The Modern Irrationalities of American Criminal Codes: An Empirical Study of Offense Grading

The Model Penal Code made great advances in clarity and legality, moving most of the states from a mix of common law and ad hoc statutes to the modern American form of a comprehensive, succinct code that has served as a model around the world. Yet the decades since the wave of Model Code-based codifications have seen a steady degradation of American codes brought on by a relentless and accelerating rate of criminal law amendments that ignore the style, format, and content of the existing codes. The most damaging aspect of this trend is the exponentially increasing number of offense grading irrationalities found in most modern American codes. This Article documents the practical and prudential importance of getting offense grading right – that is, having the grade of each offense or suboffense reflect its relative seriousness in relation to all other offenses – then illustrates just how wrong things have gone, using a case study of offense grading in Pennsylvania, one of the better modern American codes. The critique of Pennsylvania, and its conclusions, does not rely upon the value judgments of the authors but rather upon an empirical study of the judgments of Pennsylvania residents regarding the relative seriousness of more than a hundred existing Pennsylvania offenses. The results suggest a startling conflict between the law\u27s grading judgments and those of the community it governs, as well as a variety of kinds of logical irrationalities and internal inconsistencies. The process by which these grading irrationalities have been and continue to be created is examined, and solutions for fixing and, perhaps, avoiding these problems in the future, are explored

Bounding the first exit from the basin : Independence times and finite-time basin stability

9 pages, 3 figuresPeer reviewedPublisher PD

Machine Learning Prediction of Critical Cooling Rate for Metallic Glasses From Expanded Datasets and Elemental Features

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and indirect RC data for metallic glasses to expand the directly measured RC database of less than 100 values to a training set of over 2,000 values. The model error on 5-fold cross validation is 0.66 orders of magnitude in K/s. The error on leave out one group cross validation on alloy system groups is 0.59 log units in K/s when the target alloy constituents appear more than 500 times in training data. Using this model, we make predictions for the set of compositions with melt-spun glasses in the database, and for the full set of quaternary alloys that have constituents which appear more than 500 times in training data. These predictions identify a number of potential new bulk metallic glass (BMG) systems for future study, but the model is most useful for identification of alloy systems likely to contain good glass formers, rather than detailed discovery of bulk glass composition regions within known glassy systems

Ibrutinib Unmasks Critical Role of Bruton Tyrosine Kinase in Primary CNS Lymphoma.

Bruton tyrosine kinase (BTK) links the B-cell antigen receptor (BCR) and Toll-like receptors with NF-κB. The role of BTK in primary central nervous system (CNS) lymphoma (PCNSL) is unknown. We performed a phase I clinical trial with ibrutinib, the first-in-class BTK inhibitor, for patients with relapsed or refractory CNS lymphoma. Clinical responses to ibrutinib occurred in 10 of 13 (77%) patients with PCNSL, including five complete responses. The only PCNSL with complete ibrutinib resistance harbored a mutation within the coiled-coil domain of CARD11, a known ibrutinib resistance mechanism. Incomplete tumor responses were associated with mutations in the B-cell antigen receptor-associated protein CD79B

Mechanisms explaining transitions between tonic and phasic firing in neuronal populations as predicted by a low dimensional firing rate model

Several firing patterns experimentally observed in neural populations have been successfully correlated to animal behavior. Population bursting, hereby regarded as a period of high firing rate followed by a period of quiescence, is typically observed in groups of neurons during behavior. Biophysical membrane-potential models of single cell bursting involve at least three equations. Extending such models to study the collective behavior of neural populations involves thousands of equations and can be very expensive computationally. For this reason, low dimensional population models that capture biophysical aspects of networks are needed. \noindent The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing. These mechanisms are captured through a two-dimensional system, which can potentially be extended to include interactions between different areas of the nervous system with a small number of equations. The typical behavior of midbrain dopaminergic neurons in the rodent is used as an example to illustrate and interpret our results. \noindent The model presented here can be used as a building block to study interactions between networks of neurons. This theoretical approach may help contextualize and understand the factors involved in regulating burst firing in populations and how it may modulate distinct aspects of behavior.Comment: 25 pages (including references and appendices); 12 figures uploaded as separate file