219,646 research outputs found
Efficient inference about the tail weight in multivariate Student distributions
We propose a new testing procedure about the tail weight parameter of
multivariate Student distributions by having recourse to the Le Cam
methodology. Our test is asymptotically as efficient as the classical
likelihood ratio test, but outperforms the latter by its flexibility and
simplicity: indeed, our approach allows to estimate the location and scatter
nuisance parameters by any root- consistent estimators, hereby avoiding
numerically complex maximum likelihood estimation. The finite-sample properties
of our test are analyzed in a Monte Carlo simulation study, and we apply our
method on a financial data set. We conclude the paper by indicating how to use
this framework for efficient point estimation.Comment: 23 page
Conventional Space-Vector Modulation Techniques versus the Single-Phase Modulator for Multilevel Converters
Space-vector modulation is a well-suited technique
to be applied to multilevel converters and is an important
research focus in the last 25 years. Recently, a single-phase
multilevel modulator has been introduced showing its conceptual
simplicity and its very low computational cost. In this paper,
some of the most conventional multilevel space-vector modulation
techniques have been chosen to compare their results with those
obtained with single-phase multilevel modulators. The obtained
results demonstrate that the single-phase multilevel modulators
applied to each phase are equivalent with the chosen wellknown
multilevel space-vector modulation techniques. In this
way, single-phase multilevel modulators can be applied to a
converter with any number of levels and phases avoiding the
use of conceptually and mathematically complex space-vector
modulation strategies. Analytical calculations and experimental
results are shown validating the proposed concepts
Comparison of different fractal dimension measuring algorithms for RE-TM M-O films
Noise in magneto-optical recording devices is discussed. In general, it appears that either the divider technique or amplitude spectrum technique may be used interchangeably to measure the fractal dimension (D) in the domain wall structure of ideal images. However, some caveats must be observed for best results. The divider technique is attractive for its simplicity and relatively modest computation requirements. However, it is sensitive to noise, in that noise pixels that touch the domain boundary are interpreted as being part of the boundary, skewing the measurement. Also, it is not useful in measuring nucleation-dominated films or domains that have significant amounts of structure within the interior of the domain wall. The amplitude spectrum method is more complex, and less intuitive than the divider method, and somewhat more expensive to implement computationally. However, since the camera noise tends to be white, the noise can be avoided in the measurement of D by avoiding that portion of the curve that is flat (due to the white noise) when the least squares line is fit to the plot. Also, many image processing software packages include a Fast Fourier Transformation (FFT) facility, while the user will most likely have to write his own edge extraction routine for the divider method. The amplitude spectrum method is a true two dimensional technique that probes the interior of the domain wall, and in fact, can measure arbitrary clusters of domains. It can also be used to measure grey-level images, further reducing processing steps needed to threshold the image
Self-regulation of a network of Kuramoto oscillators
Treballs Finals de MĂ ster en FĂsica dels Sistemes Complexos i BiofĂsica, Facultat de FĂsica, Universitat de Barcelona. Curs: 2022-2023. Tutors: Albert DĂaz-Guilera, Jordi Soriano FraderaPersistent global synchronization of a neuronal network is considered a pathological, undesired state. Such as synchronization is often caused by the loss of neurons that regulate network dynamics, or cells that assist these neurons such as glial cells. Here we propose a self-regulation model in the framework of complex networks in which we assume that, for sake of simplicity, glial cells prevent the over synchronization of the neuronal network. We have considered a brain-like network characterized by a modular organization combined with a dynamic description of the nodes as Kuramoto oscillators. We have applied a self-regulation mechanism to keep local synchronization while avoiding global synchronization at the same time. To do so, we have added self-regulation to the system by switching off for a certain period of time a selection of edges that link nodes showing a synchronization above a certain threshold. Despite the simplicity of the approximation, our results
show that it is possible to maintain a high local synchronization (module level) while keeping low the global one. In addition, characteristic dynamic patterns have been observed when analysing synchronization between modules in large modular networks. Our work could help to understand the effects of localized regulatory actions on modular systems with synchronous phenomena, such as neuroscience and other fields
Improving Usability of Interactive Graphics Specification and Implementation with Picking Views and Inverse Transformations
Specifying and programming graphical interactions are difficult tasks,
notably because designers have difficulties to express the dynamics of the
interaction. This paper shows how the MDPC architecture improves the usability
of the specification and the implementation of graphical interaction. The
architecture is based on the use of picking views and inverse transforms from
the graphics to the data. With three examples of graphical interaction, we show
how to express them with the architecture, how to implement them, and how this
improves programming usability. Moreover, we show that it enables implementing
graphical interaction without a scene graph. This kind of code prevents from
errors due to cache consistency management
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