Extracting constraints from direct detection searches of supersymmetric dark matter in the light of null results from the LHC in the squark sector

The comparison of the results of direct detection of Dark Matter, obtained with various target nuclei, requires model-dependent, or even arbitrary, assumptions. Indeed, to draw conclusions either the spin-dependent (SD) or the spin-independent (SI) interaction has to be neglected. In the light of the null results from supersymmetry searches at the LHC, the squark sector is pushed to high masses. We show that for a squark sector at the TeV scale, the framework used to extract contraints from direct detection searches can be redefined as the number of free parameters is reduced. Moreover, the correlation observed between SI and SD proton cross sections constitutes a key issue for the development of the next generation of Dark Matter detectors.Comment: Figure 3 has been updated. Conclusions unchange

Modelling stochastic bivariate mortality

Stochastic mortality, i.e. modelling death arrival via a jump process with stochastic intensity, is gaining increasing reputation as a way to represent mortality risk. This paper represents a first attempt to model the mortality risk of couples of individuals, according to the stochastic intensity approach. On the theoretical side, we extend to couples the Cox processes set up, i.e. the idea that mortality is driven by a jump process whose intensity is itself a stochastic process, proper of a particular generation within each gender. Dependence between the survival times of the members of a couple is captured by an Archimedean copula. On the calibration side, we fit the joint survival function by calibrating separately the (analytical) copula and the (analytical) margins. First, we select the best fit copula according to the methodology of Wang and Wells (2000) for censored data. Then, we provide a sample-based calibration for the intensity, using a time-homogeneous, non mean-reverting, affine process: this gives the analytical marginal survival functions. Coupling the best fit copula with the calibrated margins we obtain, on a sample generation, a joint survival function which incorporates the stochastic nature of mortality improvements and is far from representing independency.On the contrary, since the best fit copula turns out to be a Nelsen one, dependency is increasing with age and long-term dependence exists

Signals from R-parity violating top quark decays at LHC

We evaluate the potential of the CERN LHC collider to observe rare decays of the top quark in channels involving R-parity violating (RPV) interactions. We stress the importance of calculating top quark production and decay simultaneously as a true 2->4 process. The process of tt-bar pair production followed by RPV decay of one of the top quarks is analyzed with fast detector simulation. We show that intermediate supersymmetric particles can be observed as resonances even if they are heavier than the top quark due to the significant off-shell top-quark mass effects. The approach where the top quark is produced on-mass-shell and then decays into 2- or 3-body final state would in general lead to incorrect kinematical distributions and rates. The rates of the 2 -> 4 process with top quark production and RPV 3-body decay depend on the total width of the heavy intermediate sfermion which could,therefore, be measured indirectly. We find that the LHC collider offers a unique potential to study rare top quark decays in the framework of supersymmetry with broken R-parity for branching fractions of RPV top decays as low as 10^{-6}Comment: 23 pages, 22 figure

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC

A Pure Graph-Based Solution to the SCG-1 Initiative

Abstract. This paper answers the SCG-1 initiative. The room allocation problem provided has been solved in a generic and automatic way. The solution is based on a totally declarative formal model. Basic constructs are simple graphs and the fundamental operation for doing reasonings is the graph morphism known as projection. The other formal constructs are rules and constraints defined in terms of simple graphs. The modeling framework built upon the formal model allows one to describe a problem with asserted facts, rules representing implicit knowledge about the do-main, validity constraints and rules transforming the world. A prototype implementing this framework has been built upon the tool CoGITaNT. It has been used to test our modelization of the room allocation problem.

Requirements modelling and formal analysis using graph operations

The increasing complexity of enterprise systems requires a more advanced analysis of the representation of services expected than is currently possible. Consequently, the specification stage, which could be facilitated by formal verification, becomes very important to the system life-cycle. This paper presents a formal modelling approach, which may be used in order to better represent the reality of the system and to verify the awaited or existing system’s properties, taking into account the environmental characteristics. For that, we firstly propose a formalization process based upon properties specification, and secondly we use Conceptual Graphs operations to develop reasoning mechanisms of verifying requirements statements. The graphic visualization of these reasoning enables us to correctly capture the system specifications by making it easier to determine if desired properties hold. It is applied to the field of Enterprise modelling

Factor copula models for item response data

Factor or conditional independence models based on copulas are proposed for multivariate discrete data such as item responses. The factor copula models have interpretations of latent maxima/minima (in comparison with latent means) and can lead to more probability in the joint upper or lower tail compared with factor models based on the discretized multivariate normal distribution (or multidimensional normal ogive model). Details on maximum likelihood estimation of parameters for the factor copula model are given, as well as analysis of the behavior of the log-likelihood. Our general methodology is illustrated with several item response data sets, and it is shown that there is a substantial improvement on existing models both conceptually and in fit to data

Genetic Diagnostics in Routine Osteological Assessment of Adult Low Bone Mass Disorders

Context Many different inherited and acquired conditions can result in premature bone fragility / low bone mass disorders (LBMD). Objective We aimed at elucidating the impact of genetic testing on differential diagnosis of adult LBMD and at defining clinical criteria for predicting monogenic forms. Methods Four clinical centers broadly recruited a cohort of 394 unrelated adult women before menopause and men younger than 55 years with a bone mineral density (BMD) Z-score 2), and a high normal BMI. In contrast, mutation frequencies did not correlate with age, prevalent vertebral fractures, BMD, or biochemical parameters. In individuals without monogenic disease-causing rare variants, common variants predisposing for low BMD, e.g. in LRP5, were overrepresented. Conclusion The overlapping spectra of monogenic adult LBMD can be easily disentangled by genetic testing and the proposed clinical criteria can help to maximize the diagnostic yield