2,558 research outputs found
Scalable Exploration of Complex Objects and Environments Beyond Plain Visual Replication
Digital multimedia content and presentation means are rapidly increasing their sophistication and are now capable of describing detailed representations of the physical world. 3D exploration experiences allow people to appreciate, understand and interact with intrinsically virtual objects.
Communicating information on objects requires the ability to explore them under different angles, as well as to mix highly photorealistic or illustrative presentations of the object themselves with additional data that provides additional insights on these objects, typically represented in the form of annotations. Effectively providing these capabilities requires the solution of important problems in visualization and user interaction.
In this thesis, I studied these problems in the cultural heritage-computing-domain, focusing on the very common and important special case of mostly planar, but visually, geometrically, and semantically rich objects. These could be generally roughly flat objects with a standard frontal viewing direction (e.g., paintings, inscriptions, bas-reliefs), as well as visualizations of fully 3D objects from a particular point of views (e.g., canonical views of buildings or statues). Selecting a precise application domain and a specific presentation mode allowed me to concentrate on the well defined use-case of the exploration of annotated relightable stratigraphic models (in particular, for local and remote museum presentation).
My main results and contributions to the state of the art have been a novel technique for interactively controlling visualization lenses while automatically maintaining good focus-and-context parameters, a novel approach for avoiding clutter in an annotated model and for guiding users towards interesting areas, and a method for structuring audio-visual object annotations into a graph and for using that graph to improve guidance and support storytelling and automated tours.
We demonstrated the effectiveness and potential of our techniques by performing interactive exploration sessions on various screen sizes and types ranging from desktop devices to large-screen displays for a walk-up-and-use museum installation.
KEYWORDS - Computer Graphics, Human-Computer Interaction, Interactive Lenses, Focus-and-Context, Annotated Models, Cultural Heritage Computing
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Simulating substrate binding sites in the S. aureus Type II NADH Dehydrogenase
"Type II NADH Oxidoreductase (NDH-2) from Staphylococcus aureus was established as a therapeutic target against the virulency of this bacterium and an alternative to treat Complex I-derived diseases. To accurately model interactions of NDH-2 with its substrates such as menaquinones and NADH, Coarse-Grain (CG) simulations were employed. "N/
Riemannian statistical techniques with applications in fMRI
Over the past 30 years functional magnetic resonance imaging (fMRI) has become a fundamental
tool in cognitive neuroimaging studies. In particular, the emergence of restingstate
fMRI has gained popularity in determining biomarkers of mental health disorders
(Woodward & Cascio, 2015). Resting-state fMRI can be analysed using the functional
connectivity matrix, an object that encodes the temporal correlation of blood activity
within the brain. Functional connectivity matrices are symmetric positive definite (SPD)
matrices, but common analysis methods either reduce the functional connectivity matrices
to summary statistics or fail to account for the positive definite criteria. However,
through the lens of Riemannian geometry functional connectivity matrices have an intrinsic
non-linear shape that respects the positive definite criteria (the affine-invariant
geometry (Pennec, Fillard, & Ayache, 2006)). With methods from Riemannian geometric
statistics, we can begin to explore the shape of the functional brain to understand this
non-linear structure and reduce data-loss in our analyses.
This thesis o↵ers two novel methodological developments to the field of Riemannian geometric
statistics inspired by methods used in fMRI research. First we propose geometric-
MDMR, a generalisation of multivariate distance matrix regression (MDMR) (McArdle &
Anderson, 2001) to Riemannian manifolds. Our second development is Riemannian partial
least squares (R-PLS), the generalisation of the predictive modelling technique partial least squares (PLS) (H. Wold, 1975) to Riemannian manifolds. R-PLS extends geodesic
regression (Fletcher, 2013) to manifold-valued response and predictor variables, similar to
how PLS extends multiple linear regression. We also generalise the NIPALS algorithm to
Riemannian manifolds and suggest a tangent space approximation as a proposed method
to fit R-PLS.
In addition to our methodological developments, this thesis o↵ers three more contributions
to the literature. Firstly, we develop a novel simulation procedure to simulate
realistic functional connectivity matrices through a combination of bootstrapping and the
Wishart distribution. Second, we propose the R2S
statistic for measuring subspace similarity
using the theory of principal angles between subspaces. Finally, we propose an
extension of the VIP statistic from PLS (S. Wold, Johansson, & Cocchi, 1993) to describe
the relationship between individual predictors and response variables when predicting a
multivariate response with PLS.
All methods in this thesis are applied to two fMRI datasets: the COBRE dataset
relating to schizophrenia, and the ABIDE dataset relating to Autism Spectrum Disorder
(ASD). We show that geometric-MDMR can detect group-based di↵erences between ASD
and neurotypical controls (NTC), unlike its Euclidean counterparts. We also demonstrate
the efficacy of R-PLS through the detection of functional connections related to
schizophrenia and ASD. These results are encouraging for the role of Riemannian geometric
statistics in the future of neuroscientific research.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 202
Magnetic instability and spin-glass order beyond the Anderson-Mott transition in interacting power-law random banded matrix fermions
In the presence of quenched disorder, the competition between local
magnetic-moment formation and Anderson localization for electrons at a
zero-temperature, metal-insulator transition (MIT) remains a long unresolved
problem. Here, we study the interplay of these ingredients in a power-law
random banded matrix model of spin-1/2 fermions with repulsive Hubbard
interactions. Focusing on the regime of weak interactions, we perform both
analytical field theory and numerical self-consistent Hartree-Fock numerical
calculations. We show that interference-mediated effects strongly enhance the
density of states and magnetic fluctuations upon approaching the MIT from the
metallic side. These are consistent with results due to Finkel'stein obtained
four decades ago. Our numerics further show that local moments nucleate from
typical states at the Fermi energy near the MIT, with a density that grows
continuously into the insulating phase. We identify spin-glass order in the
insulator by computing the overlap distribution between converged Hartree-Fock
mean-field moment profiles. Our results indicate that itinerant interference
effects can morph smoothly into moment formation and magnetic frustration
within a single model, revealing a common origin for these disparate phenomena.Comment: 21 pages, 18 figure
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