Abstract: We describe two approaches to detecting anomalies in time series of multi-parameter clinical data: (1) metric and model-based indicators and (2) information surprise. (1) Metric and model-based indicators are commonly used as early warning signals to detect transitions between alternate states based on individual time series. Here we explore the applicability of existing indicators to distinguish critical (anomalies) from non-critical conditions in patients undergoing cardiac surgery, based on a small anonymized clinical trial dataset. We find that a combination of time-varying autoregressive model, kurtosis, and skewness indicators correctly distinguished critical from non-critical patients in 5 out of 36 blood parameters at a window size of 0.3 (average of 37 hours) or higher. (2) Information surprise quantifies how the progression of one patient's condition differs from that of rest of the population based on the cross-section of time series. With the maximum surprise and slope features we detect all critical patients at the 0.05 significance level. Moreover we show that a naive outlier detection does not work, demonstrating the need for the more sophisticated approaches explored here. Our preliminary results suggest that future developments in early warning systems for patient condition monitoring may predict the onset of critical transition and allow medical intervention preventing patient death. Further method development is needed to avoid overfitting and spurious results, and verification on large clinical datasets.

Abstract: A clinical trial is a study designed to demonstrate the ecacy and safety of a drug, procedure, medical device, or diagnostic test. Since clinical trials involve research in humans, they must be carefully designed and must comply strictly with a set of ethical conditions. Logistical disadvantages, ethical constraints, costs and high execution times could have a negative impact on the execution of the clinical trial. This article proposes the use of a simulation tool, the MRSA-T-Simulator, to design and perform "virtual clinical trials" for the purpose of studying MRSA contact transmission among hospitalized patients. The main advantage of the simulator is its flexibility when it comes to configuring the patient population, healthcare staff and the simulation environment.

Abstract: What does the informational complexity of dynamical networked systems tell us about intrinsic mechanisms and functions of these complex systems? Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions. While several numerical schemes for estimating network integrated information exist, it is instructive to pursue an analytic approach that computes integrated information as a function of network weights. Our formulation of integrated information uses a Kullback-Leibler divergence between the multi-variate distribution on the set of network states versus the corresponding factorized distribution over its parts. Implementing stochastic Gaussian dynamics, we perform computations for several prototypical network topologies. Our findings show increased informational complexity near criticality, which remains consistent across network topologies. Spectral decomposition of the system's dynamics reveals how informational complexity is governed by eigenmodes of both, the network's covariance and adjacency matrices. We find that as the dynamics of the system approach criticality, high integrated information is exclusively driven by the eigenmode corresponding to the leading eigenvalue of the covariance matrix, while sub-leading modes get suppressed. The implication of this result is that it might be favorable for complex dynamical networked systems such as the human brain or communication systems to operate near criticality so that efficient information integration might be achieved.

Abstract: Simulation has become an important tool for studying plant physiology. An important aspect of this is discovering the processes that influence leaf growth at a cellular level. To this end, we have extended an existing, morphogen-based model for the growth of Arabidopsis leaves. We have fitted parameters to match important leaf growth properties reported in experimental data. A sensitivity analysis was performed, which allowed us to estimate the effect of these different parameters on leaf growth, and identify viable strategies for increasing leaf size.

Abstract: Scientific workflows are designed to solve complex scientific problems and accelerate scientific progress. Ideally, scientific workflows should improve the reproducibility of scientific applications by making it easier to share and reuse workflows between scientists. However, scientists often find it difficult to reuse others’ workflows, which is known as workflow decay. In this paper, we explore the challenges in reproducing scientific workflows, and propose a framework for facilitating the reproducibility of scientific workflows at the task level by giving scientists complete control over the execution environments of the tasks in their workflows and integrating execution environment specifications into scientific workflow systems. Our framework allows dependencies to be archived in basic units of OS image, software and data instead of gigantic all-in-one images. We implement a prototype of our framework by integrating Umbrella, an execution environment creator, into Makeflow, a scientific workflow system. To evaluate our framework, we use it to run two bioinformatics scientific workflows, BLAST and BWA. The execution environment of the tasks in each workflow is specified as an Umbrella specification file, and sent to execution nodes where Umbrella is used to create the specified environment for running the tasks. For each workflow we evaluate the size of the Umbrella specification file, the time and space overheads of creating execution environments using Umbrella, and the heterogeneity of execution nodes contributing to each workflow. The evaluation results show that our framework improves the utilization of heterogeneous computing resources, and improves the portability and reproducibility of scientific workflows.

Abstract: Integer Programming (IP) is the most successful technique for solving hard combinatorial optimization problems. Modern IP solvers are very complex programs composed of many different procedures whose execution is embedded in the generic Branch & Bound framework. The activation of these procedures as well the definition of exploration strategies for the search tree can be done by setting different parameters. Since the success of these procedures and strategies in improving the performance of IP solvers varies widely depending on the problem being solved, the usual approach for discovering a good set of parameters considering average results is not ideal. In this work we propose a comprehensive approach for the automatic tuning of Integer Programming solvers where the characteristics of instances are considered. Computational experiments in a diverse set of 308 benchmark instances using the open source COIN-OR CBC solver were performed with different parameter sets and the results were processed by data mining algorithms. The results were encouraging: when trained with a portion of the database the algorithms were able to predict better parameters for the remaining instances in 84% of the cases. The selection of a single best parameter setting would provide an improvement in only 56% of instances, showing that great improvements can be obtained with our approach.

Abstract: We present a spectrally accurate method for solving coupled singularly perturbed second order two-point boundary value problems (BVPs). The method combines analytical coordinate transformations with a standard Chebyshev spectral collocation method; it is applicable to linear and to nonlinear problems. The method performs well in resolving very thin boundary layers. Compared to other methods which had been proposed for systems of BVPs this method is competitive in terms of accuracy, allows for different perturbation parameters in each of the equations, and does not require special properties of the coefficient functions.

Abstract: An enduring topic in Information Systems academic and practitioners’ literature is how Business and Information Technology (IT) resources can be aligned in order to generate value for companies (Gerow et al. 2014). Despite a considerable body of literature, alignment is still considered an unachieved objective in corporate practice and the topic constantly ranks on top priorities of companies’ CIOs (Kappelman et al. 2013). The inability to explain the process of alignment, i.e. how alignment is implemented in organisations, is considered one of the main reasons for the high misalignment level in companies (Chan and Reich 2007b). In an attempt to radically innovate alignment studies, researchers approached Complexity Science to investigate how Information Systems evolve in organisations (Merali 2006; Merali et al. 2012; Vessey and Ward 2013; Campbell and Peppard 2007) and derived a set of principles potentially capable of improving alignment (Benbya and Mc Kelvey 2006). However, studies have mainly adopted a qualitative and descriptive approach and alignment principles have been drawn by analogy between Information Systems and other complex systems existing in nature and extensively studied rather than as the result of a theoretical explanation and modelling (Kallinikos 2005). In our study we developed a model that describes how alignment evolves in organisations. The model adopts the fraction of persons within an organisation who are unsatisfied by IT as a state variable to measure misalignment. The evolution of misalignment is linked to key parameters, such as the capacity of the IT department to understand business needs and transform them into innovation projects, the resistance to change of the personnel, the flexibility of the Information Systems, the IT investment policies of the organisation. The model is based on an extensive literature review (Chan and Reich 2007a), through which several parameters influencing alignment have been selected, and on the study of 4 cases, i.e. alignment processes implemented in manufacturing companies. Through the analysis of the model we derived principles for effectively managing alignment implementation in organisations, such as the improvement of personnel flexibility, the exploitation of feedback loops, the development of monitoring systems, and the implementation of modular, weakly-coupled IT components. Applicability of principles in corporate practice has been tested in one company undertaking a digital transformation project. The contribution to the study of alignment is twofold. The model, despite its simplicity, is capable of describing alignment dynamics, even in cases not explicable through other approaches, and contributes to the creation of a theoretical foundation for the study of alignment as a complex process. At operational level, the derivation of principles constitutes a step towards the implementation of effective alignment strategies. References Alaa, G. (2009). “Derivation of factors facilitating organizational emergence based on complex adaptive systems and social autopoiesis theories,” Emergence: Complexity and Organization, 11(1), 19. Benbya, H., and McKelvey, B. (2006). “Using Co-evolutionary and Complexity Theories to Improve IS Alignment: A Multi-level Approach,” Journal of Information Technology (21:4), pp. 284-298. Campbell, B., & Peppard, J. (2007). The co-evolution of business information systems’ strategic alignment: an exploratory study. Chan, Y. E., & Reich, B. H. (2007a). “IT alignment: an annotated bibliography,”Journal of Information Technology, 22(4), 316-396. Chan, Y. E., and Reich, B. H. (2007b). “IT Alignment: What have we Learned?”, Journal of Information Technology (22:4), pp. 297-315. Chan, Y. E., Sabherwal, R., and Thatcher, J. B. (2006). “Antecedents and Outcomes of Strategic IS Alignment: An Empirical Investigation,” IEEE Transactions on Engineering Management (53:1), pp. 27-47. Gerow, J. E., Grover, V., Thatcher, J. B., & Roth, P. L. (2014). “Looking toward the future of IT- business strategic alignment through the past: A meta-analysis,” MIS Quarterly, 38(4), 1059-1085. Henderson, J. C., & Venkatraman, H. (1993). “Strategic alignment: Leveraging information technology for transforming organizations,” IBM Systems Journal, 32(1), 472-484. Kallinikos, J. (2005). “The order of technology: Complexity and Control in a Connected World,” Information and Organization (15:3), pp. 185-202. Kappelman, L. A., McLeon, E., Luftman, J., and Johnson, V. 2013. “Key Issues of IT Organizations and their Leadership: The 2013 SIM IT Trends Study,” MIS Quarterly Executive, (12), pp. 227- 240. Luftman, J., Papp, R., and Brier, T. (1999). “Enablers and Inhibitors of Business-IT Alignment,” Communications of the AIS, 1(3es), 1. Merali, Y. (2006). “Complexity and Information Systems: The Emergent Domain,” Journal of Information Technology (21:4), 216-228. Vessey, I., and Ward, K. 2013. “The Dynamics of Sustainable IS Alignment: The Case for IS Adaptivity,” Journal of the Association for Information Systems (14:6), pp. 283-301. Wagner, H. T., Beimborn, D., & Weitzel, T. (2014). “How social capital among information technology and business units drives operational alignment and IT business value,” Journal of Management Information Systems, 31(1), 241-272.

Abstract: While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach.

Abstract: It has been well documented for more than 30 years, that significantly more effort is needed to prepare the HPC workforce needed today and well into the future. The Blue Waters Virtual School of Computational Science (VSCSE) provides an innovative model for addressing this critical need. The VSCSE uses a Small Private Online Course (SPOC) approach to providing graduate level credit courses to students at multiple institutions. In this paper, we describe the rationale for this approach, a description of the implementation, findings from external evaluations, and lessons learned. The paper concludes with recommendations for future strategies to build on this work to address the workforce needs of our global society.

Abstract: In this talk we discuss design considerations for an undergraduate introductory scientific computing course, why these considerations led to a specs-based grading scheme, and some techinical detail on how specs-based grading was accomplished using unit tests. The course is part of the Computational Science major at Rose-Hulman Institute of Technology. This major attracts a wide range of students, most of whom have a second major. As such, students have a wide range of background experiences, proficiencies, and potential uses for parallel computing. In this talk we discuss: 1. Selection of material and grading scheme to keep the course approachable to students with relatively weak background, keep most of the content relevant to most of the students, and provide a challenge to the students with a strong background. 2. How specs-based grading was implemented. One of the tradiational challenges with specs-based grading is in developing specifications that are clear to the students, maintain the integrity of the project, and are easily verifiable by the instructor. This is particularly difficult in a parallel computing course, where "write code that produces the correct answer" is not an acceptable specification, but rather "write code that produces the correct answer by using a reasonable algorithm". We present a unit testing scheme that allows both the instructor and the student to asses the efficiency and basic design of student code -- without having to actually read the student code. This led to a significant improvement in student understanding, quality of submitted code, and instructor satisfaction.

Abstract: At the Workshop on Teaching Computational Science at ICCS 2015, we presented an approach to use scientific workflows in computational science education. Concretely, we described how we used a process modelling and execution framework for scientific workflow projects in the scope of a computer science course for Master students with a background in natural sciences. We also reported on a variant of the course that served the converse purpose, namely to teach the basics of scientific workflows this time to Bachelor students of a Computer Science study program (who are in the process of acquiring solid programming and software development skills, but have hardly any background in other scientific disciplines). Furthermore, we envisaged to offer a course that brings both groups together, with project work carried out in interdisciplinary pairs consisting of a student of a natural science subject and a computer science partner - a constellation that many students are likely to find themselves in after graduation. Meanwhile, we have been able to actually implement this variant of the course. Taking on the roles of the “domain expert” and the “programmer”, respectively, the interdisciplinary pairs worked together on one problem - from different perspectives, but with the workflow model being the means to bridge the gap between their areas of expertise. The experiences with this interdisciplinary project course were very positive. The students designed and implemented larger and more complex scientific workflows than those in the previous years. In the course evaluation they reported that although in the beginning they found it difficult to follow their partner from the other domain, they were impressed how much that improved during the project work, and that they enjoyed and valued this experience. Encouraged by these outcomes, we plan to continue and extend the course concept in the future.

Abstract: Hybridizing agent-based paradigm with evolutionary or memetic computation can enhance the field of meta-heuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones. In the article, an evolutionary multi-agent system (EMAS) is applied to solve difficult discrete benchmark problems without any domain-specific knowledge---thus they may be called ``black-box'' ones. As a means for comparison, a parallel evolutionary algorithm (constructed along with Michalewicz model) versus evolutionary and memetic versions of EMAS are used. The obtained results point out that EMAS is significantly more efficient than classical evolutionary algorithms and also finds better results in the examined problem instances.

Abstract: This paper presents design, implementation and performance results of a new modular, parallel, agent-based and large scale crowd simulation environment. A parallel application, implemented with C and MPI, was implemented and run in this parallel environment for simulation and visualization of an evacuation scenario at Gdansk University of Technology, Poland and further in the area of districts of Gdansk. The application uses a parallel MPI I/O run on two different clusters and a two or three node Parallel File System (PFS) to store a current state in a file. In order to make this implementation efficient, we used our previously developed and tuned Byte-addressable Non-volatile RAM Distributed Cache - a solution that allows to access small data chunks from spread locations within a file efficiently. We have presented application execution times versus the number of agents (up to 100000), versus the number of simulation iterations (up to 25000), versus map size (up to 6km^2) and versus the number of processes (up to more than 650) showing high speed-ups.

Abstract: This paper presents some of the techniques, algorithms and designs used to enable mass evacuation simulations to take advantage of high performance computing infrastructure. A brief overview of a tsunami mass evacuation simulator capable of simulating urban areas of hundreds of km2 in sub-meter detail is provided. Enhancements to the serial algorithms for path finding reducing the path finding time in 94% and a cache friendly visual boundary extraction algorithm cutting the overall simulation time in 50% are presented. Furthermore the hybrid parallel (distributed memory (MPI) + shared memory (OpenMP)) framework is described. A dynamic load balancing technique reducing the idling time from 50% of the execution time to 3% is presented. Finally measures of the thread parallel strong scalability up to 16 threads of 82.69% and distributed process strong scalability up to 2048 processes of 75.93% are presented.

Abstract: Volunteer computing is a very appealing way of utilizing vast available resources in an efficient way. However currently available platforms supporting this computing style are either difficult to use or not available at all, being the results of e.g. finished scientific projects. In this paper a novel, lightweight volunteer computing platform is presented. In order to contribute the resources to this platform, only a web-browser is required without the need to install any additional plugins or other software. In this paper, besides general considerations and presentation of the platform structure and functionalities, selected results proving its efficiency are shown.

Abstract: Agent-based modelling (ABM) is a widely recognized simulation technique that is particularly relevant for economic, environmental, security and humanitarian scenarios [1,2]. Within this work we focus particularly on escape scenarios. On this topic, ABMs have been extensively applied to model events such as fire escapes [2-4], evacuations from large scale disasters [5-7], or the movements of refugees in times of conflict [8-9]. Unfortunately, many of these ABM simulation and model validation studies are constrained by limitations in the available empirical data. Most commonly, there is insufficient empirical information to validate ABMs in any complete sense [10], a phenomenon which is further exacerbated by ABMs containing a relatively rich set of adjustable parameters. In some cases, these limitations are so extreme that they hinder the construction of these models. For example, in the case of refugee modelling, people are only registered by the UNHCR once they have reached a safe location (i.e., a refugee camp, see data.unhcr.org), yet the modellers wish to know how many people reside in the area of danger, and at what times. Similar mismatches occur in simulations of fire escape or disaster relief, when registrations and recordings are only made in safe locations. Using the registration data from safe locations directly to determine the number of people in areas of danger results in a structural underestimation of the number of occupants in safe locations, as the agents in the simulation require time to travel from the area of danger to a safe haven [9]. One common method to reduce these kind of errors is by permitting a warmup time in the simulation (see e.g., [11]). However, in escape scenarios such warmup times only eliminate these errors altogether if the time required for agents to arrive at the safe location is constant across all agents, and known in advance. In this talk I will present a timescale realignment technique that allows researchers to use safe location data as an input parameter for refugee escape simulations. Using this technique it is possible to fully eliminate the validation error in total safe location registrations, given that the number of safe location registrations remains positive, and at the expense of introducing an additional error in travel speeds. References: [1] J Doyne Farmer and Duncan Foley. The economy needs agent-based mod- elling. Nature, 460(7256):685–686, 2009. [2] Eric Bonabeau. Agent-based modeling: Methods and techniques for sim- ulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3):7280–7287, 2002. [3] Timo Korhonen, Simo Hostikka, Simo Heli ̈ovaara, and Harri Ehtamo. Fds+ evac: an agent based fire evacuation model. In Pedestrian and Evacuation Dynamics 2008, pages 109–120. Springer, 2010. [4] Fangqin Tang and Aizhu Ren. Agent-based evacuation model incorpo- rating fire scene and building geometry. Tsinghua Science & Technology, 13(5):708–714, 2008. [5] Xuwei Chen, John W Meaker, and F Benjamin Zhan. Agent-based mod- eling and analysis of hurricane evacuation procedures for the florida keys. Natural Hazards, 38(3):321–338, 2006. [6] Jianyong Shi, Aizhu Ren, and Chi Chen. Agent-based evacuation model of large public buildings under fire conditions. Automation in Construction, 18(3):338–347, 2009. [7] AS Mordvintsev, VV Krzhizhanovskaya, MH Lees, and PMA Sloot. Sim- ulation of city evacuation coupled to flood dynamics. In Pedestrian and Evacuation Dynamics 2012, pages 485–499. Springer International Pub- lishing, 2014. [8] J. A. Sokolowski, C. M. Banks, and R. L. Hayes. Modeling population displacement in the syrian city of aleppo. In Proceedings of the 2014 Winter Simulation Conference, pages 252–263. IEEE Press, 2014. [9] D. Groen. Simulating refugee movements: Where would you go? Procedia Computer Science, 80:2251–2255, 2016. [10] Andrew Crooks, Christian Castle, and Michael Batty. Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, 32(6):417–430, 2008. [11] Marek Laskowski and Shamir Mukhi. Agent-based simulation of emer- gency departments with patient diversion. In International Conference on Electronic Healthcare, pages 25–37. Springer, 2008.

Abstract: Every year, millions of forest worldwide hectares are burned causing important consequences on the atmosphere, biodiversity and economy. A correct prediction of the fire evolution allows to manage the fire fighting equipment properly. Therefore, it is crucial to use reliable and speed simulations in order to predict the evolution of the fire. WRF-SFIRE is a wildland fire simulator, which couples a meteorological model called Weather Research and Forecasting Model (WRF) and a forest fire simulator, SFIRE. The aforementioned coupling strategy reproduces the interaction between the propagation of the fire and the atmosphere surrounding it. The mesh resolution used to solve the atmosphere evolution has a deep impact in the prediction of small scale meteorological effects. At the same time, the ability of introducing these small scale meteorological events into the forest fire simulation implies enhancements in the quality of the data that drives the simulation, therefore, better fire propagation predictions. However, this improvement can be affected by the instability of the problem to solve. So, this paper states the convergence problem due to the mesh resolution when using WRF-SFIRE and a proposal to overcome it is described. The proposed scheme has been tested using a real case that took place in Catalonia (northeast of Spain) in 2005.

Abstract: This paper show the extension of the wildfire simulation tool FARSITE to allow for data assimilation capabilities on both fire perimeters and fuel adjustment factors to improve the accuracy of wildfire spread predictions. While fire perimeters characterize the overall burn scar of a wildfire, fuel adjustment factors are fuel model specific calibration numbers that adjust the rate of spread for each fuel type independently. Data assimilation updates of both fire perimeters and fuel adjustment factors are calculated from an Ensemble Kalman Filter (EnKF) that exploits the uncertainty information on the simulated fire perimeter, fuel adjustment factors and a measured fire perimeter. The effectiveness of the proposed data assimilation is illustrated on a wildfire simulation representing the 2014 Cocos fire, tracking time varying fuel adjustment factors based on noisy and limited spatial resolution observations of the fire perimeter.

Abstract: The increasing capacity to gather data of an on-going wildfire operation has triggered the methods and strategies to incorporate these data to a flexible model to improve forecasting accuracy and validity. In the present paper we discuss the optimization strategy included in an inverse model algorithm based on semi-empirical fire spread model fed with infra-red airborne acquired images. The algorithm calibrates 7 parameters and incorporates a topographic diagnosis wind model. The optimization problem is shown to be a non-smooth problem and thus, its best resolving strategy is critical regarding efficiency and times constraints. Three optimization strategies are evaluated in a synthetic real-scale scenario to select the more efficient one. Preliminary results are discussed and compared.

Abstract: This paper presents a method for automatic classification of power disturbance events in an electric grid by means of distributed parameter estimation and clustering techniques of synchro- phasor data produced by phasor measurement units (PMUs). Disturbance events detected in the PMU data are subjected to a parameter estimation routine to extract features that include oscillation frequency, participation factor, damping factor and post and pre-event frequency offset. The parameters are used to classify events and classification rules are deduced on the basis of a training set of known events using nonlinear programming. Once the classification rules are set, the approach can be used to automatically classify events not seen in the training set. The proposed event classification is illustrated on a microPMU system data developed by Power Standards Lab for which disturbance events were measured over several months.

Abstract: Data provenance is a fundamental concept in scientific experimentation. However, for their understanding and use, efficient and user-friendly mechanisms are needed. Research in software visualization, ontologies and complex networks can help in this process. This paper presents a framework to assist the understanding and use of data provenance through visualization techniques, ontologies and complex networks. The framework generates new information using ontologies and provenance graph analysis and highlights results through new visualization techniques. The framework was used in the E-SECO scientific ecosystem platform.

Abstract: Many of the atmospheric phenomena with the greatest potential impact in future warmer climates are inherently multiscale. Such meteorological systems include hurricanes and tropical cyclones, atmospheric rivers, and other types of hydrometeorological extremes. These phenomena are challenging to simulate in conventional climate models due to the relatively coarse uniform model resolutions relative to the native nonhydrostatic scales of the phenomonological dynamics. To enable studies of these systems with sufficient local resolution for the multiscale dynamics yet with sufficient speed for climate-change studies, we have developed a nonhydrostatic adaptive mesh dynamical core climate studies. In this talk, we present an adaptive, conservative finite volume approach for moist non-hydrostatic atmospheric dynamics. The approach is based on the compressible Euler equations on 3D thin spherical shells, where the radial direction is treated implicitly to eliminate time step constraints from vertical acoustic waves. The spatial discretization is the equiangular cubed-sphere mapping, with a fourth-order accurate discretization to compute flux averages on faces. By using both space-and time-adaptive mesh refinement, the solver allocates computational effort only where greater accuracy is needed. The main focus are demonstrations of the performance of this AMR dycore on idealized problems directly pertinent to atmospheric fluid dynamics. We start with test simulations of the shallow water equations on a sphere. We show that the accuracy of the AMR solutions is comparable to that of conventional, quasi-form mesh solutions at high resolution, yet the AMR solutions are attained with 10 to 100x fewer operations and hence much greater speed. The remainder of the talk concerns the performance of the dycore on a series of tests of increasing complexity from the Dynamical Core Model Intercomparison Project, including tests without and with idealized moist physics to emulate hydrological processes. The tests demonstrate that AMR dynamics is a viable and highly economical alternative for attaining the ultra-high resolutions required to reproduce atmospheric extreme phenomena with sufficient accuracy and fidelity.

Abstract: A strict throughput requirement has placed a cap on the degree to which we can depend on the execution of single, long, fine spatial grid simulations to explore global atmospheric climate behavior in more detail. Running an ensemble of short simulations is economical as compared to traditional long simulations for the same number of simulated years, making it useful for tests of climate reproducibility with non-bit for bit changes. We test the null hypothesis that the climate statistics of a full-complexity atmospheric model derived from an ensemble of independent short simulation is equivalent to that from a long simulation. The climate statistics of short simulation ensembles are statistically distinguishable from that of a long simulation in terms of the distribution of global annual means, largely due to the presence of low-frequency atmospheric intrinsic variability in the long simulation. We also find that model climate statistics of the simulation ensemble are sensitive to the choice of compiler optimizations. While some answer-changing optimization choices do not effect the climate state in terms of mean, variability and extremes, aggressive optimizations can result in significantly different climate states.

Abstract: A problem of quasi-static growth of an arbitrary shaped-crack along an interface requires many times of iterations not only for find a spatial distribution of discontinuity but also for determining the crack tip. This is crucial when refining model resolution and also when the phenomena progresses quickly from one step to another. We propose a mathematical reformulation of the problem as a nonlinear equation and adopt different numerical methods to solve it efficiently. Compared to a previous work of the authors, the resulting code shows a great improvement of performance. This gain is important for further application of aseismic slip process along the fault interface, in the context of plate convergence as well as the reactivation of fault systems in reservoirs.

Abstract: In 1974 Lawson and Hanson produced a seminal active set strategy to solve least-squares problems with non-negativity constraints that remains popular today. In this paper we present TNT-NN, a new active set method for solving non-negative least squares (NNLS) problems. TNT-NN uses a different strategy not only for the construction of the active set but also for the solution of the unconstrained least squares sub-problem. This results in dramatically improved performance over traditional active set NNLS solvers, including the Lawson and Hanson NNLS algorithm and the Fast NNLS (FNNLS) algorithm, allowing for computational investigations of new types of scientific and engineering problems. For the small systems tested (5000x5000 or smaller), it is shown that TNT-NN is up to 95x faster than FNNLS. Recent studies in rock magnetism have revealed a need for fast NNLS algorithms to address large problems (on the order of 10^5 x 10^5 or larger). We apply the TNT-NN algorithm to a representative rock magnetism inversion problem where it is 60x faster than FNNLS. We also show that TNT-NN is capable of solving large (45000x45000) problems more than 150x faster than FNNLS. These large test problems were previously considered to be unsolvable, due to the excessive execution time required by traditional methods.

Abstract: Analyses of vitamin supplements A and E in food samples are performed mostly with high performance liquid chromatography (HPLC). In majority of cases, sample preparation preceding HPLC implies saponification, a step critical to heat sensitivity of analytes. The method of saponification is clearly defined by ISO standards, however, two important factors, temperature and time of saponification are only given in value ranges instead of exact settings. Resolving this deficiency with the promise of eliminating time and cost consuming experimental probes, statistical experimental design (SED) is introduced to find optimum settings of temperature and time for the best recovery of vitamin supplements in food samples. Finding the optimum settings in SED was supported with Statsoft Statistica 7.0. For illustrating SED, margarine samples supplemented with vitamin A and E were applied.

Abstract: We present a discrete optimisation model that chooses an optimal fleet of vessels to support maintenance operations at Offshore Wind Farms (OFWs). The model is presented as a bi-level problem. On the first (tactical) level, decisions are made on the fleet composition for a certain time horizon. On the second (operational) level, the fleet is used to optimise the schedule of operations needed at the OWF, given events of failures and weather conditions.

Abstract: Low-risk prostate cancer can be treated by focal brachytherapy, wherein small radioactive seeds are implanted directly into the prostate. This clinical procedure has reduced side effects compared to conventional radiotherapy treatments that target the entire gland. The planning of such treatment is complicated by post-operative displacement of the seeds from their intended location. This reduces the planned treatment dose and increases the dose to surrounding tissue such as the urethra and rectum, potentially causing harmful side-effects. Current treatment planning methods do not explicitly incorporate the effect of post-operative seed displacement. To address this, we modify the radiation dose rate function used during planning to reflect this displacement using convolution. This new dose rate model enables plans to be produced automatically and efficiently. Simulation experiments show that treatment plans made using the convolved dose rate function are more robust to seed displacement than those using the original unconvolved dose, preserving treatment efficacy but giving increased protection to surrounding tissue.

Abstract: This paper examines the coarse-grained approach to parallelization of the branch-and-bound (\BNB) algorithm in a distributed computing environment. This approach is based on preliminary decomposition of a feasible domain of mixed-integer programming problem into a set of subproblems. The produced subproblems are solved in parallel by a distributed pool of standalone \BNB solvers. The incumbent values found by individual solvers are intercepted and propagated to other solvers to speed up the traversal of \BNB search tree. Presented implementation of the approach is based on SCIP, a non-commercial MINLP solver, and Everest, a web-based distributed computing platform. The implementation was tested on several mixed-integer programming problems and a noticeable speedup has been achieved. In the paper, results of a number of experiments with the Traveling Salesman Problem are presented.

Abstract: Designing a partial differential equations solver is a complex task which involves making choices about the solution algorithm and its parameters. Such choices are usually done on the basis of personal preference or numerical experiments, which can introduce significant bias on the selection process. In this work we develop a methodology to drive this selection process towards the optimal choices by modelling the accuracy and the performance of the solution algorithm. We show how this methodology can be successfully applied on the linear advection problem. As a result, the selection can be optimally performed with a much lower investment on the development of high-performance versions of the solvers and without using the target architecture for numerical experiments.

Abstract: In this paper, 3D fabric drape was reconstructed by using video recorded from a smartphone. Elliptical Fourier Analysis (EFA) and Principle Component Analysis (PCA) were used to parameterize the 3D drape to reveal shape parameters. A cluster analysis of various 3D drapes was implemented to verify the proposed method. Experiment results demonstrated that the 3D drape can be reconstructed and parameterized with a mean error of 0.52 mm when the harmonic number of EFA equals to 25. The cluster result indicated that the new features detected by our method were useful to classify different drapes, which provided a novel idea for 3D drape analysis.

Abstract: The paper is devoted to the new method of high-level scientific visualization, comprehensive visual analysis and model validation tools development using new version of client-server scientific visualization system SciVi as an example. The distinctive features of the methods implemented are ontology-based automated adaptation to third-party data sources from various application domains and to specifics of the visualization problems as well as multiplatform portability of the software solution. High-level tools for semantic filtering of the rendered data are presented. These tools improve visual analytics capabilities of SciVi enabling to validate solvers’ or/and data sources’ models in more comprehensive form and to reduce uncertainties due to the explicit representation of hidden features of data.

Abstract: The brown bear (Ursus arctos) is the most widespread bear in the world. It can be found across Europe, Asia and North America in habitats ranging from forests to dry deserts and tundra. One of the best bear habitats in Europe are located in Bulgaria. They are situated in the mountains of Rhodopa, Stara planina, Rila, Pirin, Vitosha. Until 1992 the bear had been a game target. By Order 1023 dated 31.12.1992 of the Ministry of Environment and Water (MoEW)the species has been declared protected, in compliance with the Nature protection act. This status has been kept also after the Biodiversity act was passed in 2002. The Habitat directive requires strict protection of the species and declaration of special protected areas for conservation of its habitats \cite{Red11}. The main habitats of the bear in Bulgaria are included in the ecological network NATURA 2000 \cite{Nat}. For the purposes of protection of the habitats and the management of the network NATURA 2000 a mapping and determination of their environmental status was carried out in the frame of project under the EU operational programmes environment. The acquired information are used for elaboration of plans for management of the protected areas and the populations of the species as well as for regulation of the investment projects therein. That is why it is important to estimate habitat use and population dynamics of brown bears in the country. In this work we study the population of brown bears in Rhodopa Mountains, using data received from the monitoring that was carried out in Autumn 2011. Recommendations regarding the obtained estimators and the necessary sample sizes are presented, as well as some ways to improve data collection during future monitoring.

Abstract: This paper describes the methodology of estimation of sustainable development of a region on the basis of a system of weighted target indicators, adjusted in accordance with goals and tasks of regional management. The authors analyze an example of practical application of the methodology in St. Petersburg and draw the conclusions about an influence of objective indicators and subjective value orientations of residents on sustainable development of the region

Abstract: In this paper, we propose a posterior ensemble Kalman filter (EnKF) based on a modified Cholesky decomposition. The main idea behind our approach is to estimate the moments of the analysis distribution based on an ensemble of model realizations. The method proceeds as follows: initially, an estimate of the precision background error covariance matrix is computed via a modified Cholesky decomposition and then, based on rank-one updates, the Cholesky factors of the inverse background error covariance matrix are updated in order to obtain an estimate of the inverse analysis covariance matrix. The special structure of the Cholesky factors can be exploited in order to obtain a matrix-free implementation of the EnKF. Once the analysis covariance matrix is estimated, the posterior mode of the distribution can be approximated and samples about it are taken in order to build the posterior ensemble. Experimental tests are performed making use of the Lorenz 96 model in order to assess the accuracy of the proposed implementation. The results reveal that, the accuracy of the proposed implementation is similar to that of the well-known local ensemble transform Kalman filter and even more, the use of our estimator reduces the impact of sampling errors during the assimilation of observations.

Abstract: Chemical kinetic models often carry very large parameter uncertainties and show a strongly non-linear response with rapid changes over relatively small parameter ranges. Additionally, the dimensionality of the parameter space can grow large and there is not a priori known hierarchy between the parameters. Improving the accuracy of the model requires either high-level computationally demanding electronic structure simulation or a typically large number of dedicated experiments. Naturally, a practitioner would like to know which parameters should be determined with higher accuracy and which conclusions can already be drawn from the uncertain model. Using a real life model for the water splitting on a Cobalt oxide catalyst as a prototypical example, we address these problems using global sensitivity analysis (GSA) based on the Analysis Of Variances (ANOVA). For this, we discretize the parameter space by an adaptive sparse grid approach. Dimension adaptivity automatically sorts out unimportant terms in an (anchored) ANOVA expansion and, furthermore, adjusts the resolution for each direction. Using locally supported basis functions, the dimension adaptivity is combined with a local refinement strategy in order to address local, rapid changes. This allows to discretize the 19-dimensional parameter space with only modest numbers of grid points. Our findings indicate that, for the given model, it is not possible to make any quantitative statement, e.g. whether is highly active or not. However, from the GSA, we are still able to draw chemically relevant conclusions, i.e. what are the dominant reaction pathways and how they interact.

Abstract: Urban indicators provide a unique multi-disciplinary data framework which social scientists, planners and policy makers employ to understand and analyze the complex dynamics of metropolitan regions. Indicators provide an independent, quantitative measure or benchmark of an aspect of an urban environment, by combining different metrics for a given region. While the current approach to urban indicators involves the systematic accurate collection of the raw data required to produce reliable indicators and the standardization of well-known commonly accepted or widely adopted indicators, the next generation of indicators is expected to support a more dynamic, customizable, fine-grained approach to indicators, via a context of interoperability and linked open data. Within this paper, we address these emerging requirements through an ontological approach aimed at (i) establishing interoperability among heterogeneous data sets, (ii) expressing the high-level semantics of the indicators, (iii) supporting indicator adaptability and dynamic composition for specific applications and (iv) representing properly the uncertainties of the resulting ecosystem.