Unanticipated Partial Behavioral Reflection: Adapting Applications at Runtime

Dynamic, unanticipated adaptation of running systems is of interest in a variety of situations, ranging from functional upgrades to on-the-fly debugging or monitoring of critical applications. In this paper we study a particular form of computational reflection, called unanticipated partial behavioral reflection, which is particularly well-suited for unanticipated adaptation of real-world systems. Our proposal combines the dynamicity of unanticipated reflection, i.e. reflection that does not require preparation of the code of any sort, and the selectivity and efficiency of partial behavioral reflection. First, we propose unanticipated partial behavioral reflection which enables the developer to precisely select the required reifications, to flexibly engineer the metalevel and to introduce the meta behavior dynamically. Second, we present a system supporting unanticipated partial behavioral reflection in Squeak Smalltalk, called Geppetto, and illustrate its use with a concrete example of a web application. Benchmarks validate the applicability of our proposal as an extension to the standard reflective abilities of Smalltalk.

Practical Object-Oriented Back-in-Time Debugging

Back-in-time debuggers are extremely useful tools for identifying the causes of bugs, as they allow us to inspect the past states of objects no longer present in the current execution stack. Unfortunately the "omniscient" approaches that try to remember all previous states are impractical because they either consume too much space or they are far too slow. Several approaches rely on heuristics to limit these penalties, but they ultimately end up throwing out too much relevant information. In this paper we propose a practical approach to back-in-time debugging that attempts to keep track of only the relevant past data. In contrast to other approaches, we keep object history information together with the regular objects in the application memory. Although seemingly counter-intuitive, this approach has the effect that past data that is not reachable from current application objects (and hence, no longer relevant) is automatically garbage collected. In this paper we describe the technical details of our approach, and we present benchmarks that demonstrate that memory consumption stays within practical bounds. Furthermore since our approach works at the virtual machine level, the performance penalty is significantly better than with other approaches.

Automatic Labeling of Software Components and their Evolution using Log-Likelihood Ratio of Word Frequencies in Source Code

As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.

Is characterization of a single isolate sufficient for valid publication of a new genus or species? Proposal to modify recommendation 30b of the Bacteriological Code (1990 Revision)

From 1990 to 2000, the number of published named taxa based upon new isolates at species and genus levels in International Journal of Systematic and Evolutionary Microbiology, formerly International Journal of Systematic Bacteriology, have increased by approximately four- and sevenfold, respectively. New taxa based upon characterization of only a single isolate remained at around 40% for both categories. The Bacteriological Code (1990 Revision) has no recommendations on the number of strains required for definition of new taxa. For a few groups, a minimum number of 5-10 strains has been suggested in minimal standards. Since an exponential increase in new taxa can be expected in the future, the authors discuss problems related to naming new species and genera based upon descriptions of a single isolate and suggest that this practice is re-evaluated. It is proposed that the following should be added to Recommendation 30b of the Bacteriological Code: 'Descriptions should be based on as many strains as possible (minimum five), representing different sources with respect to geography and ecology in order to be well characterized both phenotypically and genotypically, to establish the centre (from which the type strain could be chosen) and the extent of the cluster to be named. In addition, comparative studies should be performed, including reference strains that represent neighbouring species and/or genera, in order to give descriptions that are sufficiently detailed to allow differentiation from these neighbours.'

Code-specific learning rules improve action selection by populations of spiking neurons

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Deciphering microRNA code in pain and inflammation: lessons from bladder pain syndrome

MicroRNAs (miRNAs), a novel class of molecules regulating gene expression, have been hailed as modulators of many biological processes and disease states. Recent studies demonstrated an important role of miRNAs in the processes of inflammation and cancer, however, there are little data implicating miRNAs in peripheral pain. Bladder pain syndrome/interstitial cystitis (BPS/IC) is a clinical syndrome of pelvic pain and urinary urgency/frequency in the absence of a specific cause. BPS is a chronic inflammatory condition that might share some of the pathogenetic mechanisms with its common co-morbidities inflammatory bowel disease (IBD), asthma and autoimmune diseases. Using miRNA profiling in BPS and the information about validated miRNA targets, we delineated the signaling pathways activated in this and other inflammatory pain disorders. This review projects the miRNA profiling and functional data originating from the research in bladder cancer and immune-mediated diseases on the BPS-specific miRNAs with the aim to gain new insight into the pathogenesis of this enigmatic disorder, and highlighting the common regulatory mechanisms of pain and inflammation.