Explaining young mortality

Stochastic modeling of mortality rates focuses on fitting linear models to logarithmically adjusted mortality data from the middle or late ages. Whilst this modeling enables insurers to project mortality rates and hence price mortality products it does not provide good fit for younger aged mortality. Mortality rates below the early 20's are important to model as they give an insight into estimates of the cohort effect for more recent years of birth. It is also important given the cumulative nature of life expectancy to be able to forecast mortality improvements at all ages. When we attempt to fit existing models to a wider age range, 5-89, rather than 20-89 or 50-89, their weaknesses are revealed as the results are not satisfactory. The linear innovations in existing models are not flexible enough to capture the non-linear profile of mortality rates that we see at the lower ages. In this paper we modify an existing 4 factor model of mortality to enable better fitting to a wider age range, and using data from seven developed countries our empirical results show that the proposed model has a better fit to the actual data, is robust, and has good forecasting ability.

Modelling of the sodium complex of a calixarene tetraester in the 1,3-alternate conformation

This paper describes the use of molecular mechanics to model the geometry of the sodium complex of a calix[4] arene tetraester, in the 1,3-alternate conformation 1. Partial charges were assigned to the calixarene on the basis of semi-empirical (AM1, PM3, MNDO, INDO, CNDO and ZINDO) calculations and the binding of the sodium ion to the calixarene was modelled using molecular mechanics. Agreement between the optimised and X-ray structures of the complex was very good. The effect of placing the cation in different starting positions on the energy-minimised geometry of the complex is described.

QCA Systolic array design

Quantum-dot Cellular Automata (QCA) technology is a promising potential alternative to CMOS technology. To explore the characteristics of QCA and suitable design methodologies, digital circuit design approaches have been investigated. Due to the inherent wire delay in QCA, pipelined architectures appear to be a particularly suitable design technique. Also, because of the pipeline nature of QCA technology, it is not suitable for complicated control system design. Systolic arrays take advantage of pipelining, parallelism and simple local control. Therefore, an investigation into these architectures in QCA technology is provided in this paper. Two case studies, (a matrix multiplier and a Galois Field multiplier) are designed and analyzed based on both multilayer and coplanar crossings. The performance of these two types of interconnections are compared and it is found that even though coplanar crossings are currently more practical, they tend to occupy a larger design area and incur slightly more delay. A general semi-conductor QCA systolic array design methodology is also proposed. It is found that by applying a systolic array structure in QCA design, significant benefits can be achieved particularly with large systolic arrays, even more so than when applied in CMOS-based technology.

Transport properties of protic ionic liquids, pure and in aqueous solutions: Effects of the anion and cation structure

Ionic conductivities of twelve protic ionic liquids (PILs) and their mixtures with water over the whole composition range are reported at 298.15 K and atmospheric pressure. The selected PILs are the pyrrolidinium-based PILs containing nitrate, acetate or formate anions; the formate-based PILs containing diisopropylethylammonium, amilaminium, quinolinium, lutidinium or collidinium cations; and the pyrrolidinium alkylcarboxylates, [Pyrr][CnH2n+1COO] with n = 5–8. This study was performed in order to investigate the influence of molecular structures of the ions on the ionic conductivities in aqueous solutions. The ionic conductivities of the aqueous solutions are 2–30 times higher than the conductivities of pure PILs. The maximum in conductivity varies from ww=0.41???to???0.74 and is related to the nature of cations and anions. The molar conductance and the molar conductance at infinite dilution for (PIL + water) solutions are then determined. Self-diffusion coefficients of the twelve protic ionic liquids in water at infinite dilution and at 298.15 K are calculated by using the Nernst–Haskell, the original and the modified Wilke–Chang equations. These calculations show that similar values are obtained using the modified Wilke–Chang and the Nernst–Haskell equations. Finally, the effective hydrodynamic (or Stokes) radius of the PILs was determined by using the Stokes–Einstein equation. A linear relationship was established in order to predict this radius as a function of the anion alkyl chain length in the case of the pyrrolidinium alkylcarboxylates PILs.

Flexible paleoclimate age-depth models using an autoregressive gamma process

Radiocarbon dating is routinely used in paleoecology to build chronolo- gies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called “Bacon”) that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma autoregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and informative priors are routinely used. Since many sediment cores are currently analyzed, using different data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Outliers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches.

From inconsistency handling to non-canonical requirements management: A logical perspective

As a class of defects in software requirements specification, inconsistency has been widely studied in both requirements engineering and software engineering. It has been increasingly recognized that maintaining consistency alone often results in some other types of non-canonical requirements, including incompleteness of a requirements specification, vague requirements statements, and redundant requirements statements. It is therefore desirable for inconsistency handling to take into account the related non-canonical requirements in requirements engineering. To address this issue, we propose an intuitive generalization of logical techniques for handling inconsistency to those that are suitable for managing non-canonical requirements, which deals with incompleteness and redundancy, in addition to inconsistency. We first argue that measuring non-canonical requirements plays a crucial role in handling them effectively. We then present a measure-driven logic framework for managing non-canonical requirements. The framework consists of five main parts, identifying non-canonical requirements, measuring them, generating candidate proposals for handling them, choosing commonly acceptable proposals, and revising them according to the chosen proposals. This generalization can be considered as an attempt to handle non-canonical requirements along with logic-based inconsistency handling in requirements engineering.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.

Approximating the Set of Local Minima in Partial RNA Folding Landscapes

Motivation: We study a stochastic method for approximating the set of local minima in partial RNA folding landscapes associated with a bounded-distance neighbourhood of folding conformations. The conformations are limited to RNA secondary structures without pseudoknots. The method aims at exploring partial energy landscapes pL induced by folding simulations and their underlying neighbourhood relations. It combines an approximation of the number of local optima devised by Garnier and Kallel (2002) with a run-time estimation for identifying sets of local optima established by Reeves and Eremeev (2004).

Results: The method is tested on nine sequences of length between 50 nt and 400 nt, which allows us to compare the results with data generated by RNAsubopt and subsequent barrier tree calculations. On the nine sequences, the method captures on average 92% of local minima with settings designed for a target of 95%. The run-time of the heuristic can be estimated by O(n2D?ln?), where n is the sequence length, ? is the number of local minima in the partial landscape pL under consideration and D is the maximum number of steepest descent steps in attraction basins associated with pL.

Mixed radix Reed-Muller expansions

The choice of radix is crucial for multi-valued logic synthesis. Practical examples, however, reveal that it is not always possible to find the optimal radix when taking into consideration actual physical parameters of multi-valued operations. In other words, each radix has its advantages and disadvantages. Our proposal is to synthesise logic in different radices, so it may benefit from their combination. The theory presented in this paper is based on Reed-Muller expansions over Galois field arithmetic. The work aims to firstly estimate the potential of the new approach and to secondly analyse its impact on circuit parameters down to the level of physical gates. The presented theory has been applied to real-life examples focusing on cryptographic circuits where Galois Fields find frequent application. The benchmark results show the approach creates a new dimension for the trade-off between circuit parameters and provides information on how the implemented functions are related to different radices.