245,373 research outputs found
Tools for Exploring Multivariate Data: The Package ICS
Invariant coordinate selection (ICS) has recently been introduced as a method for exploring multivariate data. It includes as a special case a method for recovering the unmixing matrix in independent components analysis (ICA). It also serves as a basis for classes of multivariate nonparametric tests, and as a tool in cluster analysis or blind discrimination. The aim of this paper is to briefly explain the (ICS) method and to illustrate how various applications can be implemented using the R package ICS. Several examples are used to show how the ICS method and ICS package can be used in analyzing a multivariate data set.
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
Exploring Design Space For An Integrated Intelligent System
Understanding the trade-offs available in the design space of intelligent systems is a major unaddressed element in the study of Artificial Intelligence. In this paper we approach this problem in two ways. First, we discuss the development of our integrated robotic system in terms of its trajectory through design space. Second, we demonstrate the practical implications of architectural design decisions by using this system as an experimental platform for comparing behaviourally similar yet architecturally different systems. The results of this show that our system occupies a "sweet spot" in design space in terms of the cost of moving information between processing components
Full Open Population Capture-Recapture Models with Individual Covariates
Traditional analyses of capture-recapture data are based on likelihood
functions that explicitly integrate out all missing data. We use a complete
data likelihood (CDL) to show how a wide range of capture-recapture models can
be easily fitted using readily available software JAGS/BUGS even when there are
individual-specific time-varying covariates. The models we describe extend
those that condition on first capture to include abundance parameters, or
parameters related to abundance, such as population size, birth rates or
lifetime. The use of a CDL means that any missing data, including uncertain
individual covariates, can be included in models without the need for
customized likelihood functions. This approach also facilitates modeling
processes of demographic interest rather than the complexities caused by
non-ignorable missing data. We illustrate using two examples, (i) open
population modeling in the presence of a censored time-varying individual
covariate in a full robust-design, and (ii) full open population multi-state
modeling in the presence of a partially observed categorical variable
Metadata and ontologies for organizing students’ memories and learning: standards and convergence models for context awareness
Este artÃculo trata de las ontologÃas que sirven para la comprensión en contexto y la Gestión de la Información Personal (PIM)y su aplicabilidad al proyecto Memex Metadata(M2). M2 es un proyecto de investigación de la Universidad de Carolina del Norte en Chapel Hill para mejorar la memoria digital de los alumnos utilizando tablet PC, la tecnologÃa SenseCam de Microsoft y otras tecnologÃas móviles(p.ej. un dispositivo de GPS) para capturar el contexto del aprendizaje. Este artÃculo presenta el proyecto M2, dicute el concepto de los portafolios digitales en las actuales tendencias educativas, relacionándolos con las tecnologÃas emergentes, revisa las ontologÃas relevantes y su relación con el proyecto CAF (Context Awareness Framework), y concluye identificando las lÃneas de investigación futuras.This paper focuses on ontologies supporting context awareness and Personal Information Management (PIM) and their
applicability in Memex Metadata (M2) project. M2 is a research project of the University of North Carolina at Chapel Hill to
improve student digital memories using the tablet PC, Microsoft’s SenseCam technology, and other mobile technologies (e.g.,
a GPS device) to capture context. The M2 project offers new opportunities studying students’ learning with digital
technologies. This paper introduces the M2 project; discusses E-portfolios and current educational trends related to pervasive
computing; reviews relevant ontologies and their relationship to the projects’ CAF (context awareness framework), and
concludes by identifying future research directions
Pilot investigation of remote sensing for intertidal oyster mapping in coastal South Carolina: a methods comparison
South Carolina’s oyster reefs are a major component of the coastal landscape. Eastern oysters Crassostrea virginica are an important economic resource to the state and serve many essential functions in the environment, including water filtration, creek bank stabilization and habitat for
other plants and animals. Effective conservation and management of oyster reefs is dependent on an understanding of their abundance, distribution, condition, and change over time. In South Carolina, over 95% of the state’s oyster habitat is intertidal. The current intertidal oyster reef database for South Carolina was developed by field assessment over several years. This database was completed in the early 1980s and is in need of an update to assess resource/habitat status and trends across the state. Anthropogenic factors such as coastal development and
associated waterway usage (e.g., boat wakes) are suspected of significantly altering the extent and health of the state’s oyster resources.
In 2002 the NOAA Coastal Services Center’s (Center) Coastal Remote Sensing Program (CRS) worked with the Marine Resources Division of the South Carolina Department of Natural Resources (SCDNR) to develop methods for mapping intertidal oyster reefs along the South Carolina coast using remote sensing technology. The objective of this project was to provide SCDNR with potential methodologies and approaches for assessing oyster resources in a more
efficiently than could be accomplished through field digitizing. The project focused on the utility of high-resolution aerial imagery and on documenting the effectiveness of various analysis techniques for accomplishing the update. (PDF contains 32 pages
Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation
This paper proposes a new method for rigid body pose estimation based on
spectrahedral representations of the tautological orbitopes of and
. The approach can use dense point cloud data from stereo vision or an
RGB-D sensor (such as the Microsoft Kinect), as well as visual appearance data.
The method is a convex relaxation of the classical pose estimation problem, and
is based on explicit linear matrix inequality (LMI) representations for the
convex hulls of and . Given these representations, the relaxed
pose estimation problem can be framed as a robust least squares problem with
the optimization variable constrained to these convex sets. Although this
formulation is a relaxation of the original problem, numerical experiments
indicate that it is indeed exact - i.e. its solution is a member of or
- in many interesting settings. We additionally show that this method
is guaranteed to be exact for a large class of pose estimation problems.Comment: ICRA 2014 Preprin
Groupware Technology and Software Reuse
Computer-Supported Cooperative Work (CSCW) is a research field concerned with the design and implementation of systems to support cooperative work. Such systems are usually called Groupware. Although Software Reusability (SR) is not commonly mentioned as an issue in the CSCW community, there are some obvious overlaps in design issues and methodologies.\ud
In this paper I will argue that reusability issues are of particular importance to groupware technology and relate our experiences in this matte
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