Redemption from the Inside-Out: The Power of Faith-Based Programming

Prisons are tense, cheerless, and often degrading places in which all inmates struggle to maintain their equilibrium despite violence, exploitation, lack of privacy, stringent limitations on family and community contacts, and a paucity of opportunities for meaningful education, work, or other productive activities. As a general matter, prisoners come to see prison as their home and try to make the most of the limited resources available in prison; they establish daily routines that allow them to find meaning and purpose in their prison lives, lives that might otherwise seem empty and hopeless. The resilience shown by prisoners should not be construed as an argument for more or longer prison sentences or for more punitive regimes of confinement, but rather is a reminder that human beings can find meaning in adversity. Prisons are meant to be settings of adversity but should strive to accommodate the human needs of their inhabitants and to promote constructive changes in behavior. Here, there are programmatic offerings that may provide prisoners with the hope, skills, and empowerment necessary to overcome barriers to achievement and success as human beings in any social context. A current line of inquiry has focused on faith based prison programs and the potential benefits that a deepened spiritual life might have on coping with the doing time experience, changing old lifestyles, and reducing the likelihood of people returning to prison. These points will be explored throughout this chapter

EXTREME PROGRAMMING AND RATIONAL UNIFIED PROCESS – CONTRASTS OR SYNONYMS?

The agile movement has received much attention in software engineering recently. Established methodologies try to surf on the wave and present their methodologies a being agile, among those Rational Unified Process (RUP). In order to evaluate the statements we evaluate the RUP against eXtreme Programming (XP) to find out to what extent they are similar end where they are different. We use a qualitative approach, utilizing a framework for comparison. RUP is a top-down solution and XP is a bottom-up approach. Which of the two is really best in different situations has to be investigated in new empirical studies.extreme programming

Dynamic Financial Analysis - Understanding Risk and Value Creation in Insurance

The changing business environment in non-life insurance and reinsurance has raised the need for new quantitative methods to analyze the impact of various types of strategic decisions on a company’s bottom line. Dynamic Financial Analysis («DFA») has become popular among practitioners as a means of addressing these new requirements. It is a systematic approach based on large-scale computer simulations for the integrated financial modeling of non-life insurance and reinsurance companies aimed at assessing the risks and the benefits associated with strategic decisions. DFA allows decision makers to understand and quantify the impact and interplay of the various risks that their company is exposed to, and – ultimately – to make better informed strategic decisions. In this brochure, we provide an overview and assessment of the state of the industry related to DFA. We investigate the DFA value proposition, we explain its elements and we explore its potential and limitations.reinsurance, dynamic financial analysis, insurance

Implementation of new regulatory rules in a multistage ALM model for Dutch pension funds

This paper discusses the implementation of new regulatory rules in a multistage recourse ALM model for Dutch pension funds. The new regulatory rules, which are called the ?Financieel Toetsingskader?, are effective as of January 2007 and have deep impact on the issues of valuation of liabilities, solvency, contribution rate, and indexation. Multistage recourse models have proved to be valuable for pension fund ALM. The ability to include the new regulatory rules would increase the practical value of these models.

Community detection and stochastic block models: recent developments

The stochastic block model (SBM) is a random graph model with planted clusters. It is widely employed as a canonical model to study clustering and community detection, and provides generally a fertile ground to study the statistical and computational tradeoffs that arise in network and data sciences. This note surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational thresholds, and for various recovery requirements such as exact, partial and weak recovery (a.k.a., detection). The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial recovery, the learning of the SBM parameters and the gap between information-theoretic and computational thresholds. The note also covers some of the algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, linearized belief propagation, classical and nonbacktracking spectral methods. A few open problems are also discussed