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Collaborative Storyboarding: Artifact-Driven Construction of Shared Understanding
Get PDFCollaborative storyboarding, with a focus on aggregating designers’ expertise in the storyboarding process, offers the opportunity for a group of designers to make progress toward creating a visual narrative for a new interface or technology, but it requires the designers to work together to explore ideas, differentiate between options, and construct a common solution. Important in collaborative storyboarding is the shared understanding that emerges among the designers and the obstacles they face in establishing that understanding. This paper defines a model for collaborative storyboarding, presents a study that explores group interactions in collaborative storyboarding, and analyzes the interactions using the distributed cognition and common ground theories. Our findings demonstrate that joint interaction and enthusiastic efforts within each phase lead to active information exchanges and shared understanding among the members of the group
Mining Posets from Linear Orders
Get PDFThere has been much research on the combinatorial problem of generating the linear extensions of a given poset. This paper focuses on the reverse of that problem, where the input is a set of linear orders, and the goal is to construct a poset or set of posets that generates the input. Such a problem ï¬nds applications in computational neuroscience,
systems biology, paleontology, and physical plant engineering. In this paper, several algorithms are presented for efficiently ï¬nding a single poset that generates the input
set of linear orders. The variation of the problem where a minimum set of posets that cover the input is also explored. It is found that the problem is polynomially
solvable for one class of simple posets (kite(2) posets) but NP-complete for a related class (hammock(2,2,2) posets)
The Effects of Finger-Walking in Place (FWIP) on Spatial Knowledge Acquisition in Virtual Environments
Get PDFSpatial knowledge, necessary for efficient navigation, comprises route knowledge (memory of landmarks along a route) and survey knowledge (overall representation like a map). Virtual environments (VEs) have been suggested as a power tool for understanding some issues associated with human navigation, such as spatial knowledge acquisition. The Finger-Walking-in-Place (FWIP) interaction technique is a locomotion technique for navigation tasks in immersive virtual environments (IVEs). The FWIP was designed to map a human’s embodied ability overlearned by natural walking for navigation, to finger-based interaction technique. Its implementation on Lemur and iPhone/iPod Touch devices was evaluated in our previous studies. In this paper, we present a comparative study of the joystick’s flying technique versus the FWIP. Our experiment results show that the FWIP results in better performance than the joystick’s flying for route knowledge acquisition in our maze navigation tasks
Convergence analysis of hybrid cellular automata for topology optimization
Get PDFThe hybrid cellular automaton (HCA) algorithm was inspired by the structural adaptation of bones to their ever changing mechanical environment. This methodology has been shown to be an effective topology synthesis tool. In previous work, it has been observed that the convergence of the HCA methodology is affected by parameters of the algorithm. As a result, questions have been raised regarding the conditions by which HCA converges to an optimal design. The objective of this investigation is to examine the conditions that guarantee convergence to a Karush-Kuhn-Tucker (KKT) point. In this paper, it is shown that the HCA algorithm is a ï¬xed point iterative scheme and the previously reported KKT optimality conditions are corrected. To demonstrate the convergence properties of the HCA algorithm, a simple cantilevered beam example is utilized. Plots of the spectral radius for projections of the design space are used to show regions of guaranteed convergence
Shortening Time-to-Discovery with Dynamic Software Updates for Parallel High Performance Applications
Get PDFDespite using multiple concurrent processors, a typical high performance parallel application is long-running, taking hours, even days to arrive at a solution. To modify a running high performance parallel application, the programmer has to stop the computation, change the code, redeploy, and enqueue the updated version to be scheduled to run, thus wasting not only the programmer’s time, but also expensive computing resources. To address these inefficiencies, this article describes how dynamic software updates can be used to modify a parallel application on the fly, thus saving the programmer’s time and using expensive computing resources more productively. The net effect of updating parallel applications dynamically reduces their time-to-discovery metrics, the total time it takes from posing a problem to arriving at a solution. To explore the benefits of dynamic updates for high performance applications, this article takes a two-pronged approach. First, we describe our experience in building and evaluating a system for dynamically updating applications running on a parallel cluster. We then review a large body of literature describing the existing state of the art in dynamic software updates and point out how this research can be applied to high performance applications. Our experimental results indicate that dynamic software updates have the potential to become a powerful tool in reducing the time-to-discovery metrics for high performance parallel applications
Power Saving Experiments for Large Scale Global Optimization
Get PDFGreen computing, an emerging ï¬eld of research that seeks to reduce excess power consumption in high performance computing (HPC), is gaining popularity among researchers. Research in this ï¬eld often relies on simulation or only uses a small cluster, typically 8 or 16 nodes, because of the lack of hardware support. In contrast, System G at Virginia Tech is a 2592 processor supercomputer equipped with power aware components suitable for large scale green computing research. DIRECT is a deterministic global optimization algorithm, implemented in the mathematical software package VTDIRECT95. This paper explores the potential energy savings for the parallel implementation of DIRECT, called pVTdirect, when used with a large scale computational biology application, parameter estimation for a budding yeast cell cycle model, on System G. Two power aware approaches for pVTdirect are developed and compared against the CPUSPEED power saving system tool. The results show that knowledge of the parallel workload of the underlying application is beneficial for power management
A Hybrid Variational/Ensemble Filter Approach to Data Assimilation
Get PDFTwo families of methods are widely used in data assimilation: the four dimensional
variational (4D-Var) approach, and the ensemble Kalman filter (EnKF) approach. The
two families have been developed largely through parallel research efforts, and each
method has its advantages and disadvantages. It is of interest to combine the two ap-
proaches and develop hybrid data assimilation algorithms. This paper investigates the
theoretical equivalence between the suboptimal 4D-Var method (where only a small
number of optimization iterations are performed) and the practical EnKF method
(where only a small number of ensemble members are used) in a linear Gaussian
setting. The analysis motivates a new hybrid algorithm: the optimization directions
obtained from a short window 4D-Var run are used to construct the EnKF initial
ensemble. Numerical results show that the proposed hybrid ensemble filter method
performs better than the regular EnKF method for both linear and nonlinear test
problems
Methods for detecting inter-protein covarying sites
Get PDFCovarying sites are defined to be sites in a protein whose rate of evolution changes over time. We design software to group protein sites into three rate pools: conserved, variant, and temporary invariant. Other software is written to find sites which are closely correlated. The algorithms used by the software require a multiple sequence alignment and phylogenetic tree as input and rely heavily on tree-corrected information
entropy. Through a study of the protein Cu, Zn Superoxide Dimutase it is shown that temporary invariant sites have interactions with at least one site which is either closely correlated or binary-switching. From this result it is reasonable to assume that temporary invariant sites which interact with no such intra-protein sites must be sites of protein-protein interaction. Temporary invariant sites are also shows to reflect the animal plant divergence
KKT conditions satisï¬ed using adaptive neighboring in hybrid cellular automata for topology optimization
Get PDFThe hybrid cellular automaton (HCA) method is a biologically inspired algorithm capable of topology synthesis that was developed to simulate the behavior of the bone functional adaptation process. In this algorithm, the design domain is divided into cells with some communication property among neighbors. Local evolutionary rules, obtained from classical control theory, iteratively establish the value of the design variables in order to minimize the local error between a ï¬eld variable and a corresponding target value. Karush-Kuhn-Tucker (KKT) optimality conditions have been derived to determine the expression for the ï¬eld variable and its target. While averaging techniques mimicking intercellular communication have been used to mitigate numerical instabilities such as checkerboard patterns and mesh dependency, some questions have been raised whether KKT conditions are fully satisï¬ed in the ï¬nal topologies. Furthermore, the averaging procedure might result in cancellation or attenuation of the error between the ï¬eld variable and its target. Several examples are presented showing that HCA converges to different ï¬nal designs for different neighborhood conï¬gurations or averaging schemes. Although it has been claimed that these ï¬nal designs are optimal, this might not be true in a precise mathematical sense—the use of the averaging procedure induces a mathematical incorrectness that has to be addressed. In this work, a new adaptive neighboring scheme will be employed that utilizes a weighting function for the influence of a cell’s neighbors that decreases to zero over time. When the weighting function reaches zero, the algorithm satisï¬es the aforementioned optimality criterion. Thus, the HCA algorithm will retain the benefits that result from utilizing neighborhood information, as well as obtain an optimal solution
MOON: MapReduce On Opportunistic eNvironments
Get PDFAbstract—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments