14,154 research outputs found
A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets
Clustering is a technique for the analysis of datasets obtained by empirical
studies in several disciplines with a major application for biomedical
research. Essentially, clustering algorithms are executed by machines aiming at
finding groups of related points in a dataset. However, the result of grouping
depends on both metrics for point-to-point similarity and rules for
point-to-group association. Indeed, non-appropriate metrics and rules can lead
to undesirable clustering artifacts. This is especially relevant for datasets,
where groups with heterogeneous structures co-exist. In this work, we propose
an algorithm that achieves clustering by exploring the paths between points.
This allows both, to evaluate the properties of the path (such as gaps, density
variations, etc.), and expressing the preference for certain paths. Moreover,
our algorithm supports the integration of existing knowledge about admissible
and non-admissible clusters by training a path classifier. We demonstrate the
accuracy of the proposed method on challenging datasets including points from
synthetic shapes in publicly available benchmarks and microscopy data
Towards guidelines for building a business case and gathering evidence of software reference architectures in industry
Background: Software reference architectures are becoming widely adopted by organizations that need to support the design and maintenance of software applications of a shared domain. For organizations that plan to adopt this architecture-centric approach, it becomes fundamental to know the return on investment and to understand how software reference architectures are designed, maintained, and used. Unfortunately, there is little evidence-based support to help organizations with these challenges.
Methods: We have conducted action research in an industry-academia collaboration between the GESSI research group and everis, a multinational IT consulting firm based in Spain.
Results: The results from such collaboration are being packaged in order to create guidelines that could be used in similar contexts as the one of everis. The main result of this paper is the construction of empirically-grounded guidelines that support organizations to decide on the adoption of software reference architectures and to gather evidence to improve RA-related practices.
Conclusions: The created guidelines could be used by other organizations outside of our industry-academia collaboration. With this goal in mind, we describe the guidelines in detail for their use.Peer ReviewedPostprint (published version
Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
Optical microscopy provides rich spatio-temporal information characterizing
in vivo molecular motion. However, effective forces and other parameters used
to summarize molecular motion change over time in live cells due to latent
state changes, e.g., changes induced by dynamic micro-environments,
photobleaching, and other heterogeneity inherent in biological processes. This
study focuses on techniques for analyzing Single Particle Tracking (SPT) data
experiencing abrupt state changes. We demonstrate the approach on GFP tagged
chromatids experiencing metaphase in yeast cells and probe the effective forces
resulting from dynamic interactions that reflect the sum of a number of
physical phenomena. State changes are induced by factors such as microtubule
dynamics exerting force through the centromere, thermal polymer fluctuations,
etc. Simulations are used to demonstrate the relevance of the approach in more
general SPT data analyses. Refined force estimates are obtained by adopting and
modifying a nonparametric Bayesian modeling technique, the Hierarchical
Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT
applications. The HDP-SLDS method shows promise in systematically identifying
dynamical regime changes induced by unobserved state changes when the number of
underlying states is unknown in advance (a common problem in SPT applications).
We expand on the relevance of the HDP-SLDS approach, review the relevant
background of Hierarchical Dirichlet Processes, show how to map discrete time
HDP-SLDS models to classic SPT models, and discuss limitations of the approach.
In addition, we demonstrate new computational techniques for tuning
hyperparameters and for checking the statistical consistency of model
assumptions directly against individual experimental trajectories; the
techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One
publication available freely as an open-access article at
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763
Geodesic distances in Liouville quantum gravity
In order to study the quantum geometry of random surfaces in Liouville
gravity, we propose a definition of geodesic distance associated to a Gaussian
free field on a regular lattice. This geodesic distance is used to numerically
determine the Hausdorff dimension associated to shortest cycles of 2d quantum
gravity on the torus coupled to conformal matter fields, showing agreement with
a conjectured formula by Y. Watabiki. Finally, the numerical tools are put to
test by quantitatively comparing the distribution of lengths of shortest cycles
to the corresponding distribution in large random triangulations.Comment: 21 pages, 8 figure
Guidelines For Pursuing and Revealing Data Abstractions
Many data abstraction types, such as networks or set relationships, remain
unfamiliar to data workers beyond the visualization research community. We
conduct a survey and series of interviews about how people describe their data,
either directly or indirectly. We refer to the latter as latent data
abstractions. We conduct a Grounded Theory analysis that (1) interprets the
extent to which latent data abstractions exist, (2) reveals the far-reaching
effects that the interventionist pursuit of such abstractions can have on data
workers, (3) describes why and when data workers may resist such explorations,
and (4) suggests how to take advantage of opportunities and mitigate risks
through transparency about visualization research perspectives and agendas. We
then use the themes and codes discovered in the Grounded Theory analysis to
develop guidelines for data abstraction in visualization projects. To continue
the discussion, we make our dataset open along with a visual interface for
further exploration
Learning From the Case Studies, How Global Software Development Process is Executed in an Agile Method Environment
. The biggest challenge in Global Software Development (GSD) is the efficiency of time to develop. GSD provides a guidance to use the process along with up-front analysis method like unified process or waterfall method. Although, it gives a benefit through comprehensive documentation and its clearness, it gives inhibits the organization which wants use GSD but in a rush. Agile methods claim an efficient and the effective approach to software development. This paper reports on how organizations combine the GSD process with agile methods like eXtreme Programming (XP), Scrum, Agile Unified Process (Agile UP), Feature Driven Development (FDD), and Microsoft Solution Framework Agile (MSF Agile). The paper uses case study to get organization experiences and describe useful practices for the organization that want to implement GSD with an agile method.
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