Pretenuring for Java

Pretenuring is a technique for reducing copying costs in garbage collectors. When pretenuring, the allocator places long-lived objects into regions that the garbage collector will rarely, if ever, collect. We extend previous work on profiling-driven pretenuring as follows. (1) We develop a collector-neutral approach to obtaining object lifetime profile information. We show that our collection of Java programs exhibits a very high degree of homogeneity of object lifetimes at each allocation site. This result is robust with respect to different inputs, and is similar to previous work on ML, but is in contrast to C programs, which require dynamic call chain context information to extract homogeneous lifetimes. Call-site homogeneity considerably simplifies the implementation of pretenuring and makes it more efficient. (2) Our pretenuring advice is neutral with respect to the collector algorithm, and we use it to improve two quite different garbage collectors: a traditional generational collector and an older-first collector. The system is also novel because it classifies and allocates objects into 3 categories: we allocate immortal objects into a permanent region that the collector will never consider, long-lived objects into a region in which the collector placed survivors of the most recent collection, and shortlived objects into the nursery, i.e., the default region. (3) We evaluate pretenuring on Java programs. Our simulation results show that pretenuring significantly reduces collector copying for generational and older-first collectors. 1

Bionic bodies, posthuman violence and the disembodied criminal subject

This article examines how the so-called disembodied criminal subject is given structure and form through the law of homicide and assault. By analysing how the body is materialised through the criminal law’s enactment of death and injury, this article suggests that the biological positioning of these harms of violence as uncontroversial, natural, and universal conditions of being ‘human’ cannot fully appreciate what makes violence wrongful for us, as embodied entities. Absent a theory of the body, and a consideration of corporeality, the criminal law risks marginalising, or altogether eliding, experiences of violence that do not align with its paradigmatic vision of what bodies can and must do when suffering its effects. Here I consider how the bionic body disrupts the criminal law’s understanding of human violence by being a body that is both organic and inorganic, and capable of experiencing and performing violence in unexpected ways. I propose that a criminal law that is more receptive to the changing, technologically mediated conditions of human existence would be one that takes the corporeal dimensions of violence more seriously and, as an extension of this, adopts an embodied, embedded, and relational understanding of human vulnerability to violence

A comparison of the performances of a bayesian algorithm and a kohonen map for clustering texture data

With many clustering algorithms available, it may be difficult to discern which is better for a given task. This study compares the performance of two clustering algorithms, the Bayesian classifier AutoClass and a Kohonen map, for the task of identifying classes of different textures in images based on statistics derived from gray-level co-occurrence matrices. The performance of the two algorithms is assessed in terms of quality of the classification. Comparisons of quality are given in terms of objective criteria such as cluster diameter, intercluster distance, etc. as well as subjective judgements by domain experts. Two different types of images are used. The first type of image consists of standard texture images in which textures classes are readily identified by novices. The second type consists of side-scanned sonar images in which the clusters are not necessarily apparent to novices and are not always classified consistently by domain experts (geologists). INTRODUCT..