Debugging mixed‐environment programs with Blink

Programmers build large‐scale systems with multiple languages to leverage legacy code and languages best suited to their problems. For instance, the same program may use Java for ease of programming and C to interface with the operating system. These programs pose significant debugging challenges, because programmers need to understand and control code across languages, which often execute in different environments. Unfortunately, traditional multilingual debuggers require a single execution environment. This paper presents a novel composition approach to building portable mixed‐environment debuggers, in which an intermediate agent interposes on language transitions, controlling and reusing single‐environment debuggers. We implement debugger composition in Blink, a debugger for Java, C, and the Jeannie programming language. We show that Blink is (i) simple: it requires modest amounts of new code; (ii) portable: it supports multiple Java virtual machines, C compilers, operating systems, and component debuggers; and (iii) powerful: composition eases debugging, while supporting new mixed‐language expression evaluation and Java native interface bug diagnostics. To demonstrate the generality of interposition, we build prototypes and demonstrate debugger language transitions with C for five of six other languages (Caml, Common Lisp, C#, Perl 5, Python, and Ruby) without modifications to their debuggers. Using real‐world case studies, we show that diagnosing language interface errors require prior single‐environment debuggers to restart execution multiple times, whereas Blink directly diagnoses them with one execution. Copyright © 2014 John Wiley & Sons, Ltd.     

How to “alternatize” a clustering algorithm

Given a clustering algorithm, how can we adapt it to find multiple, nonredundant, high-quality clusterings? We focus on algorithms based on vector quantization and describe a framework for automatic ‘alternatization’ of such algorithms. Our framework works in both simultaneous and sequential learning formulations and can mine an arbitrary number of alternative clusterings. We demonstrate its applicability to various clustering algorithms—k-means, spectral clustering, constrained clustering, and co-clustering—and effectiveness in mining a variety of datasets.     

Up in smoke: I. Smoke-derived germination cues for postfire annual,Nicotiana attenuata torr. Ex. Watson

Some postfire annuals with dormant seeds use heat or chemical cues from charred wood to synchronize their germination with the postfire environment. We report that wood smoke and polar extracts of wood smoke, but not the ash of burned wood, contain potent cue(s) that stimulate germination in the postfire annual plant,Nicotiana attenuata. We examined the responses of seeds from six populations of plants from southwest Utah to extracts of smoke and found the proportion of viable seeds that germinated in the presence of smoke cues to vary between populations but to be consistent between generations. With the most dormant genotypes, we examine three mechanisms by which smoke-derived chemical cues may stimulate germination (chemical scarification of the seed coat and nutritive- and signal-mediated stimulation of germination) and report that the response is consistent with the signal-mediated mechanism. The germination cue(s) found in smoke are produced by the burning of hay, hardwood branches, leaves, and, to a lesser degree, cellulose. Moreover, the cues are found in the common food condiment, liquid smoke, and we find no significant differences between brands. With a bioassay-driven fractionation of liquid smoke, we identified 71 compounds in active fractions by GC-MS and AA spectrometry. However, when these compounds were tested in pure form or in combinations that mimicked the composition of active fractions over a range of concentrations, they failed to stimulate germination to the same degree that smoke fractions did. Moreover, enzymatic oxidation of some of these compounds also failed to stimulate germination. In addition, we tested 43 additional compounds also reported from smoke, 85 compounds that were structurally similar to those reported from smoke and 34 compounds reported to influence germination in other species. Of the 233 compounds tested, 16 proved to inhibit germination at the concentrations tested, and none reproduced the activity of wood smoke. By thermally desorbing smoke produced by cellulose combustions that was trapped on Chromosorb 101, we demonstrate that the cue is desorbed between 125 and 150°C. We estimate that the germination cues are active at concentrations of less than 1 pg/seed and, due to their chromatographic behavior, infer that a number of different chemical structures are active. In separate experiments, we demonstrate that cues remain active for at least 53 days in soil under greenhouse conditions and that the application of aqucous extracts of smoke to soil containing seeds results in dramatic increases in germination of artificial seed banks. Hence, although the chemical nature of the germination cue remains elusive, the stability of the germination cues, their water-solubility, and their activity in low concentrations suggest that these cues could serve as powerful tools for the examination of dormant seed banks and the selective factors thought to be important in the evolution of postfire plant communities.     

Simple laser velocimeter that uses photoconductive semiconductors to measure optical frequency differences

The dc photocurrents generated by steady-state moving space-charge fields inside photoconductive semiconductors containing deep level donors and traps can be used to determine the relative frequency differences between the two interfering optical fields that establish the space-charge fields. A simple laser velocimeter that uses a semi-insulating GaAs:Cr sample to detect the Doppler frequency shift between two laser beams is demonstrated.     

A Stress and Coping Model of Medication Adherence and Viral Load in HIV-Positive Men and Women on Highly Active Antiretroviral Therapy (HAART).

The authors tested a structural model that incorporated age, time since diagnosis, social support, coping, and negative mood as predictors of medication adherence and HIV viral load on 188 men and 134 women on highly active antiretroviral therapy (HAART). The authors used psychosocial latent factors formed from baseline measures to predict latent factors of adherence, as assessed by electronic monitoring and self-report, and viral load defined by indicators assessed over a 15-month period. Results from the model indicate that greater negative mood and lower social support are related to greater use of avoidance-oriented coping strategies. Use of these coping strategies by patients on HAART is related to poorer medication adherence and, subsequently, higher viral load. This model advances researchers' understanding of the contribution of psychosocial variables in predicting treatment adherence and disease progression in HIV-positive men and women. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data

Siberia's boreal forests represent an economically and ecologically precious resource, a significant part of which is not monitored on a regular basis. Synthetic aperture radars (SARs), with their sensitivity to forest biomass, offer mapping capabilities that could provide valuable up-to-date information, for example about fire damage or logging activity. The European Commission SIBERIA project had the aim of mapping an area of approximately 1 million km² in Siberia using SAR data from two satellite sources: the tandem mission of the European Remote Sensing Satellites ERS-1/2 and the Japanese Earth Resource Satellite JERS-1. Mosaics of ERS tandem interferometric coherence and JERS backscattering coefficient show the wealth of information contained in these data but they also show large differences in radar response between neighbouring images. To create one homogeneous forest map, adaptive methods which are able to account for brightness changes due to environmental effects were required. In this paper an adaptive empirical model to determine growing stock volume classes using the ERS tandem coherence and the JERS backscatter data is described. For growing stock volume classes up to 80 m³/ha, accuracies of over 80% are achieved for over a hundred ERS frames at a spatial resolution of 50 m.