We find an invariant characterization of planar webs of maximum rank.
For 4-webs, we prove that a planar 4-web is of maximum rank three if and
only if it is linearizable and its curvature vanishes. This result leads to
the direct web-theoretical proof of the Poincar´e’s theorem: a planar 4-
web of maximum rank is linearizable. We also find an invariant intrinsic
characterization of planar 4-webs of rank two and one and prove that in
general such webs are not linearizable. This solves the Blaschke problem
“to find invariant conditions for a planar 4-web to be of rank 1 or 2 or 3”.
Finally, we find invariant characterization of planar 5-webs of maximum
rank and prove than in general such webs are not linearizable.
In this note we discuss some formal properties of universal linearization operator, relate this to brackets of non-linear differential operators and discuss application to the calculus of auxiliary integrals, used in compatibility reductions of PDEs.
Detection of objects embedded in tissue, using visible light, is difficult due to light scattering. The optical properties of the surrounding tissue will influence the spectral characteristics of the light interacting with the object, and the spectral signature observed from the object will be directly affected. A method for calibrating the spectral signature of small objects, embedded in translucent material, by the estimated local background spectrum is presented. The method is evaluated under industrial conditions in a new hyperspectral imaging system for automatic detection of nematodes in cod fillets. The system operates at a conveyor belt speed of 400 mm/s which meets the industrial required speed of assessing one fillet per second. The local calibration method reduces the number of spectra needed to be classified by 89.6%. For one or more false alarms in 60% of the fillets sampled after the trimming station, the Gaussian maximum likelihood classifier detects 70.8% and 60.3% of the dark and pale nematodes, respectively. This is better than what is previously reported using a higher resolution instrument on a slow moving conveyor belt, and comparable or better to what is reported for manual inspection under industrial conditions.
Zortea, Maciel; Skrøvseth, Stein Olav; Schopf, Thomas Roger Griesbeck; Kirchesch, Herbert M.; Godtliebsen, Fred(Journal article; Tidsskriftartikkel; Peer reviewed, 2011)
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Abstract:
Accurate detection of the borders of skin lesions is a vital first step for computer aided diagnostic systems. This paper presents a novel automatic approach to segmentation of skin lesions that is particularly suitable for analysis of dermoscopic images. Assumptions about the image acquisition, in particular, the approximate location and color, are used to derive an automatic rule to select small seed regions, likely to correspond to samples of skin and the lesion of interest. The seed regions are used as initial training samples, and the lesion segmentation problem is treated as binary classification problem. An iterative hybrid classification strategy, based on a weighted combination of estimated posteriors of a linear and quadratic classifier, is used to update both the automatically selected training samples and the segmentation, increasing reliability and final accuracy, especially for those challenging images, where the contrast between the background skin and lesion is low.
The paper proposes an approach to assessment of timescale errors in proxy-based series with chronological uncertainties. The method relies on approximation of the physical process(es) forming a proxy archive by a random Gamma process. Parameters of the process are partly data-driven and partly determined from prior assumptions. For a particular case of a linear accumulation model and absolutely dated tie points an analytical solution is found suggesting the Beta-distributed probability density on age estimates along the length of a proxy archive. In a general situation of uncertainties in the ages of the tie points the proposed method employs MCMC simulations of age-depth profiles yielding empirical confidence intervals on the constructed piecewise linear best guess timescale. It is suggested that the approach can be further extended to a more general case of a time-varying expected accumulation between the tie points. The approach is illustrated by using two ice and two lake/marine sediment cores representing the typical examples of paleoproxy archives with age models based on tie points of mixed origin
If E is an elliptic curve, then the Galois group of the extension generated by the n-torsion points acts on these points. We prove a quadratic reciprocity law involving this group action. This law is an extension of the usual quadratic reciprocity law.
Description:
This is a preprint of an article to
be published in Acta Scientiarum Mathematicarum
We give a simple criterion for the cyclicity of the m-torsion subgroup
of the group of rational points on an elliptic curve defined over a finite field of
characteristic larger than 3 for m = 2, 3, 4, 6, 12.
A stochastic theory for the toppling activity in sandpile models is
developed, based on a simple mean-field assumption about the toppling process. The
theory describes the process as an anti-persistent Gaussian walk, where the diffusion
coefficient is proportional to the activity. It is formulated as a generalization of the Itˆo
stochastic differential equation with an anti-persistent fractional Gaussian noise source.
An essential element of the theory is re-scaling to obtain a proper thermodynamic limit,
and it captures all temporal features of the toppling process obtained by numerical
simulation of the Bak-Tang-Wiesenfeld sandpile in this limit
In this thesis we generalize the Hansen and Seo test in the R package tsDyn, which tests
a linear cointegration model against a two-regime threshold cointegration model, to the
case of three regimes in the alternative hypothesis. As the Lagrange Multiplier
test statistic used in the Hansen and Seo test in tsDyn is different from the LM statistic
described in Hansen and Seo (2002), we generalize both these LM statistics, and show
that they are equal under certain conditions. The grid search algorithm, which is necessary
when maximizing this LM statistic, is also extended to the case of three regimes, and it
is rewritten such that if the cointegration value is given, it really maximizes the LM
statistic under the constraints specified by the user.
In our empirical studies we have examined thoroughly the bivariate time series consisting
of the monthly NIBOR rates of the maturities tomorrow next and 12 months. When
modeling this bivariate time series, we find strong evidence for a two-regime TVECM
being superior to a linear VECM, and in our out-of-sample forecasting the two-regime
SETAR model gives much better prediction of the cointegration relation than an
AR model. When testing a two-regime SETAR model for the cointegration relation
against a three-regime model, the two-regime model cannot be rejected at any reasonable
significance level. In addition, we show how
influential a few outliers may be by removing
them from the time series and rerunning some of the statistical tests. Also, we have
tested all the 66 possible pairs of Norwegian interest rates for cointegration, and we
have tested the term spread of each pair for threshold effects, i.e., testing a linear model
against a two-regime model, as well as testing a two-regime model against a three-regime
model. We find a lot of cointegrated pairs, and we find evidence for a two-regime model
in approximately 50 % of the cases, and evidence for a three-regime model in some cases
in this univariate time series analysis.
At last, we simulate a bivariate time series with a three-regime threshold cointegration
model as data generation process, and estimate a three-regime threshold cointegration
model from this simulated time series. Thus, we illustrate that the thresholds which our
version of the Hansen and Seo test detects as optimal, are close to the original thresholds
used in the simulation. As expected, a linear model for this bivariate time series is
strongly rejected, and there is strong evidence for a three-regime threshold model for the
cointegration relation being superior to both a linear model and a two-regime threshold
model.
This paper develops methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. This enables us to use integrated nested Laplace approximation and to considerably speed up the inferential task. In addition, methods for model comparison and model assessment facilitate the modelling process. The performance of the approach is assessed in a simulation study. To demonstrate the versatility of the approach, models are fitted to two rather different examples, a large rainforest data set with covariates and a point pattern with multiple marks.
We find a two-component generalization of the integrable case of rdDym equation. The reductions of this system include the general rdDym equation, the Boyer-Finley equation, and the deformed Boyer-Finley equation. Also we find a Bäcklund transformation between our generalization and Bodganov's two-component generalization of the universal hierarchy equatio
A Bayesian multiscale technique for the detection of statistically significant features in noisy images is proposed. The prior is defined as a stationary intrinsic Gaussian Markov random field on a toroidal graph, which enables efficient computation of the relevant posterior marginals. Hence the method is applicable to large images produced by modern digital cameras. The technique is demonstrated in two examples from medical imaging. We model digital images as intrinsic Gaussian Markov random fields. This Bayesian scale-space method detects significant gradient and curvature. Efficient computation is achieved by defining images on a toroidal graph. The technique is successfully demonstrated in two examples from medical imaging.
Kernel density estimation and kernel regression are useful ways to visualize and assess the structure of data. Using these techniques we define a temporal scale space as the vector space spanned by bandwidth and a temporal variable. In this space significance regions that reflect a significant derivative in the kernel smooth similar to those of SiZer (Significant Zero-crossings of derivatives) are indicated. Significance regions are established by hypothesis tests for significant gradient at every point in scale space. Causality is imposed onto the space by restricting to kernels with left-bounded or finite support and shifting kernels forward. We show that these adjustments to the methodology enable early detection of changes in time series constituting live surveillance systems of either count data or unevenly sampled measurements. Warning delays are comparable to standard techniques though comparison shows that other techniques may be better suited for single-scale problems. Our method reliably detects change points even with little to no knowledge about the relevant scale of the problem. Hence the technique will be applicable for a large variety of sources without tailoring. Furthermore this technique enables us to obtain a retrospective reliable interval estimate of the time of a change point rather than a point estimate. We apply the technique to disease outbreak detection based on laboratory confirmed cases for pertussis and influenza as well as blood glucose concentration obtained from patients with diabetes type 1.
We establish an efficient compatibility criterion for a system of generalized
complete intersection type in terms of certain multi-brackets of
differential operators. These multi-brackets generalize the higher Jacobi-
Mayer brackets, important in the study of evolutionary equations and the
integrability problem. We also calculate Spencer δ-cohomology of generalized
complete intersections and evaluate the formal functional dimension
of the solutions space. The results are applied to establish new integration
methods and solve several differential-geometric problems.