Modeling skull-face anatomical/morphological correspondence for craniofacial superimposition-based identification

Craniofacial superimposition (CFS) is a forensic identification technique which studies the anatomical and morphological correspondence between a skull and a face. It involves the process of overlaying a variable number of facial images with the skull. This technique has great potential since nowadays the wide majority of the people have photographs where their faces are clearly visible. In addition, the skull is a bone that hardly degrades under the effect of fire, humidity, temperature changes, etc. Three consecutive stages for the CFS process have been distinguished: the acquisition and processing of the materials; the skull-face overlay; and the decision making. This final stage consists of determining the degree of support for a match based on the previous overlays. The final decision is guided by different criteria depending on the anatomical relations between the skull and the face. In previous approaches, we proposed a framework for automating this stage at different levels taking into consideration all the information and uncertainty sources involved. In this study, we model new anatomical skull-face regions and we tackle the last level of the hierarchical decision support system. For the first time, we present a complete system which provides a final degree of craniofacial correspondence. Furthermore, we validate our system as an automatic identification tool analyzing its capabilities in closed (known information or a potential list of those involved) and open lists (little or no idea at first who may be involved) and comparing its performance with the manual results achieved by experts, obtaining a remarkable performance. The proposed system has been demonstrated to be valid for sortlisting a given data set of initial candidates (in 62,5% of the cases the positive one is ranked in the first position) and to serve as an exclusion method (97,4% and 96% of true negatives in training and test, respectively)

Neutrino Oscillations and R-parity Violating Supersymmetry

Using the neutrino oscillations and neutrinoless double beta decay experimental data we reconstructed an upper limit for the three generation neutrino mass matrix. We compared this matrix with the predictions of the minimal supersymmetric(SUSY) model with R-parity violation(\rp) and extracted stringent limits on trilinear \rp coupling constants $\lambda_{i33}, \lambda'_{i33}$. Introducing an additional $U(1)_X$ flavor symmetry which had been successful in explaining to relate various \rp parameters. In this model we found a unique scenario for the neutrino masses and the \rp couplings compatible with the neutrino oscillation data. Then we derived predictions for certain experimentally interesting observables.Comment: 19 pages, 1 figure; additional references included, minor corrections and typos fixed. Version to appear in Nucl.Phys.

Gravitational lensing by stars with angular momentum

Gravitational lensing by spinning stars, approximated as homogeneous spheres, is discussed in the weak field limit. Dragging of inertial frames, induced by angular momentum of the deflector, breaks spherical symmetry. I examine how the gravito-magnetic field affects image positions, caustics and critical curves. Distortion in microlensing-induced light curves is also considered.Comment: 9 pages, 9 figures; to appear in MNRA

A Classification Approach for Cancer Survivors from Those Cancer-Free, Based on Health Behaviors:Analysis of the Lifelines Cohort

Simple Summary Health behaviors affect health status in cancer survivors. We aimed to identify such key health behaviors using nonlinear algorithms and compare their classification performance with logistic regression, for distinguishing cancer survivors from those cancer-free in a population-based cohort. We used health behaviors and socioeconomic factors for analysis. Participants from the Lifelines population-based cohort were binary classified as cancer survivors or cancer-free using nonlinear algorithms or logistic regression. Data were collected for 107,624 cancer-free participants and 2760 cancer survivors. Using all variables, algorithms obtained an area under the receiver operator curve (AUC) of 0.75 +/- 0.01. Using only health behaviors, the algorithms differentiated cancer survivors from cancer-free participants with AUCs of 0.62 +/- 0.01 and 0.60 +/- 0.01, respectively. In the case-control analyses, both algorithms produced AUCs of 0.52 +/- 0.01. The main distinctive classifier was age. No key health behaviors were identified by linear and nonlinear algorithms to differentiate cancer survivors from cancer-free participants. Health behaviors affect health status in cancer survivors. We hypothesized that nonlinear algorithms would identify distinct key health behaviors compared to a linear algorithm and better classify cancer survivors. We aimed to use three nonlinear algorithms to identify such key health behaviors and compare their performances with that of a logistic regression for distinguishing cancer survivors from those without cancer in a population-based cohort study. We used six health behaviors and three socioeconomic factors for analysis. Participants from the Lifelines population-based cohort were binary classified into a cancer-survivors group and a cancer-free group using either nonlinear algorithms or logistic regression, and their performances were compared by the area under the curve (AUC). In addition, we performed case-control analyses (matched by age, sex, and education level) to evaluate classification performance only by health behaviors. Data were collected for 107,624 cancer free participants and 2760 cancer survivors. Using all variables resulted an AUC of 0.75 +/- 0.01, using only six health behaviors, the logistic regression and nonlinear algorithms differentiated cancer survivors from cancer-free participants with AUCs of 0.62 +/- 0.01 and 0.60 +/- 0.01, respectively. The main distinctive classifier was age. Though not relevant to classification, the main distinctive health behaviors were body mass index and alcohol consumption. In the case-control analyses, algorithms produced AUCs of 0.52 +/- 0.01. No key health behaviors were identified by linear and nonlinear algorithms to differentiate cancer survivors from cancer-free participants in this population-based cohort

New Mechanism of Flavor Symmetry Breaking from Supersymmetric Strong Dynamics

We present a class of supersymmetric models in which flavor symmetries are broken dynamically, by a set of composite flavon fields. The strong dynamics that is responsible for confinement in the flavor sector also drives flavor symmetry breaking vacuum expectation values, as a consequence of a quantum-deformed moduli space. Yukawa couplings result as a power series in the ratio of the confinement to Planck scale, and the fermion mass hierarchy depends on the differing number of preons in different flavor symmetry-breaking operators. We present viable non-Abelian and Abelian flavor models that incorporate this mechanism.Comment: 24 pp. LaTe

Relativistic MHD and black hole excision: Formulation and initial tests

A new algorithm for solving the general relativistic MHD equations is described in this paper. We design our scheme to incorporate black hole excision with smooth boundaries, and to simplify solving the combined Einstein and MHD equations with AMR. The fluid equations are solved using a finite difference Convex ENO method. Excision is implemented using overlapping grids. Elliptic and hyperbolic divergence cleaning techniques allow for maximum flexibility in choosing coordinate systems, and we compare both methods for a standard problem. Numerical results of standard test problems are presented in two-dimensional flat space using excision, overlapping grids, and elliptic and hyperbolic divergence cleaning.Comment: 22 pages, 8 figure

The Supersymmetric Standard Models with Decay and Stable Dark Matters

We propose two supersymmetric Standard Models (SMs) with decaying and stable dark matter (DM) particles. To explain the SM fermion masses and mixings and have a heavy decay DM particle S, we consider the Froggatt-Nielsen mechanism by introducing an anomalous U(1)_X gauge symmetry. Around the string scale, the U(1)_X gauge symmetry is broken down to a Z_2 symmetry under which S is odd while all the SM particles are even. S obtains a vacuum expectation value around the TeV scale, and then it can three-body decay dominantly to the second/third family of the SM leptons in Model I and to the first family of the SM leptons in Model II. Choosing a benchmark point in the constrained minimal supersymmetric SM with exact R parity, we show that the lightest neutralino DM is consistent with the CDMS II experiment. Considering S three-body decay and choosing suitable parameters, we show that the PAMELA and Fermi-LAT experiments and the PAMELA and ATIC experiments can be explained in Model I and Model II, respectively.Comment: RevTex4, 26 pages, 6 figures, references added, version to appear in EPJ

Relaxed fine-tuning in models with non-universal gaugino masses

We study, in a bottom-up approach, the fine-tuning problem between soft SUSY breaking parameters and the $\mu$-term for the successful electroweak symmetry breaking in the minimal supersymmetric standard model. It is shown that certain nontrivial ratios between gaugino masses, that is non-universal gaugino masses, are necessary at the GUT scale, in order for the fine-tuning to be reduced above 10 % order. In addition, when all the gaugino masses should be regarded as independent ones in their origins, a small gluino mass $M_3 \lesssim 120$ GeV and a non-vanishing $A$-term $A_t \sim O(M_3)$ associated to top squarks are also required at the GUT scale as well as the non-universality. On the other hand, when we consider some UV theory, which fixes ratios of soft SUSY breaking parameters as certain values with the overall magnitude, heavier spectra are allowed. It is favored that the gluino and wino masses are almost degenerate at the weak scale, while wider region of bino mass is favorable.Comment: 17 pages, 29 figure