Multiscale methods for shape constraints in deconvolution: Confidence statements for qualitative features

We derive multiscale statistics for deconvolution in order to detect qualitative features of the unknown density. An important example covered within this framework is to test for local monotonicity on all scales simultaneously. We investigate the moderately ill-posed setting, where the Fourier transform of the error density in the deconvolution model is of polynomial decay. For multiscale testing, we consider a calibration, motivated by the modulus of continuity of Brownian motion. We investigate the performance of our results from both the theoretical and simulation based point of view. A major consequence of our work is that the detection of qualitative features of a density in a deconvolution problem is a doable task, although the minimax rates for pointwise estimation are very slow.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Calibration of cosmogenic noble gas production in ordinary chondrites based on 36 Cl- 36 Ar ages. Part 1: Refined produced rates for cosmogenic 21 Ne and 38 Ar

We measured the concentrations and isotopic compositions of He, Ne, and Ar in bulk samples and metal separates of 14 ordinary chondrite falls with long exposure ages and high metamorphic grades. In addition, we measured concentrations of the cosmogenic radionuclides 10Be, 26Al, and 36Cl in metal separates and in the nonmagnetic fractions of the selected meteorites. Using cosmogenic 36Cl and 36Ar measured in the metal separates, we determined 36Cl-36Ar cosmic-ray exposure (CRE) ages, which are shielding-independent and therefore particularly reliable. Using the cosmogenic noble gases and radionuclides, we are able to decipher the CRE history for the studied objects. Based on the correlation 3He/21Ne versus 22Ne/21Ne, we demonstrate that, among the meteorites studied, only one suffered significant diffusive losses (about 35%). The data confirm that the linear correlation 3He/21Ne versus 22Ne/21Ne breaks down at high shielding. Using 36Cl-36Ar exposure ages and measured noble gas concentrations, we determine 21Ne and 38Ar production rates as a function of 22Ne/21Ne. The new data agree with recent model calculations for the relationship between 21Ne and 38Ar production rates and the 22Ne/21Ne ratio, which does not always provide unique shielding information. Based on the model calculations, we determine a new correlation line for 21Ne and 38Ar production rates as a function of the shielding indicator 22Ne/21Ne for H, L, and LL chondrites with preatmospheric radii less than about 65 cm. We also calculated the 10Be/21Ne and 26Al/21Ne production rate ratios for the investigated samples, which show good agreement with recent model calculations.

Twenty questions with noise: Bayes optimal policies for entropy loss

We consider the problem of twenty questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We formulate this problem as a dynamic program and show that any policy optimizing the one-step expected reduction in entropy is also optimal over the full horizon. Two such Bayes optimal policies are presented: one generalizes the probabilistic bisection policy due to Horstein and the other asks a deterministic set of questions. We study the structural properties of the latter, and illustrate its use in a computer vision application.

Probing the moduli dependence of refined topological amplitudes

With the aim of providing a worldsheet description of the refined topological string, we continue the study of a particular class of higher derivative couplings Fg,n in the type II string effective action compactified on a Calabi–Yau threefold. We analyse first order differential equations in the anti-holomorphic moduli of the theory, which relate the Fg,n to other component couplings. From the point of view of the topological theory, these equations describe the contribution of non-physical states to twisted correlation functions and encode an obstruction for interpreting the Fg,n as the free energy of the refined topological string theory. We investigate possibilities of lifting this obstruction by formulating conditions on the moduli dependence under which the differential equations simplify and take the form of generalised holomorphic anomaly equations. We further test this approach against explicit calculations in the dual heterotic theory.

A refined Pliocene to early Pleistocene chronostratigraphic frame at ODP Hole 653A (West Mediterranean)

Two planktonic foraminiferal oxygen isotope records of ODP Hole 653A (Tyrrhenian Sea) are presented for the time period extending from approximately 0.8 to 3.0 Ma. Six, generally accepted, synchronous bioevents were used to precise the oxygen isotope chronology and to identify the oxygen isotope stages 22 down to 114. Subsequently, this oxygen isotope chronology was used to determine the synchronism or diachronism of various other biostratigraphic events with those recorded in the Singa and Ficarazzi land sections (Italy) and those in other DSDP/ODP sites. New results concern the diachronity of the FOD of the planktonic foraminiferal species N. atlantica, G.truncatulinoides truncatulinoides and G. inflata between ODP Hole 653A and the Italian landsections. Because many species entered the Mediterranean in short term fluxes, strongly related to the southward migration of cool North Atlantic surface waters, their time distribution through the Pliocene-Pleistocene generally corresponds to alternated intervals of presence and absence. This should explain most of the apparently diachronous appearances and disappearances. Alternating presence-absence patterns are of less importance for the various nannofossil events. The LOD of D. surculus occurs during the transition of stage 100 to 101 in both ODP Hole 653A and the Singa section, which is in perfect agreement with the disappearance of this species from the open ocean. The LOD of D. pentaradiatus in the Mediterranean occurs in stages 100-99, which seems to be consistent with the extinction of this species in the southern Hemisphere. G. oceanica, which corresponds to the 4 µm < Gephyrocapsa spp <5.5 µm is recorded in stages 65 to 64 at ODP Hole 653A. The Gephyrocapsa spp. >5.5 µm first occurred in stage 51 at Hole 653A, which fits within the uncertainty interval for this event stretching from stage 51 to 47 in the open ocean and seems therefore a useful tool for conventional biostratigraphy in the Mediterranean.

Energy-Entropy-Momentum integration of discrete thermo-visco-elastic dynamics.

A novel time integration scheme is presented for the numerical solution of the dynamics of discrete systems consisting of point masses and thermo-visco-elastic springs. Even considering fully coupled constitutive laws for the elements, the obtained solutions strictly preserve the two laws of thermo dynamics and the symmetries of the continuum evolution equations. Moreover, the unconditional control over the energy and the entropy growth have the effect of stabilizing the numerical solution, allowing the use of larger time steps than those suitable for comparable implicit algorithms. Proofs for these claims are provided in the article as well as numerical examples that illustrate the performance of the method.

Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features

Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information.

3D Soil Images Structure Quantification using Relative Entropy

Soil voids manifest the cumulative effect of local pedogenic processes and ultimately influence soil behavior - especially as it pertains to aeration and hydrophysical properties. Because of the relatively weak attenuation of X-rays by air, compared with liquids or solids, non-disruptive CT scanning has become a very attractive tool for generating three-dimensional imagery of soil voids. One of the main steps involved in this analysis is the thresholding required to transform the original (greyscale) images into the type of binary representation (e.g., pores in white, solids in black) needed for fractal analysis or simulation with Lattice?Boltzmann models (Baveye et al., 2010). The objective of the current work is to apply an innovative approach to quantifying soil voids and pore networks in original X-ray CT imagery using Relative Entropy (Bird et al., 2006; Tarquis et al., 2008). These will be illustrated using typical imagery representing contrasting soil structures. Particular attention will be given to the need to consider the full 3D context of the CT imagery, as well as scaling issues, in the application and interpretation of this index.

Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance