based on the Bayesian paradigm for the simulation and analysis of complex cellular metabolic systems is also presented.

Parametric Study of a YAV-8B Harrier in Ground Effect Using Time-Dependent Navier-Stokes Computations

NASA Technical Reports Server (NTRS)

Shishir, Pandya; Chaderjian, Neal; Ahmad, Jsaim; Kwak, Dochan (Technical Monitor)

2001-01-01

Flow simulations using the time-dependent Navier-Stokes equations remain a challenge for several reasons. Principal among them are the difficulty to accurately model complex flows, and the time needed to perform the computations. A parametric study of such complex problems is not considered practical due to the large cost associated with computing many time-dependent solutions. The computation time for each solution must be reduced in order to make a parametric study possible. With successful reduction of computation time, the issue of accuracy, and appropriateness of turbulence models will become more tractable.

Surrogate assisted multidisciplinary design optimization for an all-electric GEO satellite

NASA Astrophysics Data System (ADS)

Shi, Renhe; Liu, Li; Long, Teng; Liu, Jian; Yuan, Bin

2017-09-01

State-of-the-art all-electric geostationary earth orbit (GEO) satellites use electric thrusters to execute all propulsive duties, which significantly differ from the traditional all-chemical ones in orbit-raising, station-keeping, radiation damage protection, and power budget, etc. Design optimization task of an all-electric GEO satellite is therefore a complex multidisciplinary design optimization (MDO) problem involving unique design considerations. However, solving the all-electric GEO satellite MDO problem faces big challenges in disciplinary modeling techniques and efficient optimization strategy. To address these challenges, we presents a surrogate assisted MDO framework consisting of several modules, i.e., MDO problem definition, multidisciplinary modeling, multidisciplinary analysis (MDA), and surrogate assisted optimizer. Based on the proposed framework, the all-electric GEO satellite MDO problem is formulated to minimize the total mass of the satellite system under a number of practical constraints. Then considerable efforts are spent on multidisciplinary modeling involving geosynchronous transfer, GEO station-keeping, power, thermal control, attitude control, and structure disciplines. Since orbit dynamics models and finite element structural model are computationally expensive, an adaptive response surface surrogate based optimizer is incorporated in the proposed framework to solve the satellite MDO problem with moderate computational cost, where a response surface surrogate is gradually refined to represent the computationally expensive MDA process. After optimization, the total mass of the studied GEO satellite is decreased by 185.3 kg (i.e., 7.3% of the total mass). Finally, the optimal design is further discussed to demonstrate the effectiveness of our proposed framework to cope with the all-electric GEO satellite system design optimization problems. This proposed surrogate assisted MDO framework can also provide valuable references for other all-electric spacecraft system design.

Integrating CFD, CAA, and Experiments Towards Benchmark Datasets for Airframe Noise Problems

NASA Technical Reports Server (NTRS)

Choudhari, Meelan M.; Yamamoto, Kazuomi

2012-01-01

Airframe noise corresponds to the acoustic radiation due to turbulent flow in the vicinity of airframe components such as high-lift devices and landing gears. The combination of geometric complexity, high Reynolds number turbulence, multiple regions of separation, and a strong coupling with adjacent physical components makes the problem of airframe noise highly challenging. Since 2010, the American Institute of Aeronautics and Astronautics has organized an ongoing series of workshops devoted to Benchmark Problems for Airframe Noise Computations (BANC). The BANC workshops are aimed at enabling a systematic progress in the understanding and high-fidelity predictions of airframe noise via collaborative investigations that integrate state of the art computational fluid dynamics, computational aeroacoustics, and in depth, holistic, and multifacility measurements targeting a selected set of canonical yet realistic configurations. This paper provides a brief summary of the BANC effort, including its technical objectives, strategy, and selective outcomes thus far.

The potential benefits of photonics in the computing platform

NASA Astrophysics Data System (ADS)

Bautista, Jerry

2005-03-01

The increase in computational requirements for real-time image processing, complex computational fluid dynamics, very large scale data mining in the health industry/Internet, and predictive models for financial markets are driving computer architects to consider new paradigms that rely upon very high speed interconnects within and between computing elements. Further challenges result from reduced power requirements, reduced transmission latency, and greater interconnect density. Optical interconnects may solve many of these problems with the added benefit extended reach. In addition, photonic interconnects provide relative EMI immunity which is becoming an increasing issue with a greater dependence on wireless connectivity. However, to be truly functional, the optical interconnect mesh should be able to support arbitration, addressing, etc. completely in the optical domain with a BER that is more stringent than "traditional" communication requirements. Outlined are challenges in the advanced computing environment, some possible optical architectures and relevant platform technologies, as well roughly sizing these opportunities which are quite large relative to the more "traditional" optical markets.

Learner Attrition in an Advanced Vocational Online Training: The Role of Computer Attitude, Computer Anxiety, and Online Learning Experience

ERIC Educational Resources Information Center

Stiller, Klaus D.; Köster, Annamaria

2016-01-01

Online learning has gained importance in education over the last 20 years, but the well-known problem of high dropout rates still persists. According to the multi-dimensional learning tasks model, the cognitive (over)load of learners is essential to attrition when dealing with five challenges (e.g. technology, user interface) of an online training…

Science on Sequoia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bertsch, Adam; Draeger, Erik; Richards, David

2017-01-12

With Sequoia at Lawrence Livermore National Laboratory, researchers explore grand challenging problems and are generating results at scales never before achieved. Sequoia is the first computer to have more than one million processors and is one of the fastest supercomputers in the world.

Computational Molecular Modeling for Evaluating the Toxicity of Environmental Chemicals: Prioritizing Bioassay Requirements

EPA Science Inventory

This commentary provides an overview of the challenges that arise from applying molecular modeling tools developed and commonly used for pharmaceutical discovery to the problem of predicting the potential toxicities of environmental chemicals.

A Programming Framework for Scientific Applications on CPU-GPU Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Owens, John

2013-03-24

At a high level, my research interests center around designing, programming, and evaluating computer systems that use new approaches to solve interesting problems. The rapid change of technology allows a variety of different architectural approaches to computationally difficult problems, and a constantly shifting set of constraints and trends makes the solutions to these problems both challenging and interesting. One of the most important recent trends in computing has been a move to commodity parallel architectures. This sea change is motivated by the industry’s inability to continue to profitably increase performance on a single processor and instead to move to multiplemore » parallel processors. In the period of review, my most significant work has been leading a research group looking at the use of the graphics processing unit (GPU) as a general-purpose processor. GPUs can potentially deliver superior performance on a broad range of problems than their CPU counterparts, but effectively mapping complex applications to a parallel programming model with an emerging programming environment is a significant and important research problem.« less

Computational intelligence approaches for pattern discovery in biological systems.

PubMed

Fogel, Gary B

2008-07-01

Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.

Crashworthiness simulations with DYNA3D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schauer, D.A.; Hoover, C.G.; Kay, G.J.

1996-04-01

Current progress in parallel algorithm research and applications in vehicle crash simulation is described for the explicit, finite element algorithms in DYNA3D. Problem partitioning methods and parallel algorithms for contact at material interfaces are the two challenging algorithm research problems that are addressed. Two prototype parallel contact algorithms have been developed for treating the cases of local and arbitrary contact. Demonstration problems for local contact are crashworthiness simulations with 222 locally defined contact surfaces and a vehicle/barrier collision modeled with arbitrary contact. A simulation of crash tests conducted for a vehicle impacting a U-channel small sign post embedded in soilmore » has been run on both the serial and parallel versions of DYNA3D. A significant reduction in computational time has been observed when running these problems on the parallel version. However, to achieve maximum efficiency, complex problems must be appropriately partitioned, especially when contact dominates the computation.« less

Computational biology in the cloud: methods and new insights from computing at scale.

PubMed

Kasson, Peter M

2013-01-01

The past few years have seen both explosions in the size of biological data sets and the proliferation of new, highly flexible on-demand computing capabilities. The sheer amount of information available from genomic and metagenomic sequencing, high-throughput proteomics, experimental and simulation datasets on molecular structure and dynamics affords an opportunity for greatly expanded insight, but it creates new challenges of scale for computation, storage, and interpretation of petascale data. Cloud computing resources have the potential to help solve these problems by offering a utility model of computing and storage: near-unlimited capacity, the ability to burst usage, and cheap and flexible payment models. Effective use of cloud computing on large biological datasets requires dealing with non-trivial problems of scale and robustness, since performance-limiting factors can change substantially when a dataset grows by a factor of 10,000 or more. New computing paradigms are thus often needed. The use of cloud platforms also creates new opportunities to share data, reduce duplication, and to provide easy reproducibility by making the datasets and computational methods easily available.

Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists

PubMed Central

Visser, Marco D.; McMahon, Sean M.; Merow, Cory; Dixon, Philip M.; Record, Sydne; Jongejans, Eelke

2015-01-01

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research. PMID:25811842

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

The Computing and Communications (C) Division is responsible for the Laboratory's Integrated Computing Network (ICN) as well as Laboratory-wide communications. Our computing network, used by 8,000 people distributed throughout the nation, constitutes one of the most powerful scientific computing facilities in the world. In addition to the stable production environment of the ICN, we have taken a leadership role in high-performance computing and have established the Advanced Computing Laboratory (ACL), the site of research on experimental, massively parallel computers; high-speed communication networks; distributed computing; and a broad variety of advanced applications. The computational resources available in the ACL are ofmore » the type needed to solve problems critical to national needs, the so-called Grand Challenge'' problems. The purpose of this publication is to inform our clients of our strategic and operating plans in these important areas. We review major accomplishments since late 1990 and describe our strategic planning goals and specific projects that will guide our operations over the next few years. Our mission statement, planning considerations, and management policies and practices are also included.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

The Computing and Communications (C) Division is responsible for the Laboratory`s Integrated Computing Network (ICN) as well as Laboratory-wide communications. Our computing network, used by 8,000 people distributed throughout the nation, constitutes one of the most powerful scientific computing facilities in the world. In addition to the stable production environment of the ICN, we have taken a leadership role in high-performance computing and have established the Advanced Computing Laboratory (ACL), the site of research on experimental, massively parallel computers; high-speed communication networks; distributed computing; and a broad variety of advanced applications. The computational resources available in the ACL are ofmore » the type needed to solve problems critical to national needs, the so-called ``Grand Challenge`` problems. The purpose of this publication is to inform our clients of our strategic and operating plans in these important areas. We review major accomplishments since late 1990 and describe our strategic planning goals and specific projects that will guide our operations over the next few years. Our mission statement, planning considerations, and management policies and practices are also included.« less

Learning the Digital Way: Evaluating Progress, Tackling Challenges. Technology Counts, 2015. Education Week. Volume 34, Number 35

ERIC Educational Resources Information Center

Edwards, Virginia B., Ed.

2015-01-01

Lofty ed-tech visions are always tempered by reality. Unexpected problems that arose during a launch of a 1-to-1 computing program and ambitious digital curriculum initiative in Los Angeles led to the dialing back of the effort. Financial, legal, and managerial repercussions continue to swirl in the wake. These problems should not prevent schools…

An efficient dynamic load balancing algorithm

NASA Astrophysics Data System (ADS)

Lagaros, Nikos D.

2014-01-01

In engineering problems, randomness and uncertainties are inherent. Robust design procedures, formulated in the framework of multi-objective optimization, have been proposed in order to take into account sources of randomness and uncertainty. These design procedures require orders of magnitude more computational effort than conventional analysis or optimum design processes since a very large number of finite element analyses is required to be dealt. It is therefore an imperative need to exploit the capabilities of computing resources in order to deal with this kind of problems. In particular, parallel computing can be implemented at the level of metaheuristic optimization, by exploiting the physical parallelization feature of the nondominated sorting evolution strategies method, as well as at the level of repeated structural analyses required for assessing the behavioural constraints and for calculating the objective functions. In this study an efficient dynamic load balancing algorithm for optimum exploitation of available computing resources is proposed and, without loss of generality, is applied for computing the desired Pareto front. In such problems the computation of the complete Pareto front with feasible designs only, constitutes a very challenging task. The proposed algorithm achieves linear speedup factors and almost 100% speedup factor values with reference to the sequential procedure.

Separation of ion types in tandem mass spectrometry data interpretation -- a graph-theoretic approach.

PubMed

Yan, Bo; Pan, Chongle; Olman, Victor N; Hettich, Robert L; Xu, Ying

2004-01-01

Mass spectrometry is one of the most popular analytical techniques for identification of individual proteins in a protein mixture, one of the basic problems in proteomics. It identifies a protein through identifying its unique mass spectral pattern. While the problem is theoretically solvable, it remains a challenging problem computationally. One of the key challenges comes from the difficulty in distinguishing the N- and C-terminus ions, mostly b- and y-ions respectively. In this paper, we present a graph algorithm for solving the problem of separating bfrom y-ions in a set of mass spectra. We represent each spectral peak as a node and consider two types of edges: a type-1 edge connects two peaks possibly of the same ion types and a type-2 edge connects two peaks possibly of different ion types, predicted based on local information. The ion-separation problem is then formulated and solved as a graph partition problem, which is to partition the graph into three subgraphs, namely b-, y-ions and others respectively, so to maximize the total weight of type-1 edges while minimizing the total weight of type-2 edges within each subgraph. We have developed a dynamic programming algorithm for rigorously solving this graph partition problem and implemented it as a computer program PRIME. We have tested PRIME on 18 data sets of high accurate FT-ICR tandem mass spectra and found that it achieved ~90% accuracy for separation of b- and y- ions.

Quo vadis: Hydrologic inverse analyses using high-performance computing and a D-Wave quantum annealer

NASA Astrophysics Data System (ADS)

O'Malley, D.; Vesselinov, V. V.

2017-12-01

Classical microprocessors have had a dramatic impact on hydrology for decades, due largely to the exponential growth in computing power predicted by Moore's law. However, this growth is not expected to continue indefinitely and has already begun to slow. Quantum computing is an emerging alternative to classical microprocessors. Here, we demonstrated cutting edge inverse model analyses utilizing some of the best available resources in both worlds: high-performance classical computing and a D-Wave quantum annealer. The classical high-performance computing resources are utilized to build an advanced numerical model that assimilates data from O(10^5) observations, including water levels, drawdowns, and contaminant concentrations. The developed model accurately reproduces the hydrologic conditions at a Los Alamos National Laboratory contamination site, and can be leveraged to inform decision-making about site remediation. We demonstrate the use of a D-Wave 2X quantum annealer to solve hydrologic inverse problems. This work can be seen as an early step in quantum-computational hydrology. We compare and contrast our results with an early inverse approach in classical-computational hydrology that is comparable to the approach we use with quantum annealing. Our results show that quantum annealing can be useful for identifying regions of high and low permeability within an aquifer. While the problems we consider are small-scale compared to the problems that can be solved with modern classical computers, they are large compared to the problems that could be solved with early classical CPUs. Further, the binary nature of the high/low permeability problem makes it well-suited to quantum annealing, but challenging for classical computers.

Scaling up to address data science challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wendelberger, Joanne R.

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

A survey on hair modeling: styling, simulation, and rendering.

PubMed

Ward, Kelly; Bertails, Florence; Kim, Tae-Yong; Marschner, Stephen R; Cani, Marie-Paule; Lin, Ming C

2007-01-01

Realistic hair modeling is a fundamental part of creating virtual humans in computer graphics. This paper surveys the state of the art in the major topics of hair modeling: hairstyling, hair simulation, and hair rendering. Because of the difficult, often unsolved problems that arise in all these areas, a broad diversity of approaches are used, each with strengths that make it appropriate for particular applications. We discuss each of these major topics in turn, presenting the unique challenges facing each area and describing solutions that have been presented over the years to handle these complex issues. Finally, we outline some of the remaining computational challenges in hair modeling.

Scaling up to address data science challenges

DOE PAGES

Wendelberger, Joanne R.

2017-04-27

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Cryptography and the Internet: lessons and challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCurley, K.S.

1996-12-31

The popularization of the Internet has brought fundamental changes to the world, because it allows a universal method of communication between computers. This carries enormous benefits with it, but also raises many security considerations. Cryptography is a fundamental technology used to provide security of computer networks, and there is currently a widespread engineering effort to incorporate cryptography into various aspects of the Internet. The system-level engineering required to provide security services for the Internet carries some important lessons for researchers whose study is focused on narrowly defined problems. It also offers challenges to the cryptographic research community by raising newmore » questions not adequately addressed by the existing body of knowledge. This paper attempts to summarize some of these lessons and challenges for the cryptographic research community.« less

Poised for the Millennium.

ERIC Educational Resources Information Center

Agron, Joe, Ed.

1999-01-01

Presents advice from five school administrators on how schools are meeting facility and business challenges in the new millennium. Issues discussed concern power needs, the Y2K computer problem, the explosion of new educational technology, school security, educational finance, and building deterioration. (GR)

Fault tolerance in computational grids: perspectives, challenges, and issues.

PubMed

Haider, Sajjad; Nazir, Babar

2016-01-01

Computational grids are established with the intention of providing shared access to hardware and software based resources with special reference to increased computational capabilities. Fault tolerance is one of the most important issues faced by the computational grids. The main contribution of this survey is the creation of an extended classification of problems that incur in the computational grid environments. The proposed classification will help researchers, developers, and maintainers of grids to understand the types of issues to be anticipated. Moreover, different types of problems, such as omission, interaction, and timing related have been identified that need to be handled on various layers of the computational grid. In this survey, an analysis and examination is also performed pertaining to the fault tolerance and fault detection mechanisms. Our conclusion is that a dependable and reliable grid can only be established when more emphasis is on fault identification. Moreover, our survey reveals that adaptive and intelligent fault identification, and tolerance techniques can improve the dependability of grid working environments.

Testing and Validation of Computational Methods for Mass Spectrometry.

PubMed

Gatto, Laurent; Hansen, Kasper D; Hoopmann, Michael R; Hermjakob, Henning; Kohlbacher, Oliver; Beyer, Andreas

2016-03-04

High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets ( http://compms.org/RefData ) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

Multiphysics Simulations: Challenges and Opportunities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keyes, David; McInnes, Lois C.; Woodward, Carol

2013-02-12

We consider multiphysics applications from algorithmic and architectural perspectives, where ‘‘algorithmic’’ includes both mathematical analysis and computational complexity, and ‘‘architectural’’ includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose somemore » commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.« less

Addressing the computational cost of large EIT solutions.

PubMed

Boyle, Alistair; Borsic, Andrea; Adler, Andy

2012-05-01

Electrical impedance tomography (EIT) is a soft field tomography modality based on the application of electric current to a body and measurement of voltages through electrodes at the boundary. The interior conductivity is reconstructed on a discrete representation of the domain using a finite-element method (FEM) mesh and a parametrization of that domain. The reconstruction requires a sequence of numerically intensive calculations. There is strong interest in reducing the cost of these calculations. An improvement in the compute time for current problems would encourage further exploration of computationally challenging problems such as the incorporation of time series data, wide-spread adoption of three-dimensional simulations and correlation of other modalities such as CT and ultrasound. Multicore processors offer an opportunity to reduce EIT computation times but may require some restructuring of the underlying algorithms to maximize the use of available resources. This work profiles two EIT software packages (EIDORS and NDRM) to experimentally determine where the computational costs arise in EIT as problems scale. Sparse matrix solvers, a key component for the FEM forward problem and sensitivity estimates in the inverse problem, are shown to take a considerable portion of the total compute time in these packages. A sparse matrix solver performance measurement tool, Meagre-Crowd, is developed to interface with a variety of solvers and compare their performance over a range of two- and three-dimensional problems of increasing node density. Results show that distributed sparse matrix solvers that operate on multiple cores are advantageous up to a limit that increases as the node density increases. We recommend a selection procedure to find a solver and hardware arrangement matched to the problem and provide guidance and tools to perform that selection.

Advanced Computational Methods for Security Constrained Financial Transmission Rights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kalsi, Karanjit; Elbert, Stephen T.; Vlachopoulou, Maria

Financial Transmission Rights (FTRs) are financial insurance tools to help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, first an innovative mathematical reformulationmore » of the FTR problem is presented which dramatically improves the computational efficiency of optimization problem. After having re-formulated the problem, a novel non-linear dynamic system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on both standard IEEE test systems and large-scale systems using data from the Western Electricity Coordinating Council (WECC). The performance of the NDS is demonstrated to be comparable and in some cases is shown to outperform the widely used CPLEX algorithms. The proposed formulation and NDS based solver is also easily parallelizable enabling further computational improvement.« less

Infrared stereo calibration for unmanned ground vehicle navigation

NASA Astrophysics Data System (ADS)

Harguess, Josh; Strange, Shawn

2014-06-01

The problem of calibrating two color cameras as a stereo pair has been heavily researched and many off-the-shelf software packages, such as Robot Operating System and OpenCV, include calibration routines that work in most cases. However, the problem of calibrating two infrared (IR) cameras for the purposes of sensor fusion and point could generation is relatively new and many challenges exist. We present a comparison of color camera and IR camera stereo calibration using data from an unmanned ground vehicle. There are two main challenges in IR stereo calibration; the calibration board (material, design, etc.) and the accuracy of calibration pattern detection. We present our analysis of these challenges along with our IR stereo calibration methodology. Finally, we present our results both visually and analytically with computed reprojection errors.

Testing Scientific Software: A Systematic Literature Review.

PubMed

Kanewala, Upulee; Bieman, James M

2014-10-01

Scientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code. This study aims to identify specific challenges, proposed solutions, and unsolved problems faced when testing scientific software. We conducted a systematic literature survey to identify and analyze relevant literature. We identified 62 studies that provided relevant information about testing scientific software. We found that challenges faced when testing scientific software fall into two main categories: (1) testing challenges that occur due to characteristics of scientific software such as oracle problems and (2) testing challenges that occur due to cultural differences between scientists and the software engineering community such as viewing the code and the model that it implements as inseparable entities. In addition, we identified methods to potentially overcome these challenges and their limitations. Finally we describe unsolved challenges and how software engineering researchers and practitioners can help to overcome them. Scientific software presents special challenges for testing. Specifically, cultural differences between scientist developers and software engineers, along with the characteristics of the scientific software make testing more difficult. Existing techniques such as code clone detection can help to improve the testing process. Software engineers should consider special challenges posed by scientific software such as oracle problems when developing testing techniques.

The maps problem and the mapping problem: Two challenges for a cognitive neuroscience of speech and language

PubMed Central

Poeppel, David

2012-01-01

Research on the brain basis of speech and language faces theoretical and empirical challenges. The majority of current research, dominated by imaging, deficit-lesion, and electrophysiological techniques, seeks to identify regions that underpin aspects of language processing such as phonology, syntax, or semantics. The emphasis lies on localization and spatial characterization of function. The first part of the paper deals with a practical challenge that arises in the context of such a research program. This maps problem concerns the extent to which spatial information and localization can satisfy the explanatory needs for perception and cognition. Several areas of investigation exemplify how the neural basis of speech and language is discussed in those terms (regions, streams, hemispheres, networks). The second part of the paper turns to a more troublesome challenge, namely how to formulate the formal links between neurobiology and cognition. This principled problem thus addresses the relation between the primitives of cognition (here speech, language) and neurobiology. Dealing with this mapping problem invites the development of linking hypotheses between the domains. The cognitive sciences provide granular, theoretically motivated claims about the structure of various domains (the ‘cognome’); neurobiology, similarly, provides a list of the available neural structures. However, explanatory connections will require crafting computationally explicit linking hypotheses at the right level of abstraction. For both the practical maps problem and the principled mapping problem, developmental approaches and evidence can play a central role in the resolution. PMID:23017085

Global Software Development with Cloud Platforms

NASA Astrophysics Data System (ADS)

Yara, Pavan; Ramachandran, Ramaseshan; Balasubramanian, Gayathri; Muthuswamy, Karthik; Chandrasekar, Divya

Offshore and outsourced distributed software development models and processes are facing challenges, previously unknown, with respect to computing capacity, bandwidth, storage, security, complexity, reliability, and business uncertainty. Clouds promise to address these challenges by adopting recent advances in virtualization, parallel and distributed systems, utility computing, and software services. In this paper, we envision a cloud-based platform that addresses some of these core problems. We outline a generic cloud architecture, its design and our first implementation results for three cloud forms - a compute cloud, a storage cloud and a cloud-based software service- in the context of global distributed software development (GSD). Our ”compute cloud” provides computational services such as continuous code integration and a compile server farm, ”storage cloud” offers storage (block or file-based) services with an on-line virtual storage service, whereas the on-line virtual labs represent a useful cloud service. We note some of the use cases for clouds in GSD, the lessons learned with our prototypes and identify challenges that must be conquered before realizing the full business benefits. We believe that in the future, software practitioners will focus more on these cloud computing platforms and see clouds as a means to supporting a ecosystem of clients, developers and other key stakeholders.

Model Order Reduction Algorithm for Estimating the Absorption Spectrum

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Beeumen, Roel; Williams-Young, David B.; Kasper, Joseph M.

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comesmore » at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.« less

The 1987 RIACS annual report

NASA Technical Reports Server (NTRS)

1987-01-01

The Research Institute for Advanced Computer Science (RIACS) was established at the NASA Ames Research Center in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 64 universities with graduate programs in the aerospace sciences, under several Cooperative Agreements with NASA. RIACS's goal is to provide preeminent leadership in basic and applied computer science research as partners in support of NASA's goals and missions. In pursuit of this goal, RIACS contributes to several of the grand challenges in science and engineering facing NASA: flying an airplane inside a computer; determining the chemical properties of materials under hostile conditions in the atmospheres of earth and the planets; sending intelligent machines on unmanned space missions; creating a one-world network that makes all scientific resources, including those in space, accessible to all the world's scientists; providing intelligent computational support to all stages of the process of scientific investigation from problem formulation to results dissemination; and developing accurate global models for climatic behavior throughout the world. In working with these challenges, we seek novel architectures, and novel ways to use them, that exploit the potential of parallel and distributed computation and make possible new functions that are beyond the current reach of computing machines. The investigation includes pattern computers as well as the more familiar numeric and symbolic computers, and it includes networked systems of resources distributed around the world. We believe that successful computer science research is interdisciplinary: it is driven by (and drives) important problems in other disciplines. We believe that research should be guided by a clear long-term vision with planned milestones. And we believe that our environment must foster and exploit innovation. Our activities and accomplishments for the calendar year 1987 and our plans for 1988 are reported.

OpenSees

Science.gov Websites

, through soil-structure interaction, to structural response. New computer simulation tools are necessary to of structures and soils to investigate challenging problems in soil-structure-foundation interaction including foundations and soils is used to study the effects of soil liquefaction and permanent

The Cybersecurity Challenge in Acquisition

DTIC Science & Technology

2016-04-30

problems. Scarier yet, another group took control of a car’s computers through a cellular telephone and Bluetooth connections and could access...did more extensive work, hacking their way into a 2009 midsize car through its cellular, Bluetooth , and other wireless connections. Stefan Savage, a

Neural Information Processing in Cognition: We Start to Understand the Orchestra, but Where is the Conductor?

PubMed Central

Palm, Günther

2016-01-01

Research in neural information processing has been successful in the past, providing useful approaches both to practical problems in computer science and to computational models in neuroscience. Recent developments in the area of cognitive neuroscience present new challenges for a computational or theoretical understanding asking for neural information processing models that fulfill criteria or constraints from cognitive psychology, neuroscience and computational efficiency. The most important of these criteria for the evaluation of present and future contributions to this new emerging field are listed at the end of this article. PMID:26858632

Efficient Computation Of Behavior Of Aircraft Tires

NASA Technical Reports Server (NTRS)

Tanner, John A.; Noor, Ahmed K.; Andersen, Carl M.

1989-01-01

NASA technical paper discusses challenging application of computational structural mechanics to numerical simulation of responses of aircraft tires during taxing, takeoff, and landing. Presents details of three main elements of computational strategy: use of special three-field, mixed-finite-element models; use of operator splitting; and application of technique reducing substantially number of degrees of freedom. Proposed computational strategy applied to two quasi-symmetric problems: linear analysis of anisotropic tires through use of two-dimensional-shell finite elements and nonlinear analysis of orthotropic tires subjected to unsymmetric loading. Three basic types of symmetry and combinations exhibited by response of tire identified.

The research of computer network security and protection strategy

NASA Astrophysics Data System (ADS)

He, Jian

2017-05-01

With the widespread popularity of computer network applications, its security is also received a high degree of attention. Factors affecting the safety of network is complex, for to do a good job of network security is a systematic work, has the high challenge. For safety and reliability problems of computer network system, this paper combined with practical work experience, from the threat of network security, security technology, network some Suggestions and measures for the system design principle, in order to make the masses of users in computer networks to enhance safety awareness and master certain network security technology.

CIM for 300-mm semiconductor fab

NASA Astrophysics Data System (ADS)

Luk, Arthur

1997-08-01

Five years ago, factory automation (F/A) was not prevalent in the fab. Today facing the drastically changed market and the intense competition, management request the plant floor data be forward to their desktop computer. This increased demand rapidly pushed F/A to the computer integrated manufacturing (CIM). Through personalization, we successfully reduced a computer size, let them can be stored on our desktop. PC initiates a computer new era. With the advent of the network, the network computer (NC) creates fresh problems for us. When we plan to invest more than $3 billion to build new 300 mm fab, the next generation technology raises a challenging bar.

Statistics, Computation, and Modeling in Cosmology

NASA Astrophysics Data System (ADS)

Jewell, Jeff; Guiness, Joe; SAMSI 2016 Working Group in Cosmology

2017-01-01

Current and future ground and space based missions are designed to not only detect, but map out with increasing precision, details of the universe in its infancy to the present-day. As a result we are faced with the challenge of analyzing and interpreting observations from a wide variety of instruments to form a coherent view of the universe. Finding solutions to a broad range of challenging inference problems in cosmology is one of the goals of the “Statistics, Computation, and Modeling in Cosmology” workings groups, formed as part of the year long program on ‘Statistical, Mathematical, and Computational Methods for Astronomy’, hosted by the Statistical and Applied Mathematical Sciences Institute (SAMSI), a National Science Foundation funded institute. Two application areas have emerged for focused development in the cosmology working group involving advanced algorithmic implementations of exact Bayesian inference for the Cosmic Microwave Background, and statistical modeling of galaxy formation. The former includes study and development of advanced Markov Chain Monte Carlo algorithms designed to confront challenging inference problems including inference for spatial Gaussian random fields in the presence of sources of galactic emission (an example of a source separation problem). Extending these methods to future redshift survey data probing the nonlinear regime of large scale structure formation is also included in the working group activities. In addition, the working group is also focused on the study of ‘Galacticus’, a galaxy formation model applied to dark matter-only cosmological N-body simulations operating on time-dependent halo merger trees. The working group is interested in calibrating the Galacticus model to match statistics of galaxy survey observations; specifically stellar mass functions, luminosity functions, and color-color diagrams. The group will use subsampling approaches and fractional factorial designs to statistically and computationally efficiently explore the Galacticus parameter space. The group will also use the Galacticus simulations to study the relationship between the topological and physical structure of the halo merger trees and the properties of the resulting galaxies.

The Application of the Weighted k-Partite Graph Problem to the Multiple Alignment for Metabolic Pathways.

PubMed

Chen, Wenbin; Hendrix, William; Samatova, Nagiza F

2017-12-01

The problem of aligning multiple metabolic pathways is one of very challenging problems in computational biology. A metabolic pathway consists of three types of entities: reactions, compounds, and enzymes. Based on similarities between enzymes, Tohsato et al. gave an algorithm for aligning multiple metabolic pathways. However, the algorithm given by Tohsato et al. neglects the similarities among reactions, compounds, enzymes, and pathway topology. How to design algorithms for the alignment problem of multiple metabolic pathways based on the similarity of reactions, compounds, and enzymes? It is a difficult computational problem. In this article, we propose an algorithm for the problem of aligning multiple metabolic pathways based on the similarities among reactions, compounds, enzymes, and pathway topology. First, we compute a weight between each pair of like entities in different input pathways based on the entities' similarity score and topological structure using Ay et al.'s methods. We then construct a weighted k-partite graph for the reactions, compounds, and enzymes. We extract a mapping between these entities by solving the maximum-weighted k-partite matching problem by applying a novel heuristic algorithm. By analyzing the alignment results of multiple pathways in different organisms, we show that the alignments found by our algorithm correctly identify common subnetworks among multiple pathways.

Provenance Challenges for Earth Science Dataset Publication

NASA Technical Reports Server (NTRS)

Tilmes, Curt

2011-01-01

Modern science is increasingly dependent on computational analysis of very large data sets. Organizing, referencing, publishing those data has become a complex problem. Published research that depends on such data often fails to cite the data in sufficient detail to allow an independent scientist to reproduce the original experiments and analyses. This paper explores some of the challenges related to data identification, equivalence and reproducibility in the domain of data intensive scientific processing. It will use the example of Earth Science satellite data, but the challenges also apply to other domains.

Modified stretched exponential model of computer system resources management limitations-The case of cache memory

NASA Astrophysics Data System (ADS)

Strzałka, Dominik; Dymora, Paweł; Mazurek, Mirosław

2018-02-01

In this paper we present some preliminary results in the field of computer systems management with relation to Tsallis thermostatistics and the ubiquitous problem of hardware limited resources. In the case of systems with non-deterministic behaviour, management of their resources is a key point that guarantees theirs acceptable performance and proper working. This is very wide problem that stands for many challenges in financial, transport, water and food, health, etc. areas. We focus on computer systems with attention paid to cache memory and propose to use an analytical model that is able to connect non-extensive entropy formalism, long-range dependencies, management of system resources and queuing theory. Obtained analytical results are related to the practical experiment showing interesting and valuable results.

Quantum Computation: Entangling with the Future

NASA Technical Reports Server (NTRS)

Jiang, Zhang

2017-01-01

Commercial applications of quantum computation have become viable due to the rapid progress of the field in the recent years. Efficient quantum algorithms are discovered to cope with the most challenging real-world problems that are too hard for classical computers. Manufactured quantum hardware has reached unprecedented precision and controllability, enabling fault-tolerant quantum computation. Here, I give a brief introduction on what principles in quantum mechanics promise its unparalleled computational power. I will discuss several important quantum algorithms that achieve exponential or polynomial speedup over any classical algorithm. Building a quantum computer is a daunting task, and I will talk about the criteria and various implementations of quantum computers. I conclude the talk with near-future commercial applications of a quantum computer.

A modular approach to large-scale design optimization of aerospace systems

NASA Astrophysics Data System (ADS)

Hwang, John T.

Gradient-based optimization and the adjoint method form a synergistic combination that enables the efficient solution of large-scale optimization problems. Though the gradient-based approach struggles with non-smooth or multi-modal problems, the capability to efficiently optimize up to tens of thousands of design variables provides a valuable design tool for exploring complex tradeoffs and finding unintuitive designs. However, the widespread adoption of gradient-based optimization is limited by the implementation challenges for computing derivatives efficiently and accurately, particularly in multidisciplinary and shape design problems. This thesis addresses these difficulties in two ways. First, to deal with the heterogeneity and integration challenges of multidisciplinary problems, this thesis presents a computational modeling framework that solves multidisciplinary systems and computes their derivatives in a semi-automated fashion. This framework is built upon a new mathematical formulation developed in this thesis that expresses any computational model as a system of algebraic equations and unifies all methods for computing derivatives using a single equation. The framework is applied to two engineering problems: the optimization of a nanosatellite with 7 disciplines and over 25,000 design variables; and simultaneous allocation and mission optimization for commercial aircraft involving 330 design variables, 12 of which are integer variables handled using the branch-and-bound method. In both cases, the framework makes large-scale optimization possible by reducing the implementation effort and code complexity. The second half of this thesis presents a differentiable parametrization of aircraft geometries and structures for high-fidelity shape optimization. Existing geometry parametrizations are not differentiable, or they are limited in the types of shape changes they allow. This is addressed by a novel parametrization that smoothly interpolates aircraft components, providing differentiability. An unstructured quadrilateral mesh generation algorithm is also developed to automate the creation of detailed meshes for aircraft structures, and a mesh convergence study is performed to verify that the quality of the mesh is maintained as it is refined. As a demonstration, high-fidelity aerostructural analysis is performed for two unconventional configurations with detailed structures included, and aerodynamic shape optimization is applied to the truss-braced wing, which finds and eliminates a shock in the region bounded by the struts and the wing.

Special Education Students Improve Academic Performance through Problem-Based Learning and Technology

NASA Astrophysics Data System (ADS)

Freeman, S.; Kintsch, A.

2003-12-01

Boulder High School Special Education students work in teams on donated wireless computers to solve problems created by global climate change. Their text is Richard Somerville's The Forgiving Air. They utilize Wheeling Jesuit University's remote sensing web site and private computer bulletin board. Their central source for problem-based learning (PBL) is www.cotf.edu, NASA's Classroom of the Future Global Change web site. As a result, students not only improve their abilities to write, read, do math and research, speak, and work as team members, they also improve self-esteem, resilience, and willingness to take more challenging classes. Two special education students passed AP exams, Calculus and U.S. Government, last spring and Jay Matthews of Newsweek rates Boulder High as 201st of the nation's top 1000 high schools.

Integrated Cognitive-neuroscience Architectures for Understanding Sensemaking (ICArUS): A Computational Basis for ICArUS Challenge Problem Design

DTIC Science & Technology

2014-11-01

Kullback , S., & Leibler , R. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22, 79...cognitive challenges of sensemaking only informally using conceptual notions like "framing" and "re-framing", which are not sufficient to support T&E in...appropriate frame(s) from memory. Assess the Frame: Evaluate the quality of fit between data and frame. Generate Hypotheses: Use the current

The Challenges of Human-Autonomy Teaming

NASA Technical Reports Server (NTRS)

Vera, Alonso

2017-01-01

Machine intelligence is improving rapidly based on advances in big data analytics, deep learning algorithms, networked operations, and continuing exponential growth in computing power (Moores Law). This growth in the power and applicability of increasingly intelligent systems will change the roles humans, shifting them to tasks where adaptive problem solving, reasoning and decision-making is required. This talk will address the challenges involved in engineering autonomous systems that function effectively with humans in aeronautics domains.

Learning for Semantic Parsing with Kernels under Various Forms of Supervision

DTIC Science & Technology

2007-08-01

natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing...sentences are semantically tractable. This indi- cates that Geoquery is more challenging domain for semantic parsing than ATIS. In the past, there have been a...Combining parsers. In Proceedings of the Conference on Em- pirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/ VLC -99), pp. 187–194

Fourth Computational Aeroacoustics (CAA) Workshop on Benchmark Problems

NASA Technical Reports Server (NTRS)

Dahl, Milo D. (Editor)

2004-01-01

This publication contains the proceedings of the Fourth Computational Aeroacoustics (CAA) Workshop on Benchmark Problems. In this workshop, as in previous workshops, the problems were devised to gauge the technological advancement of computational techniques to calculate all aspects of sound generation and propagation in air directly from the fundamental governing equations. A variety of benchmark problems have been previously solved ranging from simple geometries with idealized acoustic conditions to test the accuracy and effectiveness of computational algorithms and numerical boundary conditions; to sound radiation from a duct; to gust interaction with a cascade of airfoils; to the sound generated by a separating, turbulent viscous flow. By solving these and similar problems, workshop participants have shown the technical progress from the basic challenges to accurate CAA calculations to the solution of CAA problems of increasing complexity and difficulty. The fourth CAA workshop emphasized the application of CAA methods to the solution of realistic problems. The workshop was held at the Ohio Aerospace Institute in Cleveland, Ohio, on October 20 to 22, 2003. At that time, workshop participants presented their solutions to problems in one or more of five categories. Their solutions are presented in this proceedings along with the comparisons of their solutions to the benchmark solutions or experimental data. The five categories for the benchmark problems were as follows: Category 1:Basic Methods. The numerical computation of sound is affected by, among other issues, the choice of grid used and by the boundary conditions. Category 2:Complex Geometry. The ability to compute the sound in the presence of complex geometric surfaces is important in practical applications of CAA. Category 3:Sound Generation by Interacting With a Gust. The practical application of CAA for computing noise generated by turbomachinery involves the modeling of the noise source mechanism as a vortical gust interacting with an airfoil. Category 4:Sound Transmission and Radiation. Category 5:Sound Generation in Viscous Problems. Sound is generated under certain conditions by a viscous flow as the flow passes an object or a cavity.

A multiresolution approach for the convergence acceleration of multivariate curve resolution methods.

PubMed

Sawall, Mathias; Kubis, Christoph; Börner, Armin; Selent, Detlef; Neymeyr, Klaus

2015-09-03

Modern computerized spectroscopic instrumentation can result in high volumes of spectroscopic data. Such accurate measurements rise special computational challenges for multivariate curve resolution techniques since pure component factorizations are often solved via constrained minimization problems. The computational costs for these calculations rapidly grow with an increased time or frequency resolution of the spectral measurements. The key idea of this paper is to define for the given high-dimensional spectroscopic data a sequence of coarsened subproblems with reduced resolutions. The multiresolution algorithm first computes a pure component factorization for the coarsest problem with the lowest resolution. Then the factorization results are used as initial values for the next problem with a higher resolution. Good initial values result in a fast solution on the next refined level. This procedure is repeated and finally a factorization is determined for the highest level of resolution. The described multiresolution approach allows a considerable convergence acceleration. The computational procedure is analyzed and is tested for experimental spectroscopic data from the rhodium-catalyzed hydroformylation together with various soft and hard models. Copyright © 2015 Elsevier B.V. All rights reserved.

Integrating on campus problem based learning and practice based learning: issues and challenges in using computer mediated communication.

PubMed

Conway, J; Sharkey, R

2002-10-01

The Faculty of Nursing, University of Newcastle, Australia, has been keen to initiate strategies that enhance student learning and nursing practice. Two strategies are problem based learning (PBL) and clinical practice. The Faculty has maintained a comparatively high proportion of the undergraduate hours in the clinical setting in times when financial constraints suggest that simulations and on campus laboratory experiences may be less expensive.Increasingly, computer based technologies are becoming sufficiently refined to support the exploration of nursing practice in a non-traditional lecture/tutorial environment. In 1998, a group of faculty members proposed that computer mediated instruction would provide an opportunity for partnership between students, academics and clinicians that would promote more positive outcomes for all and maintain the integrity of the PBL approach. This paper discusses the similarities between problem based and practice based learning and presents the findings of an evaluative study of the implementation of a practice based learning model that uses computer mediated communication to promote integration of practice experiences with the broader goals of the undergraduate curriculum.

Defining Computational Thinking for Mathematics and Science Classrooms

NASA Astrophysics Data System (ADS)

Weintrop, David; Beheshti, Elham; Horn, Michael; Orton, Kai; Jona, Kemi; Trouille, Laura; Wilensky, Uri

2016-02-01

Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include "computational thinking" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.

Accelerating Astronomy & Astrophysics in the New Era of Parallel Computing: GPUs, Phi and Cloud Computing

NASA Astrophysics Data System (ADS)

Ford, Eric B.; Dindar, Saleh; Peters, Jorg

2015-08-01

The realism of astrophysical simulations and statistical analyses of astronomical data are set by the available computational resources. Thus, astronomers and astrophysicists are constantly pushing the limits of computational capabilities. For decades, astronomers benefited from massive improvements in computational power that were driven primarily by increasing clock speeds and required relatively little attention to details of the computational hardware. For nearly a decade, increases in computational capabilities have come primarily from increasing the degree of parallelism, rather than increasing clock speeds. Further increases in computational capabilities will likely be led by many-core architectures such as Graphical Processing Units (GPUs) and Intel Xeon Phi. Successfully harnessing these new architectures, requires significantly more understanding of the hardware architecture, cache hierarchy, compiler capabilities and network network characteristics.I will provide an astronomer's overview of the opportunities and challenges provided by modern many-core architectures and elastic cloud computing. The primary goal is to help an astronomical audience understand what types of problems are likely to yield more than order of magnitude speed-ups and which problems are unlikely to parallelize sufficiently efficiently to be worth the development time and/or costs.I will draw on my experience leading a team in developing the Swarm-NG library for parallel integration of large ensembles of small n-body systems on GPUs, as well as several smaller software projects. I will share lessons learned from collaborating with computer scientists, including both technical and soft skills. Finally, I will discuss the challenges of training the next generation of astronomers to be proficient in this new era of high-performance computing, drawing on experience teaching a graduate class on High-Performance Scientific Computing for Astrophysics and organizing a 2014 advanced summer school on Bayesian Computing for Astronomical Data Analysis with support of the Penn State Center for Astrostatistics and Institute for CyberScience.

Adjusting process count on demand for petascale global optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sosonkina, Masha; Watson, Layne T.; Radcliffe, Nicholas R.

2012-11-23

There are many challenges that need to be met before efficient and reliable computation at the petascale is possible. Many scientific and engineering codes running at the petascale are likely to be memory intensive, which makes thrashing a serious problem for many petascale applications. One way to overcome this challenge is to use a dynamic number of processes, so that the total amount of memory available for the computation can be increased on demand. This paper describes modifications made to the massively parallel global optimization code pVTdirect in order to allow for a dynamic number of processes. In particular, themore » modified version of the code monitors memory use and spawns new processes if the amount of available memory is determined to be insufficient. The primary design challenges are discussed, and performance results are presented and analyzed.« less

Delicate Balances: Collaborative Research in Language Education.

ERIC Educational Resources Information Center

Hudelson, Sarah J., Ed.; Lindfors, Judith Wells, Ed.

This book addresses the special demands, problems, challenges, and tensions of collaborative research. Following an introduction by the editors, the articles and their authors are: "Collaborative Research: More Questions Than Answers" (Carole Edelsky and Chris Boyd); "Interactive Writing on a Computer Network: A Teacher/Researcher…

Recreating History with Archimedes and Pi

ERIC Educational Resources Information Center

Santucci, Lora C.

2011-01-01

Using modern technology to examine classical mathematics problems at the high school level can reduce difficult computations and encourage generalizations. When teachers combine historical context with access to technology, they challenge advanced students to think deeply, spark interest in students whose primary interest is not mathematics, and…

CoDA 2014 special issue: Exploring data-focused research across the department of energy: Editorial

DOE PAGES

Myers, Kary Lynn

2015-10-05

Here, this collection of papers, written by researchers at the national labs, in academia, and in industry present real problems, massive and complex datasets, and novel statistical approaches motivated by the challenges presented by experimental and computational science. You'll find explorations of the trajectories of aircraft and of the light curves of supernovae, of computer network intrusions and of nuclear forensics, of photovoltaics and overhead imagery.

Method for solving the problem of nonlinear heating a cylindrical body with unknown initial temperature

NASA Astrophysics Data System (ADS)

Yaparova, N.

2017-10-01

We consider the problem of heating a cylindrical body with an internal thermal source when the main characteristics of the material such as specific heat, thermal conductivity and material density depend on the temperature at each point of the body. We can control the surface temperature and the heat flow from the surface inside the cylinder, but it is impossible to measure the temperature on axis and the initial temperature in the entire body. This problem is associated with the temperature measurement challenge and appears in non-destructive testing, in thermal monitoring of heat treatment and technical diagnostics of operating equipment. The mathematical model of heating is represented as nonlinear parabolic PDE with the unknown initial condition. In this problem, both the Dirichlet and Neumann boundary conditions are given and it is required to calculate the temperature values at the internal points of the body. To solve this problem, we propose the numerical method based on using of finite-difference equations and a regularization technique. The computational scheme involves solving the problem at each spatial step. As a result, we obtain the temperature function at each internal point of the cylinder beginning from the surface down to the axis. The application of the regularization technique ensures the stability of the scheme and allows us to significantly simplify the computational procedure. We investigate the stability of the computational scheme and prove the dependence of the stability on the discretization steps and error level of the measurement results. To obtain the experimental temperature error estimates, computational experiments were carried out. The computational results are consistent with the theoretical error estimates and confirm the efficiency and reliability of the proposed computational scheme.

Effective pedagogies for teaching math to nursing students: a literature review.

PubMed

Hunter Revell, Susan M; McCurry, Mary K

2013-11-01

Improving mathematical competency and problem-solving skills in undergraduate nursing students has been an enduring challenge for nurse educators. A number of teaching strategies have been used to address this problem with varying degrees of success. This paper discusses a literature review which examined undergraduate nursing student challenges to learning math, methods used to teach math and problem-solving skills, and the use of innovative pedagogies for teaching. The literature was searched using the Cumulative Index of Nursing and Allied Health Literature and Education Resource Information Center databases. Key search terms included: math*, nurs*, nursing student, calculation, technology, medication administration, challenges, problem-solving, personal response system, clickers, computer and multi-media. Studies included in the review were published in English from 1990 to 2011. Results support four major themes which include: student challenges to learning, traditional pedagogies, curriculum strategies, and technology and integrative methods as pedagogy. The review concludes that there is a need for more innovative pedagogical strategies for teaching math to student nurses. Nurse educators in particular play a central role in helping students learn the conceptual basis, as well as practical hands-on methods, to problem solving and math competency. It is recommended that an integrated approach inclusive of technology will benefit students through better performance, increased understanding, and improved student satisfaction. Copyright © 2012 Elsevier Ltd. All rights reserved.

Constraint Programming to Solve Maximal Density Still Life

NASA Astrophysics Data System (ADS)

Chu, Geoffrey; Petrie, Karen Elizabeth; Yorke-Smith, Neil

The Maximum Density Still Life problem fills a finite Game of Life board with a stable pattern of cells that has as many live cells as possible. Although simple to state, this problem is computationally challenging for any but the smallest sizes of board. Especially difficult is to prove that the maximum number of live cells has been found. Various approaches have been employed. The most successful are approaches based on Constraint Programming (CP). We describe the Maximum Density Still Life problem, introduce the concept of constraint programming, give an overview on how the problem can be modelled and solved with CP, and report on best-known results for the problem.

Extreme-Scale Bayesian Inference for Uncertainty Quantification of Complex Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Biros, George

Uncertainty quantification (UQ)—that is, quantifying uncertainties in complex mathematical models and their large-scale computational implementations—is widely viewed as one of the outstanding challenges facing the field of CS&E over the coming decade. The EUREKA project set to address the most difficult class of UQ problems: those for which both the underlying PDE model as well as the uncertain parameters are of extreme scale. In the project we worked on these extreme-scale challenges in the following four areas: 1. Scalable parallel algorithms for sampling and characterizing the posterior distribution that exploit the structure of the underlying PDEs and parameter-to-observable map. Thesemore » include structure-exploiting versions of the randomized maximum likelihood method, which aims to overcome the intractability of employing conventional MCMC methods for solving extreme-scale Bayesian inversion problems by appealing to and adapting ideas from large-scale PDE-constrained optimization, which have been very successful at exploring high-dimensional spaces. 2. Scalable parallel algorithms for construction of prior and likelihood functions based on learning methods and non-parametric density estimation. Constructing problem-specific priors remains a critical challenge in Bayesian inference, and more so in high dimensions. Another challenge is construction of likelihood functions that capture unmodeled couplings between observations and parameters. We will create parallel algorithms for non-parametric density estimation using high dimensional N-body methods and combine them with supervised learning techniques for the construction of priors and likelihood functions. 3. Bayesian inadequacy models, which augment physics models with stochastic models that represent their imperfections. The success of the Bayesian inference framework depends on the ability to represent the uncertainty due to imperfections of the mathematical model of the phenomena of interest. This is a central challenge in UQ, especially for large-scale models. We propose to develop the mathematical tools to address these challenges in the context of extreme-scale problems. 4. Parallel scalable algorithms for Bayesian optimal experimental design (OED). Bayesian inversion yields quantified uncertainties in the model parameters, which can be propagated forward through the model to yield uncertainty in outputs of interest. This opens the way for designing new experiments to reduce the uncertainties in the model parameters and model predictions. Such experimental design problems have been intractable for large-scale problems using conventional methods; we will create OED algorithms that exploit the structure of the PDE model and the parameter-to-output map to overcome these challenges. Parallel algorithms for these four problems were created, analyzed, prototyped, implemented, tuned, and scaled up for leading-edge supercomputers, including UT-Austin’s own 10 petaflops Stampede system, ANL’s Mira system, and ORNL’s Titan system. While our focus is on fundamental mathematical/computational methods and algorithms, we will assess our methods on model problems derived from several DOE mission applications, including multiscale mechanics and ice sheet dynamics.« less

Cost Savings Associated with the Adoption of a Cloud Computing Data Transfer System for Trauma Patients.

PubMed

Feeney, James M; Montgomery, Stephanie C; Wolf, Laura; Jayaraman, Vijay; Twohig, Michael

2016-09-01

Among transferred trauma patients, challenges with the transfer of radiographic studies include problems loading or viewing the studies at the receiving hospitals, and problems manipulating, reconstructing, or evalu- ating the transferred images. Cloud-based image transfer systems may address some ofthese problems. We reviewed the charts of patients trans- ferred during one year surrounding the adoption of a cloud computing data transfer system. We compared the rates of repeat imaging before (precloud) and af- ter (postcloud) the adoption of the cloud-based data transfer system. During the precloud period, 28 out of 100 patients required 90 repeat studies. With the cloud computing transfer system in place, three out of 134 patients required seven repeat films. There was a statistically significant decrease in the proportion of patients requiring repeat films (28% to 2.2%, P < .0001). Based on an annualized volume of 200 trauma patient transfers, the cost savings estimated using three methods of cost analysis, is between $30,272 and $192,453.

Solving a Hamiltonian Path Problem with a bacterial computer

PubMed Central

Baumgardner, Jordan; Acker, Karen; Adefuye, Oyinade; Crowley, Samuel Thomas; DeLoache, Will; Dickson, James O; Heard, Lane; Martens, Andrew T; Morton, Nickolaus; Ritter, Michelle; Shoecraft, Amber; Treece, Jessica; Unzicker, Matthew; Valencia, Amanda; Waters, Mike; Campbell, A Malcolm; Heyer, Laurie J; Poet, Jeffrey L; Eckdahl, Todd T

2009-01-01

Background The Hamiltonian Path Problem asks whether there is a route in a directed graph from a beginning node to an ending node, visiting each node exactly once. The Hamiltonian Path Problem is NP complete, achieving surprising computational complexity with modest increases in size. This challenge has inspired researchers to broaden the definition of a computer. DNA computers have been developed that solve NP complete problems. Bacterial computers can be programmed by constructing genetic circuits to execute an algorithm that is responsive to the environment and whose result can be observed. Each bacterium can examine a solution to a mathematical problem and billions of them can explore billions of possible solutions. Bacterial computers can be automated, made responsive to selection, and reproduce themselves so that more processing capacity is applied to problems over time. Results We programmed bacteria with a genetic circuit that enables them to evaluate all possible paths in a directed graph in order to find a Hamiltonian path. We encoded a three node directed graph as DNA segments that were autonomously shuffled randomly inside bacteria by a Hin/hixC recombination system we previously adapted from Salmonella typhimurium for use in Escherichia coli. We represented nodes in the graph as linked halves of two different genes encoding red or green fluorescent proteins. Bacterial populations displayed phenotypes that reflected random ordering of edges in the graph. Individual bacterial clones that found a Hamiltonian path reported their success by fluorescing both red and green, resulting in yellow colonies. We used DNA sequencing to verify that the yellow phenotype resulted from genotypes that represented Hamiltonian path solutions, demonstrating that our bacterial computer functioned as expected. Conclusion We successfully designed, constructed, and tested a bacterial computer capable of finding a Hamiltonian path in a three node directed graph. This proof-of-concept experiment demonstrates that bacterial computing is a new way to address NP-complete problems using the inherent advantages of genetic systems. The results of our experiments also validate synthetic biology as a valuable approach to biological engineering. We designed and constructed basic parts, devices, and systems using synthetic biology principles of standardization and abstraction. PMID:19630940

WFIRST: Microlensing Analysis Data Challenge

NASA Astrophysics Data System (ADS)

Street, Rachel; WFIRST Microlensing Science Investigation Team

2018-01-01

WFIRST will produce thousands of high cadence, high photometric precision lightcurves of microlensing events, from which a wealth of planetary and stellar systems will be discovered. However, the analysis of such lightcurves has historically been very time consuming and expensive in both labor and computing facilities. This poses a potential bottleneck to deriving the full science potential of the WFIRST mission. To address this problem, the WFIRST Microlensing Science Investigation Team designing a series of data challenges to stimulate research to address outstanding problems of microlensing analysis. These range from the classification and modeling of triple lens events to methods to efficiently yet thoroughly search a high-dimensional parameter space for the best fitting models.

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

PubMed Central

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-01-01

Background We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems. PMID:17081289

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.

PubMed

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-11-02

We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.

Robust optimization modelling with applications to industry and environmental problems

NASA Astrophysics Data System (ADS)

Chaerani, Diah; Dewanto, Stanley P.; Lesmana, Eman

2017-10-01

Robust Optimization (RO) modeling is one of the existing methodology for handling data uncertainty in optimization problem. The main challenge in this RO methodology is how and when we can reformulate the robust counterpart of uncertain problems as a computationally tractable optimization problem or at least approximate the robust counterpart by a tractable problem. Due to its definition the robust counterpart highly depends on how we choose the uncertainty set. As a consequence we can meet this challenge only if this set is chosen in a suitable way. The development on RO grows fast, since 2004, a new approach of RO called Adjustable Robust Optimization (ARO) is introduced to handle uncertain problems when the decision variables must be decided as a ”wait and see” decision variables. Different than the classic Robust Optimization (RO) that models decision variables as ”here and now”. In ARO, the uncertain problems can be considered as a multistage decision problem, thus decision variables involved are now become the wait and see decision variables. In this paper we present the applications of both RO and ARO. We present briefly all results to strengthen the importance of RO and ARO in many real life problems.

Emergent Network Defense

ERIC Educational Resources Information Center

Crane, Earl Newell

2013-01-01

The research problem that inspired this effort is the challenge of managing the security of systems in large-scale heterogeneous networked environments. Human intervention is slow and limited: humans operate at much slower speeds than networked computer communications and there are few humans associated with each network. Enabling each node in the…

Testing Scientific Software: A Systematic Literature Review

PubMed Central

Kanewala, Upulee; Bieman, James M.

2014-01-01

Context Scientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code. Objective This study aims to identify specific challenges, proposed solutions, and unsolved problems faced when testing scientific software. Method We conducted a systematic literature survey to identify and analyze relevant literature. We identified 62 studies that provided relevant information about testing scientific software. Results We found that challenges faced when testing scientific software fall into two main categories: (1) testing challenges that occur due to characteristics of scientific software such as oracle problems and (2) testing challenges that occur due to cultural differences between scientists and the software engineering community such as viewing the code and the model that it implements as inseparable entities. In addition, we identified methods to potentially overcome these challenges and their limitations. Finally we describe unsolved challenges and how software engineering researchers and practitioners can help to overcome them. Conclusions Scientific software presents special challenges for testing. Specifically, cultural differences between scientist developers and software engineers, along with the characteristics of the scientific software make testing more difficult. Existing techniques such as code clone detection can help to improve the testing process. Software engineers should consider special challenges posed by scientific software such as oracle problems when developing testing techniques. PMID:25125798

A memetic optimization algorithm for multi-constrained multicast routing in ad hoc networks.

PubMed

Ramadan, Rahab M; Gasser, Safa M; El-Mahallawy, Mohamed S; Hammad, Karim; El Bakly, Ahmed M

2018-01-01

A mobile ad hoc network is a conventional self-configuring network where the routing optimization problem-subject to various Quality-of-Service (QoS) constraints-represents a major challenge. Unlike previously proposed solutions, in this paper, we propose a memetic algorithm (MA) employing an adaptive mutation parameter, to solve the multicast routing problem with higher search ability and computational efficiency. The proposed algorithm utilizes an updated scheme, based on statistical analysis, to estimate the best values for all MA parameters and enhance MA performance. The numerical results show that the proposed MA improved the delay and jitter of the network, while reducing computational complexity as compared to existing algorithms.

Multiple-Swarm Ensembles: Improving the Predictive Power and Robustness of Predictive Models and Its Use in Computational Biology.

PubMed

Alves, Pedro; Liu, Shuang; Wang, Daifeng; Gerstein, Mark

2018-01-01

Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.

A comparison of several computational auditory scene analysis (CASA) techniques for monaural speech segregation.

PubMed

Zeremdini, Jihen; Ben Messaoud, Mohamed Anouar; Bouzid, Aicha

2015-09-01

Humans have the ability to easily separate a composed speech and to form perceptual representations of the constituent sources in an acoustic mixture thanks to their ears. Until recently, researchers attempt to build computer models of high-level functions of the auditory system. The problem of the composed speech segregation is still a very challenging problem for these researchers. In our case, we are interested in approaches that are addressed to the monaural speech segregation. For this purpose, we study in this paper the computational auditory scene analysis (CASA) to segregate speech from monaural mixtures. CASA is the reproduction of the source organization achieved by listeners. It is based on two main stages: segmentation and grouping. In this work, we have presented, and compared several studies that have used CASA for speech separation and recognition.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, Edmond

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Application of artificial intelligence to pharmacy and medicine.

PubMed

Dasta, J F

1992-04-01

Artificial intelligence (AI) is a branch of computer science dealing with solving problems using symbolic programming. It has evolved into a problem solving science with applications in business, engineering, and health care. One application of AI is expert system development. An expert system consists of a knowledge base and inference engine, coupled with a user interface. A crucial aspect of expert system development is knowledge acquisition and implementing computable ways to solve problems. There have been several expert systems developed in medicine to assist physicians with medical diagnosis. Recently, several programs focusing on drug therapy have been described. They provide guidance on drug interactions, drug therapy monitoring, and drug formulary selection. There are many aspects of pharmacy that AI can have an impact on and the reader is challenged to consider these possibilities because they may some day become a reality in pharmacy.

The SGI/CRAY T3E: Experiences and Insights

NASA Technical Reports Server (NTRS)

Bernard, Lisa Hamet

1999-01-01

The focus of the HPCC Earth and Space Sciences (ESS) Project is capability computing - pushing highly scalable computing testbeds to their performance limits. The drivers of this focus are the Grand Challenge problems in Earth and space science: those that could not be addressed in a capacity computing environment where large jobs must continually compete for resources. These Grand Challenge codes require a high degree of communication, large memory, and very large I/O (throughout the duration of the processing, not just in loading initial conditions and saving final results). This set of parameters led to the selection of an SGI/Cray T3E as the current ESS Computing Testbed. The T3E at the Goddard Space Flight Center is a unique computational resource within NASA. As such, it must be managed to effectively support the diverse research efforts across the NASA research community yet still enable the ESS Grand Challenge Investigator teams to achieve their performance milestones, for which the system was intended. To date, all Grand Challenge Investigator teams have achieved the 10 GFLOPS milestone, eight of nine have achieved the 50 GFLOPS milestone, and three have achieved the 100 GFLOPS milestone. In addition, many technical papers have been published highlighting results achieved on the NASA T3E, including some at this Workshop. The successes enabled by the NASA T3E computing environment are best illustrated by the 512 PE upgrade funded by the NASA Earth Science Enterprise earlier this year. Never before has an HPCC computing testbed been so well received by the general NASA science community that it was deemed critical to the success of a core NASA science effort. NASA looks forward to many more success stories before the conclusion of the NASA-SGI/Cray cooperative agreement in June 1999.

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

PubMed

Yin, Zekun; Lan, Haidong; Tan, Guangming; Lu, Mian; Vasilakos, Athanasios V; Liu, Weiguo

2017-01-01

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

Metagenomic Assembly: Overview, Challenges and Applications

PubMed Central

Ghurye, Jay S.; Cepeda-Espinoza, Victoria; Pop, Mihai

2016-01-01

Advances in sequencing technologies have led to the increased use of high throughput sequencing in characterizing the microbial communities associated with our bodies and our environment. Critical to the analysis of the resulting data are sequence assembly algorithms able to reconstruct genes and organisms from complex mixtures. Metagenomic assembly involves new computational challenges due to the specific characteristics of the metagenomic data. In this survey, we focus on major algorithmic approaches for genome and metagenome assembly, and discuss the new challenges and opportunities afforded by this new field. We also review several applications of metagenome assembly in addressing interesting biological problems. PMID:27698619

Arithmetic 400. A Computer Educational Program.

ERIC Educational Resources Information Center

Firestein, Laurie

"ARITHMETIC 400" is the first of the next generation of educational programs designed to encourage thinking about arithmetic problems. Presented in video game format, performance is a measure of correctness, speed, accuracy, and fortune as well. Play presents a challenge to individuals at various skill levels. The program, run on an Apple…

Smart Clothing Challenge

ERIC Educational Resources Information Center

Roman, Harry T.

2011-01-01

As sensors and computers become smaller and smaller, it becomes possible to add intelligence or smartness to common items. This is already seen in smart appliances, cars that diagnose their own maintenance problems, and military hardware that is something straight out of a science fiction book. In this article, the author looks at a design…

Towards a Virtual Teaching Assistant to Answer Questions Asked by Students in Introductory Computer Science

ERIC Educational Resources Information Center

Heiner, Cecily

2009-01-01

Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This dissertation analyzes 411 questions from an introductory Java programming course by reducing the…

Intersectional Computer-Supported Collaboration in Business Writing: Learning through Challenged Performance

ERIC Educational Resources Information Center

Remley, Dirk

2009-01-01

Carter (2007) identifies four meta-genres associated with writing activities that can help students learn discipline-specific writing skills relative to standards within a given field: these include problem solving, empirical approaches to analysis, selection of sources to use within research, and production of materials that meet accepted…

STREAM: A First Programming Process

ERIC Educational Resources Information Center

Caspersen, Michael E.; Kolling, Michael

2009-01-01

Programming is recognized as one of seven grand challenges in computing education. Decades of research have shown that the major problems novices experience are composition-based---they may know what the individual programming language constructs are, but they do not know how to put them together. Despite this fact, textbooks, educational…

Remote access to very large image repositories, a high performance computing perspective

NASA Technical Reports Server (NTRS)

Plesea, Lucian

2005-01-01

The main challenges of using the increasingly large repositories of remote imagery data can be summarized in one word: efficiency. In this paper, a number of concrete problems and the chosen solutions are described, based on the construction of a 5TB global Landsat 7 mosaic.

Silent Reading Fluency Using Underlining: Evidence for an Alternative Method of Assessment

ERIC Educational Resources Information Center

Price, Katherine W.; Meisinger, Elizabeth B.; Louwerse, Max M.; D'Mello, Sidney K.

2012-01-01

Assessing silent reading fluency in classroom environments is challenging. This article reports on a method of assessing silent reading using underlining, an approach that solves many problems other silent reading fluency assessment measures face. This method computationally monitors readers' silent reading fluency by the speed they underline…

Driving into the future: how imaging technology is shaping the future of cars

NASA Astrophysics Data System (ADS)

Zhang, Buyue

2015-03-01

Fueled by the development of advanced driver assistance system (ADAS), autonomous vehicles, and the proliferation of cameras and sensors, automotive is becoming a rich new domain for innovations in imaging technology. This paper presents an overview of ADAS, the important imaging and computer vision problems to solve for automotive, and examples of how some of these problems are solved, through which we highlight the challenges and opportunities in the automotive imaging space.

Supercomputer requirements for selected disciplines important to aerospace

NASA Technical Reports Server (NTRS)

Peterson, Victor L.; Kim, John; Holst, Terry L.; Deiwert, George S.; Cooper, David M.; Watson, Andrew B.; Bailey, F. Ron

1989-01-01

Speed and memory requirements placed on supercomputers by five different disciplines important to aerospace are discussed and compared with the capabilities of various existing computers and those projected to be available before the end of this century. The disciplines chosen for consideration are turbulence physics, aerodynamics, aerothermodynamics, chemistry, and human vision modeling. Example results for problems illustrative of those currently being solved in each of the disciplines are presented and discussed. Limitations imposed on physical modeling and geometrical complexity by the need to obtain solutions in practical amounts of time are identified. Computational challenges for the future, for which either some or all of the current limitations are removed, are described. Meeting some of the challenges will require computer speeds in excess of exaflop/s (10 to the 18th flop/s) and memories in excess of petawords (10 to the 15th words).

Increased Diels-Alderase activity through backbone remodeling guided by Foldit players.

PubMed

Eiben, Christopher B; Siegel, Justin B; Bale, Jacob B; Cooper, Seth; Khatib, Firas; Shen, Betty W; Players, Foldit; Stoddard, Barry L; Popovic, Zoran; Baker, David

2012-01-22

Computational enzyme design holds promise for the production of renewable fuels, drugs and chemicals. De novo enzyme design has generated catalysts for several reactions, but with lower catalytic efficiencies than naturally occurring enzymes. Here we report the use of game-driven crowdsourcing to enhance the activity of a computationally designed enzyme through the functional remodeling of its structure. Players of the online game Foldit were challenged to remodel the backbone of a computationally designed bimolecular Diels-Alderase to enable additional interactions with substrates. Several iterations of design and characterization generated a 24-residue helix-turn-helix motif, including a 13-residue insertion, that increased enzyme activity >18-fold. X-ray crystallography showed that the large insertion adopts a helix-turn-helix structure positioned as in the Foldit model. These results demonstrate that human creativity can extend beyond the macroscopic challenges encountered in everyday life to molecular-scale design problems.

Fuzzy logic, neural networks, and soft computing

NASA Technical Reports Server (NTRS)

Zadeh, Lofti A.

1994-01-01

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Wasatch: An architecture-proof multiphysics development environment using a Domain Specific Language and graph theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saad, Tony; Sutherland, James C.

To address the coding and software challenges of modern hybrid architectures, we propose an approach to multiphysics code development for high-performance computing. This approach is based on using a Domain Specific Language (DSL) in tandem with a directed acyclic graph (DAG) representation of the problem to be solved that allows runtime algorithm generation. When coupled with a large-scale parallel framework, the result is a portable development framework capable of executing on hybrid platforms and handling the challenges of multiphysics applications. In addition, we share our experience developing a code in such an environment – an effort that spans an interdisciplinarymore » team of engineers and computer scientists.« less

Wasatch: An architecture-proof multiphysics development environment using a Domain Specific Language and graph theory

DOE PAGES

Saad, Tony; Sutherland, James C.

2016-05-04

To address the coding and software challenges of modern hybrid architectures, we propose an approach to multiphysics code development for high-performance computing. This approach is based on using a Domain Specific Language (DSL) in tandem with a directed acyclic graph (DAG) representation of the problem to be solved that allows runtime algorithm generation. When coupled with a large-scale parallel framework, the result is a portable development framework capable of executing on hybrid platforms and handling the challenges of multiphysics applications. In addition, we share our experience developing a code in such an environment – an effort that spans an interdisciplinarymore » team of engineers and computer scientists.« less

Modelling DC responses of 3D complex fracture networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beskardes, Gungor Didem; Weiss, Chester Joseph

Here, the determination of the geometrical properties of fractures plays a critical role in many engineering problems to assess the current hydrological and mechanical states of geological media and to predict their future states. However, numerical modeling of geoelectrical responses in realistic fractured media has been challenging due to the explosive computational cost imposed by the explicit discretizations of fractures at multiple length scales, which often brings about a tradeoff between computational efficiency and geologic realism. Here, we use the hierarchical finite element method to model electrostatic response of realistically complex 3D conductive fracture networks with minimal computational cost.

Modelling DC responses of 3D complex fracture networks

DOE PAGES

Beskardes, Gungor Didem; Weiss, Chester Joseph

2018-03-01

Here, the determination of the geometrical properties of fractures plays a critical role in many engineering problems to assess the current hydrological and mechanical states of geological media and to predict their future states. However, numerical modeling of geoelectrical responses in realistic fractured media has been challenging due to the explosive computational cost imposed by the explicit discretizations of fractures at multiple length scales, which often brings about a tradeoff between computational efficiency and geologic realism. Here, we use the hierarchical finite element method to model electrostatic response of realistically complex 3D conductive fracture networks with minimal computational cost.

Accuracy of Time Integration Approaches for Stiff Magnetohydrodynamics Problems

NASA Astrophysics Data System (ADS)

Knoll, D. A.; Chacon, L.

2003-10-01

The simulation of complex physical processes with multiple time scales presents a continuing challenge to the computational plasma physisist due to the co-existence of fast and slow time scales. Within computational plasma physics, practitioners have developed and used linearized methods, semi-implicit methods, and time splitting in an attempt to tackle such problems. All of these methods are understood to generate numerical error. We are currently developing algorithms which remove such error for MHD problems [1,2]. These methods do not rely on linearization or time splitting. We are also attempting to analyze the errors introduced by existing ``implicit'' methods using modified equation analysis (MEA) [3]. In this presentation we will briefly cover the major findings in [3]. We will then extend this work further into MHD. This analysis will be augmented with numerical experiments with the hope of gaining insight, particularly into how these errors accumulate over many time steps. [1] L. Chacon,. D.A. Knoll, J.M. Finn, J. Comput. Phys., vol. 178, pp. 15-36 (2002) [2] L. Chacon and D.A. Knoll, J. Comput. Phys., vol. 188, pp. 573-592 (2003) [3] D.A. Knoll , L. Chacon, L.G. Margolin, V.A. Mousseau, J. Comput. Phys., vol. 185, pp. 583-611 (2003)

Quantum Metropolis sampling.

PubMed

Temme, K; Osborne, T J; Vollbrecht, K G; Poulin, D; Verstraete, F

2011-03-03

The original motivation to build a quantum computer came from Feynman, who imagined a machine capable of simulating generic quantum mechanical systems--a task that is believed to be intractable for classical computers. Such a machine could have far-reaching applications in the simulation of many-body quantum physics in condensed-matter, chemical and high-energy systems. Part of Feynman's challenge was met by Lloyd, who showed how to approximately decompose the time evolution operator of interacting quantum particles into a short sequence of elementary gates, suitable for operation on a quantum computer. However, this left open the problem of how to simulate the equilibrium and static properties of quantum systems. This requires the preparation of ground and Gibbs states on a quantum computer. For classical systems, this problem is solved by the ubiquitous Metropolis algorithm, a method that has basically acquired a monopoly on the simulation of interacting particles. Here we demonstrate how to implement a quantum version of the Metropolis algorithm. This algorithm permits sampling directly from the eigenstates of the Hamiltonian, and thus evades the sign problem present in classical simulations. A small-scale implementation of this algorithm should be achievable with today's technology.

A Survey of Computational Intelligence Techniques in Protein Function Prediction

PubMed Central

Tiwari, Arvind Kumar; Srivastava, Rajeev

2014-01-01

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

Peripheral controllers and devices--Part 1.

PubMed

Pinkert, J R; Wear, L L

1992-10-01

In this article, we looked at several peripherals, described their characteristics, and described how they are connected to computers. We included some discussions of problems caused by electrical and mechanical differences between computers and peripheral devices. During the past few years, many companies have addressed such problems. Numerous standards have been defined as a result of this work. These standards specify everything from what type of connectors will be used to the timing of electrical signals. They make it easier for peripheral manufacturers to design their devices for a wide range of computers. Peripherals and their controllers are important components of any computer system. Sometimes, however, other parts of the system, such as the control unit and main memory, receive more attention. Many engineers want to design new processors, but shy away from the design of peripherals and controllers; they consider such designs less glamorous. In reality, designs for some peripherals and their controllers can be more challenging than the design of the CPU itself. A computer without peripherals is of little use, other than as a paper weight. Until we attach peripherals to the computer, none of its power is accessible to the user. Peripherals turn computers into useful tools.

Security and Cloud Outsourcing Framework for Economic Dispatch

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sarker, Mushfiqur R.; Wang, Jianhui; Li, Zuyi

The computational complexity and problem sizes of power grid applications have increased significantly with the advent of renewable resources and smart grid technologies. The current paradigm of solving these issues consist of inhouse high performance computing infrastructures, which have drawbacks of high capital expenditures, maintenance, and limited scalability. Cloud computing is an ideal alternative due to its powerful computational capacity, rapid scalability, and high cost-effectiveness. A major challenge, however, remains in that the highly confidential grid data is susceptible for potential cyberattacks when outsourced to the cloud. In this work, a security and cloud outsourcing framework is developed for themore » Economic Dispatch (ED) linear programming application. As a result, the security framework transforms the ED linear program into a confidentiality-preserving linear program, that masks both the data and problem structure, thus enabling secure outsourcing to the cloud. Results show that for large grid test cases the performance gain and costs outperforms the in-house infrastructure.« less

Security and Cloud Outsourcing Framework for Economic Dispatch

DOE PAGES

Sarker, Mushfiqur R.; Wang, Jianhui; Li, Zuyi; ...

2017-04-24

The computational complexity and problem sizes of power grid applications have increased significantly with the advent of renewable resources and smart grid technologies. The current paradigm of solving these issues consist of inhouse high performance computing infrastructures, which have drawbacks of high capital expenditures, maintenance, and limited scalability. Cloud computing is an ideal alternative due to its powerful computational capacity, rapid scalability, and high cost-effectiveness. A major challenge, however, remains in that the highly confidential grid data is susceptible for potential cyberattacks when outsourced to the cloud. In this work, a security and cloud outsourcing framework is developed for themore » Economic Dispatch (ED) linear programming application. As a result, the security framework transforms the ED linear program into a confidentiality-preserving linear program, that masks both the data and problem structure, thus enabling secure outsourcing to the cloud. Results show that for large grid test cases the performance gain and costs outperforms the in-house infrastructure.« less

DualTrust: A Trust Management Model for Swarm-Based Autonomic Computing Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maiden, Wendy M.

Trust management techniques must be adapted to the unique needs of the application architectures and problem domains to which they are applied. For autonomic computing systems that utilize mobile agents and ant colony algorithms for their sensor layer, certain characteristics of the mobile agent ant swarm -- their lightweight, ephemeral nature and indirect communication -- make this adaptation especially challenging. This thesis looks at the trust issues and opportunities in swarm-based autonomic computing systems and finds that by monitoring the trustworthiness of the autonomic managers rather than the swarming sensors, the trust management problem becomes much more scalable and stillmore » serves to protect the swarm. After analyzing the applicability of trust management research as it has been applied to architectures with similar characteristics, this thesis specifies the required characteristics for trust management mechanisms used to monitor the trustworthiness of entities in a swarm-based autonomic computing system and describes a trust model that meets these requirements.« less

Enhanced computer vision with Microsoft Kinect sensor: a review.

PubMed

Han, Jungong; Shao, Ling; Xu, Dong; Shotton, Jamie

2013-10-01

With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.

The role of synergies within generative models of action execution and recognition: A computational perspective. Comment on "Grasping synergies: A motor-control approach to the mirror neuron mechanism" by A. D'Ausilio et al.

NASA Astrophysics Data System (ADS)

Pezzulo, Giovanni; Donnarumma, Francesco; Iodice, Pierpaolo; Prevete, Roberto; Dindo, Haris

2015-03-01

Controlling the body - given its huge number of degrees of freedom - poses severe computational challenges. Mounting evidence suggests that the brain alleviates this problem by exploiting "synergies", or patterns of muscle activities (and/or movement dynamics and kinematics) that can be combined to control action, rather than controlling individual muscles of joints [1-10].

Computational cameras for moving iris recognition

NASA Astrophysics Data System (ADS)

McCloskey, Scott; Venkatesha, Sharath

2015-05-01

Iris-based biometric identification is increasingly used for facility access and other security applications. Like all methods that exploit visual information, however, iris systems are limited by the quality of captured images. Optical defocus due to a small depth of field (DOF) is one such challenge, as is the acquisition of sharply-focused iris images from subjects in motion. This manuscript describes the application of computational motion-deblurring cameras to the problem of moving iris capture, from the underlying theory to system considerations and performance data.

Systems engineering for very large systems

NASA Technical Reports Server (NTRS)

Lewkowicz, Paul E.

1993-01-01

Very large integrated systems have always posed special problems for engineers. Whether they are power generation systems, computer networks or space vehicles, whenever there are multiple interfaces, complex technologies or just demanding customers, the challenges are unique. 'Systems engineering' has evolved as a discipline in order to meet these challenges by providing a structured, top-down design and development methodology for the engineer. This paper attempts to define the general class of problems requiring the complete systems engineering treatment and to show how systems engineering can be utilized to improve customer satisfaction and profit ability. Specifically, this work will focus on a design methodology for the largest of systems, not necessarily in terms of physical size, but in terms of complexity and interconnectivity.

Systems engineering for very large systems

NASA Astrophysics Data System (ADS)

Lewkowicz, Paul E.

Very large integrated systems have always posed special problems for engineers. Whether they are power generation systems, computer networks or space vehicles, whenever there are multiple interfaces, complex technologies or just demanding customers, the challenges are unique. 'Systems engineering' has evolved as a discipline in order to meet these challenges by providing a structured, top-down design and development methodology for the engineer. This paper attempts to define the general class of problems requiring the complete systems engineering treatment and to show how systems engineering can be utilized to improve customer satisfaction and profit ability. Specifically, this work will focus on a design methodology for the largest of systems, not necessarily in terms of physical size, but in terms of complexity and interconnectivity.

Computational Analysis of a Prototype Martian Rotorcraft Experiment

NASA Technical Reports Server (NTRS)

Corfeld, Kelly J.; Strawn, Roger C.; Long, Lyle N.

2002-01-01

This paper presents Reynolds-averaged Navier-Stokes calculations for a prototype Martian rotorcraft. The computations are intended for comparison with an ongoing Mars rotor hover test at NASA Ames Research Center. These computational simulations present a new and challenging problem, since rotors that operate on Mars will experience a unique low Reynolds number and high Mach number environment. Computed results for the 3-D rotor differ substantially from 2-D sectional computations in that the 3-D results exhibit a stall delay phenomenon caused by rotational forces along the blade span. Computational results have yet to be compared to experimental data, but computed performance predictions match the experimental design goals fairly well. In addition, the computed results provide a high level of detail in the rotor wake and blade surface aerodynamics. These details provide an important supplement to the expected experimental performance data.

Extensible Computational Chemistry Environment

DOE Office of Scientific and Technical Information (OSTI.GOV)

2012-08-09

ECCE provides a sophisticated graphical user interface, scientific visualization tools, and the underlying data management framework enabling scientists to efficiently set up calculations and store, retrieve, and analyze the rapidly growing volumes of data produced by computational chemistry studies. ECCE was conceived as part of the Environmental Molecular Sciences Laboratory construction to solve the problem of researchers being able to effectively utilize complex computational chemistry codes and massively parallel high performance compute resources. Bringing the power of these codes and resources to the desktops of researcher and thus enabling world class research without users needing a detailed understanding of themore » inner workings of either the theoretical codes or the supercomputers needed to run them was a grand challenge problem in the original version of the EMSL. ECCE allows collaboration among researchers using a web-based data repository where the inputs and results for all calculations done within ECCE are organized. ECCE is a first of kind end-to-end problem solving environment for all phases of computational chemistry research: setting up calculations with sophisticated GUI and direct manipulation visualization tools, submitting and monitoring calculations on remote high performance supercomputers without having to be familiar with the details of using these compute resources, and performing results visualization and analysis including creating publication quality images. ECCE is a suite of tightly integrated applications that are employed as the user moves through the modeling process.« less

Perceiving emotion: towards a realistic understanding of the task.

PubMed

Cowie, Roddy

2009-12-12

A decade ago, perceiving emotion was generally equated with taking a sample (a still photograph or a few seconds of speech) that unquestionably signified an archetypal emotional state, and attaching the appropriate label. Computational research has shifted that paradigm in multiple ways. Concern with realism is key. Emotion generally colours ongoing action and interaction: describing that colouring is a different problem from categorizing brief episodes of relatively pure emotion. Multiple challenges flow from that. Describing emotional colouring is a challenge in itself. One approach is to use everyday categories describing states that are partly emotional and partly cognitive. Another approach is to use dimensions. Both approaches need ways to deal with gradual changes over time and mixed emotions. Attaching target descriptions to a sample poses problems of both procedure and validation. Cues are likely to be distributed both in time and across modalities, and key decisions may depend heavily on context. The usefulness of acted data is limited because it tends not to reproduce these features. By engaging with these challenging issues, research is not only achieving impressive results, but also offering a much deeper understanding of the problem.

An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

DOE PAGES

Li, Weixuan; Lin, Guang

2015-03-21

Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less

Virtual patient simulator for distributed collaborative medical education.

PubMed

Caudell, Thomas P; Summers, Kenneth L; Holten, Jim; Hakamata, Takeshi; Mowafi, Moad; Jacobs, Joshua; Lozanoff, Beth K; Lozanoff, Scott; Wilks, David; Keep, Marcus F; Saiki, Stanley; Alverson, Dale

2003-01-01

Project TOUCH (Telehealth Outreach for Unified Community Health; http://hsc.unm.edu/touch) investigates the feasibility of using advanced technologies to enhance education in an innovative problem-based learning format currently being used in medical school curricula, applying specific clinical case models, and deploying to remote sites/workstations. The University of New Mexico's School of Medicine and the John A. Burns School of Medicine at the University of Hawai'i face similar health care challenges in providing and delivering services and training to remote and rural areas. Recognizing that health care needs are local and require local solutions, both states are committed to improving health care delivery to their unique populations by sharing information and experiences through emerging telehealth technologies by using high-performance computing and communications resources. The purpose of this study is to describe the deployment of a problem-based learning case distributed over the National Computational Science Alliance's Access Grid. Emphasis is placed on the underlying technical components of the TOUCH project, including the virtual reality development tool Flatland, the artificial intelligence-based simulation engine, the Access Grid, high-performance computing platforms, and the software that connects them all. In addition, educational and technical challenges for Project TOUCH are identified. Copyright 2003 Wiley-Liss, Inc.

An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Weixuan; Lin, Guang, E-mail: guanglin@purdue.edu

2015-08-01

Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less

Computer-assisted learning and simulation systems in dentistry--a challenge to society.

PubMed

Welk, A; Splieth, Ch; Wierinck, E; Gilpatrick, R O; Meyer, G

2006-07-01

Computer technology is increasingly used in practical training at universities. However, in spite of their potential, computer-assisted learning (CAL) and computer-assisted simulation (CAS) systems still appear to be underutilized in dental education. Advantages, challenges, problems, and solutions of computer-assisted learning and simulation in dentistry are discussed by means of MEDLINE, open Internet platform searches, and key results of a study among German dental schools. The advantages of computer-assisted learning are seen for example in self-paced and self-directed learning and increased motivation. It is useful for both objective theoretical and practical tests and for training students to handle complex cases. CAL can lead to more structured learning and can support training in evidence-based decision-making. The reasons for the still relatively rare implementation of CAL/CAS systems in dental education include an inability to finance, lack of studies of CAL/CAS, and too much effort required to integrate CAL/CAS systems into the curriculum. To overcome the reasons for the relative low degree of computer technology use, we should strive for multicenter research and development projects monitored by the appropriate national and international scientific societies, so that the potential of computer technology can be fully realized in graduate, postgraduate, and continuing dental education.

The Application of a Massively Parallel Computer to the Simulation of Electrical Wave Propagation Phenomena in the Heart Muscle Using Simplified Models

NASA Technical Reports Server (NTRS)

Karpoukhin, Mikhii G.; Kogan, Boris Y.; Karplus, Walter J.

1995-01-01

The simulation of heart arrhythmia and fibrillation are very important and challenging tasks. The solution of these problems using sophisticated mathematical models is beyond the capabilities of modern super computers. To overcome these difficulties it is proposed to break the whole simulation problem into two tightly coupled stages: generation of the action potential using sophisticated models. and propagation of the action potential using simplified models. The well known simplified models are compared and modified to bring the rate of depolarization and action potential duration restitution closer to reality. The modified method of lines is used to parallelize the computational process. The conditions for the appearance of 2D spiral waves after the application of a premature beat and the subsequent traveling of the spiral wave inside the simulated tissue are studied.

A variational eigenvalue solver on a photonic quantum processor

PubMed Central

Peruzzo, Alberto; McClean, Jarrod; Shadbolt, Peter; Yung, Man-Hong; Zhou, Xiao-Qi; Love, Peter J.; Aspuru-Guzik, Alán; O’Brien, Jeremy L.

2014-01-01

Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. For quantum systems, where the physical dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable problem and remains a fundamental challenge. The quantum phase estimation algorithm efficiently finds the eigenvalue of a given eigenvector but requires fully coherent evolution. Here we present an alternative approach that greatly reduces the requirements for coherent evolution and combine this method with a new approach to state preparation based on ansätze and classical optimization. We implement the algorithm by combining a highly reconfigurable photonic quantum processor with a conventional computer. We experimentally demonstrate the feasibility of this approach with an example from quantum chemistry—calculating the ground-state molecular energy for He–H+. The proposed approach drastically reduces the coherence time requirements, enhancing the potential of quantum resources available today and in the near future. PMID:25055053

An interactive parallel programming environment applied in atmospheric science

NASA Technical Reports Server (NTRS)

vonLaszewski, G.

1996-01-01

This article introduces an interactive parallel programming environment (IPPE) that simplifies the generation and execution of parallel programs. One of the tasks of the environment is to generate message-passing parallel programs for homogeneous and heterogeneous computing platforms. The parallel programs are represented by using visual objects. This is accomplished with the help of a graphical programming editor that is implemented in Java and enables portability to a wide variety of computer platforms. In contrast to other graphical programming systems, reusable parts of the programs can be stored in a program library to support rapid prototyping. In addition, runtime performance data on different computing platforms is collected in a database. A selection process determines dynamically the software and the hardware platform to be used to solve the problem in minimal wall-clock time. The environment is currently being tested on a Grand Challenge problem, the NASA four-dimensional data assimilation system.

An Intelligent Model for Pairs Trading Using Genetic Algorithms.

PubMed

Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

2015-01-01

Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

An Intelligent Model for Pairs Trading Using Genetic Algorithms

PubMed Central

Hsu, Chi-Jen; Chen, Chi-Chung; Li, Chen-An

2015-01-01

Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236

The menu-setting problem and subsidized prices: drug formulary illustration.

PubMed

Olmstead, T; Zeckhauser, R

1999-10-01

The menu-setting problem (MSP) determines the goods and services an institution offers and the prices charged. It appears widely in health care, from choosing the services an insurance arrangement offers, to selecting the health plans an employer proffers. The challenge arises because purchases are subsidized, and consumers (or their physician agents) may make cost-ineffective choices. The intuitively comprehensible MSP model--readily solved by computer using actual data--helps structure thinking and support decision making about such problems. The analysis uses drug formularies--lists of approved drugs in a plan or institution--to illustrate the framework.

Parallel programming of gradient-based iterative image reconstruction schemes for optical tomography.

PubMed

Hielscher, Andreas H; Bartel, Sebastian

2004-02-01

Optical tomography (OT) is a fast developing novel imaging modality that uses near-infrared (NIR) light to obtain cross-sectional views of optical properties inside the human body. A major challenge remains the time-consuming, computational-intensive image reconstruction problem that converts NIR transmission measurements into cross-sectional images. To increase the speed of iterative image reconstruction schemes that are commonly applied for OT, we have developed and implemented several parallel algorithms on a cluster of workstations. Static process distribution as well as dynamic load balancing schemes suitable for heterogeneous clusters and varying machine performances are introduced and tested. The resulting algorithms are shown to accelerate the reconstruction process to various degrees, substantially reducing the computation times for clinically relevant problems.

Helicopter-V/STOL dynamic wind and turbulence design methodology

NASA Technical Reports Server (NTRS)

Bailey, J. Earl

1987-01-01

Aircraft and helicopter accidents due to severe dynamic wind and turbulence continue to present challenging design problems. The development of the current set of design analysis tools for a aircraft wind and turbulence design began in the 1940's and 1950's. The areas of helicopter dynamic wind and turbulence modeling and vehicle response to severe dynamic wind inputs (microburst type phenomena) during takeoff and landing remain as major unsolved design problems from a lack of both environmental data and computational methodology. The development of helicopter and V/STOL dynamic wind and turbulence response computation methology is reviewed, the current state of the design art in industry is outlined, and comments on design methodology are made which may serve to improve future flight vehicle design.

A Localized Ensemble Kalman Smoother

NASA Technical Reports Server (NTRS)

Butala, Mark D.

2012-01-01

Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother.

DoD HPC Insights Fall 2016A publication of the Department of Defense High Performance Computing Modernization Program

DTIC Science & Technology

2016-09-01

HPCMP will continue to be a key resource in solving challenging problems for the Department of Defense . 1 Fall 2016 High-F idel i ty Simulat ions of...laser interactions. The group had studied plasma expansion experimentally, but this wasn’t sufficient to understand the problem . Feister adapted and...focused on increasing the efficiency of jet turbine engines and extending aircraft flight ranges by changing the shape (articulation) of the turbine

Exploring biological interaction networks with tailored weighted quasi-bicliques

PubMed Central

2012-01-01

Background Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges. Results We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. Conclusions We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks. PMID:22759421

Simulation and optimization of an experimental membrane wastewater treatment plant using computational intelligence methods.

PubMed

Ludwig, T; Kern, P; Bongards, M; Wolf, C

2011-01-01

The optimization of relaxation and filtration times of submerged microfiltration flat modules in membrane bioreactors used for municipal wastewater treatment is essential for efficient plant operation. However, the optimization and control of such plants and their filtration processes is a challenging problem due to the underlying highly nonlinear and complex processes. This paper presents the use of genetic algorithms for this optimization problem in conjunction with a fully calibrated simulation model, as computational intelligence methods are perfectly suited to the nonconvex multi-objective nature of the optimization problems posed by these complex systems. The simulation model is developed and calibrated using membrane modules from the wastewater simulation software GPS-X based on the Activated Sludge Model No.1 (ASM1). Simulation results have been validated at a technical reference plant. They clearly show that filtration process costs for cleaning and energy can be reduced significantly by intelligent process optimization.

A New Framework for Effective and Efficient Global Sensitivity Analysis of Earth and Environmental Systems Models

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin

2015-04-01

Earth and Environmental Systems (EES) models are essential components of research, development, and decision-making in science and engineering disciplines. With continuous advances in understanding and computing power, such models are becoming more complex with increasingly more factors to be specified (model parameters, forcings, boundary conditions, etc.). To facilitate better understanding of the role and importance of different factors in producing the model responses, the procedure known as 'Sensitivity Analysis' (SA) can be very helpful. Despite the availability of a large body of literature on the development and application of various SA approaches, two issues continue to pose major challenges: (1) Ambiguous Definition of Sensitivity - Different SA methods are based in different philosophies and theoretical definitions of sensitivity, and can result in different, even conflicting, assessments of the underlying sensitivities for a given problem, (2) Computational Cost - The cost of carrying out SA can be large, even excessive, for high-dimensional problems and/or computationally intensive models. In this presentation, we propose a new approach to sensitivity analysis that addresses the dual aspects of 'effectiveness' and 'efficiency'. By effective, we mean achieving an assessment that is both meaningful and clearly reflective of the objective of the analysis (the first challenge above), while by efficiency we mean achieving statistically robust results with minimal computational cost (the second challenge above). Based on this approach, we develop a 'global' sensitivity analysis framework that efficiently generates a newly-defined set of sensitivity indices that characterize a range of important properties of metric 'response surfaces' encountered when performing SA on EES models. Further, we show how this framework embraces, and is consistent with, a spectrum of different concepts regarding 'sensitivity', and that commonly-used SA approaches (e.g., Sobol, Morris, etc.) are actually limiting cases of our approach under specific conditions. Multiple case studies are used to demonstrate the value of the new framework. The results show that the new framework provides a fundamental understanding of the underlying sensitivities for any given problem, while requiring orders of magnitude fewer model runs.

Transfer of Learning: The Effects of Different Instruction Methods on Software Application Learning

ERIC Educational Resources Information Center

Larson, Mark E.

2010-01-01

Human Resource Departments (HRD), especially instructors, are challenged to keep pace with rapidly changing computer software applications and technology. The problem under investigation revealed after instruction of a software application if a particular method of instruction was a predictor of transfer of learning, when other risk factors were…

Neural Network Research: A Personal Perspective,

DTIC Science & Technology

1988-03-01

problems in computer science and technology today. Still others do both. Whatever the focus, here isafidred to adre efforts of a wide variety of gifted ...Still others do both. Whatever the focus, here is a field ready to challenge and reward the sustained efforts of a wide variety of gifted people. 14 7eN. a rcb

Prospects and Challenges for Using Microcomputers in School. Technical Report No. 7.

ERIC Educational Resources Information Center

Pea, Roy D.

Prepared as an address for educator groups, this paper provides a theoretical perspective for thinking about problems and prospects for integrating microcomputer uses in school activities. Six major aspects of the perspective are defined: (1) the computer as general-purpose symbolic device; (2) the importance of developmental studies of children's…

Wildlife Conservation Planning Using Stochastic Optimization and Importance Sampling

Treesearch

Robert G. Haight; Laurel E. Travis

1997-01-01

Formulations for determining conservation plans for sensitive wildlife species must account for economic costs of habitat protection and uncertainties about how wildlife populations will respond. This paper describes such a formulation and addresses the computational challenge of solving it. The problem is to determine the cost-efficient level of habitat protection...

Security Metrics: A Solution in Search of a Problem

ERIC Educational Resources Information Center

Rosenblatt, Joel

2008-01-01

Computer security is one of the most complicated and challenging fields in technology today. A security metrics program provides a major benefit: looking at the metrics on a regular basis offers early clues to changes in attack patterns or environmental factors that may require changes in security strategy. The term "security metrics"…

Challenging Elementary Learners with Programmable Robots during Free Play and Direct Instruction

ERIC Educational Resources Information Center

McCoy-Parker, Kimberly S.; Paull, Lindsey N.; Rule, Audrey C.; Montgomery, Sarah E.

2017-01-01

Computer programming skills are important to many current careers; teaching robot coding to elementary students can start a positive foundation for technological careers, develop problem-solving skills, and growth mindsets. This study, through a repeated measures design involving students in two classrooms at two widely-separated grade levels…

Advancing Diagnostic Skills for Technology and Engineering Undergraduates: A Summary of the Validation Data

ERIC Educational Resources Information Center

Foster, W. Tad; Shahhosseini, A. Mehran; Maughan, George

2016-01-01

Facilitating student growth and development in diagnosing and solving technical problems remains a challenge for technology and engineering educators. With funding from the National Science Foundation, this team of researchers developed a self-guided, computer-based instructional program to experiment with conceptual mapping as a treatment to…

Pen-based computers: Computers without keys

NASA Technical Reports Server (NTRS)

Conklin, Cheryl L.

1994-01-01

The National Space Transportation System (NSTS) is comprised of many diverse and highly complex systems incorporating the latest technologies. Data collection associated with ground processing of the various Space Shuttle system elements is extremely challenging due to the many separate processing locations where data is generated. This presents a significant problem when the timely collection, transfer, collation, and storage of data is required. This paper describes how new technology, referred to as Pen-Based computers, is being used to transform the data collection process at Kennedy Space Center (KSC). Pen-Based computers have streamlined procedures, increased data accuracy, and now provide more complete information than previous methods. The end results is the elimination of Shuttle processing delays associated with data deficiencies.

Challenges in Soft Computing: Case Study with Louisville MSD CSO Modeling

NASA Astrophysics Data System (ADS)

Ormsbee, L.; Tufail, M.

2005-12-01

The principal constituents of soft computing include fuzzy logic, neural computing, evolutionary computation, machine learning, and probabilistic reasoning. There are numerous applications of these constituents (both individually and combination of two or more) in the area of water resources and environmental systems. These range from development of data driven models to optimal control strategies to assist in more informed and intelligent decision making process. Availability of data is critical to such applications and having scarce data may lead to models that do not represent the response function over the entire domain. At the same time, too much data has a tendency to lead to over-constraining of the problem. This paper will describe the application of a subset of these soft computing techniques (neural computing and genetic algorithms) to the Beargrass Creek watershed in Louisville, Kentucky. The application include development of inductive models as substitutes for more complex process-based models to predict water quality of key constituents (such as dissolved oxygen) and use them in an optimization framework for optimal load reductions. Such a process will facilitate the development of total maximum daily loads for the impaired water bodies in the watershed. Some of the challenges faced in this application include 1) uncertainty in data sets, 2) model application, and 3) development of cause-and-effect relationships between water quality constituents and watershed parameters through use of inductive models. The paper will discuss these challenges and how they affect the desired goals of the project.

Towards a 'siliconeural computer': technological successes and challenges.

PubMed

Hughes, Mark A; Shipston, Mike J; Murray, Alan F

2015-07-28

Electronic signals govern the function of both nervous systems and computers, albeit in different ways. As such, hybridizing both systems to create an iono-electric brain-computer interface is a realistic goal; and one that promises exciting advances in both heterotic computing and neuroprosthetics capable of circumventing devastating neuropathology. 'Neural networks' were, in the 1980s, viewed naively as a potential panacea for all computational problems that did not fit well with conventional computing. The field bifurcated during the 1990s into a highly successful and much more realistic machine learning community and an equally pragmatic, biologically oriented 'neuromorphic computing' community. Algorithms found in nature that use the non-synchronous, spiking nature of neuronal signals have been found to be (i) implementable efficiently in silicon and (ii) computationally useful. As a result, interest has grown in techniques that could create mixed 'siliconeural' computers. Here, we discuss potential approaches and focus on one particular platform using parylene-patterned silicon dioxide.

Toward integration of in vivo molecular computing devices: successes and challenges

PubMed Central

Hayat, Sikander; Hinze, Thomas

2008-01-01

The computing power unleashed by biomolecule based massively parallel computational units has been the focus of many interdisciplinary studies that couple state of the art ideas from mathematical logic, theoretical computer science, bioengineering, and nanotechnology to fulfill some computational task. The output can influence, for instance, release of a drug at a specific target, gene expression, cell population, or be a purely mathematical entity. Analysis of the results of several studies has led to the emergence of a general set of rules concerning the implementation and optimization of in vivo computational units. Taking two recent studies on in vivo computing as examples, we discuss the impact of mathematical modeling and simulation in the field of synthetic biology and on in vivo computing. The impact of the emergence of gene regulatory networks and the potential of proteins acting as “circuit wires” on the problem of interconnecting molecular computing device subunits is also highlighted. PMID:19404433

Phase unwrapping with graph cuts optimization and dual decomposition acceleration for 3D high-resolution MRI data.

PubMed

Dong, Jianwu; Chen, Feng; Zhou, Dong; Liu, Tian; Yu, Zhaofei; Wang, Yi

2017-03-01

Existence of low SNR regions and rapid-phase variations pose challenges to spatial phase unwrapping algorithms. Global optimization-based phase unwrapping methods are widely used, but are significantly slower than greedy methods. In this paper, dual decomposition acceleration is introduced to speed up a three-dimensional graph cut-based phase unwrapping algorithm. The phase unwrapping problem is formulated as a global discrete energy minimization problem, whereas the technique of dual decomposition is used to increase the computational efficiency by splitting the full problem into overlapping subproblems and enforcing the congruence of overlapping variables. Using three dimensional (3D) multiecho gradient echo images from an agarose phantom and five brain hemorrhage patients, we compared this proposed method with an unaccelerated graph cut-based method. Experimental results show up to 18-fold acceleration in computation time. Dual decomposition significantly improves the computational efficiency of 3D graph cut-based phase unwrapping algorithms. Magn Reson Med 77:1353-1358, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

A Computational Approach for Probabilistic Analysis of LS-DYNA Water Impact Simulations

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Mason, Brian H.; Lyle, Karen H.

2010-01-01

NASA s development of new concepts for the Crew Exploration Vehicle Orion presents many similar challenges to those worked in the sixties during the Apollo program. However, with improved modeling capabilities, new challenges arise. For example, the use of the commercial code LS-DYNA, although widely used and accepted in the technical community, often involves high-dimensional, time consuming, and computationally intensive simulations. Because of the computational cost, these tools are often used to evaluate specific conditions and rarely used for statistical analysis. The challenge is to capture what is learned from a limited number of LS-DYNA simulations to develop models that allow users to conduct interpolation of solutions at a fraction of the computational time. For this problem, response surface models are used to predict the system time responses to a water landing as a function of capsule speed, direction, attitude, water speed, and water direction. Furthermore, these models can also be used to ascertain the adequacy of the design in terms of probability measures. This paper presents a description of the LS-DYNA model, a brief summary of the response surface techniques, the analysis of variance approach used in the sensitivity studies, equations used to estimate impact parameters, results showing conditions that might cause injuries, and concluding remarks.

Semantic Web Services Challenge, Results from the First Year. Series: Semantic Web And Beyond, Volume 8.

NASA Astrophysics Data System (ADS)

Petrie, C.; Margaria, T.; Lausen, H.; Zaremba, M.

Explores trade-offs among existing approaches. Reveals strengths and weaknesses of proposed approaches, as well as which aspects of the problem are not yet covered. Introduces software engineering approach to evaluating semantic web services. Service-Oriented Computing is one of the most promising software engineering trends because of the potential to reduce the programming effort for future distributed industrial systems. However, only a small part of this potential rests on the standardization of tools offered by the web services stack. The larger part of this potential rests upon the development of sufficient semantics to automate service orchestration. Currently there are many different approaches to semantic web service descriptions and many frameworks built around them. A common understanding, evaluation scheme, and test bed to compare and classify these frameworks in terms of their capabilities and shortcomings, is necessary to make progress in developing the full potential of Service-Oriented Computing. The Semantic Web Services Challenge is an open source initiative that provides a public evaluation and certification of multiple frameworks on common industrially-relevant problem sets. This edited volume reports on the first results in developing common understanding of the various technologies intended to facilitate the automation of mediation, choreography and discovery for Web Services using semantic annotations. Semantic Web Services Challenge: Results from the First Year is designed for a professional audience composed of practitioners and researchers in industry. Professionals can use this book to evaluate SWS technology for their potential practical use. The book is also suitable for advanced-level students in computer science.

Distributed Parallel Processing and Dynamic Load Balancing Techniques for Multidisciplinary High Speed Aircraft Design

NASA Technical Reports Server (NTRS)

Krasteva, Denitza T.

1998-01-01

Multidisciplinary design optimization (MDO) for large-scale engineering problems poses many challenges (e.g., the design of an efficient concurrent paradigm for global optimization based on disciplinary analyses, expensive computations over vast data sets, etc.) This work focuses on the application of distributed schemes for massively parallel architectures to MDO problems, as a tool for reducing computation time and solving larger problems. The specific problem considered here is configuration optimization of a high speed civil transport (HSCT), and the efficient parallelization of the embedded paradigm for reasonable design space identification. Two distributed dynamic load balancing techniques (random polling and global round robin with message combining) and two necessary termination detection schemes (global task count and token passing) were implemented and evaluated in terms of effectiveness and scalability to large problem sizes and a thousand processors. The effect of certain parameters on execution time was also inspected. Empirical results demonstrated stable performance and effectiveness for all schemes, and the parametric study showed that the selected algorithmic parameters have a negligible effect on performance.

Algorithms Bridging Quantum Computation and Chemistry

NASA Astrophysics Data System (ADS)

McClean, Jarrod Ryan

The design of new materials and chemicals derived entirely from computation has long been a goal of computational chemistry, and the governing equation whose solution would permit this dream is known. Unfortunately, the exact solution to this equation has been far too expensive and clever approximations fail in critical situations. Quantum computers offer a novel solution to this problem. In this work, we develop not only new algorithms to use quantum computers to study hard problems in chemistry, but also explore how such algorithms can help us to better understand and improve our traditional approaches. In particular, we first introduce a new method, the variational quantum eigensolver, which is designed to maximally utilize the quantum resources available in a device to solve chemical problems. We apply this method in a real quantum photonic device in the lab to study the dissociation of the helium hydride (HeH+) molecule. We also enhance this methodology with architecture specific optimizations on ion trap computers and show how linear-scaling techniques from traditional quantum chemistry can be used to improve the outlook of similar algorithms on quantum computers. We then show how studying quantum algorithms such as these can be used to understand and enhance the development of classical algorithms. In particular we use a tool from adiabatic quantum computation, Feynman's Clock, to develop a new discrete time variational principle and further establish a connection between real-time quantum dynamics and ground state eigenvalue problems. We use these tools to develop two novel parallel-in-time quantum algorithms that outperform competitive algorithms as well as offer new insights into the connection between the fermion sign problem of ground states and the dynamical sign problem of quantum dynamics. Finally we use insights gained in the study of quantum circuits to explore a general notion of sparsity in many-body quantum systems. In particular we use developments from the field of compressed sensing to find compact representations of ground states. As an application we study electronic systems and find solutions dramatically more compact than traditional configuration interaction expansions, offering hope to extend this methodology to challenging systems in chemical and material design.

A reduced successive quadratic programming strategy for errors-in-variables estimation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tjoa, I.-B.; Biegler, L. T.; Carnegie-Mellon Univ.

Parameter estimation problems in process engineering represent a special class of nonlinear optimization problems, because the maximum likelihood structure of the objective function can be exploited. Within this class, the errors in variables method (EVM) is particularly interesting. Here we seek a weighted least-squares fit to the measurements with an underdetermined process model. Thus, both the number of variables and degrees of freedom available for optimization increase linearly with the number of data sets. Large optimization problems of this type can be particularly challenging and expensive to solve because, for general-purpose nonlinear programming (NLP) algorithms, the computational effort increases atmore » least quadratically with problem size. In this study we develop a tailored NLP strategy for EVM problems. The method is based on a reduced Hessian approach to successive quadratic programming (SQP), but with the decomposition performed separately for each data set. This leads to the elimination of all variables but the model parameters, which are determined by a QP coordination step. In this way the computational effort remains linear in the number of data sets. Moreover, unlike previous approaches to the EVM problem, global and superlinear properties of the SQP algorithm apply naturally. Also, the method directly incorporates inequality constraints on the model parameters (although not on the fitted variables). This approach is demonstrated on five example problems with up to 102 degrees of freedom. Compared to general-purpose NLP algorithms, large improvements in computational performance are observed.« less

Big Data, Deep Learning and Tianhe-2 at Sun Yat-Sen University, Guangzhou

NASA Astrophysics Data System (ADS)

Yuen, D. A.; Dzwinel, W.; Liu, J.; Zhang, K.

2014-12-01

In this decade the big data revolution has permeated in many fields, ranging from financial transactions, medical surveys and scientific endeavors, because of the big opportunities people see ahead. What to do with all this data remains an intriguing question. This is where computer scientists together with applied mathematicians have made some significant inroads in developing deep learning techniques for unraveling new relationships among the different variables by means of correlation analysis and data-assimilation methods. Deep-learning and big data taken together is a grand challenge task in High-performance computing which demand both ultrafast speed and large memory. The Tianhe-2 recently installed at Sun Yat-Sen University in Guangzhou is well positioned to take up this challenge because it is currently the world's fastest computer at 34 Petaflops. Each compute node of Tianhe-2 has two CPUs of Intel Xeon E5-2600 and three Xeon Phi accelerators. The Tianhe-2 has a very large fast memory RAM of 88 Gigabytes on each node. The system has a total memory of 1,375 Terabytes. All of these technical features will allow very high dimensional (more than 10) problem in deep learning to be explored carefully on the Tianhe-2. Problems in seismology which can be solved include three-dimensional seismic wave simulations of the whole Earth with a few km resolution and the recognition of new phases in seismic wave form from assemblage of large data sets.

Evaluation of Full Reynolds Stress Turbulence Models in FUN3D

NASA Technical Reports Server (NTRS)

Dudek, Julianne C.; Carlson, Jan-Renee

2017-01-01

Full seven-equation Reynolds stress turbulence models are promising tools for today’s aerospace technology challenges. This paper examines two such models for computing challenging turbulent flows including shock-wave boundary layer interactions, separation and mixing layers. The Wilcox and the SSG/LRR full second-moment Reynolds stress models have been implemented into the FUN3D (Fully Unstructured Navier-Stokes Three Dimensional) unstructured Navier-Stokes code and were evaluated for four problems: a transonic two-dimensional diffuser, a supersonic axisymmetric compression corner, a compressible planar shear layer, and a subsonic axisymmetric jet. Simulation results are compared with experimental data and results computed using the more commonly used Spalart-Allmaras (SA) one-equation and the Menter Shear Stress Transport (SST-V) two-equation turbulence models.

Towards Modeling False Memory With Computational Knowledge Bases.

PubMed

Li, Justin; Kohanyi, Emma

2017-01-01

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. Copyright © 2016 Cognitive Science Society, Inc.

Decomposing Large Inverse Problems with an Augmented Lagrangian Approach: Application to Joint Inversion of Body-Wave Travel Times and Surface-Wave Dispersion Measurements

NASA Astrophysics Data System (ADS)

Reiter, D. T.; Rodi, W. L.

2015-12-01

Constructing 3D Earth models through the joint inversion of large geophysical data sets presents numerous theoretical and practical challenges, especially when diverse types of data and model parameters are involved. Among the challenges are the computational complexity associated with large data and model vectors and the need to unify differing model parameterizations, forward modeling methods and regularization schemes within a common inversion framework. The challenges can be addressed in part by decomposing the inverse problem into smaller, simpler inverse problems that can be solved separately, providing one knows how to merge the separate inversion results into an optimal solution of the full problem. We have formulated an approach to the decomposition of large inverse problems based on the augmented Lagrangian technique from optimization theory. As commonly done, we define a solution to the full inverse problem as the Earth model minimizing an objective function motivated, for example, by a Bayesian inference formulation. Our decomposition approach recasts the minimization problem equivalently as the minimization of component objective functions, corresponding to specified data subsets, subject to the constraints that the minimizing models be equal. A standard optimization algorithm solves the resulting constrained minimization problems by alternating between the separate solution of the component problems and the updating of Lagrange multipliers that serve to steer the individual solution models toward a common model solving the full problem. We are applying our inversion method to the reconstruction of the·crust and upper-mantle seismic velocity structure across Eurasia.· Data for the inversion comprise a large set of P and S body-wave travel times·and fundamental and first-higher mode Rayleigh-wave group velocities.

Computational approaches to protein inference in shotgun proteomics

PubMed Central

2012-01-01

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programing and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area. PMID:23176300

Improved Hybrid Modeling of Spent Fuel Storage Facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bibber, Karl van

This work developed a new computational method for improving the ability to calculate the neutron flux in deep-penetration radiation shielding problems that contain areas with strong streaming. The “gold standard” method for radiation transport is Monte Carlo (MC) as it samples the physics exactly and requires few approximations. Historically, however, MC was not useful for shielding problems because of the computational challenge of following particles through dense shields. Instead, deterministic methods, which are superior in term of computational effort for these problems types but are not as accurate, were used. Hybrid methods, which use deterministic solutions to improve MC calculationsmore » through a process called variance reduction, can make it tractable from a computational time and resource use perspective to use MC for deep-penetration shielding. Perhaps the most widespread and accessible of these methods are the Consistent Adjoint Driven Importance Sampling (CADIS) and Forward-Weighted CADIS (FW-CADIS) methods. For problems containing strong anisotropies, such as power plants with pipes through walls, spent fuel cask arrays, active interrogation, and locations with small air gaps or plates embedded in water or concrete, hybrid methods are still insufficiently accurate. In this work, a new method for generating variance reduction parameters for strongly anisotropic, deep penetration radiation shielding studies was developed. This method generates an alternate form of the adjoint scalar flux quantity, Φ Ω, which is used by both CADIS and FW-CADIS to generate variance reduction parameters for local and global response functions, respectively. The new method, called CADIS-Ω, was implemented in the Denovo/ADVANTG software. Results indicate that the flux generated by CADIS-Ω incorporates localized angular anisotropies in the flux more effectively than standard methods. CADIS-Ω outperformed CADIS in several test problems. This initial work indicates that CADIS- may be highly useful for shielding problems with strong angular anisotropies. This is a benefit to the public by increasing accuracy for lower computational effort for many problems that have energy, security, and economic importance.« less

Citizens unite for computational immunology!

PubMed

Belden, Orrin S; Baker, Sarah Catherine; Baker, Brian M

2015-07-01

Recruiting volunteers who can provide computational time, programming expertise, or puzzle-solving talent has emerged as a powerful tool for biomedical research. Recent projects demonstrate the potential for such 'crowdsourcing' efforts in immunology. Tools for developing applications, new funding opportunities, and an eager public make crowdsourcing a serious option for creative solutions for computationally-challenging problems. Expanded uses of crowdsourcing in immunology will allow for more efficient large-scale data collection and analysis. It will also involve, inspire, educate, and engage the public in a variety of meaningful ways. The benefits are real - it is time to jump in! Copyright © 2015 Elsevier Ltd. All rights reserved.

Introduction to focus issue: intrinsic and designed computation: information processing in dynamical systems--beyond the digital hegemony.

PubMed

Crutchfield, James P; Ditto, William L; Sinha, Sudeshna

2010-09-01

How dynamical systems store and process information is a fundamental question that touches a remarkably wide set of contemporary issues: from the breakdown of Moore's scaling laws--that predicted the inexorable improvement in digital circuitry--to basic philosophical problems of pattern in the natural world. It is a question that also returns one to the earliest days of the foundations of dynamical systems theory, probability theory, mathematical logic, communication theory, and theoretical computer science. We introduce the broad and rather eclectic set of articles in this Focus Issue that highlights a range of current challenges in computing and dynamical systems.

Multiphase Fluid Dynamics for Spacecraft Applications

NASA Astrophysics Data System (ADS)

Shyy, W.; Sim, J.

2011-09-01

Multiphase flows involving moving interfaces between different fluids/phases are observed in nature as well as in a wide range of engineering applications. With the recent development of high fidelity computational techniques, a number of challenging multiphase flow problems can now be computed. We introduce the basic notion of the main categories of multiphase flow computation; Lagrangian, Eulerian, and Eulerian-Lagrangian techniques to represent and follow interface, and sharp and continuous interface methods to model interfacial dynamics. The marker-based adaptive Eulerian-Lagrangian method, which is one of the most popular methods, is highlighted with microgravity and space applications including droplet collision and spacecraft liquid fuel tank surface stability.

Computing in Hydraulic Engineering Education

NASA Astrophysics Data System (ADS)

Duan, J. G.

2011-12-01

Civil engineers, pioneers of our civilization, are rarely perceived as leaders and innovators in modern society because of retardations in technology innovation. This crisis has resulted in the decline of the prestige of civil engineering profession, reduction of federal funding on deteriorating infrastructures, and problems with attracting the most talented high-school students. Infusion of cutting-edge computer technology and stimulating creativity and innovation therefore are the critical challenge to civil engineering education. To better prepare our graduates to innovate, this paper discussed the adaption of problem-based collaborative learning technique and integration of civil engineering computing into a traditional civil engineering curriculum. Three interconnected courses: Open Channel Flow, Computational Hydraulics, and Sedimentation Engineering, were developed with emphasis on computational simulations. In Open Channel flow, the focuses are principles of free surface flow and the application of computational models. This prepares students to the 2nd course, Computational Hydraulics, that introduce the fundamental principles of computational hydraulics, including finite difference and finite element methods. This course complements the Open Channel Flow class to provide students with in-depth understandings of computational methods. The 3rd course, Sedimentation Engineering, covers the fundamentals of sediment transport and river engineering, so students can apply the knowledge and programming skills gained from previous courses to develop computational models for simulating sediment transport. These courses effectively equipped students with important skills and knowledge to complete thesis and dissertation research.

Numerical algebraic geometry for model selection and its application to the life sciences

PubMed Central

Gross, Elizabeth; Davis, Brent; Ho, Kenneth L.; Bates, Daniel J.

2016-01-01

Researchers working with mathematical models are often confronted by the related problems of parameter estimation, model validation and model selection. These are all optimization problems, well known to be challenging due to nonlinearity, non-convexity and multiple local optima. Furthermore, the challenges are compounded when only partial data are available. Here, we consider polynomial models (e.g. mass-action chemical reaction networks at steady state) and describe a framework for their analysis based on optimization using numerical algebraic geometry. Specifically, we use probability-one polynomial homotopy continuation methods to compute all critical points of the objective function, then filter to recover the global optima. Our approach exploits the geometrical structures relating models and data, and we demonstrate its utility on examples from cell signalling, synthetic biology and epidemiology. PMID:27733697

Mesoscale Models of Fluid Dynamics

NASA Astrophysics Data System (ADS)

Boghosian, Bruce M.; Hadjiconstantinou, Nicolas G.

During the last half century, enormous progress has been made in the field of computational materials modeling, to the extent that in many cases computational approaches are used in a predictive fashion. Despite this progress, modeling of general hydrodynamic behavior remains a challenging task. One of the main challenges stems from the fact that hydrodynamics manifests itself over a very wide range of length and time scales. On one end of the spectrum, one finds the fluid's "internal" scale characteristic of its molecular structure (in the absence of quantum effects, which we omit in this chapter). On the other end, the "outer" scale is set by the characteristic sizes of the problem's domain. The resulting scale separation or lack thereof as well as the existence of intermediate scales are key to determining the optimal approach. Successful treatments require a judicious choice of the level of description which is a delicate balancing act between the conflicting requirements of fidelity and manageable computational cost: a coarse description typically requires models for underlying processes occuring at smaller length and time scales; on the other hand, a fine-scale model will incur a significantly larger computational cost.

Mining High-Dimensional Data

NASA Astrophysics Data System (ADS)

Wang, Wei; Yang, Jiong

With the rapid growth of computational biology and e-commerce applications, high-dimensional data becomes very common. Thus, mining high-dimensional data is an urgent problem of great practical importance. However, there are some unique challenges for mining data of high dimensions, including (1) the curse of dimensionality and more crucial (2) the meaningfulness of the similarity measure in the high dimension space. In this chapter, we present several state-of-art techniques for analyzing high-dimensional data, e.g., frequent pattern mining, clustering, and classification. We will discuss how these methods deal with the challenges of high dimensionality.

[Medical cooperation on the internet].

PubMed

Meier, N; Lenzen, H; Renger, B C

1998-01-01

Post-1999, the economically united EEC will pose new challenges to European business, industry and citizen. It is a key objective that in the domain of European "infostructure" these problems are challenged and overcome, and that "advanced communications technologies and services" (ACTS) become the cement which binds the Community together. Within ACTS, 130 different projects are building new services. The consortium Emerald develops a telemedicine platform, setting up teleworking with teleconference, computer supported co-operative work (cscw, joint editing), demonstration and teleteaching for radiology, cardiology, nuclear medicine and radio surgery working environments.

Observations on Student Misconceptions--A Case Study of the Build-Heap Algorithm

ERIC Educational Resources Information Center

Seppala, Otto; Malmi, Lauri; Korhonen, Ari

2006-01-01

Data structures and algorithms are core issues in computer programming. However, learning them is challenging for most students and many of them have various types of misconceptions on how algorithms work. In this study, we discuss the problem of identifying misconceptions on the principles of how algorithms work. Our context is algorithm…

An Informal Discussion on Internet Matters. Moral Construction for Children and Young People

ERIC Educational Resources Information Center

Shaoguang, Yang

2006-01-01

The social problems triggered by Internet are legion. Computer games and such high-tech achievements of the Internet that used to be regarded as "angels" are today frequently playing the role of "demons." As the times advance, Internet ethics have become a new challenge facing educational workers. Today, when our country is…

The Paradigm Recursion: Is It More Accessible When Introduced in Middle School?

ERIC Educational Resources Information Center

Gunion, Katherine; Milford, Todd; Stege, Ulrike

2009-01-01

Recursion is a programming paradigm as well as a problem solving strategy thought to be very challenging to grasp for university students. This article outlines a pilot study, which expands the age range of students exposed to the concept of recursion in computer science through instruction in a series of interesting and engaging activities. In…

A Predictive Coding Account of Psychotic Symptoms in Autism Spectrum Disorder

ERIC Educational Resources Information Center

van Schalkwyk, Gerrit I.; Volkmar, Fred R.; Corlett, Philip R.

2017-01-01

The co-occurrence of psychotic and autism spectrum disorder (ASD) symptoms represents an important clinical challenge. Here we consider this problem in the context of a computational psychiatry approach that has been applied to both conditions--predictive coding. Some symptoms of schizophrenia have been explained in terms of a failure of top-down…

An Explanatory Item Response Theory Approach for a Computer-Based Case Simulation Test

ERIC Educational Resources Information Center

Kahraman, Nilüfer

2014-01-01

Problem: Practitioners working with multiple-choice tests have long utilized Item Response Theory (IRT) models to evaluate the performance of test items for quality assurance. The use of similar applications for performance tests, however, is often encumbered due to the challenges encountered in working with complicated data sets in which local…

Communication Challenges Learners Face Online: Why Addressing CMC and Language Proficiency Will Not Solve Learners' Problems

ERIC Educational Resources Information Center

Jung-Ivannikova, Liubov

2016-01-01

Computer-mediated communication (CMC) has been argued to cause (mis)communication issues. Research and practice suggest a range of tactics and strategies for educators focused on how to encourage and foster communication in a virtual learning environment (VLE) (eg, Salmon). However, while frameworks such as Salmon's support the effective…

Parameters Free Computational Characterization of Defects in Transition Metal Oxides with Diffusion Quantum Monte Carlo

NASA Astrophysics Data System (ADS)

Santana, Juan A.; Krogel, Jaron T.; Kent, Paul R.; Reboredo, Fernando

Materials based on transition metal oxides (TMO's) are among the most challenging systems for computational characterization. Reliable and practical computations are possible by directly solving the many-body problem for TMO's with quantum Monte Carlo (QMC) methods. These methods are very computationally intensive, but recent developments in algorithms and computational infrastructures have enabled their application to real materials. We will show our efforts on the application of the diffusion quantum Monte Carlo (DMC) method to study the formation of defects in binary and ternary TMO and heterostructures of TMO. We will also outline current limitations in hardware and algorithms. This work is supported by the Materials Sciences & Engineering Division of the Office of Basic Energy Sciences, U.S. Department of Energy (DOE).

GPU acceleration of Dock6's Amber scoring computation.

PubMed

Yang, Hailong; Zhou, Qiongqiong; Li, Bo; Wang, Yongjian; Luan, Zhongzhi; Qian, Depei; Li, Hanlu

2010-01-01

Dressing the problem of virtual screening is a long-term goal in the drug discovery field, which if properly solved, can significantly shorten new drugs' R&D cycle. The scoring functionality that evaluates the fitness of the docking result is one of the major challenges in virtual screening. In general, scoring functionality in docking requires a large amount of floating-point calculations, which usually takes several weeks or even months to be finished. This time-consuming procedure is unacceptable, especially when highly fatal and infectious virus arises such as SARS and H1N1, which forces the scoring task to be done in a limited time. This paper presents how to leverage the computational power of GPU to accelerate Dock6's (http://dock.compbio.ucsf.edu/DOCK_6/) Amber (J. Comput. Chem. 25: 1157-1174, 2004) scoring with NVIDIA CUDA (NVIDIA Corporation Technical Staff, Compute Unified Device Architecture - Programming Guide, NVIDIA Corporation, 2008) (Compute Unified Device Architecture) platform. We also discuss many factors that will greatly influence the performance after porting the Amber scoring to GPU, including thread management, data transfer, and divergence hidden. Our experiments show that the GPU-accelerated Amber scoring achieves a 6.5× speedup with respect to the original version running on AMD dual-core CPU for the same problem size. This acceleration makes the Amber scoring more competitive and efficient for large-scale virtual screening problems.

Report of the Panel on Computer and Information Technology

NASA Technical Reports Server (NTRS)

Lundstrom, Stephen F.; Larsen, Ronald L.

1984-01-01

Aircraft have become more and more dependent on computers (information processing) for improved performance and safety. It is clear that this activity will grow, since information processing technology has advanced by a factor of 10 every 5 years for the past 35 years and will continue to do so. Breakthroughs in device technology, from vacuum tubes through transistors to integrated circuits, contribute to this rapid pace. This progress is nearly matched by similar, though not as dramatic, advances in numerical software and algorithms. Progress has not been easy. Many technical and nontechnical challenges were surmounted. The outlook is for continued growth in capability but will require surmounting new challenges. The technology forecast presented in this report has been developed by extrapolating current trends and assessing the possibilities of several high-risk research topics. In the process, critical problem areas that require research and development emphasis have been identified. The outlook assumes a positive perspective; the projected capabilities are possible by the year 2000, and adequate resources will be made available to achieve them. Computer and information technology forecasts and the potential impacts of this technology on aeronautics are identified. Critical issues and technical challenges underlying the achievement of forecasted performance and benefits are addressed.

Model and controller reduction of large-scale structures based on projection methods

NASA Astrophysics Data System (ADS)

Gildin, Eduardo

The design of low-order controllers for high-order plants is a challenging problem theoretically as well as from a computational point of view. Frequently, robust controller design techniques result in high-order controllers. It is then interesting to achieve reduced-order models and controllers while maintaining robustness properties. Controller designed for large structures based on models obtained by finite element techniques yield large state-space dimensions. In this case, problems related to storage, accuracy and computational speed may arise. Thus, model reduction methods capable of addressing controller reduction problems are of primary importance to allow the practical applicability of advanced controller design methods for high-order systems. A challenging large-scale control problem that has emerged recently is the protection of civil structures, such as high-rise buildings and long-span bridges, from dynamic loadings such as earthquakes, high wind, heavy traffic, and deliberate attacks. Even though significant effort has been spent in the application of control theory to the design of civil structures in order increase their safety and reliability, several challenging issues are open problems for real-time implementation. This dissertation addresses with the development of methodologies for controller reduction for real-time implementation in seismic protection of civil structures using projection methods. Three classes of schemes are analyzed for model and controller reduction: nodal truncation, singular value decomposition methods and Krylov-based methods. A family of benchmark problems for structural control are used as a framework for a comparative study of model and controller reduction techniques. It is shown that classical model and controller reduction techniques, such as balanced truncation, modal truncation and moment matching by Krylov techniques, yield reduced-order controllers that do not guarantee stability of the closed-loop system, that is, the reduced-order controller implemented with the full-order plant. A controller reduction approach is proposed such that to guarantee closed-loop stability. It is based on the concept of dissipativity (or positivity) of linear dynamical systems. Utilizing passivity preserving model reduction together with dissipative-LQG controllers, effective low-order optimal controllers are obtained. Results are shown through simulations.

Methods of Mathematical and Computational Physics for Industry, Science, and Technology

NASA Astrophysics Data System (ADS)

Melnik, Roderick V. N.; Voss, Frands

2006-11-01

Many industrial problems provide scientists with important and challenging problems that need to be solved today rather than tomorrow. The key role of mathematical physics, modelling, and computational methodologies in addressing such problems continues to increase. Science has never been exogenous to applied research. Gigantic ships and steam engines, repeating catapult of Dionysius and the Antikythera `computer' invented around 80BC are just a few examples demonstrating a profound link between theoretical and applied science in the ancient world. Nowadays, many industrial problems are typically approached by groups of researchers who are working as a team bringing their expertise to the success of the entire enterprise. Since the late 1960s several groups of European mathematicians and scientists have started organizing regular meetings, seeking new challenges from industry and contributing to the solution of important industrial problems. In particular, this often took the format of week-long workshops originally initiated by the Oxford Study Groups with Industry in 1968. Such workshops are now held in many European countries (typically under the auspices of the European Study Groups with Industry - ESGI), as well as in Australia, Canada, the United States, and other countries around the world. Problems given by industrial partners are sometimes very difficult to complete within a week. However, during a week of brainstorming activities these problems inevitably stimulate developing fruitful new ideas, new approaches, and new collaborations. At the same time, there are cases where as soon as the problem is formulated mathematically, it is relatively easy to solve. Hence, putting the industrial problem into a mathematical framework, based on physical laws, often provides a key element to the success. In addition to this important first step, the value in such cases is the real, practical applicability of the results obtained for an industrial partner who presents the problem. Under both outlined scenarios, scientists and mathematicians are provided with an opportunity to challenge themselves with real-world problems and to work together in a team on important industrial issues. This issue is a result of selected contributions by participants of the meeting that took place in the Sønderborg area of Denmark, one of the most important centers for information technology, telecommunication and electronics in the country. The meeting was hosted by the University of Southern Denmark in a picturesque area of Southern Jutland. It brought together about 65 participants, among whom were professional mathematicians, engineers, physicists, and industrial participants. The meeting was a truly international one, with delegates from four major Danish Universities, the UK, Norway, Italy, Czech Republic, Turkey, China, Germany, Latvia, Canada, the United States, and Finland. Five challenging projects were presented by leading industrial companies, including Grundfos, Danfoss Industrial Control, Unisensor, and Danfoss Flow Division (now Siemens). The meeting featured also the Mathematics for Industry Workshop with several distinguished international speakers. This volume of Journal of Physics: Conference Series on `Methods of Mathematical and Computational Physics for Industry, Science, and Technology' contains contributions from some of the participants of the workshop as well as the papers produced as a result of collaborative efforts with the above mentioned industrial companies. We would like to thank all authors and participants for their contributions and for bearing with us during the review process and preparation of this issue. We thank also all our referees for their timely and detailed reports. The publication of the proceedings of this meeting in Denmark was delayed due to problems with a previous publisher. We are very grateful that Journal of Physics: Conference Series kindly agreed to publish the proceedings rapidly at this late stage. As industrial problems become increasingly multidisciplinary, their successful solutions are often contingent on effective collaborative efforts between scientists, mathematicians, industrialists, and engineers. This volume has provided several examples of such collaborative efforts in the context of real-world industrial problems along with the analysis of important physics-based mathematical models applicable in a range of industrial contexts. Roderick V N Melnik, Professor of Mathematical Modelling, Syddansk Universitet (Denmark) and Professor and Canada Research Chair, Wilfrid Laurier University, Waterloo, Canada E-mail: rmelnik@wlu.ca Frands Voss, Director of the Mads Clausen Institute, Syddansk Universitet (Denmark)

Translational Biomedical Informatics in the Cloud: Present and Future

PubMed Central

Chen, Jiajia; Qian, Fuliang; Yan, Wenying; Shen, Bairong

2013-01-01

Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration. In this paper we demonstrated the utility and promise of cloud computing for tackling the big data problems. We also outline our vision that cloud computing could be an enabling tool to facilitate translational bioinformatics research. PMID:23586054

Virtualization in education: Information Security lab in your hands

NASA Astrophysics Data System (ADS)

Karlov, A. A.

2016-09-01

The growing demand for qualified specialists in advanced information technologies poses serious challenges to the education and training of young personnel for science, industry and social problems. Virtualization as a way to isolate the user from the physical characteristics of computing resources (processors, servers, operating systems, networks, applications, etc.), has, in particular, an enormous influence in the field of education, increasing its efficiency, reducing the cost, making it more widely and readily available. The study of Information Security of computer systems is considered as an example of use of virtualization in education.

Organizational diagnosis of computer and information learning needs: the process and product.

PubMed

Nelson, R; Anton, B

1997-01-01

Organizational diagnosis views the organization as a single entity with problems and challenges that are unique to the organization as a whole. This paper describes the process of establishing organizational diagnoses related to computer and information learning needs within a clinical or academic health care institution. The assessment of a college within a state-owned university in the U.S.A. is used to demonstrate the process of organizational diagnosis. The diagnoses identified include the need to improve information seeking skills and the information presentation skills of faculty.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

PubMed Central

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently. PMID:28255297

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

PubMed

Ni, Jianjun; Wu, Liuying; Shi, Pengfei; Yang, Simon X

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

NASA Astrophysics Data System (ADS)

Fujii, Keisuke; Nakajima, Kohei

2017-08-01

The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

Computational protein design with backbone plasticity

PubMed Central

MacDonald, James T.; Freemont, Paul S.

2016-01-01

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process. PMID:27911735

The application of quantum mechanics in structure-based drug design.

PubMed

Mucs, Daniel; Bryce, Richard A

2013-03-01

Computational chemistry has become an established and valuable component in structure-based drug design. However the chemical complexity of many ligands and active sites challenges the accuracy of the empirical potentials commonly used to describe these systems. Consequently, there is a growing interest in utilizing electronic structure methods for addressing problems in protein-ligand recognition. In this review, the authors discuss recent progress in the development and application of quantum chemical approaches to modeling protein-ligand interactions. The authors specifically consider the development of quantum mechanics (QM) approaches for studying large molecular systems pertinent to biology, focusing on protein-ligand docking, protein-ligand binding affinities and ligand strain on binding. Although computation of binding energies remains a challenging and evolving area, current QM methods can underpin improved docking approaches and offer detailed insights into ligand strain and into the nature and relative strengths of complex active site interactions. The authors envisage that QM will become an increasingly routine and valued tool of the computational medicinal chemist.

QM/QM approach to model energy disorder in amorphous organic semiconductors.

PubMed

Friederich, Pascal; Meded, Velimir; Symalla, Franz; Elstner, Marcus; Wenzel, Wolfgang

2015-02-10

It is an outstanding challenge to model the electronic properties of organic amorphous materials utilized in organic electronics. Computation of the charge carrier mobility is a challenging problem as it requires integration of morphological and electronic degrees of freedom in a coherent methodology and depends strongly on the distribution of polaron energies in the system. Here we represent a QM/QM model to compute the polaron energies combining density functional methods for molecules in the vicinity of the polaron with computationally efficient density functional based tight binding methods in the rest of the environment. For seven widely used amorphous organic semiconductor materials, we show that the calculations are accelerated up to 1 order of magnitude without any loss in accuracy. Considering that the quantum chemical step is the efficiency bottleneck of a workflow to model the carrier mobility, these results are an important step toward accurate and efficient disordered organic semiconductors simulations, a prerequisite for accelerated materials screening and consequent component optimization in the organic electronics industry.

A Secure and Verifiable Outsourced Access Control Scheme in Fog-Cloud Computing.

PubMed

Fan, Kai; Wang, Junxiong; Wang, Xin; Li, Hui; Yang, Yintang

2017-07-24

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient.

Deep Space Network (DSN), Network Operations Control Center (NOCC) computer-human interfaces

NASA Technical Reports Server (NTRS)

Ellman, Alvin; Carlton, Magdi

1993-01-01

The Network Operations Control Center (NOCC) of the DSN is responsible for scheduling the resources of DSN, and monitoring all multi-mission spacecraft tracking activities in real-time. Operations performs this job with computer systems at JPL connected to over 100 computers at Goldstone, Australia and Spain. The old computer system became obsolete, and the first version of the new system was installed in 1991. Significant improvements for the computer-human interfaces became the dominant theme for the replacement project. Major issues required innovating problem solving. Among these issues were: How to present several thousand data elements on displays without overloading the operator? What is the best graphical representation of DSN end-to-end data flow? How to operate the system without memorizing mnemonics of hundreds of operator directives? Which computing environment will meet the competing performance requirements? This paper presents the technical challenges, engineering solutions, and results of the NOCC computer-human interface design.

MapReduce SVM Game

DOE PAGES

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; ...

2015-08-10

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

MapReduce SVM Game

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

On Computing Breakpoint Distances for Genomes with Duplicate Genes.

PubMed

Shao, Mingfu; Moret, Bernard M E

2017-06-01

A fundamental problem in comparative genomics is to compute the distance between two genomes in terms of its higher level organization (given by genes or syntenic blocks). For two genomes without duplicate genes, we can easily define (and almost always efficiently compute) a variety of distance measures, but the problem is NP-hard under most models when genomes contain duplicate genes. To tackle duplicate genes, three formulations (exemplar, maximum matching, and any matching) have been proposed, all of which aim to build a matching between homologous genes so as to minimize some distance measure. Of the many distance measures, the breakpoint distance (the number of nonconserved adjacencies) was the first one to be studied and remains of significant interest because of its simplicity and model-free property. The three breakpoint distance problems corresponding to the three formulations have been widely studied. Although we provided last year a solution for the exemplar problem that runs very fast on full genomes, computing optimal solutions for the other two problems has remained challenging. In this article, we describe very fast, exact algorithms for these two problems. Our algorithms rely on a compact integer-linear program that we further simplify by developing an algorithm to remove variables, based on new results on the structure of adjacencies and matchings. Through extensive experiments using both simulations and biological data sets, we show that our algorithms run very fast (in seconds) on mammalian genomes and scale well beyond. We also apply these algorithms (as well as the classic orthology tool MSOAR) to create orthology assignment, then compare their quality in terms of both accuracy and coverage. We find that our algorithm for the "any matching" formulation significantly outperforms other methods in terms of accuracy while achieving nearly maximum coverage.

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression

PubMed Central

Liu, Yu-Ying; Li, Shuang; Li, Fuxin; Song, Le; Rehg, James M.

2016-01-01

The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer’s disease dataset. PMID:27019571

Human Computation in Visualization: Using Purpose Driven Games for Robust Evaluation of Visualization Algorithms.

PubMed

Ahmed, N; Zheng, Ziyi; Mueller, K

2012-12-01

Due to the inherent characteristics of the visualization process, most of the problems in this field have strong ties with human cognition and perception. This makes the human brain and sensory system the only truly appropriate evaluation platform for evaluating and fine-tuning a new visualization method or paradigm. However, getting humans to volunteer for these purposes has always been a significant obstacle, and thus this phase of the development process has traditionally formed a bottleneck, slowing down progress in visualization research. We propose to take advantage of the newly emerging field of Human Computation (HC) to overcome these challenges. HC promotes the idea that rather than considering humans as users of the computational system, they can be made part of a hybrid computational loop consisting of traditional computation resources and the human brain and sensory system. This approach is particularly successful in cases where part of the computational problem is considered intractable using known computer algorithms but is trivial to common sense human knowledge. In this paper, we focus on HC from the perspective of solving visualization problems and also outline a framework by which humans can be easily seduced to volunteer their HC resources. We introduce a purpose-driven game titled "Disguise" which serves as a prototypical example for how the evaluation of visualization algorithms can be mapped into a fun and addicting activity, allowing this task to be accomplished in an extensive yet cost effective way. Finally, we sketch out a framework that transcends from the pure evaluation of existing visualization methods to the design of a new one.

Towards large scale multi-target tracking

NASA Astrophysics Data System (ADS)

Vo, Ba-Ngu; Vo, Ba-Tuong; Reuter, Stephan; Lam, Quang; Dietmayer, Klaus

2014-06-01

Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving a large number of targets is extremely challenging. This article demonstrates the capability of the random finite set approach to provide large scale multi-target tracking algorithms. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer.

Ability evaluation by binary tests: Problems, challenges & recent advances

NASA Astrophysics Data System (ADS)

Bashkansky, E.; Turetsky, V.

2016-11-01

Binary tests designed to measure abilities of objects under test (OUTs) are widely used in different fields of measurement theory and practice. The number of test items in such tests is usually very limited. The response to each test item provides only one bit of information per OUT. The problem of correct ability assessment is even more complicated, when the levels of difficulty of the test items are unknown beforehand. This fact makes the search for effective ways of planning and processing the results of such tests highly relevant. In recent years, there has been some progress in this direction, generated by both the development of computational tools and the emergence of new ideas. The latter are associated with the use of so-called “scale invariant item response models”. Together with maximum likelihood estimation (MLE) approach, they helped to solve some problems of engineering and proficiency testing. However, several issues related to the assessment of uncertainties, replications scheduling, the use of placebo, as well as evaluation of multidimensional abilities still present a challenge for researchers. The authors attempt to outline the ways to solve the above problems.

Turbocharged molecular discovery of OLED emitters: from high-throughput quantum simulation to highly efficient TADF devices

NASA Astrophysics Data System (ADS)

Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D.; Ha, Dong-Gwang; Einzinger, Markus; Wu, Tony; Baldo, Marc A.; Aspuru-Guzik, Alán.

2016-09-01

Discovering new OLED emitters requires many experiments to synthesize candidates and test performance in devices. Large scale computer simulation can greatly speed this search process but the problem remains challenging enough that brute force application of massive computing power is not enough to successfully identify novel structures. We report a successful High Throughput Virtual Screening study that leveraged a range of methods to optimize the search process. The generation of candidate structures was constrained to contain combinatorial explosion. Simulations were tuned to the specific problem and calibrated with experimental results. Experimentalists and theorists actively collaborated such that experimental feedback was regularly utilized to update and shape the computational search. Supervised machine learning methods prioritized candidate structures prior to quantum chemistry simulation to prevent wasting compute on likely poor performers. With this combination of techniques, each multiplying the strength of the search, this effort managed to navigate an area of molecular space and identify hundreds of promising OLED candidate structures. An experimentally validated selection of this set shows emitters with external quantum efficiencies as high as 22%.

Performance of a Block Structured, Hierarchical Adaptive MeshRefinement Code on the 64k Node IBM BlueGene/L Computer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Greenough, Jeffrey A.; de Supinski, Bronis R.; Yates, Robert K.

2005-04-25

We describe the performance of the block-structured Adaptive Mesh Refinement (AMR) code Raptor on the 32k node IBM BlueGene/L computer. This machine represents a significant step forward towards petascale computing. As such, it presents Raptor with many challenges for utilizing the hardware efficiently. In terms of performance, Raptor shows excellent weak and strong scaling when running in single level mode (no adaptivity). Hardware performance monitors show Raptor achieves an aggregate performance of 3:0 Tflops in the main integration kernel on the 32k system. Results from preliminary AMR runs on a prototype astrophysical problem demonstrate the efficiency of the current softwaremore » when running at large scale. The BG/L system is enabling a physics problem to be considered that represents a factor of 64 increase in overall size compared to the largest ones of this type computed to date. Finally, we provide a description of the development work currently underway to address our inefficiencies.« less

Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography

PubMed Central

Sidky, Emil Y.; Kraemer, David N.; Roth, Erin G.; Ullberg, Christer; Reiser, Ingrid S.; Pan, Xiaochuan

2014-01-01

Abstract. One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data. PMID:25685824

Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography.

PubMed

Sidky, Emil Y; Kraemer, David N; Roth, Erin G; Ullberg, Christer; Reiser, Ingrid S; Pan, Xiaochuan

2014-10-03

One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

A new graph-based method for pairwise global network alignment

PubMed Central

Klau, Gunnar W

2009-01-01

Background In addition to component-based comparative approaches, network alignments provide the means to study conserved network topology such as common pathways and more complex network motifs. Yet, unlike in classical sequence alignment, the comparison of networks becomes computationally more challenging, as most meaningful assumptions instantly lead to NP-hard problems. Most previous algorithmic work on network alignments is heuristic in nature. Results We introduce the graph-based maximum structural matching formulation for pairwise global network alignment. We relate the formulation to previous work and prove NP-hardness of the problem. Based on the new formulation we build upon recent results in computational structural biology and present a novel Lagrangian relaxation approach that, in combination with a branch-and-bound method, computes provably optimal network alignments. The Lagrangian algorithm alone is a powerful heuristic method, which produces solutions that are often near-optimal and – unlike those computed by pure heuristics – come with a quality guarantee. Conclusion Computational experiments on the alignment of protein-protein interaction networks and on the classification of metabolic subnetworks demonstrate that the new method is reasonably fast and has advantages over pure heuristics. Our software tool is freely available as part of the LISA library. PMID:19208162

Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdel-Khalik, Hany S.; Turinsky, Paul J.

2005-07-15

Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. A meaningful adaption will result in high-fidelity and robust adapted core simulator models. To perform adaption, we propose an inverse theory approach in which the multitudes of input data to core simulators, i.e., reactor physics and thermal-hydraulic data, are to be adjusted to improve agreement withmore » measured observables while keeping core simulator models unadapted. At first glance, devising such adaption for typical core simulators with millions of input and observables data would spawn not only several prohibitive challenges but also numerous disparaging concerns. The challenges include the computational burdens of the sensitivity-type calculations required to construct Jacobian operators for the core simulator models. Also, the computational burdens of the uncertainty-type calculations required to estimate the uncertainty information of core simulator input data present a demanding challenge. The concerns however are mainly related to the reliability of the adjusted input data. The methodologies of adaptive simulation are well established in the literature of data adjustment. We adopt the same general framework for data adjustment; however, we refrain from solving the fundamental adjustment equations in a conventional manner. We demonstrate the use of our so-called Efficient Subspace Methods (ESMs) to overcome the computational and storage burdens associated with the core adaption problem. We illustrate the successful use of ESM-based adaptive techniques for a typical boiling water reactor core simulator adaption problem.« less

Verifying the interactive convergence clock synchronization algorithm using the Boyer-Moore theorem prover

NASA Technical Reports Server (NTRS)

Young, William D.

1992-01-01

The application of formal methods to the analysis of computing systems promises to provide higher and higher levels of assurance as the sophistication of our tools and techniques increases. Improvements in tools and techniques come about as we pit the current state of the art against new and challenging problems. A promising area for the application of formal methods is in real-time and distributed computing. Some of the algorithms in this area are both subtle and important. In response to this challenge and as part of an ongoing attempt to verify an implementation of the Interactive Convergence Clock Synchronization Algorithm (ICCSA), we decided to undertake a proof of the correctness of the algorithm using the Boyer-Moore theorem prover. This paper describes our approach to proving the ICCSA using the Boyer-Moore prover.

The IS-GEO RCN: Fostering Collaborations for Intelligent Systems Research to Support Geosciences

NASA Astrophysics Data System (ADS)

Gil, Y.; Pierce, S. A.

2016-12-01

Geoscience problems are complex and often involve data that changes across space and time. Frequently geoscience knowledge and understanding provides valuable information and insight for problems related to energy, water, climate, mineral resources, and our understanding of how the Earth evolves through time. Simultaneously, many grand challenges in the geosciences cannot be addressed without the aid of computational support and innovations. Intelligent and Information Systems (IS) research includes a broad range of computational methods and topics such as knowledge representation, information integration, machine learning, robotics, adaptive sensors, and intelligent interfaces. IS research has a very important role to play in accelerating the speed of scientific discovery in geosciences and thus in solving challenges in geosciences. Many aspects of geosciences (GEO) research pose novel open problems for intelligent systems researchers. To develop intelligent systems with sound knowledge of theory and practice, it is important that GEO and IS experts collaborate. The EarthCube Research Coordination Network for Intelligent Systems for Geosciences (IS-GEO RCN) represents an emerging community of interdisciplinary researchers producing fundamental new capabilities for understanding Earth systems. Furthermore, the educational component aims to identify new approaches to teaching students in this new interdisciplinary area, seeking to raise a new generation of scientists that are better able to apply IS methods and tools to geoscience challenges of the future. By providing avenues for IS and GEO researchers to work together, the IS-GEO RCN will serve as both a point of contact, as well as an avenue for educational outreach across the disciplines for the nascent community of research and practice. The initial efforts are focused on connecting the communities in ways that help researchers understand opportunities and challenges that can benefit from IS-GEO collaborations. The IS-GEO RCN will jumpstart interdisciplinary research collaborations in this emerging new area so that progress across both disciplines can be accelerated.

Improving Multi-Objective Management of Water Quality Tipping Points: Revisiting the Classical Shallow Lake Problem

NASA Astrophysics Data System (ADS)

Quinn, J. D.; Reed, P. M.; Keller, K.

2015-12-01

Recent multi-objective extensions of the classical shallow lake problem are useful for exploring the conceptual and computational challenges that emerge when managing irreversible water quality tipping points. Building on this work, we explore a four objective version of the lake problem where a hypothetical town derives economic benefits from polluting a nearby lake, but at the risk of irreversibly tipping the lake into a permanently polluted state. The trophic state of the lake exhibits non-linear threshold dynamics; below some critical phosphorus (P) threshold it is healthy and oligotrophic, but above this threshold it is irreversibly eutrophic. The town must decide how much P to discharge each year, a decision complicated by uncertainty in the natural P inflow to the lake. The shallow lake problem provides a conceptually rich set of dynamics, low computational demands, and a high level of mathematical difficulty. These properties maximize its value for benchmarking the relative merits and limitations of emerging decision support frameworks, such as Direct Policy Search (DPS). Here, we explore the use of DPS as a formal means of developing robust environmental pollution control rules that effectively account for deeply uncertain system states and conflicting objectives. The DPS reformulation of the shallow lake problem shows promise in formalizing pollution control triggers and signposts, while dramatically reducing the computational complexity of the multi-objective pollution control problem. More broadly, the insights from the DPS variant of the shallow lake problem formulated in this study bridge emerging work related to socio-ecological systems management, tipping points, robust decision making, and robust control.

Sketch Matching on Topology Product Graph.

PubMed

Liang, Shuang; Luo, Jun; Liu, Wenyin; Wei, Yichen

2015-08-01

Sketch matching is the fundamental problem in sketch based interfaces. After years of study, it remains challenging when there exists large irregularity and variations in the hand drawn sketch shapes. While most existing works exploit topology relations and graph representations for this problem, they are usually limited by the coarse topology exploration and heuristic (thus suboptimal) similarity metrics between graphs. We present a new sketch matching method with two novel contributions. We introduce a comprehensive definition of topology relations, which results in a rich and informative graph representation of sketches. For graph matching, we propose topology product graph that retains the full correspondence for matching two graphs. Based on it, we derive an intuitive sketch similarity metric whose exact solution is easy to compute. In addition, the graph representation and new metric naturally support partial matching, an important practical problem that received less attention in the literature. Extensive experimental results on a real challenging dataset and the superior performance of our method show that it outperforms the state-of-the-art.

The challenge of big data in public health: an opportunity for visual analytics.

PubMed

Ola, Oluwakemi; Sedig, Kamran

2014-01-01

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data's volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research.

The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics

PubMed Central

Ola, Oluwakemi; Sedig, Kamran

2014-01-01

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data’s volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research. PMID:24678376

Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach

NASA Technical Reports Server (NTRS)

Globus, Al; Crawford, James; Lohn, Jason; Morris, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

We describe work in progress concerning multi-instrument, multi-satellite scheduling. Most, although not all, Earth observing instruments currently in orbit are unique. In the relatively near future, however, we expect to see fleets of Earth observing spacecraft, many carrying nearly identical instruments. This presents a substantially new scheduling challenge. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of evolutionary algorithms to solve a set of Earth observing related model problems. Both the model problems and the software are described. Since the larger problems will require substantial computation and evolutionary algorithms are embarrassingly parallel, we discuss our parallelization techniques using dedicated and cycle-scavenged workstations.

Fast reconstruction of optical properties for complex segmentations in near infrared imaging

NASA Astrophysics Data System (ADS)

Jiang, Jingjing; Wolf, Martin; Sánchez Majos, Salvador

2017-04-01

The intrinsic ill-posed nature of the inverse problem in near infrared imaging makes the reconstruction of fine details of objects deeply embedded in turbid media challenging even for the large amounts of data provided by time-resolved cameras. In addition, most reconstruction algorithms for this type of measurements are only suitable for highly symmetric geometries and rely on a linear approximation to the diffusion equation since a numerical solution of the fully non-linear problem is computationally too expensive. In this paper, we will show that a problem of practical interest can be successfully addressed making efficient use of the totality of the information supplied by time-resolved cameras. We set aside the goal of achieving high spatial resolution for deep structures and focus on the reconstruction of complex arrangements of large regions. We show numerical results based on a combined approach of wavelength-normalized data and prior geometrical information, defining a fully parallelizable problem in arbitrary geometries for time-resolved measurements. Fast reconstructions are obtained using a diffusion approximation and Monte-Carlo simulations, parallelized in a multicore computer and a GPU respectively.

Computational Studies of Strongly Correlated Quantum Matter

NASA Astrophysics Data System (ADS)

Shi, Hao

The study of strongly correlated quantum many-body systems is an outstanding challenge. Highly accurate results are needed for the understanding of practical and fundamental problems in condensed-matter physics, high energy physics, material science, quantum chemistry and so on. Our familiar mean-field or perturbative methods tend to be ineffective. Numerical simulations provide a promising approach for studying such systems. The fundamental difficulty of numerical simulation is that the dimension of the Hilbert space needed to describe interacting systems increases exponentially with the system size. Quantum Monte Carlo (QMC) methods are one of the best approaches to tackle the problem of enormous Hilbert space. They have been highly successful for boson systems and unfrustrated spin models. For systems with fermions, the exchange symmetry in general causes the infamous sign problem, making the statistical noise in the computed results grow exponentially with the system size. This hinders our understanding of interesting physics such as high-temperature superconductivity, metal-insulator phase transition. In this thesis, we present a variety of new developments in the auxiliary-field quantum Monte Carlo (AFQMC) methods, including the incorporation of symmetry in both the trial wave function and the projector, developing the constraint release method, using the force-bias to drastically improve the efficiency in Metropolis framework, identifying and solving the infinite variance problem, and sampling Hartree-Fock-Bogoliubov wave function. With these developments, some of the most challenging many-electron problems are now under control. We obtain an exact numerical solution of two-dimensional strongly interacting Fermi atomic gas, determine the ground state properties of the 2D Fermi gas with Rashba spin-orbit coupling, provide benchmark results for the ground state of the two-dimensional Hubbard model, and establish that the Hubbard model has a stripe order in the underdoped region.

Beyond computational difficulties: Survey of the two decades from the elaboration to the extensive application of the Hartree-Fock method

NASA Astrophysics Data System (ADS)

Martinez, Jean-Philippe

2017-11-01

The Hartree-Fock method, one of the first applications of the new quantum mechanics in the frame of the many-body problem, had been elaborated by Rayner Douglas Hartree in 1928 and Vladimir Fock in 1930. Promptly, the challenge of tedious computations was being discussed and it is well known that the application of the method benefited greatly from the development of computers from the mid-to-late 1950s. However, the years from 1930 to 1950 were by no means years of stagnation, as the method was the object of several considerations related to its mathematical formulation, possible extension, and conceptual understanding. Thus, with a focus on the respective attitudes of Hartree and Fock, in particular with respect to the concept of quantum exchange, the present work puts forward some mathematical and conceptual clarifications, which played an important role for a better understanding of the many-body problem in quantum mechanics.

Autonomous stair-climbing with miniature jumping robots.

PubMed

Stoeter, Sascha A; Papanikolopoulos, Nikolaos

2005-04-01

The problem of vision-guided control of miniature mobile robots is investigated. Untethered mobile robots with small physical dimensions of around 10 cm or less do not permit powerful onboard computers because of size and power constraints. These challenges have, in the past, reduced the functionality of such devices to that of a complex remote control vehicle with fancy sensors. With the help of a computationally more powerful entity such as a larger companion robot, the control loop can be closed. Using the miniature robot's video transmission or that of an observer to localize it in the world, control commands can be computed and relayed to the inept robot. The result is a system that exhibits autonomous capabilities. The framework presented here solves the problem of climbing stairs with the miniature Scout robot. The robot's unique locomotion mode, the jump, is employed to hop one step at a time. Methods for externally tracking the Scout are developed. A large number of real-world experiments are conducted and the results discussed.

Efficient Parallelization of a Dynamic Unstructured Application on the Tera MTA

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Biswas, Rupak

1999-01-01

The success of parallel computing in solving real-life computationally-intensive problems relies on their efficient mapping and execution on large-scale multiprocessor architectures. Many important applications are both unstructured and dynamic in nature, making their efficient parallel implementation a daunting task. This paper presents the parallelization of a dynamic unstructured mesh adaptation algorithm using three popular programming paradigms on three leading supercomputers. We examine an MPI message-passing implementation on the Cray T3E and the SGI Origin2OOO, a shared-memory implementation using cache coherent nonuniform memory access (CC-NUMA) of the Origin2OOO, and a multi-threaded version on the newly-released Tera Multi-threaded Architecture (MTA). We compare several critical factors of this parallel code development, including runtime, scalability, programmability, and memory overhead. Our overall results demonstrate that multi-threaded systems offer tremendous potential for quickly and efficiently solving some of the most challenging real-life problems on parallel computers.

Overcoming time scale and finite size limitations to compute nucleation rates from small scale well tempered metadynamics simulations.

PubMed

Salvalaglio, Matteo; Tiwary, Pratyush; Maggioni, Giovanni Maria; Mazzotti, Marco; Parrinello, Michele

2016-12-07

Condensation of a liquid droplet from a supersaturated vapour phase is initiated by a prototypical nucleation event. As such it is challenging to compute its rate from atomistic molecular dynamics simulations. In fact at realistic supersaturation conditions condensation occurs on time scales that far exceed what can be reached with conventional molecular dynamics methods. Another known problem in this context is the distortion of the free energy profile associated to nucleation due to the small, finite size of typical simulation boxes. In this work the problem of time scale is addressed with a recently developed enhanced sampling method while contextually correcting for finite size effects. We demonstrate our approach by studying the condensation of argon, and showing that characteristic nucleation times of the order of magnitude of hours can be reliably calculated. Nucleation rates spanning a range of 10 orders of magnitude are computed at moderate supersaturation levels, thus bridging the gap between what standard molecular dynamics simulations can do and real physical systems.

Overcoming time scale and finite size limitations to compute nucleation rates from small scale well tempered metadynamics simulations

NASA Astrophysics Data System (ADS)

Salvalaglio, Matteo; Tiwary, Pratyush; Maggioni, Giovanni Maria; Mazzotti, Marco; Parrinello, Michele

2016-12-01

Condensation of a liquid droplet from a supersaturated vapour phase is initiated by a prototypical nucleation event. As such it is challenging to compute its rate from atomistic molecular dynamics simulations. In fact at realistic supersaturation conditions condensation occurs on time scales that far exceed what can be reached with conventional molecular dynamics methods. Another known problem in this context is the distortion of the free energy profile associated to nucleation due to the small, finite size of typical simulation boxes. In this work the problem of time scale is addressed with a recently developed enhanced sampling method while contextually correcting for finite size effects. We demonstrate our approach by studying the condensation of argon, and showing that characteristic nucleation times of the order of magnitude of hours can be reliably calculated. Nucleation rates spanning a range of 10 orders of magnitude are computed at moderate supersaturation levels, thus bridging the gap between what standard molecular dynamics simulations can do and real physical systems.

From video to computation of biological fluid-structure interaction problems

NASA Astrophysics Data System (ADS)

Dillard, Seth I.; Buchholz, James H. J.; Udaykumar, H. S.

2016-04-01

This work deals with the techniques necessary to obtain a purely Eulerian procedure to conduct CFD simulations of biological systems with moving boundary flow phenomena. Eulerian approaches obviate difficulties associated with mesh generation to describe or fit flow meshes to body surfaces. The challenges associated with constructing embedded boundary information, body motions and applying boundary conditions on the moving bodies for flow computation are addressed in the work. The overall approach is applied to the study of a fluid-structure interaction problem, i.e., the hydrodynamics of swimming of an American eel, where the motion of the eel is derived from video imaging. It is shown that some first-blush approaches do not work, and therefore, careful consideration of appropriate techniques to connect moving images to flow simulations is necessary and forms the main contribution of the paper. A combination of level set-based active contour segmentation with optical flow and image morphing is shown to enable the image-to-computation process.

Shifting the Load: A Peer Dialogue Agent That Encourages Its Human Collaborator to Contribute More to Problem Solving

ERIC Educational Resources Information Center

Howard, Cynthia; Jordan, Pamela; Di Eugenio, Barbara; Katz, Sandra

2017-01-01

Despite a growing need for educational tools that support students at the earliest phases of undergraduate Computer Science (CS) curricula, relatively few such tools exist--the majority being Intelligent Tutoring Systems. Since peer interactions more readily give rise to challenges and negotiations, another way in which students can become more…

Supporting Students' Learning and Socioscientific Reasoning about Climate Change--The Effect of Computer-Based Concept Mapping Scaffolds

ERIC Educational Resources Information Center

Eggert, Sabina; Nitsch, Anne; Boone, William J.; Nückles, Matthias; Bögeholz, Susanne

2017-01-01

Climate change is one of the most challenging problems facing today's global society (e.g., IPCC 2013). While climate change is a widely covered topic in the media, and abundant information is made available through the internet, the causes and consequences of climate change in its full complexity are difficult for individuals, especially…

Automatic Generation of Analogy Questions for Student Assessment: An Ontology-Based Approach

ERIC Educational Resources Information Center

Alsubait, Tahani; Parsia, Bijan; Sattler, Uli

2012-01-01

Different computational models for generating analogies of the form "A is to B as C is to D" have been proposed over the past 35 years. However, analogy generation is a challenging problem that requires further research. In this article, we present a new approach for generating analogies in Multiple Choice Question (MCQ) format that can be used…

Website on Protein Interaction and Protein Structure Related Work

NASA Technical Reports Server (NTRS)

Samanta, Manoj; Liang, Shoudan; Biegel, Bryan (Technical Monitor)

2003-01-01

In today's world, three seemingly diverse fields - computer information technology, nanotechnology and biotechnology are joining forces to enlarge our scientific knowledge and solve complex technological problems. Our group is dedicated to conduct theoretical research exploring the challenges in this area. The major areas of research include: 1) Yeast Protein Interactions; 2) Protein Structures; and 3) Current Transport through Small Molecules.

Improved Flux Formulations for Unsteady Low Mach Number Flows

DTIC Science & Technology

2012-07-01

challenging problem since it requires the resolution of disparate time scales. Unsteady effects may arise from a combination of hydrodynamic effects...Many practical applications including rotorcraft flows, jets and shear layers include a combination of both acoustic and hydrodynamic effects...are computed independently as scalar formulations thus making it possible to independently tailor the dissipation for hydrodynamic and acoustic

The Contribution of Reasoning to the Utilization of Feedback from Software When Solving Mathematical Problems

ERIC Educational Resources Information Center

Olsson, Jan

2018-01-01

This study investigates how students' reasoning contributes to their utilization of computer-generated feedback. Sixteen 16-year-old students solved a linear function task designed to present a challenge to them using dynamic software, GeoGebra, for assistance. The data were analysed with respect both to character of reasoning and to the use of…

A Blended Learning Approach for Teaching Computer Programming: Design for Large Classes in Sub-Saharan Africa

ERIC Educational Resources Information Center

Bati, Tesfaye Bayu; Gelderblom, Helene; van Biljon, Judy

2014-01-01

The challenge of teaching programming in higher education is complicated by problems associated with large class teaching, a prevalent situation in many developing countries. This paper reports on an investigation into the use of a blended learning approach to teaching and learning of programming in a class of more than 200 students. A course and…

Evolving Information Technology: A Case Study of the Effects of Constant Change on Information Technology Instructional Design Architecture

ERIC Educational Resources Information Center

Helps, Richard

2010-01-01

A major challenge for Information Technology (IT) programs is that the rapid pace of evolution of computing technology leads to frequent redesign of IT courses. The problem is exacerbated by several factors. Firstly, the changing technology is the subject matter of the discipline and is also frequently used to support instruction; secondly, this…

Advancing Capabilities for Understanding the Earth System Through Intelligent Systems, the NSF Perspective

NASA Astrophysics Data System (ADS)

Gil, Y.; Zanzerkia, E. E.; Munoz-Avila, H.

2015-12-01

The National Science Foundation (NSF) Directorate for Geosciences (GEO) and Directorate for Computer and Information Science (CISE) acknowledge the significant scientific challenges required to understand the fundamental processes of the Earth system, within the atmospheric and geospace, Earth, ocean and polar sciences, and across those boundaries. A broad view of the opportunities and directions for GEO are described in the report "Dynamic Earth: GEO imperative and Frontiers 2015-2020." Many of the aspects of geosciences research, highlighted both in this document and other community grand challenges, pose novel problems for researchers in intelligent systems. Geosciences research will require solutions for data-intensive science, advanced computational capabilities, and transformative concepts for visualizing, using, analyzing and understanding geo phenomena and data. Opportunities for the scientific community to engage in addressing these challenges are available and being developed through NSF's portfolio of investments and activities. The NSF-wide initiative, Cyberinfrastructure Framework for 21st Century Science and Engineering (CIF21), looks to accelerate research and education through new capabilities in data, computation, software and other aspects of cyberinfrastructure. EarthCube, a joint program between GEO and the Advanced Cyberinfrastructure Division, aims to create a well-connected and facile environment to share data and knowledge in an open, transparent, and inclusive manner, thus accelerating our ability to understand and predict the Earth system. EarthCube's mission opens an opportunity for collaborative research on novel information systems enhancing and supporting geosciences research efforts. NSF encourages true, collaborative partnerships between scientists in computer sciences and the geosciences to meet these challenges.

Numerical simulations of flying and swimming of biological systems with the viscous vortex particle method

NASA Astrophysics Data System (ADS)

Eldredge, Jeff

2005-11-01

Many biological mechanisms of locomotion involve the interaction of a fluid with a deformable surface undergoing large unsteady motion. Analysis of such problems poses a significant challenge to conventional grid-based computational approaches. Particularly in the moderate Reynolds number regime where many insects and fish function, viscous and inertial processes are both important, and vorticity serves a crucial role. In this work, the viscous vortex particle method is shown to provide an efficient, intuitive simulation approach for investigation of these biological systems. In contrast with a grid-based approach, the method solves the Navier--Stokes equations by tracking computational particles that carry smooth blobs of vorticity and exchange strength with one another to account for viscous diffusion. Thus, computational resources are focused on the physically relevant features of the flow, and there is no need for artificial boundary conditions. Building from previously-developed techniques for the creation of vorticity to enforce no-throughflow and no-slip conditions, the present method is extended to problems of coupled fluid--body dynamics by enforcement of global conservation of momenta. The application to several two-dimensional model problems is demonstrated, including single and multiple flapping wings and free swimming of a three-linkage fish.

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Jinglai, E-mail: jinglaili@sjtu.edu.cn; Lin, Guang, E-mail: lin491@purdue.edu; Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352

2015-09-01

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by threemore » steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.« less

2016 Final Reports from the Los Alamos National Laboratory Computational Physics Student Summer Workshop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Runnels, Scott Robert; Bachrach, Harrison Ian; Carlson, Nils

The two primary purposes of LANL’s Computational Physics Student Summer Workshop are (1) To educate graduate and exceptional undergraduate students in the challenges and applications of computational physics of interest to LANL, and (2) Entice their interest toward those challenges. Computational physics is emerging as a discipline in its own right, combining expertise in mathematics, physics, and computer science. The mathematical aspects focus on numerical methods for solving equations on the computer as well as developing test problems with analytical solutions. The physics aspects are very broad, ranging from low-temperature material modeling to extremely high temperature plasma physics, radiation transportmore » and neutron transport. The computer science issues are concerned with matching numerical algorithms to emerging architectures and maintaining the quality of extremely large codes built to perform multi-physics calculations. Although graduate programs associated with computational physics are emerging, it is apparent that the pool of U.S. citizens in this multi-disciplinary field is relatively small and is typically not focused on the aspects that are of primary interest to LANL. Furthermore, more structured foundations for LANL interaction with universities in computational physics is needed; historically interactions rely heavily on individuals’ personalities and personal contacts. Thus a tertiary purpose of the Summer Workshop is to build an educational network of LANL researchers, university professors, and emerging students to advance the field and LANL’s involvement in it.« less

Knowledge-based control for robot self-localization

NASA Technical Reports Server (NTRS)

Bennett, Bonnie Kathleen Holte

1993-01-01

Autonomous robot systems are being proposed for a variety of missions including the Mars rover/sample return mission. Prior to any other mission objectives being met, an autonomous robot must be able to determine its own location. This will be especially challenging because location sensors like GPS, which are available on Earth, will not be useful, nor will INS sensors because their drift is too large. Another approach to self-localization is required. In this paper, we describe a novel approach to localization by applying a problem solving methodology. The term 'problem solving' implies a computational technique based on logical representational and control steps. In this research, these steps are derived from observing experts solving localization problems. The objective is not specifically to simulate human expertise but rather to apply its techniques where appropriate for computational systems. In doing this, we describe a model for solving the problem and a system built on that model, called localization control and logic expert (LOCALE), which is a demonstration of concept for the approach and the model. The results of this work represent the first successful solution to high-level control aspects of the localization problem.

Automated error correction in IBM quantum computer and explicit generalization

NASA Astrophysics Data System (ADS)

Ghosh, Debjit; Agarwal, Pratik; Pandey, Pratyush; Behera, Bikash K.; Panigrahi, Prasanta K.

2018-06-01

Construction of a fault-tolerant quantum computer remains a challenging problem due to unavoidable noise and fragile quantum states. However, this goal can be achieved by introducing quantum error-correcting codes. Here, we experimentally realize an automated error correction code and demonstrate the nondestructive discrimination of GHZ states in IBM 5-qubit quantum computer. After performing quantum state tomography, we obtain the experimental results with a high fidelity. Finally, we generalize the investigated code for maximally entangled n-qudit case, which could both detect and automatically correct any arbitrary phase-change error, or any phase-flip error, or any bit-flip error, or combined error of all types of error.

The Montage architecture for grid-enabled science processing of large, distributed datasets

NASA Technical Reports Server (NTRS)

Jacob, Joseph C.; Katz, Daniel S .; Prince, Thomas; Berriman, Bruce G.; Good, John C.; Laity, Anastasia C.; Deelman, Ewa; Singh, Gurmeet; Su, Mei-Hui

2004-01-01

Montage is an Earth Science Technology Office (ESTO) Computational Technologies (CT) Round III Grand Challenge investigation to deploy a portable, compute-intensive, custom astronomical image mosaicking service for the National Virtual Observatory (NVO). Although Montage is developing a compute- and data-intensive service for the astronomy community, we are also helping to address a problem that spans both Earth and Space science, namely how to efficiently access and process multi-terabyte, distributed datasets. In both communities, the datasets are massive, and are stored in distributed archives that are, in most cases, remote from the available Computational resources. Therefore, state of the art computational grid technologies are a key element of the Montage portal architecture. This paper describes the aspects of the Montage design that are applicable to both the Earth and Space science communities.

Depth compensating calculation method of computer-generated holograms using symmetry and similarity of zone plates

NASA Astrophysics Data System (ADS)

Wei, Hui; Gong, Guanghong; Li, Ni

2017-10-01

Computer-generated hologram (CGH) is a promising 3D display technology while it is challenged by heavy computation load and vast memory requirement. To solve these problems, a depth compensating CGH calculation method based on symmetry and similarity of zone plates is proposed and implemented on graphics processing unit (GPU). An improved LUT method is put forward to compute the distances between object points and hologram pixels in the XY direction. The concept of depth compensating factor is defined and used for calculating the holograms of points with different depth positions instead of layer-based methods. The proposed method is suitable for arbitrary sampling objects with lower memory usage and higher computational efficiency compared to other CGH methods. The effectiveness of the proposed method is validated by numerical and optical experiments.

Multi-Agent Patrolling under Uncertainty and Threats.

PubMed

Chen, Shaofei; Wu, Feng; Shen, Lincheng; Chen, Jing; Ramchurn, Sarvapali D

2015-01-01

We investigate a multi-agent patrolling problem where information is distributed alongside threats in environments with uncertainties. Specifically, the information and threat at each location are independently modelled as multi-state Markov chains, whose states are not observed until the location is visited by an agent. While agents will obtain information at a location, they may also suffer damage from the threat at that location. Therefore, the goal of the agents is to gather as much information as possible while mitigating the damage incurred. To address this challenge, we formulate the single-agent patrolling problem as a Partially Observable Markov Decision Process (POMDP) and propose a computationally efficient algorithm to solve this model. Building upon this, to compute patrols for multiple agents, the single-agent algorithm is extended for each agent with the aim of maximising its marginal contribution to the team. We empirically evaluate our algorithm on problems of multi-agent patrolling and show that it outperforms a baseline algorithm up to 44% for 10 agents and by 21% for 15 agents in large domains.

Neuromorphic computing enabled by physics of electron spins: Prospects and perspectives

NASA Astrophysics Data System (ADS)

Sengupta, Abhronil; Roy, Kaushik

2018-03-01

“Spintronics” refers to the understanding of the physics of electron spin-related phenomena. While most of the significant advancements in this field has been driven primarily by memory, recent research has demonstrated that various facets of the underlying physics of spin transport and manipulation can directly mimic the functionalities of the computational primitives in neuromorphic computation, i.e., the neurons and synapses. Given the potential of these spintronic devices to implement bio-mimetic computations at very low terminal voltages, several spin-device structures have been proposed as the core building blocks of neuromorphic circuits and systems to implement brain-inspired computing. Such an approach is expected to play a key role in circumventing the problems of ever-increasing power dissipation and hardware requirements for implementing neuro-inspired algorithms in conventional digital CMOS technology. Perspectives on spin-enabled neuromorphic computing, its status, and challenges and future prospects are outlined in this review article.

Self-Scheduling Parallel Methods for Multiple Serial Codes with Application to WOPWOP

NASA Technical Reports Server (NTRS)

Long, Lyle N.; Brentner, Kenneth S.

2000-01-01

This paper presents a scheme for efficiently running a large number of serial jobs on parallel computers. Two examples are given of computer programs that run relatively quickly, but often they must be run numerous times to obtain all the results needed. It is very common in science and engineering to have codes that are not massive computing challenges in themselves, but due to the number of instances that must be run, they do become large-scale computing problems. The two examples given here represent common problems in aerospace engineering: aerodynamic panel methods and aeroacoustic integral methods. The first example simply solves many systems of linear equations. This is representative of an aerodynamic panel code where someone would like to solve for numerous angles of attack. The complete code for this first example is included in the appendix so that it can be readily used by others as a template. The second example is an aeroacoustics code (WOPWOP) that solves the Ffowcs Williams Hawkings equation to predict the far-field sound due to rotating blades. In this example, one quite often needs to compute the sound at numerous observer locations, hence parallelization is utilized to automate the noise computation for a large number of observers.

Weaving a Formal Methods Education with Problem-Based Learning

NASA Astrophysics Data System (ADS)

Gibson, J. Paul

The idea of weaving formal methods through computing (or software engineering) degrees is not a new one. However, there has been little success in developing and implementing such a curriculum. Formal methods continue to be taught as stand-alone modules and students, in general, fail to see how fundamental these methods are to the engineering of software. A major problem is one of motivation — how can the students be expected to enthusiastically embrace a challenging subject when the learning benefits, beyond passing an exam and achieving curriculum credits, are not clear? Problem-based learning has gradually moved from being an innovative pedagogique technique, commonly used to better-motivate students, to being widely adopted in the teaching of many different disciplines, including computer science and software engineering. Our experience shows that a good problem can be re-used throughout a student's academic life. In fact, the best computing problems can be used with children (young and old), undergraduates and postgraduates. In this paper we present a process for weaving formal methods through a University curriculum that is founded on the application of problem-based learning and a library of good software engineering problems, where students learn about formal methods without sitting a traditional formal methods module. The process of constructing good problems and integrating them into the curriculum is shown to be analagous to the process of engineering software. This approach is not intended to replace more traditional formal methods modules: it will better prepare students for such specialised modules and ensure that all students have an understanding and appreciation for formal methods even if they do not go on to specialise in them.

Assessment of Hybrid RANS/LES Turbulence Models for Aeroacoustics Applications

NASA Technical Reports Server (NTRS)

Vatsa, Veer N.; Lockard, David P.

2010-01-01

Predicting the noise from aircraft with exposed landing gear remains a challenging problem for the aeroacoustics community. Although computational fluid dynamics (CFD) has shown promise as a technique that could produce high-fidelity flow solutions, generating grids that can resolve the pertinent physics around complex configurations can be very challenging. Structured grids are often impractical for such configurations. Unstructured grids offer a path forward for simulating complex configurations. However, few unstructured grid codes have been thoroughly tested for unsteady flow problems in the manner needed for aeroacoustic prediction. A widely used unstructured grid code, FUN3D, is examined for resolving the near field in unsteady flow problems. Although the ultimate goal is to compute the flow around complex geometries such as the landing gear, simpler problems that include some of the relevant physics, and are easily amenable to the structured grid approaches are used for testing the unstructured grid approach. The test cases chosen for this study correspond to the experimental work on single and tandem cylinders conducted in the Basic Aerodynamic Research Tunnel (BART) and the Quiet Flow Facility (QFF) at NASA Langley Research Center. These configurations offer an excellent opportunity to assess the performance of hybrid RANS/LES turbulence models that transition from RANS in unresolved regions near solid bodies to LES in the outer flow field. Several of these models have been implemented and tested in both structured and unstructured grid codes to evaluate their dependence on the solver and mesh type. Comparison of FUN3D solutions with experimental data and numerical solutions from a structured grid flow solver are found to be encouraging.

BATEMANATER: a computer program to estimate and bootstrap mating system variables based on Bateman's principles.

PubMed

Jones, Adam G

2015-11-01

Bateman's principles continue to play a major role in the characterization of genetic mating systems in natural populations. The modern manifestations of Bateman's ideas include the opportunity for sexual selection (i.e. I(s) - the variance in relative mating success), the opportunity for selection (i.e. I - the variance in relative reproductive success) and the Bateman gradient (i.e. β(ss) - the slope of the least-squares regression of reproductive success on mating success). These variables serve as the foundation for one convenient approach for the quantification of mating systems. However, their estimation presents at least two challenges, which I address here with a new Windows-based computer software package called BATEMANATER. The first challenge is that confidence intervals for these variables are not easy to calculate. BATEMANATER solves this problem using a bootstrapping approach. The second, more serious, problem is that direct estimates of mating system variables from open populations will typically be biased if some potential progeny or adults are missing from the analysed sample. BATEMANATER addresses this problem using a maximum-likelihood approach to estimate mating system variables from incompletely sampled breeding populations. The current version of BATEMANATER addresses the problem for systems in which progeny can be collected in groups of half- or full-siblings, as would occur when eggs are laid in discrete masses or offspring occur in pregnant females. BATEMANATER has a user-friendly graphical interface and thus represents a new, convenient tool for the characterization and comparison of genetic mating systems. © 2015 John Wiley & Sons Ltd.

Parallel Optimization of Polynomials for Large-scale Problems in Stability and Control

NASA Astrophysics Data System (ADS)

Kamyar, Reza

In this thesis, we focus on some of the NP-hard problems in control theory. Thanks to the converse Lyapunov theory, these problems can often be modeled as optimization over polynomials. To avoid the problem of intractability, we establish a trade off between accuracy and complexity. In particular, we develop a sequence of tractable optimization problems --- in the form of Linear Programs (LPs) and/or Semi-Definite Programs (SDPs) --- whose solutions converge to the exact solution of the NP-hard problem. However, the computational and memory complexity of these LPs and SDPs grow exponentially with the progress of the sequence - meaning that improving the accuracy of the solutions requires solving SDPs with tens of thousands of decision variables and constraints. Setting up and solving such problems is a significant challenge. The existing optimization algorithms and software are only designed to use desktop computers or small cluster computers --- machines which do not have sufficient memory for solving such large SDPs. Moreover, the speed-up of these algorithms does not scale beyond dozens of processors. This in fact is the reason we seek parallel algorithms for setting-up and solving large SDPs on large cluster- and/or super-computers. We propose parallel algorithms for stability analysis of two classes of systems: 1) Linear systems with a large number of uncertain parameters; 2) Nonlinear systems defined by polynomial vector fields. First, we develop a distributed parallel algorithm which applies Polya's and/or Handelman's theorems to some variants of parameter-dependent Lyapunov inequalities with parameters defined over the standard simplex. The result is a sequence of SDPs which possess a block-diagonal structure. We then develop a parallel SDP solver which exploits this structure in order to map the computation, memory and communication to a distributed parallel environment. Numerical tests on a supercomputer demonstrate the ability of the algorithm to efficiently utilize hundreds and potentially thousands of processors, and analyze systems with 100+ dimensional state-space. Furthermore, we extend our algorithms to analyze robust stability over more complicated geometries such as hypercubes and arbitrary convex polytopes. Our algorithms can be readily extended to address a wide variety of problems in control such as Hinfinity synthesis for systems with parametric uncertainty and computing control Lyapunov functions.

cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design

PubMed Central

Pan, Yuchao; Dong, Yuxi; Zhou, Jingtian; Hallen, Mark; Donald, Bruce R.; Xu, Wei

2016-01-01

Abstract Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches. PMID:27154509

Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network.

PubMed

Kang, Eunhee; Chang, Won; Yoo, Jaejun; Ye, Jong Chul

2018-06-01

Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography (CT) are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the textures were not fully recovered. To address this problem, here we propose a novel framelet-based denoising algorithm using wavelet residual network which synergistically combines the expressive power of deep learning and the performance guarantee from the framelet-based denoising algorithms. The new algorithms were inspired by the recent interpretation of the deep CNN as a cascaded convolution framelet signal representation. Extensive experimental results confirm that the proposed networks have significantly improved performance and preserve the detail texture of the original images.

The application of CFD to the modelling of fires in complex geometries

NASA Astrophysics Data System (ADS)

Burns, A. D.; Clarke, D. S.; Guilbert, P.; Jones, I. P.; Simcox, S.; Wilkes, N. S.

The application of Computational Fluid Dynamics (CFD) to industrial safety is a challenging activity. In particular it involves the interaction of several different physical processes, including turbulence, combustion, radiation, buoyancy, compressible flow and shock waves in complex three-dimensional geometries. In addition, there may be multi-phase effects arising, for example, from sprinkler systems for extinguishing fires. The FLOW3D software (1-3) from Computational Fluid Dynamics Services (CFDS) is in widespread use in industrial safety problems, both within AEA Technology, and also by CFDS's commercial customers, for example references (4-13). This paper discusses some other applications of FLOW3D to safety problems. These applications illustrate the coupling of the gas flows with radiation models and combustion models, particularly for complex geometries where simpler radiation models are not applicable.

Grand challenges in mass storage: A systems integrators perspective

NASA Technical Reports Server (NTRS)

Lee, Richard R.; Mintz, Daniel G.

1993-01-01

Within today's much ballyhooed supercomputing environment, with its CFLOPS of CPU power, and Gigabit networks, there exists a major roadblock to computing success; that of Mass Storage. The solution to this mass storage problem is considered to be one of the 'Grand Challenges' facing the computer industry today, as well as long into the future. It has become obvious to us, as well as many others in the industry, that there is no clear single solution in sight. The Systems Integrator today is faced with a myriad of quandaries in approaching this challenge. He must first be innovative in approach, second choose hardware solutions that are volumetric efficient; high in signal bandwidth; available from multiple sources; competitively priced, and have forward growth extendibility. In addition he must also comply with a variety of mandated, and often conflicting software standards (GOSIP, POSIX, IEEE, MSRM 4.0, and others), and finally he must deliver a systems solution with the 'most bang for the buck' in terms of cost vs. performance factors. These quandaries challenge the Systems Integrator to 'push the envelope' in terms of his or her ingenuity and innovation on an almost daily basis. This dynamic is explored further, and an attempt to acquaint the audience with rational approaches to this 'Grand Challenge' is made.

Application of Artificial Intelligence technology to the analysis and synthesis of reliable software systems

NASA Technical Reports Server (NTRS)

Wild, Christian; Eckhardt, Dave

1987-01-01

The development of a methodology for the production of highly reliable software is one of the greatest challenges facing the computer industry. Meeting this challenge will undoubtably involve the integration of many technologies. This paper describes the use of Artificial Intelligence technologies in the automated analysis of the formal algebraic specifications of abstract data types. These technologies include symbolic execution of specifications using techniques of automated deduction and machine learning through the use of examples. On-going research into the role of knowledge representation and problem solving in the process of developing software is also discussed.

Large-scale inverse model analyses employing fast randomized data reduction

NASA Astrophysics Data System (ADS)

Lin, Youzuo; Le, Ellen B.; O'Malley, Daniel; Vesselinov, Velimir V.; Bui-Thanh, Tan

2017-08-01

When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 107 or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called "sketching" matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Most importantly, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Therefore, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity.

Dynamic resource allocation in conservation planning

USGS Publications Warehouse

Golovin, D.; Krause, A.; Gardner, B.; Converse, S.J.; Morey, S.

2011-01-01

Consider the problem of protecting endangered species by selecting patches of land to be used for conservation purposes. Typically, the availability of patches changes over time, and recommendations must be made dynamically. This is a challenging prototypical example of a sequential optimization problem under uncertainty in computational sustainability. Existing techniques do not scale to problems of realistic size. In this paper, we develop an efficient algorithm for adaptively making recommendations for dynamic conservation planning, and prove that it obtains near-optimal performance. We further evaluate our approach on a detailed reserve design case study of conservation planning for three rare species in the Pacific Northwest of the United States. Copyright ?? 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

Spectral Collocation Time-Domain Modeling of Diffractive Optical Elements

NASA Astrophysics Data System (ADS)

Hesthaven, J. S.; Dinesen, P. G.; Lynov, J. P.

1999-11-01

A spectral collocation multi-domain scheme is developed for the accurate and efficient time-domain solution of Maxwell's equations within multi-layered diffractive optical elements. Special attention is being paid to the modeling of out-of-plane waveguide couplers. Emphasis is given to the proper construction of high-order schemes with the ability to handle very general problems of considerable geometric and material complexity. Central questions regarding efficient absorbing boundary conditions and time-stepping issues are also addressed. The efficacy of the overall scheme for the time-domain modeling of electrically large, and computationally challenging, problems is illustrated by solving a number of plane as well as non-plane waveguide problems.

An optimization-based approach for solving a time-harmonic multiphysical wave problem with higher-order schemes

NASA Astrophysics Data System (ADS)

Mönkölä, Sanna

2013-06-01

This study considers developing numerical solution techniques for the computer simulations of time-harmonic fluid-structure interaction between acoustic and elastic waves. The focus is on the efficiency of an iterative solution method based on a controllability approach and spectral elements. We concentrate on the model, in which the acoustic waves in the fluid domain are modeled by using the velocity potential and the elastic waves in the structure domain are modeled by using displacement. Traditionally, the complex-valued time-harmonic equations are used for solving the time-harmonic problems. Instead of that, we focus on finding periodic solutions without solving the time-harmonic problems directly. The time-dependent equations can be simulated with respect to time until a time-harmonic solution is reached, but the approach suffers from poor convergence. To overcome this challenge, we follow the approach first suggested and developed for the acoustic wave equations by Bristeau, Glowinski, and Périaux. Thus, we accelerate the convergence rate by employing a controllability method. The problem is formulated as a least-squares optimization problem, which is solved with the conjugate gradient (CG) algorithm. Computation of the gradient of the functional is done directly for the discretized problem. A graph-based multigrid method is used for preconditioning the CG algorithm.

Adaptive Importance Sampling for Control and Inference

NASA Astrophysics Data System (ADS)

Kappen, H. J.; Ruiz, H. C.

2016-03-01

Path integral (PI) control problems are a restricted class of non-linear control problems that can be solved formally as a Feynman-Kac PI and can be estimated using Monte Carlo sampling. In this contribution we review PI control theory in the finite horizon case. We subsequently focus on the problem how to compute and represent control solutions. We review the most commonly used methods in robotics and control. Within the PI theory, the question of how to compute becomes the question of importance sampling. Efficient importance samplers are state feedback controllers and the use of these requires an efficient representation. Learning and representing effective state-feedback controllers for non-linear stochastic control problems is a very challenging, and largely unsolved, problem. We show how to learn and represent such controllers using ideas from the cross entropy method. We derive a gradient descent method that allows to learn feed-back controllers using an arbitrary parametrisation. We refer to this method as the path integral cross entropy method or PICE. We illustrate this method for some simple examples. The PI control methods can be used to estimate the posterior distribution in latent state models. In neuroscience these problems arise when estimating connectivity from neural recording data using EM. We demonstrate the PI control method as an accurate alternative to particle filtering.

Big Computing in Astronomy: Perspectives and Challenges

NASA Astrophysics Data System (ADS)

Pankratius, Victor

2014-06-01

Hardware progress in recent years has led to astronomical instruments gathering large volumes of data. In radio astronomy for instance, the current generation of antenna arrays produces data at Tbits per second, and forthcoming instruments will expand these rates much further. As instruments are increasingly becoming software-based, astronomers will get more exposed to computer science. This talk therefore outlines key challenges that arise at the intersection of computer science and astronomy and presents perspectives on how both communities can collaborate to overcome these challenges.Major problems are emerging due to increases in data rates that are much larger than in storage and transmission capacity, as well as humans being cognitively overwhelmed when attempting to opportunistically scan through Big Data. As a consequence, the generation of scientific insight will become more dependent on automation and algorithmic instrument control. Intelligent data reduction will have to be considered across the entire acquisition pipeline. In this context, the presentation will outline the enabling role of machine learning and parallel computing.BioVictor Pankratius is a computer scientist who joined MIT Haystack Observatory following his passion for astronomy. He is currently leading efforts to advance astronomy through cutting-edge computer science and parallel computing. Victor is also involved in projects such as ALMA Phasing to enhance the ALMA Observatory with Very-Long Baseline Interferometry capabilities, the Event Horizon Telescope, as well as in the Radio Array of Portable Interferometric Detectors (RAPID) to create an analysis environment using parallel computing in the cloud. He has an extensive track record of research in parallel multicore systems and software engineering, with contributions to auto-tuning, debugging, and empirical experiments studying programmers. Victor has worked with major industry partners such as Intel, Sun Labs, and Oracle. He holds a distinguished doctorate and a Habilitation degree in Computer Science from the University of Karlsruhe. Contact him at pankrat@mit.edu, victorpankratius.com, or Twitter @vpankratius.

An Integrative and Collaborative Approach to Creating a Diverse and Computationally Competent Geoscience Workforce

NASA Astrophysics Data System (ADS)

Moore, S. L.; Kar, A.; Gomez, R.

2015-12-01

A partnership between Fort Valley State University (FVSU), the Jackson School of Geosciences at The University of Texas (UT) at Austin, and the Texas Advanced Computing Center (TACC) is engaging computational geoscience faculty and researchers with academically talented underrepresented minority (URM) students, training them to solve grand challenges . These next generation computational geoscientists are being trained to solve some of the world's most challenging geoscience grand challenges requiring data intensive large scale modeling and simulation on high performance computers . UT Austin's geoscience outreach program GeoFORCE, recently awarded the Presidential Award in Excellence in Science, Mathematics and Engineering Mentoring, contributes to the collaborative best practices in engaging researchers with URM students. Collaborative efforts over the past decade are providing data demonstrating that integrative pipeline programs with mentoring and paid internship opportunities, multi-year scholarships, computational training, and communication skills development are having an impact on URMs developing middle skills for geoscience careers. Since 1997, the Cooperative Developmental Energy Program at FVSU and its collaborating universities have graduated 87 engineers, 33 geoscientists, and eight health physicists. Recruited as early as high school, students enroll for three years at FVSU majoring in mathematics, chemistry or biology, and then transfer to UT Austin or other partner institutions to complete a second STEM degree, including geosciences. A partnership with the Integrative Computational Education and Research Traineeship (ICERT), a National Science Foundation (NSF) Research Experience for Undergraduates (REU) Site at TACC provides students with a 10-week summer research experience at UT Austin. Mentored by TACC researchers, students with no previous background in computational science learn to use some of the world's most powerful high performance computing resources to address a grand geosciences problem. Students increase their ability to understand and explain the societal impact of their research and communicate the research to multidisciplinary and lay audiences via near-peer mentoring, poster presentations, and publication opportunities.

Discovering Tradeoffs, Vulnerabilities, and Dependencies within Water Resources Systems

NASA Astrophysics Data System (ADS)

Reed, P. M.

2015-12-01

There is a growing recognition and interest in using emerging computational tools for discovering the tradeoffs that emerge across complex combinations infrastructure options, adaptive operations, and sign posts. As a field concerned with "deep uncertainties", it is logically consistent to include a more direct acknowledgement that our choices for dealing with computationally demanding simulations, advanced search algorithms, and sensitivity analysis tools are themselves subject to failures that could adversely bias our understanding of how systems' vulnerabilities change with proposed actions. Balancing simplicity versus complexity in our computational frameworks is nontrivial given that we are often exploring high impact irreversible decisions. It is not always clear that accepted models even encompass important failure modes. Moreover as they become more complex and computationally demanding the benefits and consequences of simplifications are often untested. This presentation discusses our efforts to address these challenges through our "many-objective robust decision making" (MORDM) framework for the design and management water resources systems. The MORDM framework has four core components: (1) elicited problem conception and formulation, (2) parallel many-objective search, (3) interactive visual analytics, and (4) negotiated selection of robust alternatives. Problem conception and formulation is the process of abstracting a practical design problem into a mathematical representation. We build on the emerging work in visual analytics to exploit interactive visualization of both the design space and the objective space in multiple heterogeneous linked views that permit exploration and discovery. Many-objective search produces tradeoff solutions from potentially competing problem formulations that can each consider up to ten conflicting objectives based on current computational search capabilities. Negotiated design selection uses interactive visualization, reformulation, and optimization to discover desirable designs for implementation. Multi-city urban water supply portfolio planning will be used to illustrate the MORDM framework.

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction.

PubMed

Singh, Shailendra; Kaur, Sukhbir; Goel, Neelam

2015-01-01

In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA that helps in transcription of a gene. Promoter is one such region and to find its location is a challenging problem. Various computational methods for promoter prediction have been developed over the past few years. This paper reviews these promoter prediction methods. Several difficulties and pitfalls encountered by these methods are also detailed, along with future research directions.

An Efficient Identity-Based Key Management Scheme for Wireless Sensor Networks Using the Bloom Filter

PubMed Central

Qin, Zhongyuan; Zhang, Xinshuai; Feng, Kerong; Zhang, Qunfang; Huang, Jie

2014-01-01

With the rapid development and widespread adoption of wireless sensor networks (WSNs), security has become an increasingly prominent problem. How to establish a session key in node communication is a challenging task for WSNs. Considering the limitations in WSNs, such as low computing capacity, small memory, power supply limitations and price, we propose an efficient identity-based key management (IBKM) scheme, which exploits the Bloom filter to authenticate the communication sensor node with storage efficiency. The security analysis shows that IBKM can prevent several attacks effectively with acceptable computation and communication overhead. PMID:25264955

Interaction Entropy: A New Paradigm for Highly Efficient and Reliable Computation of Protein-Ligand Binding Free Energy.

PubMed

Duan, Lili; Liu, Xiao; Zhang, John Z H

2016-05-04

Efficient and reliable calculation of protein-ligand binding free energy is a grand challenge in computational biology and is of critical importance in drug design and many other molecular recognition problems. The main challenge lies in the calculation of entropic contribution to protein-ligand binding or interaction systems. In this report, we present a new interaction entropy method which is theoretically rigorous, computationally efficient, and numerically reliable for calculating entropic contribution to free energy in protein-ligand binding and other interaction processes. Drastically different from the widely employed but extremely expensive normal mode method for calculating entropy change in protein-ligand binding, the new method calculates the entropic component (interaction entropy or -TΔS) of the binding free energy directly from molecular dynamics simulation without any extra computational cost. Extensive study of over a dozen randomly selected protein-ligand binding systems demonstrated that this interaction entropy method is both computationally efficient and numerically reliable and is vastly superior to the standard normal mode approach. This interaction entropy paradigm introduces a novel and intuitive conceptual understanding of the entropic effect in protein-ligand binding and other general interaction systems as well as a practical method for highly efficient calculation of this effect.

Image-Based Modeling Techniques for Architectural Heritage 3d Digitalization: Limits and Potentialities

NASA Astrophysics Data System (ADS)

Santagati, C.; Inzerillo, L.; Di Paola, F.

2013-07-01

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose.

Computational AeroAcoustics for Fan Noise Prediction

NASA Technical Reports Server (NTRS)

Envia, Ed; Hixon, Ray; Dyson, Rodger; Huff, Dennis (Technical Monitor)

2002-01-01

An overview of the current state-of-the-art in computational aeroacoustics as applied to fan noise prediction at NASA Glenn is presented. Results from recent modeling efforts using three dimensional inviscid formulations in both frequency and time domains are summarized. In particular, the application of a frequency domain method, called LINFLUX, to the computation of rotor-stator interaction tone noise is reviewed and the influence of the background inviscid flow on the acoustic results is analyzed. It has been shown that the noise levels are very sensitive to the gradients of the mean flow near the surface and that the correct computation of these gradients for highly loaded airfoils is especially problematic using an inviscid formulation. The ongoing development of a finite difference time marching code that is based on a sixth order compact scheme is also reviewed. Preliminary results from the nonlinear computation of a gust-airfoil interaction model problem demonstrate the fidelity and accuracy of this approach. Spatial and temporal features of the code as well as its multi-block nature are discussed. Finally, latest results from an ongoing effort in the area of arbitrarily high order methods are reviewed and technical challenges associated with implementing correct high order boundary conditions are discussed and possible strategies for addressing these challenges ore outlined.

Challenges in reducing the computational time of QSTS simulations for distribution system analysis.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deboever, Jeremiah; Zhang, Xiaochen; Reno, Matthew J.

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modelling and impact analysis. Unlike conventional scenario - based studies , quasi - static time - series (QSTS) simulation s can realistically model time - dependent voltage controllers and the diversity of potential impacts that can occur at different times of year . However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1 - second resolution is often required , which could take conventional computers a computational time of 10more » to 120 hours when an actual unbalanced distribution feeder is modeled . This computational burden is a clear l imitation to the adoption of QSTS simulation s in interconnection studies and for determining optimal control solutions for utility operations . Our ongoing research to improve the speed of QSTS simulation has revealed many unique aspects of distribution system modelling and sequential power flow analysis that make fast QSTS a very difficult problem to solve. In this report , the most relevant challenges in reducing the computational time of QSTS simulations are presented: number of power flows to solve, circuit complexity, time dependence between time steps, multiple valid power flow solutions, controllable element interactions, and extensive accurate simulation analysis.« less

From Three-Photon Greenberger-Horne-Zeilinger States to Ballistic Universal Quantum Computation.

PubMed

Gimeno-Segovia, Mercedes; Shadbolt, Pete; Browne, Dan E; Rudolph, Terry

2015-07-10

Single photons, manipulated using integrated linear optics, constitute a promising platform for universal quantum computation. A series of increasingly efficient proposals have shown linear-optical quantum computing to be formally scalable. However, existing schemes typically require extensive adaptive switching, which is experimentally challenging and noisy, thousands of photon sources per renormalized qubit, and/or large quantum memories for repeat-until-success strategies. Our work overcomes all these problems. We present a scheme to construct a cluster state universal for quantum computation, which uses no adaptive switching, no large memories, and which is at least an order of magnitude more resource efficient than previous passive schemes. Unlike previous proposals, it is constructed entirely from loss-detecting gates and offers a robustness to photon loss. Even without the use of an active loss-tolerant encoding, our scheme naturally tolerates a total loss rate ∼1.6% in the photons detected in the gates. This scheme uses only 3 Greenberger-Horne-Zeilinger states as a resource, together with a passive linear-optical network. We fully describe and model the iterative process of cluster generation, including photon loss and gate failure. This demonstrates that building a linear-optical quantum computer needs to be less challenging than previously thought.

Fully implicit adaptive mesh refinement algorithm for reduced MHD

NASA Astrophysics Data System (ADS)

Philip, Bobby; Pernice, Michael; Chacon, Luis

2006-10-01

In the macroscopic simulation of plasmas, the numerical modeler is faced with the challenge of dealing with multiple time and length scales. Traditional approaches based on explicit time integration techniques and fixed meshes are not suitable for this challenge, as such approaches prevent the modeler from using realistic plasma parameters to keep the computation feasible. We propose here a novel approach, based on implicit methods and structured adaptive mesh refinement (SAMR). Our emphasis is on both accuracy and scalability with the number of degrees of freedom. As a proof-of-principle, we focus on the reduced resistive MHD model as a basic MHD model paradigm, which is truly multiscale. The approach taken here is to adapt mature physics-based technology to AMR grids, and employ AMR-aware multilevel techniques (such as fast adaptive composite grid --FAC-- algorithms) for scalability. We demonstrate that the concept is indeed feasible, featuring near-optimal scalability under grid refinement. Results of fully-implicit, dynamically-adaptive AMR simulations in challenging dissipation regimes will be presented on a variety of problems that benefit from this capability, including tearing modes, the island coalescence instability, and the tilt mode instability. L. Chac'on et al., J. Comput. Phys. 178 (1), 15- 36 (2002) B. Philip, M. Pernice, and L. Chac'on, Lecture Notes in Computational Science and Engineering, accepted (2006)

The pKa Cooperative: A Collaborative Effort to Advance Structure-Based Calculations of pKa values and Electrostatic Effects in Proteins

PubMed Central

Nielsen, Jens E.; Gunner, M. R.; Bertrand García-Moreno, E.

2012-01-01

The pKa Cooperative http://www.pkacoop.org was organized to advance development of accurate and useful computational methods for structure-based calculation of pKa values and electrostatic energy in proteins. The Cooperative brings together laboratories with expertise and interest in theoretical, computational and experimental studies of protein electrostatics. To improve structure-based energy calculations it is necessary to better understand the physical character and molecular determinants of electrostatic effects. The Cooperative thus intends to foment experimental research into fundamental aspects of proteins that depend on electrostatic interactions. It will maintain a depository for experimental data useful for critical assessment of methods for structure-based electrostatics calculations. To help guide the development of computational methods the Cooperative will organize blind prediction exercises. As a first step, computational laboratories were invited to reproduce an unpublished set of experimental pKa values of acidic and basic residues introduced in the interior of staphylococcal nuclease by site-directed mutagenesis. The pKa values of these groups are unique and challenging to simulate owing to the large magnitude of their shifts relative to normal pKa values in water. Many computational methods were tested in this 1st Blind Prediction Challenge and critical assessment exercise. A workshop was organized in the Telluride Science Research Center to assess objectively the performance of many computational methods tested on this one extensive dataset. This volume of PROTEINS: Structure, Function, and Bioinformatics introduces the pKa Cooperative, presents reports submitted by participants in the blind prediction challenge, and highlights some of the problems in structure-based calculations identified during this exercise. PMID:22002877

Identifying problem and compulsive gamblers.

PubMed Central

van Es, R.

2000-01-01

OBJECTIVE: To present a meta-analysis of current research on the prevalence, identification, and treatment of problem and compulsive gamblers. QUALITY OF EVIDENCE: Problem and compulsive gambling was not a socio-scientific concern until the last two decades. Hence research on this topic is limited. The summary and analysis for this paper relied on computer searches of journal and news abstracts in addition to direct contact with organizations addressing the identification and treatment of compulsive gamblers. MAIN MESSAGE: An estimated 5% of those who gamble run into problems. About 1% of those who gamble are predicted to experience serious problems. Successful treatment of problem and compulsive gambling continues to be a challenge. Although cognitive therapy has been the favoured approach, a combination of several therapeutic approaches is advocated. CONCLUSIONS: Problem and compulsive gambling can present a real health threat. As with other addictions, treatment strategies continue to be a baffling social problem. Aware and informed physicians can have a pivotal role in the difficult process of identifying, acknowledging, and remediating problem and compulsive gambling. PMID:10907572

Perspectives on biological growth and remodeling

PubMed Central

Ambrosi, D.; Ateshian, G. A.; Arruda, E. M.; Cowin, S. C.; Dumais, J.; Goriely, A.; Holzapfel, G. A.; Humphrey, J. D.; Kemkemer, R.; Kuhl, E.; Olberding, J. E.; Taber, L. A.; Garikipati, K.

2011-01-01

The continuum mechanical treatment of biological growth and remodeling has attracted considerable attention over the past fifteen years. Many aspects of these problems are now well-understood, yet there remain areas in need of significant development from the standpoint of experiments, theory, and computation. In this perspective paper we review the state of the field and highlight open questions, challenges, and avenues for further development. PMID:21532929

Investigating the Role of Computer-Supported Annotation in Problem-Solving-Based Teaching: An Empirical Study of a Scratch Programming Pedagogy

ERIC Educational Resources Information Center

Su, Addison Y. S.; Yang, Stephen J. H.; Hwang, Wu-Yuin; Huang, Chester S. J.; Tern, Ming-Yu

2014-01-01

For more than 2 years, Scratch programming has been taught in Taiwanese elementary schools. However, past studies have shown that it is difficult to find appropriate learning methods or tools to boost students' Scratch programming performance. This inability to readily identify tutoring tools has become one of the primary challenges addressed in…

Teaching Sustainability through System Dynamics: Exploring Stocks and Flows Embedded in Dynamic Computer Models of an Agricultural Land Management System

ERIC Educational Resources Information Center

Pallant, Amy; Lee, Hee-Sun

2017-01-01

During the past several decades, there has been a growing awareness of the ways humans affect Earth systems. As global problems emerge, educating the next generation of citizens to be able to make informed choices related to future outcomes is increasingly important. The challenge for educators is figuring out how to prepare students to think…

Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pal, Ranjan; Chelmis, Charalampos; Aman, Saima

The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face of the “increasing dimensionality” problem where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethinkmore » the way cluster points are created and designed, and then design an efficient online clustering technique for demand response (DR) in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. Our online algorithm is randomized in nature, and provides optimal performance guarantees in a computationally efficient manner. Unlike prior work we (i) study the consumption properties of the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a ‘killer’ approach that breaks the “curse of dimensionality” in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field of sociology, where collective behavior often emerges as the result of individual patterns and lifestyles.« less

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar

2016-01-01

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Probabilistic models, learning algorithms, and response variability: sampling in cognitive development.

PubMed

Bonawitz, Elizabeth; Denison, Stephanie; Griffiths, Thomas L; Gopnik, Alison

2014-10-01

Although probabilistic models of cognitive development have become increasingly prevalent, one challenge is to account for how children might cope with a potentially vast number of possible hypotheses. We propose that children might address this problem by 'sampling' hypotheses from a probability distribution. We discuss empirical results demonstrating signatures of sampling, which offer an explanation for the variability of children's responses. The sampling hypothesis provides an algorithmic account of how children might address computationally intractable problems and suggests a way to make sense of their 'noisy' behavior. Copyright © 2014 Elsevier Ltd. All rights reserved.

Artificial intelligence and design: Opportunities, research problems and directions

NASA Technical Reports Server (NTRS)

Amarel, Saul

1990-01-01

The issues of industrial productivity and economic competitiveness are of major significance in the U.S. at present. By advancing the science of design, and by creating a broad computer-based methodology for automating the design of artifacts and of industrial processes, we can attain dramatic improvements in productivity. It is our thesis that developments in computer science, especially in Artificial Intelligence (AI) and in related areas of advanced computing, provide us with a unique opportunity to push beyond the present level of computer aided automation technology and to attain substantial advances in the understanding and mechanization of design processes. To attain these goals, we need to build on top of the present state of AI, and to accelerate research and development in areas that are especially relevant to design problems of realistic complexity. We propose an approach to the special challenges in this area, which combines 'core work' in AI with the development of systems for handling significant design tasks. We discuss the general nature of design problems, the scientific issues involved in studying them with the help of AI approaches, and the methodological/technical issues that one must face in developing AI systems for handling advanced design tasks. Looking at basic work in AI from the perspective of design automation, we identify a number of research problems that need special attention. These include finding solution methods for handling multiple interacting goals, formation problems, problem decompositions, and redesign problems; choosing representations for design problems with emphasis on the concept of a design record; and developing approaches for the acquisition and structuring of domain knowledge with emphasis on finding useful approximations to domain theories. Progress in handling these research problems will have major impact both on our understanding of design processes and their automation, and also on several fundamental questions that are of intrinsic concern to AI. We present examples of current AI work on specific design tasks, and discuss new directions of research, both as extensions of current work and in the context of new design tasks where domain knowledge is either intractable or incomplete. The domains discussed include Digital Circuit Design, Mechanical Design of Rotational Transmissions, Design of Computer Architectures, Marine Design, Aircraft Design, and Design of Chemical Processes and Materials. Work in these domains is significant on technical grounds, and it is also important for economic and policy reasons.

Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems

PubMed Central

Teodoro, George; Kurc, Tahsin M.; Pan, Tony; Cooper, Lee A.D.; Kong, Jun; Widener, Patrick; Saltz, Joel H.

2014-01-01

The past decade has witnessed a major paradigm shift in high performance computing with the introduction of accelerators as general purpose processors. These computing devices make available very high parallel computing power at low cost and power consumption, transforming current high performance platforms into heterogeneous CPU-GPU equipped systems. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of this computing power remains a very challenging problem. Most applications are still deployed to either GPU or CPU, leaving the other resource under- or un-utilized. In this paper, we propose, implement, and evaluate a performance aware scheduling technique along with optimizations to make efficient collaborative use of CPUs and GPUs on a parallel system. In the context of feature computations in large scale image analysis applications, our evaluations show that intelligently co-scheduling CPUs and GPUs can significantly improve performance over GPU-only or multi-core CPU-only approaches. PMID:25419545

Improved look-up table method of computer-generated holograms.

PubMed

Wei, Hui; Gong, Guanghong; Li, Ni

2016-11-10

Heavy computation load and vast memory requirements are major bottlenecks of computer-generated holograms (CGHs), which are promising and challenging in three-dimensional displays. To solve these problems, an improved look-up table (LUT) method suitable for arbitrarily sampled object points is proposed and implemented on a graphics processing unit (GPU) whose reconstructed object quality is consistent with that of the coherent ray-trace (CRT) method. The concept of distance factor is defined, and the distance factors are pre-computed off-line and stored in a look-up table. The results show that while reconstruction quality close to that of the CRT method is obtained, the on-line computation time is dramatically reduced compared with the LUT method on the GPU and the memory usage is lower than that of the novel-LUT considerably. Optical experiments are carried out to validate the effectiveness of the proposed method.

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.

PubMed

Penas, David R; González, Patricia; Egea, Jose A; Doallo, Ramón; Banga, Julio R

2017-01-21

The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

Application of Stereo Vision to the Reconnection Scaling Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klarenbeek, Johnny; Sears, Jason A.; Gao, Kevin W.

The measurement and simulation of the three-dimensional structure of magnetic reconnection in astrophysical and lab plasmas is a challenging problem. At Los Alamos National Laboratory we use the Reconnection Scaling Experiment (RSX) to model 3D magnetohydrodynamic (MHD) relaxation of plasma filled tubes. These magnetic flux tubes are called flux ropes. In RSX, the 3D structure of the flux ropes is explored with insertable probes. Stereo triangulation can be used to compute the 3D position of a probe from point correspondences in images from two calibrated cameras. While common applications of stereo triangulation include 3D scene reconstruction and robotics navigation, wemore » will investigate the novel application of stereo triangulation in plasma physics to aid reconstruction of 3D data for RSX plasmas. Several challenges will be explored and addressed, such as minimizing 3D reconstruction errors in stereo camera systems and dealing with point correspondence problems.« less

Computational complexity in entanglement transformations

NASA Astrophysics Data System (ADS)

Chitambar, Eric A.

In physics, systems having three parts are typically much more difficult to analyze than those having just two. Even in classical mechanics, predicting the motion of three interacting celestial bodies remains an insurmountable challenge while the analogous two-body problem has an elementary solution. It is as if just by adding a third party, a fundamental change occurs in the structure of the problem that renders it unsolvable. In this thesis, we demonstrate how such an effect is likewise present in the theory of quantum entanglement. In fact, the complexity differences between two-party and three-party entanglement become quite conspicuous when comparing the difficulty in deciding what state changes are possible for these systems when no additional entanglement is consumed in the transformation process. We examine this entanglement transformation question and its variants in the language of computational complexity theory, a powerful subject that formalizes the concept of problem difficulty. Since deciding feasibility of a specified bipartite transformation is relatively easy, this task belongs to the complexity class P. On the other hand, for tripartite systems, we find the problem to be NP-Hard, meaning that its solution is at least as hard as the solution to some of the most difficult problems humans have encountered. One can then rigorously defend the assertion that a fundamental complexity difference exists between bipartite and tripartite entanglement since unlike the former, the full range of forms realizable by the latter is incalculable (assuming P≠NP). However, similar to the three-body celestial problem, when one examines a special subclass of the problem---invertible transformations on systems having at least one qubit subsystem---we prove that the problem can be solved efficiently. As a hybrid of the two questions, we find that the question of tripartite to bipartite transformations can be solved by an efficient randomized algorithm. Our results are obtained by encoding well-studied computational problems such as polynomial identity testing and tensor rank into questions of entanglement transformation. In this way, entanglement theory provides a physical manifestation of some of the most puzzling and abstract classical computation questions.

A Secure and Verifiable Outsourced Access Control Scheme in Fog-Cloud Computing

PubMed Central

Fan, Kai; Wang, Junxiong; Wang, Xin; Li, Hui; Yang, Yintang

2017-01-01

With the rapid development of big data and Internet of things (IOT), the number of networking devices and data volume are increasing dramatically. Fog computing, which extends cloud computing to the edge of the network can effectively solve the bottleneck problems of data transmission and data storage. However, security and privacy challenges are also arising in the fog-cloud computing environment. Ciphertext-policy attribute-based encryption (CP-ABE) can be adopted to realize data access control in fog-cloud computing systems. In this paper, we propose a verifiable outsourced multi-authority access control scheme, named VO-MAACS. In our construction, most encryption and decryption computations are outsourced to fog devices and the computation results can be verified by using our verification method. Meanwhile, to address the revocation issue, we design an efficient user and attribute revocation method for it. Finally, analysis and simulation results show that our scheme is both secure and highly efficient. PMID:28737733

Progress in computer vision.

NASA Astrophysics Data System (ADS)

Jain, A. K.; Dorai, C.

Computer vision has emerged as a challenging and important area of research, both as an engineering and a scientific discipline. The growing importance of computer vision is evident from the fact that it was identified as one of the "Grand Challenges" and also from its prominent role in the National Information Infrastructure. While the design of a general-purpose vision system continues to be elusive machine vision systems are being used successfully in specific application elusive, machine vision systems are being used successfully in specific application domains. Building a practical vision system requires a careful selection of appropriate sensors, extraction and integration of information from available cues in the sensed data, and evaluation of system robustness and performance. The authors discuss and demonstrate advantages of (1) multi-sensor fusion, (2) combination of features and classifiers, (3) integration of visual modules, and (IV) admissibility and goal-directed evaluation of vision algorithms. The requirements of several prominent real world applications such as biometry, document image analysis, image and video database retrieval, and automatic object model construction offer exciting problems and new opportunities to design and evaluate vision algorithms.

solveME: fast and reliable solution of nonlinear ME models.

PubMed

Yang, Laurence; Ma, Ding; Ebrahim, Ali; Lloyd, Colton J; Saunders, Michael A; Palsson, Bernhard O

2016-09-22

Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints. Just as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.

Parallel Processing of Images in Mobile Devices using BOINC

NASA Astrophysics Data System (ADS)

Curiel, Mariela; Calle, David F.; Santamaría, Alfredo S.; Suarez, David F.; Flórez, Leonardo

2018-04-01

Medical image processing helps health professionals make decisions for the diagnosis and treatment of patients. Since some algorithms for processing images require substantial amounts of resources, one could take advantage of distributed or parallel computing. A mobile grid can be an adequate computing infrastructure for this problem. A mobile grid is a grid that includes mobile devices as resource providers. In a previous step of this research, we selected BOINC as the infrastructure to build our mobile grid. However, parallel processing of images in mobile devices poses at least two important challenges: the execution of standard libraries for processing images and obtaining adequate performance when compared to desktop computers grids. By the time we started our research, the use of BOINC in mobile devices also involved two issues: a) the execution of programs in mobile devices required to modify the code to insert calls to the BOINC API, and b) the division of the image among the mobile devices as well as its merging required additional code in some BOINC components. This article presents answers to these four challenges.

Modeling and Simulation of Explosively Driven Electromechanical Devices

NASA Astrophysics Data System (ADS)

Demmie, Paul N.

2002-07-01

Components that store electrical energy in ferroelectric materials and produce currents when their permittivity is explosively reduced are used in a variety of applications. The modeling and simulation of such devices is a challenging problem since one has to represent the coupled physics of detonation, shock propagation, and electromagnetic field generation. The high fidelity modeling and simulation of complicated electromechanical devices was not feasible prior to having the Accelerated Strategic Computing Initiative (ASCI) computers and the ASCI developed codes at Sandia National Laboratories (SNL). The EMMA computer code is used to model such devices and simulate their operation. In this paper, I discuss the capabilities of the EMMA code for the modeling and simulation of one such electromechanical device, a slim-loop ferroelectric (SFE) firing set.

TeleMed: Wide-area, secure, collaborative object computing with Java and CORBA for healthcare

DOE Office of Scientific and Technical Information (OSTI.GOV)

Forslund, D.W.; George, J.E.; Gavrilov, E.M.

1998-12-31

Distributed computing is becoming commonplace in a variety of industries with healthcare being a particularly important one for society. The authors describe the development and deployment of TeleMed in a few healthcare domains. TeleMed is a 100% Java distributed application build on CORBA and OMG standards enabling the collaboration on the treatment of chronically ill patients in a secure manner over the Internet. These standards enable other systems to work interoperably with TeleMed and provide transparent access to high performance distributed computing to the healthcare domain. The goal of wide scale integration of electronic medical records is a grand-challenge scalemore » problem of global proportions with far-reaching social benefits.« less

A forward-adjoint operator pair based on the elastic wave equation for use in transcranial photoacoustic computed tomography

PubMed Central

Mitsuhashi, Kenji; Poudel, Joemini; Matthews, Thomas P.; Garcia-Uribe, Alejandro; Wang, Lihong V.; Anastasio, Mark A.

2017-01-01

Photoacoustic computed tomography (PACT) is an emerging imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to an inverse source problem in which the initial pressure distribution is recovered from measurements of the radiated wavefield. A major challenge in transcranial PACT brain imaging is compensation for aberrations in the measured data due to the presence of the skull. Ultrasonic waves undergo absorption, scattering and longitudinal-to-shear wave mode conversion as they propagate through the skull. To properly account for these effects, a wave-equation-based inversion method should be employed that can model the heterogeneous elastic properties of the skull. In this work, a forward model based on a finite-difference time-domain discretization of the three-dimensional elastic wave equation is established and a procedure for computing the corresponding adjoint of the forward operator is presented. Massively parallel implementations of these operators employing multiple graphics processing units (GPUs) are also developed. The developed numerical framework is validated and investigated in computer19 simulation and experimental phantom studies whose designs are motivated by transcranial PACT applications. PMID:29387291

Iterative image reconstruction in elastic inhomogenous media with application to transcranial photoacoustic tomography

NASA Astrophysics Data System (ADS)

Poudel, Joemini; Matthews, Thomas P.; Mitsuhashi, Kenji; Garcia-Uribe, Alejandro; Wang, Lihong V.; Anastasio, Mark A.

2017-03-01

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the measurements recorded on an aperture outside the support of the source. A major challenge in transcranial PACT brain imaging is to compensate for aberrations in the measured data due to the propagation of the photoacoustic wavefields through the skull. To properly account for these effects, a wave equation-based inversion method should be employed that can model the heterogeneous elastic properties of the medium. In this study, an iterative image reconstruction method for 3D transcranial PACT is developed based on the elastic wave equation. To accomplish this, a forward model based on a finite-difference time-domain discretization of the elastic wave equation is established. Subsequently, gradient-based methods are employed for computing penalized least squares estimates of the initial source distribution that produced the measured photoacoustic data. The developed reconstruction algorithm is validated and investigated through computer-simulation studies.

MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

NASA Astrophysics Data System (ADS)

Zheng, Mao-Kuan; Ming, Xin-Guo; Zhang, Xian-Yu; Li, Guo-Ming

2017-09-01

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

2015 Final Reports from the Los Alamos National Laboratory Computational Physics Student Summer Workshop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Runnels, Scott Robert; Caldwell, Wendy; Brown, Barton Jed

The two primary purposes of LANL’s Computational Physics Student Summer Workshop are (1) To educate graduate and exceptional undergraduate students in the challenges and applications of computational physics of interest to LANL, and (2) Entice their interest toward those challenges. Computational physics is emerging as a discipline in its own right, combining expertise in mathematics, physics, and computer science. The mathematical aspects focus on numerical methods for solving equations on the computer as well as developing test problems with analytical solutions. The physics aspects are very broad, ranging from low-temperature material modeling to extremely high temperature plasma physics, radiation transportmore » and neutron transport. The computer science issues are concerned with matching numerical algorithms to emerging architectures and maintaining the quality of extremely large codes built to perform multi-physics calculations. Although graduate programs associated with computational physics are emerging, it is apparent that the pool of U.S. citizens in this multi-disciplinary field is relatively small and is typically not focused on the aspects that are of primary interest to LANL. Furthermore, more structured foundations for LANL interaction with universities in computational physics is needed; historically interactions rely heavily on individuals’ personalities and personal contacts. Thus a tertiary purpose of the Summer Workshop is to build an educational network of LANL researchers, university professors, and emerging students to advance the field and LANL’s involvement in it. This report includes both the background for the program and the reports from the students.« less

Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range.

PubMed

He, Chenlong; Feng, Zuren; Ren, Zhigang

2018-02-03

For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham's Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm.

Distributed Algorithm for Voronoi Partition of Wireless Sensor Networks with a Limited Sensing Range

PubMed Central

Feng, Zuren; Ren, Zhigang

2018-01-01

For Wireless Sensor Networks (WSNs), the Voronoi partition of a region is a challenging problem owing to the limited sensing ability of each sensor and the distributed organization of the network. In this paper, an algorithm is proposed for each sensor having a limited sensing range to compute its limited Voronoi cell autonomously, so that the limited Voronoi partition of the entire WSN is generated in a distributed manner. Inspired by Graham’s Scan (GS) algorithm used to compute the convex hull of a point set, the limited Voronoi cell of each sensor is obtained by sequentially scanning two consecutive bisectors between the sensor and its neighbors. The proposed algorithm called the Boundary Scan (BS) algorithm has a lower computational complexity than the existing Range-Constrained Voronoi Cell (RCVC) algorithm and reaches the lower bound of the computational complexity of the algorithms used to solve the problem of this kind. Moreover, it also improves the time efficiency of a key step in the Adjust-Sensing-Radius (ASR) algorithm used to compute the exact Voronoi cell. Extensive numerical simulations are performed to demonstrate the correctness and effectiveness of the BS algorithm. The distributed realization of the BS combined with a localization algorithm in WSNs is used to justify the WSN nature of the proposed algorithm. PMID:29401649

The second Sandia Fracture Challenge. Predictions of ductile failure under quasi-static and moderate-rate dynamic loading

DOE PAGES

Boyce, B. L.; Kramer, S. L. B.; Bosiljevac, T. R.; ...

2016-03-14

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Instead of evaluating the predictions of a single simulation approach, the Sandia Fracture Challenge relied on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive modelsmore » to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in ~ 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. In addition, shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.« less

The second Sandia Fracture Challenge. Predictions of ductile failure under quasi-static and moderate-rate dynamic loading

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boyce, B. L.; Kramer, S. L. B.; Bosiljevac, T. R.

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Instead of evaluating the predictions of a single simulation approach, the Sandia Fracture Challenge relied on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive modelsmore » to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in ~ 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. In addition, shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.« less

Efficient multi-scenario Model Predictive Control for water resources management with ensemble streamflow forecasts

NASA Astrophysics Data System (ADS)

Tian, Xin; Negenborn, Rudy R.; van Overloop, Peter-Jules; María Maestre, José; Sadowska, Anna; van de Giesen, Nick

2017-11-01

Model Predictive Control (MPC) is one of the most advanced real-time control techniques that has been widely applied to Water Resources Management (WRM). MPC can manage the water system in a holistic manner and has a flexible structure to incorporate specific elements, such as setpoints and constraints. Therefore, MPC has shown its versatile performance in many branches of WRM. Nonetheless, with the in-depth understanding of stochastic hydrology in recent studies, MPC also faces the challenge of how to cope with hydrological uncertainty in its decision-making process. A possible way to embed the uncertainty is to generate an Ensemble Forecast (EF) of hydrological variables, rather than a deterministic one. The combination of MPC and EF results in a more comprehensive approach: Multi-scenario MPC (MS-MPC). In this study, we will first assess the model performance of MS-MPC, considering an ensemble streamflow forecast. Noticeably, the computational inefficiency may be a critical obstacle that hinders applicability of MS-MPC. In fact, with more scenarios taken into account, the computational burden of solving an optimization problem in MS-MPC accordingly increases. To deal with this challenge, we propose the Adaptive Control Resolution (ACR) approach as a computationally efficient scheme to practically reduce the number of control variables in MS-MPC. In brief, the ACR approach uses a mixed-resolution control time step from the near future to the distant future. The ACR-MPC approach is tested on a real-world case study: an integrated flood control and navigation problem in the North Sea Canal of the Netherlands. Such an approach reduces the computation time by 18% and up in our case study. At the same time, the model performance of ACR-MPC remains close to that of conventional MPC.

Human connectome module pattern detection using a new multi-graph MinMax cut model.

PubMed

De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng

2014-01-01

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.

Making big data useful for health care: a summary of the inaugural mit critical data conference.

PubMed

Badawi, Omar; Brennan, Thomas; Celi, Leo Anthony; Feng, Mengling; Ghassemi, Marzyeh; Ippolito, Andrea; Johnson, Alistair; Mark, Roger G; Mayaud, Louis; Moody, George; Moses, Christopher; Naumann, Tristan; Pimentel, Marco; Pollard, Tom J; Santos, Mauro; Stone, David J; Zimolzak, Andrew

2014-08-22

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines-including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology-gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

Dictionary learning-based CT detection of pulmonary nodules

NASA Astrophysics Data System (ADS)

Wu, Panpan; Xia, Kewen; Zhang, Yanbo; Qian, Xiaohua; Wang, Ge; Yu, Hengyong

2016-10-01

Segmentation of lung features is one of the most important steps for computer-aided detection (CAD) of pulmonary nodules with computed tomography (CT). However, irregular shapes, complicated anatomical background and poor pulmonary nodule contrast make CAD a very challenging problem. Here, we propose a novel scheme for feature extraction and classification of pulmonary nodules through dictionary learning from training CT images, which does not require accurately segmented pulmonary nodules. Specifically, two classification-oriented dictionaries and one background dictionary are learnt to solve a two-category problem. In terms of the classification-oriented dictionaries, we calculate sparse coefficient matrices to extract intrinsic features for pulmonary nodule classification. The support vector machine (SVM) classifier is then designed to optimize the performance. Our proposed methodology is evaluated with the lung image database consortium and image database resource initiative (LIDC-IDRI) database, and the results demonstrate that the proposed strategy is promising.

High-resolution coded-aperture design for compressive X-ray tomography using low resolution detectors

NASA Astrophysics Data System (ADS)

Mojica, Edson; Pertuz, Said; Arguello, Henry

2017-12-01

One of the main challenges in Computed Tomography (CT) is obtaining accurate reconstructions of the imaged object while keeping a low radiation dose in the acquisition process. In order to solve this problem, several researchers have proposed the use of compressed sensing for reducing the amount of measurements required to perform CT. This paper tackles the problem of designing high-resolution coded apertures for compressed sensing computed tomography. In contrast to previous approaches, we aim at designing apertures to be used with low-resolution detectors in order to achieve super-resolution. The proposed method iteratively improves random coded apertures using a gradient descent algorithm subject to constraints in the coherence and homogeneity of the compressive sensing matrix induced by the coded aperture. Experiments with different test sets show consistent results for different transmittances, number of shots and super-resolution factors.

Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures

NASA Technical Reports Server (NTRS)

Ma, Kwan-Liu

1995-01-01

As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructured in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image composing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without using dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

PubMed Central

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

Numerical Simulation of Black Holes

NASA Astrophysics Data System (ADS)

Teukolsky, Saul

2003-04-01

Einstein's equations of general relativity are prime candidates for numerical solution on supercomputers. There is some urgency in being able to carry out such simulations: Large-scale gravitational wave detectors are now coming on line, and the most important expected signals cannot be predicted except numerically. Problems involving black holes are perhaps the most interesting, yet also particularly challenging computationally. One difficulty is that inside a black hole there is a physical singularity that cannot be part of the computational domain. A second difficulty is the disparity in length scales between the size of the black hole and the wavelength of the gravitational radiation emitted. A third difficulty is that all existing methods of evolving black holes in three spatial dimensions are plagued by instabilities that prohibit long-term evolution. I will describe the ideas that are being introduced in numerical relativity to deal with these problems, and discuss the results of recent calculations of black hole collisions.

Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference

PubMed Central

2014-01-01

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines—including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology—gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability. PMID:25600172

BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology.

PubMed

Villaverde, Alejandro F; Henriques, David; Smallbone, Kieran; Bongard, Sophia; Schmid, Joachim; Cicin-Sain, Damjan; Crombach, Anton; Saez-Rodriguez, Julio; Mauch, Klaus; Balsa-Canto, Eva; Mendes, Pedro; Jaeger, Johannes; Banga, Julio R

2015-02-20

Dynamic modelling is one of the cornerstones of systems biology. Many research efforts are currently being invested in the development and exploitation of large-scale kinetic models. The associated problems of parameter estimation (model calibration) and optimal experimental design are particularly challenging. The community has already developed many methods and software packages which aim to facilitate these tasks. However, there is a lack of suitable benchmark problems which allow a fair and systematic evaluation and comparison of these contributions. Here we present BioPreDyn-bench, a set of challenging parameter estimation problems which aspire to serve as reference test cases in this area. This set comprises six problems including medium and large-scale kinetic models of the bacterium E. coli, baker's yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The level of description includes metabolism, transcription, signal transduction, and development. For each problem we provide (i) a basic description and formulation, (ii) implementations ready-to-run in several formats, (iii) computational results obtained with specific solvers, (iv) a basic analysis and interpretation. This suite of benchmark problems can be readily used to evaluate and compare parameter estimation methods. Further, it can also be used to build test problems for sensitivity and identifiability analysis, model reduction and optimal experimental design methods. The suite, including codes and documentation, can be freely downloaded from the BioPreDyn-bench website, https://sites.google.com/site/biopredynbenchmarks/ .

Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework.

PubMed

Xiao, Bo; Imel, Zac E; Georgiou, Panayiotis; Atkins, David C; Narayanan, Shrikanth S

2016-05-01

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, and facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation and offer a series of open problems for future research.

Designing, programming, and optimizing a (small) quantum computer

NASA Astrophysics Data System (ADS)

Svore, Krysta

In 1982, Richard Feynman proposed to use a computer founded on the laws of quantum physics to simulate physical systems. In the more than thirty years since, quantum computers have shown promise to solve problems in number theory, chemistry, and materials science that would otherwise take longer than the lifetime of the universe to solve on an exascale classical machine. The practical realization of a quantum computer requires understanding and manipulating subtle quantum states while experimentally controlling quantum interference. It also requires an end-to-end software architecture for programming, optimizing, and implementing a quantum algorithm on the quantum device hardware. In this talk, we will introduce recent advances in connecting abstract theory to present-day real-world applications through software. We will highlight recent advancement of quantum algorithms and the challenges in ultimately performing a scalable solution on a quantum device.

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

RIACS/USRA

NASA Technical Reports Server (NTRS)

Oliger, Joseph

1993-01-01

The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on 6 June 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under contract with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Parallel Computing, Advanced Methods for Scientific Computing, High Performance Networks and Technology, and Learning Systems. Parallel compiler techniques, adaptive numerical methods for flows in complicated geometries, and optimization were identified as important problems to investigate for ARC's involvement in the Computational Grand Challenges of the next decade.

Visualization of Unsteady Computational Fluid Dynamics

NASA Technical Reports Server (NTRS)

Haimes, Robert

1997-01-01

The current compute environment that most researchers are using for the calculation of 3D unsteady Computational Fluid Dynamic (CFD) results is a super-computer class machine. The Massively Parallel Processors (MPP's) such as the 160 node IBM SP2 at NAS and clusters of workstations acting as a single MPP (like NAS's SGI Power-Challenge array and the J90 cluster) provide the required computation bandwidth for CFD calculations of transient problems. If we follow the traditional computational analysis steps for CFD (and we wish to construct an interactive visualizer) we need to be aware of the following: (1) Disk space requirements. A single snap-shot must contain at least the values (primitive variables) stored at the appropriate locations within the mesh. For most simple 3D Euler solvers that means 5 floating point words. Navier-Stokes solutions with turbulence models may contain 7 state-variables. (2) Disk speed vs. Computational speeds. The time required to read the complete solution of a saved time frame from disk is now longer than the compute time for a set number of iterations from an explicit solver. Depending, on the hardware and solver an iteration of an implicit code may also take less time than reading the solution from disk. If one examines the performance improvements in the last decade or two, it is easy to see that depending on disk performance (vs. CPU improvement) may not be the best method for enhancing interactivity. (3) Cluster and Parallel Machine I/O problems. Disk access time is much worse within current parallel machines and cluster of workstations that are acting in concert to solve a single problem. In this case we are not trying to read the volume of data, but are running the solver and the solver outputs the solution. These traditional network interfaces must be used for the file system. (4) Numerics of particle traces. Most visualization tools can work upon a single snap shot of the data but some visualization tools for transient problems require dealing with time.

Aeroelastic analysis of versatile thermal insulation (VTI) panels with pinched boundary conditions

NASA Astrophysics Data System (ADS)

Carrera, Erasmo; Zappino, Enrico; Patočka, Karel; Komarek, Martin; Ferrarese, Adriano; Montabone, Mauro; Kotzias, Bernhard; Huermann, Brian; Schwane, Richard

2014-03-01

Launch vehicle design and analysis is a crucial problem in space engineering. The large range of external conditions and the complexity of space vehicles make the solution of the problem really challenging. The problem considered in the present work deals with the versatile thermal insulation (VTI) panel. This thermal protection system is designed to reduce heat fluxes on the LH2 tank during the long coasting phases. Because of the unconventional boundary conditions and the large-scale geometry of the panel, the aeroelastic behaviour of VTI is investigated in the present work. Known available results from literature related to similar problem, are reviewed by considering the effect of various Mach regimes, including boundary layer thickness effects, in-plane mechanical and thermal loads, non-linear effects and amplitude of limit cycle oscillations. A dedicated finite element model is developed for the supersonic regime. The models used for coupling the orthotropic layered structural model with Piston Theory aerodynamic models allow the calculations of flutter conditions in case of curved panels supported in a discrete number of points. An advanced computational aeroelasticity tool is developed using various dedicated commercial softwares (CFX, ZAERO, EDGE). A wind tunnel test campaign is carried out to assess the computational tool in the analysis of this type of problem.

Graph-cut based discrete-valued image reconstruction.

PubMed

Tuysuzoglu, Ahmet; Karl, W Clem; Stojanovic, Ivana; Castañòn, David; Ünlü, M Selim

2015-05-01

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

Stochastic Dynamic Mixed-Integer Programming (SD-MIP)

DTIC Science & Technology

2015-05-05

stochastic linear programming ( SLP ) problems. By using a combination of ideas from cutting plane theory of deterministic MIP (especially disjunctive...developed to date. b) As part of this project, we have also developed tools for very large scale Stochastic Linear Programming ( SLP ). There are...several reasons for this. First, SLP models continue to challenge many of the fastest computers to date, and many applications within the DoD (e.g

Research on Interactive Acquisition and Use of Knowledge.

DTIC Science & Technology

1983-11-01

complex and challenging endeavor. Computer scientists faced with the problem of managing software complexity have de - veloped strict design disciplines...handle most-indeed, probably all-- phenomena in the syntax and semantics of natural language. It has also turned out to be well suited for the classes of...Semantics The previous grammar performs a de facto coordination of syntax and semantics by requiring that the (syntactically) preverbal NP play the

Annual Research Briefs - 2006

DTIC Science & Technology

2006-12-01

IACCARINO AND Q. WANG 3 Strain and stress analysis of uncertain engineering systems . D. GHOSH, C. FARHAT AND P. AVERY 17 Separated flow in a three...research in predictive science in complex systems , CTR has strived to maintain a critical mass in numerical analysis , computer science and physics based... analysis for a linear problem: heat conduction The design and analysis of complex engineering systems is challenging not only be- cause of the physical

Methods for solving reasoning problems in abstract argumentation – A survey

PubMed Central

Charwat, Günther; Dvořák, Wolfgang; Gaggl, Sarah A.; Wallner, Johannes P.; Woltran, Stefan

2015-01-01

Within the last decade, abstract argumentation has emerged as a central field in Artificial Intelligence. Besides providing a core formalism for many advanced argumentation systems, abstract argumentation has also served to capture several non-monotonic logics and other AI related principles. Although the idea of abstract argumentation is appealingly simple, several reasoning problems in this formalism exhibit high computational complexity. This calls for advanced techniques when it comes to implementation issues, a challenge which has been recently faced from different angles. In this survey, we give an overview on different methods for solving reasoning problems in abstract argumentation and compare their particular features. Moreover, we highlight available state-of-the-art systems for abstract argumentation, which put these methods to practice. PMID:25737590

Optimization of a Small Scale Linear Reluctance Accelerator

NASA Astrophysics Data System (ADS)

Barrera, Thor; Beard, Robby

2011-11-01

Reluctance accelerators are extremely promising future methods of transportation. Several problems still plague these devices, most prominently low efficiency. Variables to overcoming efficiency problems are many and difficult to correlate how they affect our accelerator. The study examined several differing variables that present potential challenges in optimizing the efficiency of reluctance accelerators. These include coil and projectile design, power supplies, switching, and the elusive gradient inductance problem. Extensive research in these areas has been performed from computational and theoretical to experimental. Findings show that these parameters share significant similarity to transformer design elements, thus general findings show current optimized parameters the research suggests as a baseline for further research and design. Demonstration of these current findings will be offered at the time of presentation.

NMESys: An expert system for network fault detection

NASA Technical Reports Server (NTRS)

Nelson, Peter C.; Warpinski, Janet

1991-01-01

The problem of network management is becoming an increasingly difficult and challenging task. It is very common today to find heterogeneous networks consisting of many different types of computers, operating systems, and protocols. The complexity of implementing a network with this many components is difficult enough, while the maintenance of such a network is an even larger problem. A prototype network management expert system, NMESys, implemented in the C Language Integrated Production System (CLIPS). NMESys concentrates on solving some of the critical problems encountered in managing a large network. The major goal of NMESys is to provide a network operator with an expert system tool to quickly and accurately detect hard failures, potential failures, and to minimize or eliminate user down time in a large network.

Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap

PubMed Central

Al-Widyan, Khalid

2017-01-01

Extrinsic calibration of a camera and a 2D laser range finder (lidar) sensors is crucial in sensor data fusion applications; for example SLAM algorithms used in mobile robot platforms. The fundamental challenge of extrinsic calibration is when the camera-lidar sensors do not overlap or share the same field of view. In this paper we propose a novel and flexible approach for the extrinsic calibration of a camera-lidar system without overlap, which can be used for robotic platform self-calibration. The approach is based on the robot–world hand–eye calibration (RWHE) problem; proven to have efficient and accurate solutions. First, the system was mapped to the RWHE calibration problem modeled as the linear relationship AX=ZB, where X and Z are unknown calibration matrices. Then, we computed the transformation matrix B, which was the main challenge in the above mapping. The computation is based on reasonable assumptions about geometric structure in the calibration environment. The reliability and accuracy of the proposed approach is compared to a state-of-the-art method in extrinsic 2D lidar to camera calibration. Experimental results from real datasets indicate that the proposed approach provides better results with an L2 norm translational and rotational deviations of 314 mm and 0.12∘ respectively. PMID:29036905

Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap.

PubMed

Ahmad Yousef, Khalil M; Mohd, Bassam J; Al-Widyan, Khalid; Hayajneh, Thaier

2017-10-14

Extrinsic calibration of a camera and a 2D laser range finder (lidar) sensors is crucial in sensor data fusion applications; for example SLAM algorithms used in mobile robot platforms. The fundamental challenge of extrinsic calibration is when the camera-lidar sensors do not overlap or share the same field of view. In this paper we propose a novel and flexible approach for the extrinsic calibration of a camera-lidar system without overlap, which can be used for robotic platform self-calibration. The approach is based on the robot-world hand-eye calibration (RWHE) problem; proven to have efficient and accurate solutions. First, the system was mapped to the RWHE calibration problem modeled as the linear relationship AX = ZB , where X and Z are unknown calibration matrices. Then, we computed the transformation matrix B , which was the main challenge in the above mapping. The computation is based on reasonable assumptions about geometric structure in the calibration environment. The reliability and accuracy of the proposed approach is compared to a state-of-the-art method in extrinsic 2D lidar to camera calibration. Experimental results from real datasets indicate that the proposed approach provides better results with an L2 norm translational and rotational deviations of 314 mm and 0 . 12 ∘ respectively.

Lattice Boltzmann simulation of nonequilibrium effects in oscillatory gas flow.

PubMed

Tang, G H; Gu, X J; Barber, R W; Emerson, D R; Zhang, Y H

2008-08-01

Accurate evaluation of damping in laterally oscillating microstructures is challenging due to the complex flow behavior. In addition, device fabrication techniques and surface properties will have an important effect on the flow characteristics. Although kinetic approaches such as the direct simulation Monte Carlo (DSMC) method and directly solving the Boltzmann equation can address these challenges, they are beyond the reach of current computer technology for large scale simulation. As the continuum Navier-Stokes equations become invalid for nonequilibrium flows, we take advantage of the computationally efficient lattice Boltzmann method to investigate nonequilibrium oscillating flows. We have analyzed the effects of the Stokes number, Knudsen number, and tangential momentum accommodation coefficient for oscillating Couette flow and Stokes' second problem. Our results are in excellent agreement with DSMC data for Knudsen numbers up to Kn=O(1) and show good agreement for Knudsen numbers as large as 2.5. In addition to increasing the Stokes number, we demonstrate that increasing the Knudsen number or decreasing the accommodation coefficient can also expedite the breakdown of symmetry for oscillating Couette flow. This results in an earlier transition from quasisteady to unsteady flow. Our paper also highlights the deviation in velocity slip between Stokes' second problem and the confined Couette case.

An interior-point method-based solver for simulation of aircraft parts riveting

NASA Astrophysics Data System (ADS)

Stefanova, Maria; Yakunin, Sergey; Petukhova, Margarita; Lupuleac, Sergey; Kokkolaras, Michael

2018-05-01

The particularities of the aircraft parts riveting process simulation necessitate the solution of a large amount of contact problems. A primal-dual interior-point method-based solver is proposed for solving such problems efficiently. The proposed method features a worst case polynomial complexity bound ? on the number of iterations, where n is the dimension of the problem and ε is a threshold related to desired accuracy. In practice, the convergence is often faster than this worst case bound, which makes the method applicable to large-scale problems. The computational challenge is solving the system of linear equations because the associated matrix is ill conditioned. To that end, the authors introduce a preconditioner and a strategy for determining effective initial guesses based on the physics of the problem. Numerical results are compared with ones obtained using the Goldfarb-Idnani algorithm. The results demonstrate the efficiency of the proposed method.

(Extreme) Core-collapse Supernova Simulations

NASA Astrophysics Data System (ADS)

Mösta, Philipp

2017-01-01

In this talk I will present recent progress on modeling core-collapse supernovae with massively parallel simulations on the largest supercomputers available. I will discuss the unique challenges in both input physics and computational modeling that come with a problem involving all four fundamental forces and relativistic effects and will highlight recent breakthroughs overcoming these challenges in full 3D simulations. I will pay particular attention to how these simulations can be used to reveal the engines driving some of the most extreme explosions and conclude by discussing what remains to be done in simulation work to maximize what we can learn from current and future time-domain astronomy transient surveys.

e-Collaboration for Earth observation (E-CEO): the Cloud4SAR interferometry data challenge

NASA Astrophysics Data System (ADS)

Casu, Francesco; Manunta, Michele; Boissier, Enguerran; Brito, Fabrice; Aas, Christina; Lavender, Samantha; Ribeiro, Rita; Farres, Jordi

2014-05-01

The e-Collaboration for Earth Observation (E-CEO) project addresses the technologies and architectures needed to provide a collaborative research Platform for automating data mining and processing, and information extraction experiments. The Platform serves for the implementation of Data Challenge Contests focusing on Information Extraction for Earth Observations (EO) applications. The possibility to implement multiple processors within a Common Software Environment facilitates the validation, evaluation and transparent peer comparison among different methodologies, which is one of the main requirements rose by scientists who develop algorithms in the EO field. In this scenario, we set up a Data Challenge, referred to as Cloud4SAR (http://wiki.services.eoportal.org/tiki-index.php?page=ECEO), to foster the deployment of Interferometric SAR (InSAR) processing chains within a Cloud Computing platform. While a large variety of InSAR processing software tools are available, they require a high level of expertise and a complex user interaction to be effectively run. Computing a co-seismic interferogram or a 20-years deformation time series on a volcanic area are not easy tasks to be performed in a fully unsupervised way and/or in very short time (hours or less). Benefiting from ESA's E-CEO platform, participants can optimise algorithms on a Virtual Sandbox environment without being expert programmers, and compute results on high performing Cloud platforms. Cloud4SAR requires solving a relatively easy InSAR problem by trying to maximize the exploitation of the processing capabilities provided by a Cloud Computing infrastructure. The proposed challenge offers two different frameworks, each dedicated to participants with different skills, identified as Beginners and Experts. For both of them, the contest mainly resides in the degree of automation of the deployed algorithms, no matter which one is used, as well as in the capability of taking effective benefit from a parallel computing environment.

Advanced Computational Methods for Security Constrained Financial Transmission Rights: Structure and Parallelism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elbert, Stephen T.; Kalsi, Karanjit; Vlachopoulou, Maria

Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed tomore » solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to outperform the widely used CPLEX algorithms while exhibiting superior scalability. Furthermore, the NDS based solver can be easily parallelized which results in significant computational improvement.« less

Statistical benchmark for BosonSampling

NASA Astrophysics Data System (ADS)

Walschaers, Mattia; Kuipers, Jack; Urbina, Juan-Diego; Mayer, Klaus; Tichy, Malte Christopher; Richter, Klaus; Buchleitner, Andreas

2016-03-01

Boson samplers—set-ups that generate complex many-particle output states through the transmission of elementary many-particle input states across a multitude of mutually coupled modes—promise the efficient quantum simulation of a classically intractable computational task, and challenge the extended Church-Turing thesis, one of the fundamental dogmas of computer science. However, as in all experimental quantum simulations of truly complex systems, one crucial problem remains: how to certify that a given experimental measurement record unambiguously results from enforcing the claimed dynamics, on bosons, fermions or distinguishable particles? Here we offer a statistical solution to the certification problem, identifying an unambiguous statistical signature of many-body quantum interference upon transmission across a multimode, random scattering device. We show that statistical analysis of only partial information on the output state allows to characterise the imparted dynamics through particle type-specific features of the emerging interference patterns. The relevant statistical quantifiers are classically computable, define a falsifiable benchmark for BosonSampling, and reveal distinctive features of many-particle quantum dynamics, which go much beyond mere bunching or anti-bunching effects.

A Comparative Study of Three Methodologies for Modeling Dynamic Stall

NASA Technical Reports Server (NTRS)

Sankar, L.; Rhee, M.; Tung, C.; ZibiBailly, J.; LeBalleur, J. C.; Blaise, D.; Rouzaud, O.

2002-01-01

During the past two decades, there has been an increased reliance on the use of computational fluid dynamics methods for modeling rotors in high speed forward flight. Computational methods are being developed for modeling the shock induced loads on the advancing side, first-principles based modeling of the trailing wake evolution, and for retreating blade stall. The retreating blade dynamic stall problem has received particular attention, because the large variations in lift and pitching moments encountered in dynamic stall can lead to blade vibrations and pitch link fatigue. Restricting to aerodynamics, the numerical prediction of dynamic stall is still a complex and challenging CFD problem, that, even in two dimensions at low speed, gathers the major difficulties of aerodynamics, such as the grid resolution requirements for the viscous phenomena at leading-edge bubbles or in mixing-layers, the bias of the numerical viscosity, and the major difficulties of the physical modeling, such as the turbulence models, the transition models, whose both determinant influences, already present in static maximal-lift or stall computations, are emphasized by the dynamic aspect of the phenomena.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, Zhaojun; Yang, Chao

What is common among electronic structure calculation, design of MEMS devices, vibrational analysis of high speed railways, and simulation of the electromagnetic field of a particle accelerator? The answer: they all require solving large scale nonlinear eigenvalue problems. In fact, these are just a handful of examples in which solving nonlinear eigenvalue problems accurately and efficiently is becoming increasingly important. Recognizing the importance of this class of problems, an invited minisymposium dedicated to nonlinear eigenvalue problems was held at the 2005 SIAM Annual Meeting. The purpose of the minisymposium was to bring together numerical analysts and application scientists to showcasemore » some of the cutting edge results from both communities and to discuss the challenges they are still facing. The minisymposium consisted of eight talks divided into two sessions. The first three talks focused on a type of nonlinear eigenvalue problem arising from electronic structure calculations. In this type of problem, the matrix Hamiltonian H depends, in a non-trivial way, on the set of eigenvectors X to be computed. The invariant subspace spanned by these eigenvectors also minimizes a total energy function that is highly nonlinear with respect to X on a manifold defined by a set of orthonormality constraints. In other applications, the nonlinearity of the matrix eigenvalue problem is restricted to the dependency of the matrix on the eigenvalues to be computed. These problems are often called polynomial or rational eigenvalue problems In the second session, Christian Mehl from Technical University of Berlin described numerical techniques for solving a special type of polynomial eigenvalue problem arising from vibration analysis of rail tracks excited by high-speed trains.« less

[INVITED] Computational intelligence for smart laser materials processing

NASA Astrophysics Data System (ADS)

Casalino, Giuseppe

2018-03-01

Computational intelligence (CI) involves using a computer algorithm to capture hidden knowledge from data and to use them for training ;intelligent machine; to make complex decisions without human intervention. As simulation is becoming more prevalent from design and planning to manufacturing and operations, laser material processing can also benefit from computer generating knowledge through soft computing. This work is a review of the state-of-the-art on the methodology and applications of CI in laser materials processing (LMP), which is nowadays receiving increasing interest from world class manufacturers and 4.0 industry. The focus is on the methods that have been proven effective and robust in solving several problems in welding, cutting, drilling, surface treating and additive manufacturing using the laser beam. After a basic description of the most common computational intelligences employed in manufacturing, four sections, namely, laser joining, machining, surface, and additive covered the most recent applications in the already extensive literature regarding the CI in LMP. Eventually, emerging trends and future challenges were identified and discussed.

Understanding Emergency Care Delivery Through Computer Simulation Modeling.

PubMed

Laker, Lauren F; Torabi, Elham; France, Daniel J; Froehle, Craig M; Goldlust, Eric J; Hoot, Nathan R; Kasaie, Parastu; Lyons, Michael S; Barg-Walkow, Laura H; Ward, Michael J; Wears, Robert L

2018-02-01

In 2017, Academic Emergency Medicine convened a consensus conference entitled, "Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes." This article, a product of the breakout session on "understanding complex interactions through systems modeling," explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation, system dynamics modeling, discrete-event simulation, and agent-based simulation), along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research, along with a research agenda for computer simulation modeling. Through this article, our goal is to enhance adoption of computer simulation, a set of methods that hold great promise in addressing emergency care organization and design challenges. © 2017 by the Society for Academic Emergency Medicine.

Transport synthetic acceleration for long-characteristics assembly-level transport problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zika, M.R.; Adams, M.L.

2000-02-01

The authors apply the transport synthetic acceleration (TSA) scheme to the long-characteristics spatial discretization for the two-dimensional assembly-level transport problem. This synthetic method employs a simplified transport operator as its low-order approximation. Thus, in the acceleration step, the authors take advantage of features of the long-characteristics discretization that make it particularly well suited to assembly-level transport problems. The main contribution is to address difficulties unique to the long-characteristics discretization and produce a computationally efficient acceleration scheme. The combination of the long-characteristics discretization, opposing reflecting boundary conditions (which are present in assembly-level transport problems), and TSA presents several challenges. The authorsmore » devise methods for overcoming each of them in a computationally efficient way. Since the boundary angular data exist on different grids in the high- and low-order problems, they define restriction and prolongation operations specific to the method of long characteristics to map between the two grids. They implement the conjugate gradient (CG) method in the presence of opposing reflection boundary conditions to solve the TSA low-order equations. The CG iteration may be applied only to symmetric positive definite (SPD) matrices; they prove that the long-characteristics discretization yields an SPD matrix. They present results of the acceleration scheme on a simple test problem, a typical pressurized water reactor assembly, and a typical boiling water reactor assembly.« less

Perspective: Machine learning potentials for atomistic simulations

NASA Astrophysics Data System (ADS)

Behler, Jörg

2016-11-01

Nowadays, computer simulations have become a standard tool in essentially all fields of chemistry, condensed matter physics, and materials science. In order to keep up with state-of-the-art experiments and the ever growing complexity of the investigated problems, there is a constantly increasing need for simulations of more realistic, i.e., larger, model systems with improved accuracy. In many cases, the availability of sufficiently efficient interatomic potentials providing reliable energies and forces has become a serious bottleneck for performing these simulations. To address this problem, currently a paradigm change is taking place in the development of interatomic potentials. Since the early days of computer simulations simplified potentials have been derived using physical approximations whenever the direct application of electronic structure methods has been too demanding. Recent advances in machine learning (ML) now offer an alternative approach for the representation of potential-energy surfaces by fitting large data sets from electronic structure calculations. In this perspective, the central ideas underlying these ML potentials, solved problems and remaining challenges are reviewed along with a discussion of their current applicability and limitations.

New variational bounds on convective transport. II. Computations and implications

NASA Astrophysics Data System (ADS)

Souza, Andre; Tobasco, Ian; Doering, Charles R.

2016-11-01

We study the maximal rate of scalar transport between parallel walls separated by distance h, by an incompressible fluid with scalar diffusion coefficient κ. Given velocity vector field u with intensity measured by the Péclet number Pe =h2 < | ∇ u |2 >1/2 / κ (where < . > is space-time average) the challenge is to determine the largest enhancement of wall-to-wall scalar flux over purely diffusive transport, i.e., the Nusselt number Nu . Variational formulations of the problem are studied numerically and optimizing flow fields are computed over a range of Pe . Implications of this optimal wall-to-wall transport problem for the classical problem of Rayleigh-Bénard convection are discussed: the maximal scaling Nu Pe 2 / 3 corresponds, via the identity Pe2 = Ra (Nu - 1) where Ra is the usual Rayleigh number, to Nu Ra 1 / 2 as Ra -> ∞ . Supported in part by National Science Foundation Graduate Research Fellowship DGE-0813964, awards OISE-0967140, PHY-1205219, DMS-1311833, and DMS-1515161, and the John Simon Guggenheim Memorial Foundation.

A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models

NASA Technical Reports Server (NTRS)

Giunta, Anthony A.; Watson, Layne T.

1998-01-01

Two methods of creating approximation models are compared through the calculation of the modeling accuracy on test problems involving one, five, and ten independent variables. Here, the test problems are representative of the modeling challenges typically encountered in realistic engineering optimization problems. The first approximation model is a quadratic polynomial created using the method of least squares. This type of polynomial model has seen considerable use in recent engineering optimization studies due to its computational simplicity and ease of use. However, quadratic polynomial models may be of limited accuracy when the response data to be modeled have multiple local extrema. The second approximation model employs an interpolation scheme known as kriging developed in the fields of spatial statistics and geostatistics. This class of interpolating model has the flexibility to model response data with multiple local extrema. However, this flexibility is obtained at an increase in computational expense and a decrease in ease of use. The intent of this study is to provide an initial exploration of the accuracy and modeling capabilities of these two approximation methods.

Beyond the Renderer: Software Architecture for Parallel Graphics and Visualization

NASA Technical Reports Server (NTRS)

Crockett, Thomas W.

1996-01-01

As numerous implementations have demonstrated, software-based parallel rendering is an effective way to obtain the needed computational power for a variety of challenging applications in computer graphics and scientific visualization. To fully realize their potential, however, parallel renderers need to be integrated into a complete environment for generating, manipulating, and delivering visual data. We examine the structure and components of such an environment, including the programming and user interfaces, rendering engines, and image delivery systems. We consider some of the constraints imposed by real-world applications and discuss the problems and issues involved in bringing parallel rendering out of the lab and into production.

A contact algorithm for shell problems via Delaunay-based meshing of the contact domain

NASA Astrophysics Data System (ADS)

Kamran, K.; Rossi, R.; Oñate, E.

2013-07-01

The simulation of the contact within shells, with all of its different facets, represents still an open challenge in Computational Mechanics. Despite the effort spent in the development of techniques for the simulation of general contact problems, an all-seasons algorithm applicable to complex shell contact problems is yet to be developed. This work focuses on the solution of the contact between thin shells by using a technique derived from the particle finite element method together with a rotation-free shell triangle. The key concept is to define a discretization of the contact domain (CD) by constructing a finite element mesh of four-noded tetrahedra that describes the potential contact volume. The problem is completed by using an assumed-strain approach to define an elastic contact strain over the CD.

Object tracking using plenoptic image sequences

NASA Astrophysics Data System (ADS)

Kim, Jae Woo; Bae, Seong-Joon; Park, Seongjin; Kim, Do Hyung

2017-05-01

Object tracking is a very important problem in computer vision research. Among the difficulties of object tracking, partial occlusion problem is one of the most serious and challenging problems. To address the problem, we proposed novel approaches to object tracking on plenoptic image sequences. Our approaches take advantage of the refocusing capability that plenoptic images provide. Our approaches input the sequences of focal stacks constructed from plenoptic image sequences. The proposed image selection algorithms select the sequence of optimal images that can maximize the tracking accuracy from the sequence of focal stacks. Focus measure approach and confidence measure approach were proposed for image selection and both of the approaches were validated by the experiments using thirteen plenoptic image sequences that include heavily occluded target objects. The experimental results showed that the proposed approaches were satisfactory comparing to the conventional 2D object tracking algorithms.

A Walsh Function Module Users' Manual

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.

2014-01-01

The solution of partial differential equations (PDEs) with Walsh functions offers new opportunities to simulate many challenging problems in mathematical physics. The approach was developed to better simulate hypersonic flows with shocks on unstructured grids. It is unique in that integrals and derivatives are computed using simple matrix multiplication of series representations of functions without the need for divided differences. The product of any two Walsh functions is another Walsh function - a feature that radically changes an algorithm for solving PDEs. A FORTRAN module for supporting Walsh function simulations is documented. A FORTRAN code is also documented with options for solving time-dependent problems: an advection equation, a Burgers equation, and a Riemann problem. The sample problems demonstrate the usage of the Walsh function module including such features as operator overloading, Fast Walsh Transforms in multi-dimensions, and a Fast Walsh reciprocal.

Formulation for Simultaneous Aerodynamic Analysis and Design Optimization

NASA Technical Reports Server (NTRS)

Hou, G. W.; Taylor, A. C., III; Mani, S. V.; Newman, P. A.

1993-01-01

An efficient approach for simultaneous aerodynamic analysis and design optimization is presented. This approach does not require the performance of many flow analyses at each design optimization step, which can be an expensive procedure. Thus, this approach brings us one step closer to meeting the challenge of incorporating computational fluid dynamic codes into gradient-based optimization techniques for aerodynamic design. An adjoint-variable method is introduced to nullify the effect of the increased number of design variables in the problem formulation. The method has been successfully tested on one-dimensional nozzle flow problems, including a sample problem with a normal shock. Implementations of the above algorithm are also presented that incorporate Newton iterations to secure a high-quality flow solution at the end of the design process. Implementations with iterative flow solvers are possible and will be required for large, multidimensional flow problems.

The Human Toxome Collaboratorium: A Shared Environment for Multi-Omic Computational Collaboration within a Consortium.

PubMed

Fasani, Rick A; Livi, Carolina B; Choudhury, Dipanwita R; Kleensang, Andre; Bouhifd, Mounir; Pendse, Salil N; McMullen, Patrick D; Andersen, Melvin E; Hartung, Thomas; Rosenberg, Michael

2015-01-01

The Human Toxome Project is part of a long-term vision to modernize toxicity testing for the 21st century. In the initial phase of the project, a consortium of six academic, commercial, and government organizations has partnered to map pathways of toxicity, using endocrine disruption as a model hazard. Experimental data is generated at multiple sites, and analyzed using a range of computational tools. While effectively gathering, managing, and analyzing the data for high-content experiments is a challenge in its own right, doing so for a growing number of -omics technologies, with larger data sets, across multiple institutions complicates the process. Interestingly, one of the most difficult, ongoing challenges has been the computational collaboration between the geographically separate institutions. Existing solutions cannot handle the growing heterogeneous data, provide a computational environment for consistent analysis, accommodate different workflows, and adapt to the constantly evolving methods and goals of a research project. To meet the needs of the project, we have created and managed The Human Toxome Collaboratorium, a shared computational environment hosted on third-party cloud services. The Collaboratorium provides a familiar virtual desktop, with a mix of commercial, open-source, and custom-built applications. It shares some of the challenges of traditional information technology, but with unique and unexpected constraints that emerge from the cloud. Here we describe the problems we faced, the current architecture of the solution, an example of its use, the major lessons we learned, and the future potential of the concept. In particular, the Collaboratorium represents a novel distribution method that could increase the reproducibility and reusability of results from similar large, multi-omic studies.

Computational dynamic approaches for temporal omics data with applications to systems medicine.

PubMed

Liang, Yulan; Kelemen, Arpad

2017-01-01

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

Discrete Surface Evolution and Mesh Deformation for Aircraft Icing Applications

NASA Technical Reports Server (NTRS)

Thompson, David; Tong, Xiaoling; Arnoldus, Qiuhan; Collins, Eric; McLaurin, David; Luke, Edward; Bidwell, Colin S.

2013-01-01

Robust, automated mesh generation for problems with deforming geometries, such as ice accreting on aerodynamic surfaces, remains a challenging problem. Here we describe a technique to deform a discrete surface as it evolves due to the accretion of ice. The surface evolution algorithm is based on a smoothed, face-offsetting approach. We also describe a fast algebraic technique to propagate the computed surface deformations into the surrounding volume mesh while maintaining geometric mesh quality. Preliminary results presented here demonstrate the ecacy of the approach for a sphere with a prescribed accretion rate, a rime ice accretion, and a more complex glaze ice accretion.

Location Estimation of Urban Images Based on Geographical Neighborhoods

NASA Astrophysics Data System (ADS)

Huang, Jie; Lo, Sio-Long

2018-04-01

Estimating the location of an image is a challenging computer vision problem, and the recent decade has witnessed increasing research efforts towards the solution of this problem. In this paper, we propose a new approach to the location estimation of images taken in urban environments. Experiments are conducted to quantitatively compare the estimation accuracy of our approach, against three representative approaches in the existing literature, using a recently published dataset of over 150 thousand Google Street View images and 259 user uploaded images as queries. According to the experimental results, our approach outperforms three baseline approaches and shows its robustness across different distance thresholds.

Applied Distributed Model Predictive Control for Energy Efficient Buildings and Ramp Metering

NASA Astrophysics Data System (ADS)

Koehler, Sarah Muraoka

Industrial large-scale control problems present an interesting algorithmic design challenge. A number of controllers must cooperate in real-time on a network of embedded hardware with limited computing power in order to maximize system efficiency while respecting constraints and despite communication delays. Model predictive control (MPC) can automatically synthesize a centralized controller which optimizes an objective function subject to a system model, constraints, and predictions of disturbance. Unfortunately, the computations required by model predictive controllers for large-scale systems often limit its industrial implementation only to medium-scale slow processes. Distributed model predictive control (DMPC) enters the picture as a way to decentralize a large-scale model predictive control problem. The main idea of DMPC is to split the computations required by the MPC problem amongst distributed processors that can compute in parallel and communicate iteratively to find a solution. Some popularly proposed solutions are distributed optimization algorithms such as dual decomposition and the alternating direction method of multipliers (ADMM). However, these algorithms ignore two practical challenges: substantial communication delays present in control systems and also problem non-convexity. This thesis presents two novel and practically effective DMPC algorithms. The first DMPC algorithm is based on a primal-dual active-set method which achieves fast convergence, making it suitable for large-scale control applications which have a large communication delay across its communication network. In particular, this algorithm is suited for MPC problems with a quadratic cost, linear dynamics, forecasted demand, and box constraints. We measure the performance of this algorithm and show that it significantly outperforms both dual decomposition and ADMM in the presence of communication delay. The second DMPC algorithm is based on an inexact interior point method which is suited for nonlinear optimization problems. The parallel computation of the algorithm exploits iterative linear algebra methods for the main linear algebra computations in the algorithm. We show that the splitting of the algorithm is flexible and can thus be applied to various distributed platform configurations. The two proposed algorithms are applied to two main energy and transportation control problems. The first application is energy efficient building control. Buildings represent 40% of energy consumption in the United States. Thus, it is significant to improve the energy efficiency of buildings. The goal is to minimize energy consumption subject to the physics of the building (e.g. heat transfer laws), the constraints of the actuators as well as the desired operating constraints (thermal comfort of the occupants), and heat load on the system. In this thesis, we describe the control systems of forced air building systems in practice. We discuss the "Trim and Respond" algorithm which is a distributed control algorithm that is used in practice, and show that it performs similarly to a one-step explicit DMPC algorithm. Then, we apply the novel distributed primal-dual active-set method and provide extensive numerical results for the building MPC problem. The second main application is the control of ramp metering signals to optimize traffic flow through a freeway system. This application is particularly important since urban congestion has more than doubled in the past few decades. The ramp metering problem is to maximize freeway throughput subject to freeway dynamics (derived from mass conservation), actuation constraints, freeway capacity constraints, and predicted traffic demand. In this thesis, we develop a hybrid model predictive controller for ramp metering that is guaranteed to be persistently feasible and stable. This contrasts to previous work on MPC for ramp metering where such guarantees are absent. We apply a smoothing method to the hybrid model predictive controller and apply the inexact interior point method to this nonlinear non-convex ramp metering problem.

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

PubMed Central

Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

2014-01-01

Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor). Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1) a 2-class problem (strengthening/weakening PPI mutations), (2) another 2-class problem (mutations that disrupt/preserve a PPI), and (3) a 3-class classification (detrimental/neutral/beneficial mutation effects). In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the rewiring of large-scale protein-protein interaction networks, and can be useful for functional annotation of disease-associated SNPs. SNIP-IN tool is freely accessible as a web-server at http://korkinlab.org/snpintool/. PMID:24784581

Participatory Design of Human-Centered Cyberinfrastructure (Invited)

NASA Astrophysics Data System (ADS)

Pennington, D. D.; Gates, A. Q.

2010-12-01

Cyberinfrastructure, by definition, is about people sharing resources to achieve outcomes that cannot be reached independently. CI depends not just on creating discoverable resources, or tools that allow those resources to be processed, integrated, and visualized -- but on human activation of flows of information across those resources. CI must be centered on human activities. Yet for those CI projects that are directed towards observational science, there are few models for organizing collaborative research in ways that align individual research interests into a collective vision of CI-enabled science. Given that the emerging technologies are themselves expected to change the way science is conducted, it is not simply a matter of conducting requirements analysis on how scientists currently work, or building consensus among the scientists on what is needed. Developing effective CI depends on generating a new, creative vision of problem solving within a community based on computational concepts that are, in some cases, still very abstract and theoretical. The computer science theory may (or may not) be well formalized, but the potential for impact on any particular domain is typically ill-defined. In this presentation we will describe approaches being developed and tested at the CyberShARE Center of Excellence at University of Texas in El Paso for ill-structured problem solving within cross-disciplinary teams of scientists and computer scientists working on data intensive environmental and geoscience. These approaches deal with the challenges associated with sharing and integrating knowledge across disciplines; the challenges of developing effective teamwork skills in a culture that favors independent effort; and the challenges of evolving shared, focused research goals from ill-structured, vague starting points - all issues that must be confronted by every interdisciplinary CI project. We will introduce visual and semantic-based tools that can enable the collaborative research design process and illustrate their application in designing and developing useful end-to-end data solutions for scientists. Lastly, we will outline areas of future investigation within CyberShARE that we believe have the potential for high impact.

First-arrival traveltime computation for quasi-P waves in 2D transversely isotropic media using Fermat’s principle-based fast marching

NASA Astrophysics Data System (ADS)

Hu, Jiangtao; Cao, Junxing; Wang, Huazhong; Wang, Xingjian; Jiang, Xudong

2017-12-01

First-arrival traveltime computation for quasi-P waves in transversely isotropic (TI) media is the key component of tomography and depth migration. It is appealing to use the fast marching method in isotropic media as it efficiently computes traveltime along an expanding wavefront. It uses the finite difference method to solve the eikonal equation. However, applying the fast marching method in anisotropic media faces challenges because the anisotropy introduces additional nonlinearity in the eikonal equation and solving this nonlinear eikonal equation with the finite difference method is challenging. To address this problem, we present a Fermat’s principle-based fast marching method to compute traveltime in two-dimensional TI media. This method is applicable in both vertical and tilted TI (VTI and TTI) media. It computes traveltime along an expanding wavefront using Fermat’s principle instead of the eikonal equation. Thus, it does not suffer from the nonlinearity of the eikonal equation in TI media. To compute traveltime using Fermat’s principle, the explicit expression of group velocity in TI media is required to describe the ray propagation. The moveout approximation is adopted to obtain the explicit expression of group velocity. Numerical examples on both VTI and TTI models show that the traveltime contour obtained by the proposed method matches well with the wavefront from the wave equation. This shows that the proposed method could be used in depth migration and tomography.

Progress and challenges in bioinformatics approaches for enhancer identification

PubMed Central

Kleftogiannis, Dimitrios; Kalnis, Panos

2016-01-01

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration. PMID:26634919

Confronting Decision Cliffs: Diagnostic Assessment of Multi-Objective Evolutionary Algorithms' Performance for Addressing Uncertain Environmental Thresholds

NASA Astrophysics Data System (ADS)

Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.

2014-12-01

As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a useful and nontrivial benchmarking problem.

Toward Scalable Trustworthy Computing Using the Human-Physiology-Immunity Metaphor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hively, Lee M; Sheldon, Frederick T

The cybersecurity landscape consists of an ad hoc patchwork of solutions. Optimal cybersecurity is difficult for various reasons: complexity, immense data and processing requirements, resource-agnostic cloud computing, practical time-space-energy constraints, inherent flaws in 'Maginot Line' defenses, and the growing number and sophistication of cyberattacks. This article defines the high-priority problems and examines the potential solution space. In that space, achieving scalable trustworthy computing and communications is possible through real-time knowledge-based decisions about cyber trust. This vision is based on the human-physiology-immunity metaphor and the human brain's ability to extract knowledge from data and information. The article outlines future steps towardmore » scalable trustworthy systems requiring a long-term commitment to solve the well-known challenges.« less

Advances in mixed-integer programming methods for chemical production scheduling.

PubMed

Velez, Sara; Maravelias, Christos T

2014-01-01

The goal of this paper is to critically review advances in the area of chemical production scheduling over the past three decades and then present two recently proposed solution methods that have led to dramatic computational enhancements. First, we present a general framework and problem classification and discuss modeling and solution methods with an emphasis on mixed-integer programming (MIP) techniques. Second, we present two solution methods: (a) a constraint propagation algorithm that allows us to compute parameters that are then used to tighten MIP scheduling models and (b) a reformulation that introduces new variables, thus leading to effective branching. We also present computational results and an example illustrating how these methods are implemented, as well as the resulting enhancements. We close with a discussion of open research challenges and future research directions.

Technique for Calculating Solution Derivatives With Respect to Geometry Parameters in a CFD Code

NASA Technical Reports Server (NTRS)

Mathur, Sanjay

2011-01-01

A solution has been developed to the challenges of computation of derivatives with respect to geometry, which is not straightforward because these are not typically direct inputs to the computational fluid dynamics (CFD) solver. To overcome these issues, a procedure has been devised that can be used without having access to the mesh generator, while still being applicable to all types of meshes. The basic approach is inspired by the mesh motion algorithms used to deform the interior mesh nodes in a smooth manner when the surface nodes, for example, are in a fluid structure interaction problem. The general idea is to model the mesh edges and nodes as constituting a spring-mass system. Changes to boundary node locations are propagated to interior nodes by allowing them to assume their new equilibrium positions, for instance, one where the forces on each node are in balance. The main advantage of the technique is that it is independent of the volumetric mesh generator, and can be applied to structured, unstructured, single- and multi-block meshes. It essentially reduces the problem down to defining the surface mesh node derivatives with respect to the geometry parameters of interest. For analytical geometries, this is quite straightforward. In the more general case, one would need to be able to interrogate the underlying parametric CAD (computer aided design) model and to evaluate the derivatives either analytically, or by a finite difference technique. Because the technique is based on a partial differential equation (PDE), it is applicable not only to forward mode problems (where derivatives of all the output quantities are computed with respect to a single input), but it could also be extended to the adjoint problem, either by using an analytical adjoint of the PDE or a discrete analog.

Assessing problem-solving skills in construction education with the virtual construction simulator

NASA Astrophysics Data System (ADS)

Castronovo, Fadi

The ability to solve complex problems is an essential skill that a construction and project manager must possess when entering the architectural, engineering, and construction industry. Such ability requires a mixture of problem-solving skills, ranging from lower to higher order thinking skills, composed of cognitive and metacognitive processes. These skills include the ability to develop and evaluate construction plans and manage the execution of such plans. However, in a typical construction program, introducing students to such complex problems can be a challenge, and most commonly the learner is presented with only part of a complex problem. To support this challenge, the traditional methodology of delivering design, engineering, and construction instruction has been going through a technological revolution, due to the rise of computer-based technology. For example, in construction classrooms, and other disciplines, simulations and educational games are being utilized to support the development of problem-solving skills. Previous engineering education research has illustrated the high potential that simulations and educational games have in engaging in lower and higher order thinking skills. Such research illustrated their capacity to support the development of problem-solving skills. This research presents evidence supporting the theory that educational simulation games can help with the learning and retention of transferable problem-solving skills, which are necessary to solve complex construction problems. The educational simulation game employed in this study is the Virtual Construction Simulator (VCS). The VCS is a game developed to provide students in an engaging learning activity that simulates the planning and managing phases of a construction project. Assessment of the third iteration of the VCS(3) game has shown pedagogical value in promoting students' motivation and a basic understanding of construction concepts. To further evaluate the benefits on problem-solving skills, a new version of the VCS(4) was developed, with new building modules and assessment framework. The design and development of the VCS4 leveraged research in educational psychology, multimedia learning, human-computer interaction, and Building Information Modeling. In this dissertation the researcher aimed to evaluate the pedagogical value of the VCS4 in fostering problem-solving skills. To answer the research questions, a crossover repeated measures quasi-experiment was designed to assess the educational gains that the VCS can provide to construction education. A group of 34 students, attending a fourth-year construction course at a university in the United States was chosen to participate in the experiment. The three learning modules of the VCS were used, which challenged the students to plan and manage the construction process of a wooden pavilion, the steel erection of a dormitory, and the concrete placement of the same dormitory. Based on the results the researcher was able to provide evidence supporting the hypothesis that the chosen sample of construction students were able to gain and retain problem-solving skills necessary to solve complex construction simulation problems, no matter what the sequence with which these modules were played. In conclusion, the presented results provide evidence supporting the theory that educational simulation games can help the learning and retention of transferable problem-solving skills, which are necessary to solve complex construction problems.

A comparative analysis of soft computing techniques for gene prediction.

PubMed

Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand

2013-07-01

The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.

An Integrated Development Environment for Adiabatic Quantum Programming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humble, Travis S; McCaskey, Alex; Bennink, Ryan S

2014-01-01

Adiabatic quantum computing is a promising route to the computational power afforded by quantum information processing. The recent availability of adiabatic hardware raises the question of how well quantum programs perform. Benchmarking behavior is challenging since the multiple steps to synthesize an adiabatic quantum program are highly tunable. We present an adiabatic quantum programming environment called JADE that provides control over all the steps taken during program development. JADE captures the workflow needed to rigorously benchmark performance while also allowing a variety of problem types, programming techniques, and processor configurations. We have also integrated JADE with a quantum simulation enginemore » that enables program profiling using numerical calculation. The computational engine supports plug-ins for simulation methodologies tailored to various metrics and computing resources. We present the design, integration, and deployment of JADE and discuss its use for benchmarking adiabatic quantum programs.« less

Trusted computing strengthens cloud authentication.

PubMed

Ghazizadeh, Eghbal; Zamani, Mazdak; Ab Manan, Jamalul-lail; Alizadeh, Mojtaba

2014-01-01

Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model.

Trusted Computing Strengthens Cloud Authentication

PubMed Central

2014-01-01

Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model. PMID:24701149

Optimal nonlinear information processing capacity in delay-based reservoir computers

NASA Astrophysics Data System (ADS)

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

2015-09-01

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.

Computational Analysis and Simulation of Empathic Behaviors: A Survey of Empathy Modeling with Behavioral Signal Processing Framework

PubMed Central

Xiao, Bo; Imel, Zac E.; Georgiou, Panayiotis; Atkins, David C.; Narayanan, Shrikanth S.

2017-01-01

Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation, and offer a series of open problems for future research. PMID:27017830

Requirements for fault-tolerant factoring on an atom-optics quantum computer.

PubMed

Devitt, Simon J; Stephens, Ashley M; Munro, William J; Nemoto, Kae

2013-01-01

Quantum information processing and its associated technologies have reached a pivotal stage in their development, with many experiments having established the basic building blocks. Moving forward, the challenge is to scale up to larger machines capable of performing computational tasks not possible today. This raises questions that need to be urgently addressed, such as what resources these machines will consume and how large will they be. Here we estimate the resources required to execute Shor's factoring algorithm on an atom-optics quantum computer architecture. We determine the runtime and size of the computer as a function of the problem size and physical error rate. Our results suggest that once the physical error rate is low enough to allow quantum error correction, optimization to reduce resources and increase performance will come mostly from integrating algorithms and circuits within the error correction environment, rather than from improving the physical hardware.

Optimal nonlinear information processing capacity in delay-based reservoir computers.

PubMed

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

2015-09-11

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.

Optimal nonlinear information processing capacity in delay-based reservoir computers

PubMed Central

Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

2015-01-01

Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature. PMID:26358528

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bailey, David

In the January 2002 edition of SIAM News, Nick Trefethen announced the '$100, 100-Digit Challenge'. In this note he presented ten easy-to-state but hard-to-solve problems of numerical analysis, and challenged readers to find each answer to ten-digit accuracy. Trefethen closed with the enticing comment: 'Hint: They're hard! If anyone gets 50 digits in total, I will be impressed.' This challenge obviously struck a chord in hundreds of numerical mathematicians worldwide, as 94 teams from 25 nations later submitted entries. Many of these submissions exceeded the target of 50 correct digits; in fact, 20 teams achieved a perfect score of 100more » correct digits. Trefethen had offered $100 for the best submission. Given the overwhelming response, a generous donor (William Browning, founder of Applied Mathematics, Inc.) provided additional funds to provide a $100 award to each of the 20 winning teams. Soon after the results were out, four participants, each from a winning team, got together and agreed to write a book about the problems and their solutions. The team is truly international: Bornemann is from Germany, Laurie is from South Africa, Wagon is from the USA, and Waldvogel is from Switzerland. This book provides some mathematical background for each problem, and then shows in detail how each of them can be solved. In fact, multiple solution techniques are mentioned in each case. The book describes how to extend these solutions to much larger problems and much higher numeric precision (hundreds or thousands of digit accuracy). The authors also show how to compute error bounds for the results, so that one can say with confidence that one's results are accurate to the level stated. Numerous numerical software tools are demonstrated in the process, including the commercial products Mathematica, Maple and Matlab. Computer programs that perform many of the algorithms mentioned in the book are provided, both in an appendix to the book and on a website. In the process, the authors take the reader on a wide-ranging tour of modern numerical mathematics, with enough background material so that even readers with little or no training in numerical analysis can follow. Here is a list of just a few of the topics visited: numerical quadrature (i.e., numerical integration), series summation, sequence extrapolation, contour integration, Fourier integrals, high-precision arithmetic, interval arithmetic, symbolic computing, numerical linear algebra, perturbation theory, Euler-Maclaurin summation, global minimization, eigenvalue methods, evolutionary algorithms, matrix preconditioning, random walks, special functions, elliptic functions, Monte-Carlo methods, and numerical differentiation.« less

RPT: A Low Overhead Single-End Probing Tool for Detecting Network Congestion Positions

DTIC Science & Technology

2003-12-20

complete evaluation on the Internet , we need to know the real available bandwidth on all the links of a network path. But that information is hard to...School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract Detecting the points of network congestion is an intriguing...research problem, because this infor- mation can benefit both regular network users and Internet Service Providers. This is also a highly challenging

Mixed-Integer Conic Linear Programming: Challenges and Perspectives

DTIC Science & Technology

2013-10-01

The novel DCCs for MISOCO may be used in branch- and-cut algorithms when solving MISOCO problems. The experimental software CICLO was developed to...perform limited, but rigorous computational experiments. The CICLO solver utilizes continuous SOCO solvers, MOSEK, CPLES or SeDuMi, builds on the open...submitted Fall 2013. Software: 1. CICLO : Integer conic linear optimization package. Authors: J.C. Góez, T.K. Ralphs, Y. Fu, and T. Terlaky

Extremal Optimization for Quadratic Unconstrained Binary Problems

NASA Astrophysics Data System (ADS)

Boettcher, S.

We present an implementation of τ-EO for quadratic unconstrained binary optimization (QUBO) problems. To this end, we transform modify QUBO from its conventional Boolean presentation into a spin glass with a random external field on each site. These fields tend to be rather large compared to the typical coupling, presenting EO with a challenging two-scale problem, exploring smaller differences in couplings effectively while sufficiently aligning with those strong external fields. However, we also find a simple solution to that problem that indicates that those external fields apparently tilt the energy landscape to a such a degree such that global minima become more easy to find than those of spin glasses without (or very small) fields. We explore the impact of the weight distribution of the QUBO formulation in the operations research literature and analyze their meaning in a spin-glass language. This is significant because QUBO problems are considered among the main contenders for NP-hard problems that could be solved efficiently on a quantum computer such as D-Wave.

Problems Related to Parallelization of CFD Algorithms on GPU, Multi-GPU and Hybrid Architectures

NASA Astrophysics Data System (ADS)

Biazewicz, Marek; Kurowski, Krzysztof; Ludwiczak, Bogdan; Napieraia, Krystyna

2010-09-01

Computational Fluid Dynamics (CFD) is one of the branches of fluid mechanics, which uses numerical methods and algorithms to solve and analyze fluid flows. CFD is used in various domains, such as oil and gas reservoir uncertainty analysis, aerodynamic body shapes optimization (e.g. planes, cars, ships, sport helmets, skis), natural phenomena analysis, numerical simulation for weather forecasting or realistic visualizations. CFD problem is very complex and needs a lot of computational power to obtain the results in a reasonable time. We have implemented a parallel application for two-dimensional CFD simulation with a free surface approximation (MAC method) using new hardware architectures, in particular multi-GPU and hybrid computing environments. For this purpose we decided to use NVIDIA graphic cards with CUDA environment due to its simplicity of programming and good computations performance. We used finite difference discretization of Navier-Stokes equations, where fluid is propagated over an Eulerian Grid. In this model, the behavior of the fluid inside the cell depends only on the properties of local, surrounding cells, therefore it is well suited for the GPU-based architecture. In this paper we demonstrate how to use efficiently the computing power of GPUs for CFD. Additionally, we present some best practices to help users analyze and improve the performance of CFD applications executed on GPU. Finally, we discuss various challenges around the multi-GPU implementation on the example of matrix multiplication.

Finite Dimensional Approximations for Continuum Multiscale Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berlyand, Leonid

2017-01-24

The completed research project concerns the development of novel computational techniques for modeling nonlinear multiscale physical and biological phenomena. Specifically, it addresses the theoretical development and applications of the homogenization theory (coarse graining) approach to calculation of the effective properties of highly heterogenous biological and bio-inspired materials with many spatial scales and nonlinear behavior. This theory studies properties of strongly heterogeneous media in problems arising in materials science, geoscience, biology, etc. Modeling of such media raises fundamental mathematical questions, primarily in partial differential equations (PDEs) and calculus of variations, the subject of the PI’s research. The focus of completed researchmore » was on mathematical models of biological and bio-inspired materials with the common theme of multiscale analysis and coarse grain computational techniques. Biological and bio-inspired materials offer the unique ability to create environmentally clean functional materials used for energy conversion and storage. These materials are intrinsically complex, with hierarchical organization occurring on many nested length and time scales. The potential to rationally design and tailor the properties of these materials for broad energy applications has been hampered by the lack of computational techniques, which are able to bridge from the molecular to the macroscopic scale. The project addressed the challenge of computational treatments of such complex materials by the development of a synergistic approach that combines innovative multiscale modeling/analysis techniques with high performance computing.« less

Challenges to the development of complex virtual reality surgical simulations.

PubMed

Seymour, N E; Røtnes, J S

2006-11-01

Virtual reality simulation in surgical training has become more widely used and intensely investigated in an effort to develop safer, more efficient, measurable training processes. The development of virtual reality simulation of surgical procedures has begun, but well-described technical obstacles must be overcome to permit varied training in a clinically realistic computer-generated environment. These challenges include development of realistic surgical interfaces and physical objects within the computer-generated environment, modeling of realistic interactions between objects, rendering of the surgical field, and development of signal processing for complex events associated with surgery. Of these, the realistic modeling of tissue objects that are fully responsive to surgical manipulations is the most challenging. Threats to early success include relatively limited resources for development and procurement, as well as smaller potential for return on investment than in other simulation industries that face similar problems. Despite these difficulties, steady progress continues to be made in these areas. If executed properly, virtual reality offers inherent advantages over other training systems in creating a realistic surgical environment and facilitating measurement of surgeon performance. Once developed, complex new virtual reality training devices must be validated for their usefulness in formative training and assessment of skill to be established.

The iterative thermal emission method: A more implicit modification of IMC

DOE PAGES

Long, A. R.; Gentile, N. A.; Palmer, T. S.

2014-08-19

For over 40 years, the Implicit Monte Carlo (IMC) method has been used to solve challenging problems in thermal radiative transfer. These problems typically contain regions that are optically thick and diffusive, as a consequence of the high degree of “pseudo-scattering” introduced to model the absorption and reemission of photons from a tightly-coupled, radiating material. IMC has several well-known features that could be improved: a) it can be prohibitively computationally expensive, b) it introduces statistical noise into the material and radiation temperatures, which may be problematic in multiphysics simulations, and c) under certain conditions, solutions can be nonphysical, in thatmore » they violate a maximum principle, where IMC-calculated temperatures can be greater than the maximum temperature used to drive the problem.« less

BCM: toolkit for Bayesian analysis of Computational Models using samplers.

PubMed

Thijssen, Bram; Dijkstra, Tjeerd M H; Heskes, Tom; Wessels, Lodewyk F A

2016-10-21

Computational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model. We present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved. BCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.

Limits on efficient computation in the physical world

NASA Astrophysics Data System (ADS)

Aaronson, Scott Joel

More than a speculative technology, quantum computing seems to challenge our most basic intuitions about how the physical world should behave. In this thesis I show that, while some intuitions from classical computer science must be jettisoned in the light of modern physics, many others emerge nearly unscathed; and I use powerful tools from computational complexity theory to help determine which are which. In the first part of the thesis, I attack the common belief that quantum computing resembles classical exponential parallelism, by showing that quantum computers would face serious limitations on a wider range of problems than was previously known. In particular, any quantum algorithm that solves the collision problem---that of deciding whether a sequence of n integers is one-to-one or two-to-one---must query the sequence O (n1/5) times. This resolves a question that was open for years; previously no lower bound better than constant was known. A corollary is that there is no "black-box" quantum algorithm to break cryptographic hash functions or solve the Graph Isomorphism problem in polynomial time. I also show that relative to an oracle, quantum computers could not solve NP-complete problems in polynomial time, even with the help of nonuniform "quantum advice states"; and that any quantum algorithm needs O (2n/4/n) queries to find a local minimum of a black-box function on the n-dimensional hypercube. Surprisingly, the latter result also leads to new classical lower bounds for the local search problem. Finally, I give new lower bounds on quantum one-way communication complexity, and on the quantum query complexity of total Boolean functions and recursive Fourier sampling. The second part of the thesis studies the relationship of the quantum computing model to physical reality. I first examine the arguments of Leonid Levin, Stephen Wolfram, and others who believe quantum computing to be fundamentally impossible. I find their arguments unconvincing without a "Sure/Shor separator"---a criterion that separates the already-verified quantum states from those that appear in Shor's factoring algorithm. I argue that such a separator should be based on a complexity classification of quantum states, and go on to create such a classification. Next I ask what happens to the quantum computing model if we take into account that the speed of light is finite---and in particular, whether Grover's algorithm still yields a quadratic speedup for searching a database. Refuting a claim by Benioff, I show that the surprising answer is yes. Finally, I analyze hypothetical models of computation that go even beyond quantum computing. I show that many such models would be as powerful as the complexity class PP, and use this fact to give a simple, quantum computing based proof that PP is closed under intersection. On the other hand, I also present one model---wherein we could sample the entire history of a hidden variable---that appears to be more powerful than standard quantum computing, but only slightly so.

Algorithmic aspects for the reconstruction of spatio-spectral data cubes in the perspective of the SKA

NASA Astrophysics Data System (ADS)

Mary, D.; Ferrari, A.; Ferrari, C.; Deguignet, J.; Vannier, M.

2016-12-01

With millions of receivers leading to TerraByte data cubes, the story of the giant SKA telescope is also that of collaborative efforts from radioastronomy, signal processing, optimization and computer sciences. Reconstructing SKA cubes poses two challenges. First, the majority of existing algorithms work in 2D and cannot be directly translated into 3D. Second, the reconstruction implies solving an inverse problem and it is not clear what ultimate limit we can expect on the error of this solution. This study addresses (of course partially) both challenges. We consider an extremely simple data acquisition model, and we focus on strategies making it possible to implement 3D reconstruction algorithms that use state-of-the-art image/spectral regularization. The proposed approach has two main features: (i) reduced memory storage with respect to a previous approach; (ii) efficient parallelization and ventilation of the computational load over the spectral bands. This work will allow to implement and compare various 3D reconstruction approaches in a large scale framework.

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

PubMed

Korkut, Anil; Wang, Weiqing; Demir, Emek; Aksoy, Bülent Arman; Jing, Xiaohong; Molinelli, Evan J; Babur, Özgün; Bemis, Debra L; Onur Sumer, Selcuk; Solit, David B; Pratilas, Christine A; Sander, Chris

2015-08-18

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.