The Design of Private Reinsurance Contracts

This paper examines the role of reinsurance relationships in the trading of underwriting risk when this trade takes place in an environment that is characterized by asymmetric information and in which information is revealed only over time. It begins by explaining how information problems affect the efficiency of the allocation of risk between insurer and reinsurer, and how long-term implicit contracts between insurers and reinsurers allow the inclusion of new information in the pricing of both future and past reinsurance coverage. Because of these features, the ceding company purchases a more efficient quantity of reinsurance. Specifically, such arrangements lead to more reinsurance coverage, higher insurer profits, and lower expected distress in the industry. It is, in short, Pareto improving.

Raman spectra of misoriented bilayer graphene

We compare the main feature of the measured Raman scattering spectra from single layer graphene with a bilayer in which the two layers are arbitrarily misoriented. The profiles of the 2D bands are very similar having only one component, contrary to the four found for commensurate Bernal bilayers. These results agree with recent theoretical calculations and point to the similarity of the electronic structures of single layer graphene and misoriented bilayer graphene. Another new aspect is that the dependance of the 2D frequency on the laser excitation energy is different in these two latter systems

Sparsity and adaptivity for the blind separation of partially correlated sources

Blind source separation (BSS) is a very popular technique to analyze multichannel data. In this context, the data are modeled as the linear combination of sources to be retrieved. For that purpose, standard BSS methods all rely on some discrimination principle, whether it is statistical independence or morphological diversity, to distinguish between the sources. However, dealing with real-world data reveals that such assumptions are rarely valid in practice: the signals of interest are more likely partially correlated, which generally hampers the performances of standard BSS methods. In this article, we introduce a novel sparsity-enforcing BSS method coined Adaptive Morphological Component Analysis (AMCA), which is designed to retrieve sparse and partially correlated sources. More precisely, it makes profit of an adaptive re-weighting scheme to favor/penalize samples based on their level of correlation. Extensive numerical experiments have been carried out which show that the proposed method is robust to the partial correlation of sources while standard BSS techniques fail. The AMCA algorithm is evaluated in the field of astrophysics for the separation of physical components from microwave data.Comment: submitted to IEEE Transactions on signal processin

Sparse and Non-Negative BSS for Noisy Data

Non-negative blind source separation (BSS) has raised interest in various fields of research, as testified by the wide literature on the topic of non-negative matrix factorization (NMF). In this context, it is fundamental that the sources to be estimated present some diversity in order to be efficiently retrieved. Sparsity is known to enhance such contrast between the sources while producing very robust approaches, especially to noise. In this paper we introduce a new algorithm in order to tackle the blind separation of non-negative sparse sources from noisy measurements. We first show that sparsity and non-negativity constraints have to be carefully applied on the sought-after solution. In fact, improperly constrained solutions are unlikely to be stable and are therefore sub-optimal. The proposed algorithm, named nGMCA (non-negative Generalized Morphological Component Analysis), makes use of proximal calculus techniques to provide properly constrained solutions. The performance of nGMCA compared to other state-of-the-art algorithms is demonstrated by numerical experiments encompassing a wide variety of settings, with negligible parameter tuning. In particular, nGMCA is shown to provide robustness to noise and performs well on synthetic mixtures of real NMR spectra.Comment: 13 pages, 18 figures, to be published in IEEE Transactions on Signal Processin

Alternative definition of excitation amplitudes in Multi-Reference state-specific Coupled Cluster

A central difficulty of state-specific Multi-Reference Coupled Cluster (MR-CC) formalisms concerns the definition of the amplitudes of the single and double excitation operators appearing in the exponential wave operator. If the reference space is a complete active space (CAS) the number of these amplitudes is larger than the number of singly and doubly excited determinants on which one may project the eigenequation, and one must impose additional conditions. The present work first defines a state-specific reference-independent operator $\hat{\tilde{T}}^m$ which acting on the CAS component of the wave function $|\Psi_0^m \rangle$ maximizes the overlap between $(1+\hat{\tilde{T}}^m)|\Psi_0^m \rangle$ and the eigenvector of the CAS-SD CI matrix $|\Psi_{\rm CAS-SD}^m \rangle$. This operator may be used to generate approximate coefficients of the Triples and Quadruples, and a dressing of the CAS-SD CI matrix, according to the intermediate Hamiltonian formalism. The process may be iterated to convergence. As a refinement towards a strict Coupled Cluster formalism, one may exploit reference-independent amplitudes provided by $(1+\hat{\tilde{T}}^m)|\Psi_0^m \rangle$ to define a reference-dependent operator $\hat{T}^m$ by fitting the eigenvector of the (dressed) CAS-SD CI matrix. The two variants, which are internally uncontracted, give rather similar results. The new MR-CC version has been tested on the ground state potential energy curves of 6 molecules (up to triple-bond breaking) and a two excited states. The non-parallelism error with respect to the Full-CI curves is of the order of 1 m$E_{\rm h}$.Comment: 11 page

Effect of tart cherry juice on risk of gout attacks: protocol for a randomised controlled trial

Introduction: Gout is a painful form of inflammatory arthritis associated with several comorbidities, particularly cardiovascular disease. Cherries, which are rich in anti-inflammatory and antioxidative bioactive compounds, are proposed to be efficacious in preventing and treating gout, but recommendations to patients are conflicting. Cherry consumption has been demonstrated to lower serum urate levels and inflammation in several small studies. One observational case cross-over study reported that cherry consumption was associated with reduced risk of recurrent gout attacks. This preliminary evidence requires substantiation. The proposed randomised clinical trial aims to test the effect of consumption of tart cherry juice on risk of gout attacks. Methods and analysis: This 12-month, parallel, double-blind, randomised, placebo-controlled trial will recruit 120 individuals (aged 18–80 years) with a clinical diagnosis of gout who have self-reported a gout flare in the previous year. Participants will be randomly assigned to an intervention group, which will receive Montmorency tart cherry juice daily for a 12-month period, or a corresponding placebo group, which will receive a cherry-flavoured placebo drink. The primary study outcome is change in frequency of self-reported gout attacks. Secondary outcome measures include attack intensity, serum urate concentration, fractional excretion of uric acid, biomarkers of inflammation, blood lipids and other markers of cardiovascular risk. Other secondary outcome measures will be changes in physical activity and functional status. Statistical analysis will be conducted on an intention-to-treat basis. Ethics and dissemination: This study has been granted ethical approval by the National Research Ethics Service, Yorkshire and The Humber—Leeds West Research Ethics Committee (ref: 18/SW/0262). Results of the trial will be submitted for publication in a peer-reviewed journal. Trial registration number: NCT03621215