Penal Strategies Research Articles

Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design

The adoption of high-fidelity models in multidisciplinary design optimization (MDO) permits to enhance the identification of superior design configurations, but would prohibitively rise the demand for computational resources and time. Multifidelity Bayesian Optimization (MFBO) efficiently combines information from multiple models at different levels of fidelity to accelerate the MDO procedure. State-of-the-art MFBO methods currently meet two major limitations: (i) the sequential adaptive sampling precludes parallel computations of high-fidelity models, and (ii) the search scheme measures the utility of new design evaluations only at the immediate next iteration. This paper proposes a Non-Myopic Multipoint Multifidelity Bayesian Optimization (NM3-BO) algorithm to sensitively accelerate MDO overcoming the limitations of standard methods. NM3-BO selects a batch of promising design configurations to be evaluated in parallel, and quantifies the expected long-term improvement of these designs at future steps of the optimization. Our learning scheme leverages an original acquisition function based on the combination of a two-step lookahead policy and a local penalization strategy to measure the future utility achieved evaluating multiple design configurations simultaneously. We observe that the proposed framework permits to sensitively accelerate the MDO of a space vehicle and outperforms popular algorithms.

Efficient micromagnetic–elastic simulations based on a perturbed Lagrangian function

Micromagnetic simulations require the numerically challenging preservation of the Euclidean norm during the whole simulation. This can be accomplished by applying a priori length preserving methods, renormalization algorithms, or penalization strategies. The latter one includes both the penalty method and the Lagrangian multiplier. The penalty method requires the definition of a penalty parameter during the initiation of the simulation which, depending on its size, can lead to an unsatisfied constraint or stiff and difficult to solve systems of equations. The Lagrange multiplier always penalizes in problem-dependent intensity, hence, an additional degree of freedom is added to the system of equations to the drawback of higher computational costs. This paper proposes a method that utilizes a perturbed Lagrangian multiplier and an element level static condensation to condensate the additional degree of freedom. This guarantees fast simulations, and no parameter fitting in advance. Suitable numerical examples are conducted to prove the workability of the outlined scheme and to highlight the efficiency compared to the non-condensed formulation.

Criminologists’ Perspective of Factors Contributing to Unreported Narcotics Crimes

Efforts to address and eliminate narcotics crimes heavily depend on community involvement, particularly in reporting such offenses, yet many hesitate due to various reasons. This research explores factors that hinder individuals from reporting narcotics crimes and seeks effective strategies to combat this issue, employing both normative juridical and empirical juridical approaches, including literature reviews and field studies, and analyzing data qualitatively. An example from Decision Number: 522/Pid.Sus/2022/PN Tjk, where defendant Ali Amarsyah Bin Misran was sentenced to eight months for a narcotics offense, highlights the legal consequences of such crimes. The research identifies key factors discouraging reports: the inconvenience of legal processes, fear of retaliation from criminals, and a lack of awareness about legal protections for informants. To counteract these issues, the research recommends non-penal measures such as public education on legal rights and protections, alongside penal strategies that involve thorough inquiries and investigations to collect evidence and identify suspects in unreported narcotics activities. This dual approach emphasizes a holistic strategy to boost public cooperation with law enforcement and enhance the effectiveness of narcotics crime prevention and prosecution.

Economic topology optimization of District Heating Networks using a pipe penalization approach

In the presented study, a pipe penalization approach for the economic topology optimization of District Heating Networks is proposed, drawing inspiration from density-based topology optimization. For District Heating Networks, the upfront investment is a crucial factor for the rollout of this technology. Today, the pipe routing is usually designed relying on a linearization of the underlying heat transport problem. This study proposes to solve the optimal pipe routing problem as a non-linear topology optimization problem, drawing inspiration from density-based topology optimization. The optimization problem is formulated around a non-linear heat transport model and minimizes a detailed net present value representation of the heating network cost. By relaxing the combinatorial problem of pipe placement, this approach remains scalable for large-scale applications. In a design study on a realistic medium-sized network with 160 houses, a strong influence of economic parameters on the optimal network topology was observed. For this case, the optimization algorithm converges to a discrete network topology and near-discrete pipe design in about 10 min by using the proposed intermediate pipe penalization strategy. The optimal discrete network design found by the algorithm showed to outperform simple rounding post-processing steps by up to 2.8% of their respective net present value.

Penal Welfare or Penal Sovereignty? A Political Sociology of Recent Formalization of Chinese Community Corrections

In this study, we address two observed gaps in existing accounts on Chinese community corrections (hereafter CCC): 1) lack of multilevel understanding of this penal institution’s local variations in a highly centralized penal regime; 2) inadequate scrutiny of political logics of, and the authoritarian state’s significance in, its recent formal introduction. Those limits may inhibit adequate understandings of state power and punishment in an authoritarian polity like China. To that end, we argue for a multilayered and hybrid conceptualization of CCC as an assemblage of penal welfare and penal sovereignty to understand CCC’s formation and function. Fracturing the holistic entity of CCC, our study challenges the approach to viewing it as a system of singular logics and unifying structure, and contrasts three modes of operational practices across localities—bureaucratic, professionalization, and technology-dominant models. Moreover, our analysis of its political functions suggests that in effect penal sovereignty subjugates penal welfare within contemporary Chinese penality. Far from heralding the full-fledged rise of Chinese penal welfare, this legal formalization represents a space created for the authoritarian state to penetrate political ideologies, and to reclaim, consolidate and exercise sovereign power through managerial penal strategies in a rapidly developing and differentiating society.

Uncertainty quantification for robust variable selection and multiple testing

We study the problem of identifying the set of active variables, termed in the literature as variable selection or multiple hypothesis testing, depending on the pursued criteria. For a general robust setting of non-normal, possibly dependent observations and a generalized notion of active set, we propose a procedure that is used simultaneously for the both tasks, variable selection and multiple testing. The procedure is based on the risk hull minimization method, but can also be obtained as a result of an empirical Bayes approach or a penalization strategy. We address its quality via various criteria: the Hamming risk, FDR, FPR, FWER, NDR, FNR, and various multiple testing risks, e.g., MTR=FDR+NDR; and discuss a weak optimality of our results. Finally, we introduce and study, for the first time, the uncertainty quantification problem in the variable selection and multiple testing context in our robust setting.

High-dimensional sufficient dimension reduction through principal projections

We develop in this work a new dimension reduction method for high-dimensional settings. The proposed procedure is based on a principal support vector machine framework where principal projections are used in order to overcome the non-invertibility of the covariance matrix. Using a series of equivalences we show that one can accurately recover the central subspace using a projection on a lower dimensional subspace and then applying an ℓ1 penalization strategy to obtain sparse estimators of the sufficient directions. Based next on a desparsified estimator, we provide an inferential procedure for high-dimensional models that allows testing for the importance of variables in determining the sufficient direction. Theoretical properties of the methodology are illustrated and computational advantages are demonstrated with simulated and real data experiments.

Dual-Weighted Residual A Posteriori Error Estimates for a Penalized Phase-Field Slit Discontinuity Problem

Abstract In this work, goal-oriented adjoint-based a posteriori error estimates are derived for a nonlinear phase-field discontinuity problem in which a scalar-valued displacement field interacts with a scalar-valued smoothed indicator function. The latter is subject to an irreversibility constraint, which is regularized using a simple penalization strategy. The main advancements in the current work are error identities, resulting estimators, and two-sided estimates employing the dual-weighted residual method, which address the influence of the phase-field regularization, penalization, and spatial discretization parameters. Some numerical tests accompany our derived estimates.

Staying with the Social Project: A Review of Feminist Criminology

Review of the Field In this issue, Social & Legal Studies is pleased to publish the third of an occasional feature: Review of the Field. Our ambition in this series is to publish articles which reflect upon fields of study and which offer a critical appraisal of the key literature and concepts. The aim is to provide not only a valuable map of the scholarly terrain but, equally, we hope that the format will give authors the opportunity to set a direction of travel for their discipline. Thus, we anticipate that reviews will ask new research questions, identify gaps in the scholarship and explore connections and discontinuities between diverse bodies of knowledge. Suggestions for future reviews are welcome and should be addressed to members of the Editorial Board. We are pleased to publish this Review of Feminist Criminology by Katharine Dunbar Winsor of Concordia University. We hope that our readers agree with us that the article provides an important addition to the literature and provides an invaluable template for contributors of future reviews. Editorial Board Social & Legal Studies The emergence of the feminist movement in the 1960s and 1970s became a primary influence of the field of feminist criminology. Feminist criminology has evolved over the past several decades and has remained impacted by and in dialogue with feminist thought and perspectives. Within the field, researchers have focused on producing and circulating women-centred knowledge. Despite this, tensions within the field highlight diverging approaches to what and who is studied. In Canada, the maturation of feminist criminology as a field has coincided with significant changes to women’s penology. In this essay, the development and changes to feminist criminology are mapped through an examination of key events and changes in Canada’s penal strategies for women. What emerges is the argument that feminist criminology must understand itself beyond narrow and discrete terms and instead must work with the tensions and debates of the field to keep women’s voices centred and the feminist social project alive.

On the aggregation of published prognostic scores for causal inference in observational studies.

As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has recently been proposed as a method for causal inference. It aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference exposure (“control”). This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Unfortunately, large cohorts for developing prognostic models are not always available. Prognostic models are therefore subject to a dilemma between overfitting and parsimony; the latter being prone to a violation of the assumption of no unmeasured confounders when important covariates are ignored. Although it is possible to limit overfitting by using penalization strategies, an alternative approach is to adopt evidence synthesis. Aggregating previously published prognostic models may improve the generalizability of PGS, while taking account of a large set of covariates—even when limited individual participant data are available. In this article, we extend a method for prediction model aggregation to PGS analysis in nonrandomized studies. We conduct extensive simulations to assess the validity of model aggregation, compared with other methods of PGS analysis for estimating marginal treatment effects. We show that aggregating existing PGS into a “meta‐score” is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes. We illustrate our methods in a setting of treatments for asthma.

An iterative staggered scheme for phase field brittle fracture propagation with stabilizing parameters

This paper concerns the analysis and implementation of a novel iterative staggered scheme for quasi-static brittle fracture propagation models, where the fracture evolution is tracked by a phase field variable. The model we consider is a two-field variational inequality system, with the phase field function and the elastic displacements of the solid material as independent variables. Using a penalization strategy, this variational inequality system is transformed into a variational equality system, which is the formulation we take as the starting point for our algorithmic developments. The proposed scheme involves a partitioning of this model into two subproblems; phase field and mechanics, with added stabilization terms to both subproblems for improved efficiency and robustness. We analyze the convergence of the proposed scheme using a fixed point argument, and find that under a natural condition, the elastic mechanical energy remains bounded, and, if the diffusive zone around crack surfaces is sufficiently thick, monotonic convergence is achieved. Finally, the proposed scheme is validated numerically with several bench-mark problems.

Marginal gains or diminishing returns? Penal bifurcation, policy change and the administration of prisoner release in England and Wales

Prisoner release has emerged as a key site of penal policy contestation in England and Wales. A series of crises have undermined public confidence in the parole system and reopened longstanding debates over the confused normative basis of prisoner release policy and practice. This article attempts to locate current concerns within an ideational interpretation of penal policy change. It will argue that prisoner release has been fundamentally re-shaped by a bifurcated penal strategy that emerged as one possible response to the unique challenges of late-modern crime-control. Over time this strategy has provided an enduring guide to collective action and a political template for successive penal reform programmes. However, there are signs that we may now be reaching the conceptual limits of this strategy. While the logic(s) of bifurcation will continue to yield marginal political gains in the short-term, this article concludes that the long-term prognosis is one of diminishing returns with significant implications for the legitimacy, effectiveness and administrative coherence of prisoner release in this jurisdiction.

The Optimal Deterrence of Crime: A Focus on the Time Preference of DWI Offenders

We develop a general model for finding the optimal penal strategy based on the behavioral traits of the offenders. We focus on how the discount rate (level of time discounting) affects criminal propensity on the individual level, and how the aggregation of these effects influences criminal activities on the population level. The effects are aggregated based on the distribution of discount rate among the population. We study this distribution empirically through a survey with 207 participants, and we show that it follows zero-inflated exponential distribution. We quantify the effectiveness of the penal strategy as its net utility for the population, and show how this quantity can be maximized. When we apply the maximization procedure on the offense of impaired driving (DWI), we discover that the effectiveness of DWI deterrence depends critically on the amount of fine and prison condition.

Penal welfarism og norsk sikkerhetspsykiatri, 1895-1940

AbstractThis article presents the early decades of Norwegian forensic psychiatry as a basis for exploring David Garland’s term «penal welfarism». While Garland focuses primarily upon penalties and prisons, I find it relevant to look at a type of sanction not officially defined as punishment according to Norwegian law. Insanity has provided exemption from criminal punishment in Norway since 1842. Yet criminals considered dangerous to themselves or others have been housed in criminal asylums since 1895, which is the same year Garland argues that a transformation in penal strategies occurred in Britain (Garland, 1985).

A one-sided procedure for monitoring variables defined on contingency tables

Nowadays the use of the multivariate statistical process control (MSPC) toolbox is efficiently generalized beyond assuring product quality through the monitoring of industrial processes in order to be used in many other non industrial fields (e.g., public health, environmental, financial monitoring, etc.). Data produced by non industrial processes usually require the development of problem-oriented monitoring procedures. In this article we develop a method for monitoring bivariate random variables defined on contingency tables and introduce an appropriate one-sided control procedure, motivated by a problem from double reading used in many medical processes. Specifically, we propose a procedure for monitoring simultaneously the measure of agreement between Cohen’s kappa defined on a contingency table associated with the process stability and one percentage associated with the process quality level, defined on the same contingency table. The procedure is based on an appropriate approximation that is assessed numerically and shows an excellent performance. Then we explore the performance of several candidate one-sided techniques for monitoring the process and we propose a new one that is based on a penalization strategy that appears to have the best performance. The new technique is very easy to implement by a non statistician, as illustrated by its application to a real case from double reading.

Robust Visual Tracking via Dirac-Weighted Cascading Correlation Filters

Correlation filter-based trackers (CFTs) have recently raised considerable attention in visual tracking and achieved competitive performance. Nevertheless, conventional structures of such CFTs build a shallow architecture with a single correlation filter, which cannot comprehensively depict the target appearance. Hence, these trackers lack strong discriminative ability and easily drift when the target suffers drastic appearance variations. To address the limitations, we propose Dirac-weighted cascading correlation filters (DWCCF) for visual tracking. It incorporates cascading characteristics of multiple filters to construct the target appearance model, and dynamically learns Dirac weights for each filter, which is accordingly robust to appearance variations. Besides, we design a boundary penalization strategy to adaptively reduce the boundary effects, which efficiently improves the detection precision for tracking. Qualitative and quantitative evaluations on OTB-2013 and OTB-2015 datasets demonstrate that the proposed DWCCF significantly outperforms other state-of-the-art methods.

Punishment and Welfare revisited

First published in 1985, Punishment and Welfare: A History of Penal Strategies develops a number of themes that continue to animate the research agenda of today’s sociology of punishment. This essa...

Punishing tax offenders in France and Great Britain: two criminal policies

PurposeSince 2008-2009, the governments in France and Great Britain have encouraged more rigorous penalization of tax evaders. This paper aims to investigate the implementation of these policies on the basis of an important and original empirical material.Design/methodology/approachThe study done in France relies on interviews conducted with representatives of law enforcement agencies on public statistics and on an innovative database compiled from nearly 600 cases submitted to the judiciary. The comparison with Great Britain is developed through interviews conducted with different participants in the fight against tax fraud and statistical information.FindingsThis paper describes the recent evolution of the machinery for screening tax-related wrongdoings in France and in the UK. It demonstrates that whilst publicly calling for harsh punishment against tax dodgers, in practice, both governments tend to seek a balance between the growing demand for tax equality and the belief that the State should not intervene in the economic realm. This strategy leads to the over-representation of certain categories of taxpayers. Despite the commonalities resulting from the numerous filters before prosecution, the penal strategy takes on two different shapes on either side of the Channel: whereas the British institutions support an “exemplary punitive” system, French regulatory system favours a “quasi-administrative” treatment. The French tax authority continues to use the criminal procedures mainly as a financial instrument for the improved restitution of stolen taxes. The policy of Her Majesty’s Revenue and Customs, supported by the “Sentencing Guidelines”, aims much more at obtaining exemplary convictions.Originality/valueBased on a large empirical material, this paper highlights the different outcomes of the criminal trials against tax evaders in the two countries.

Firth's logistic regression with rare events: accurate effect estimates and predictions?

Firth's logistic regression has become a standard approach for the analysis of binary outcomes with small samples. Whereas it reduces the bias in maximum likelihood estimates of coefficients, bias towards one-half is introduced in the predicted probabilities. The stronger the imbalance of the outcome, the more severe is the bias in the predicted probabilities. We propose two simple modifications of Firth's logistic regression resulting in unbiased predicted probabilities. The first corrects the predicted probabilities by a post hoc adjustment of the intercept. The other is based on an alternative formulation of Firth's penalization as an iterative data augmentation procedure. Our suggested modification consists in introducing an indicator variable that distinguishes between original and pseudo-observations in the augmented data. In a comprehensive simulation study, these approaches are compared with other attempts to improve predictions based on Firth's penalization and to other published penalization strategies intended for routine use. For instance, we consider a recently suggested compromise between maximum likelihood and Firth's logistic regression. Simulation results are scrutinized with regard to prediction and effect estimation. We find that both our suggested methods do not only give unbiased predicted probabilities but also improve the accuracy conditional on explanatory variables compared with Firth's penalization. While one method results in effect estimates identical to those of Firth's penalization, the other introduces some bias, but this is compensated by a decrease in the mean squared error. Finally, all methods considered are illustrated and compared for a study on arterial closure devices in minimally invasive cardiac surgery. Copyright © 2017 John Wiley & Sons, Ltd.

Modified Impact Factors

New versions of JCR IF are proposed for comparing them with JCR IF. They focus on the journal self-citation and the number of citing sources. The proposed versions are grouped into: 1) IFs penalized by self-citations, 3) IF encouraged by the number of citing sources and 3) IFs combining the penalized IFs and encouraged IF. This study evaluates the impact of journal self-citations and distribution of citations among sources on JCR IF. The study indicates that selfcitationshave little impact on the values of IF and the IF rankings, whether or not journal self-citations areincluded. The proposed indicators have been evaluated for the 30 journals in Computer Science field indexed in JCR 2013. The Spearman’s ρ correlation between the JCR IF and IFs penalized by self-citations is in the range of 0.76-0.96 and the Kendall’s τ correlation is in the range of 0.57-0.86. The study also indicates that compared to the IFs penalized by self-citations, the IF encouraged (EIF) by the number of citing sources correlated moderate with the JCR IF, the Spearman’s ρ correlation is 0.73 and the Kendall’s τ correlation is 0.59. Experiment results showed that the JCR IF moderately correlated with the combined IFs, Spearman’s ρ correlation is in the range of 0.69-0.72 and Kendall’s τ correlation is in the range of 0.55-0.58. We also showed that penalization strategy of self-citation can influence on the result. For example, IF linearly penalized (LPIF) by self-citations highly correlated with the JCR IF (Spearman’s ρ correlation is 0.99 and Kendall’s τ correlation is 0.93), and IF non-linearly penalized (nLPIF) by self-citations moderately correlated with the JCR IF (Spearman’s ρ correlation is 0.76 and Kendall’s τ correlation is 0.57). Finally, we concluded that IFs with and without penalization lowly correlated with the number of articles and the number of citing sources.