Psycho-Sociological Review of Criminal Thinking Style

Criminal thinking has been long established as a very important predictor of criminal behaviour, however far less research effort has been undertaken to understand what variables can predict the emergence of criminal thinking. Considering the importance of criminal thinking, we feel it necessary to conduct a systematic review of the literature on criminal thinking in order to bring together what is currently known regarding the factors that relate to, and predict, habitual criminal thinking styles. This paper provides a brief overview of the state of the science on criminal thinking and indicates the need for future research in this context and the areas this future research should focus upon

A fast algorithm for matrix balancing

As long as a square nonnegative matrix A contains sufficient nonzero elements, then the matrix can be balanced, that is we can find a diagonal scaling of A that is doubly stochastic. A number of algorithms have been proposed to achieve the balancing, the most well known of these being Sinkhorn-Knopp. In this paper we derive new algorithms based on inner-outer iteration schemes. We show that Sinkhorn-Knopp belongs to this family, but other members can converge much more quickly. In particular, we show that while stationary iterative methods offer little or no improvement in many cases, a scheme using a preconditioned conjugate gradient method as the inner iteration can give quadratic convergence at low cost

The role of criminal cognitions and personality traits in non-violent recidivism: Empirical investigation within a prison sample

The observation that many offenders re-engage in crime following their initial incarceration, and the effect this crime has on the prison system and society in general, has lead criminologists to investigate the factors that are associated with re-engagement in crime and based on these factors to attempt to estimate the risk that an individual will reoffend. Given the increased attention given to dangerousness in the criminal justice system, much research has focused on the prediction of violent recidivism. Less attention has been given to the study of non-violent recidivism; however, it has been demonstrated that there is no distinction between the variables that are predictive of violent and general recidivism (Bonta, Harman, Hann, and Cormier, 1996; Gendreau, Little, and Goggin, 1996). The purpose of the current study is to investigate the predictors of non-violent recidivism, in particular the role of criminal cognitions and personality factors in non-violent recidivism

How are emergent constraints quantifying uncertainty and what do they leave behind?

The use of emergent constraints to quantify uncertainty for key policy relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become increasingly widespread in recent years. Many researchers, however, claim that emergent constraints are inappropriate or even under-report uncertainty. In this paper we contribute to this discussion by examining the emergent constraints methodology in terms of its underpinning statistical assumptions. We argue that the existing frameworks are based on indefensible assumptions, then show how weakening them leads to a more transparent Bayesian framework wherein hitherto ignored sources of uncertainty, such as how reality might differ from models, can be quantified. We present a guided framework for the quantification of additional uncertainties that is linked to the confidence we can have in the underpinning physical arguments for using linear constraints. We provide a software tool for implementing our general framework for emergent constraints and use it to illustrate the framework on a number of recent emergent constraints for ECS. We find that the robustness of any constraint to additional uncertainties depends strongly on the confidence we can have in the underpinning physics, allowing a future framing of the debate over the validity of a particular constraint around the underlying physical arguments, rather than statistical assumptions

Symmetry-Based Search Space Reduction For Grid Maps

In this paper we explore a symmetry-based search space reduction technique which can speed up optimal pathfinding on undirected uniform-cost grid maps by up to 38 times. Our technique decomposes grid maps into a set of empty rectangles, removing from each rectangle all interior nodes and possibly some from along the perimeter. We then add a series of macro-edges between selected pairs of remaining perimeter nodes to facilitate provably optimal traversal through each rectangle. We also develop a novel online pruning technique to further speed up search. Our algorithm is fast, memory efficient and retains the same optimality and completeness guarantees as searching on an unmodified grid map

Soft hairy warped black hole entropy

We reconsider warped black hole solutions in topologically massive gravity and find novel boundary conditions that allow for soft hairy excitations on the horizon. To compute the associated symmetry algebra we develop a general framework to compute asymptotic symmetries in any Chern-Simons-like theory of gravity. We use this to show that the near horizon symmetry algebra consists of two u(1) current algebras and recover the surprisingly simple entropy formula $S=2\pi (J_0^+ + J_0^-)$, where $J_0^\pm$ are zero mode charges of the current algebras. This provides the first example of a locally non-maximally symmetric configuration exhibiting this entropy law and thus non-trivial evidence for its universality.Comment: 24pp, v2: added appendix C and minor edit