Pangea: A Workbench for Statically Analyzing Multi-Language Software Corpora

Software corpora facilitate reproducibility of analyses, however, static analysis for an entire corpus still requires considerable effort, often duplicated unnecessarily by multiple users. Moreover, most corpora are designed for single languages increasing the effort for cross-language analysis. To address these aspects we propose Pangea, an infrastructure allowing fast development of static analyses on multi-language corpora. Pangea uses language-independent meta-models stored as object model snapshots that can be directly loaded into memory and queried without any parsing overhead. To reduce the effort of performing static analyses, Pangea provides out-of-the box support for: creating and refining analyses in a dedicated environment, deploying an analysis on an entire corpus, using a runner that supports parallel execution, and exporting results in various formats. In this tool demonstration we introduce Pangea and provide several usage scenarios that illustrate how it reduces the cost of analysis.

Spike-based decision learning of nash equilibria in two-player games

Humans and animals face decision tasks in an uncertain multi-agent environment where an agent's strategy may change in time due to the co-adaptation of others strategies. The neuronal substrate and the computational algorithms underlying such adaptive decision making, however, is largely unknown. We propose a population coding model of spiking neurons with a policy gradient procedure that successfully acquires optimal strategies for classical game-theoretical tasks. The suggested population reinforcement learning reproduces data from human behavioral experiments for the blackjack and the inspector game. It performs optimally according to a pure (deterministic) and mixed (stochastic) Nash equilibrium, respectively. In contrast, temporal-difference(TD)-learning, covariance-learning, and basic reinforcement learning fail to perform optimally for the stochastic strategy. Spike-based population reinforcement learning, shown to follow the stochastic reward gradient, is therefore a viable candidate to explain automated decision learning of a Nash equilibrium in two-player games.

Contemplating a ‘public service navigator’: In search of new (and better) functioning public service media

This is a paper contemplating a tool of public editorial intelligence, so that key media policy objectives can be attained in the complex and multi-faceted environment of digital media. The paper is meant to be an article of a special issue of the International Journal of Communication on "Public Media and Exposure Diversity", co-edited by Natali Helberger and Mira Burri.

Meta-Environment and Executable Meta-Language using Smalltalk: an Experience Report

Object-oriented modelling languages such as EMOF are often used to specify domain specific meta-models. However, these modelling languages lack the ability to describe behavior or operational semantics. Several approaches have used a subset of Java mixed with OCL as executable meta-languages. In this experience report we show how we use Smalltalk as an executable meta-language in the context of the Moose reengineering environment. We present how we implemented EMOF and its behavioral aspects. Over the last decade we validated this approach through incrementally building a meta-described reengineering environment. Such an approach bridges the gap between a code-oriented view and a meta-model driven one. It avoids the creation of yet another language and reuses the infrastructure and run-time of the underlying implementation language. It offers an uniform way of letting developers focus on their tasks while at the same time allowing them to meta-describe their domain model. The advantage of our approach is that developers use the same tools and environment they use for their regular tasks. Still the approach is not Smalltalk specific but can be applied to language offering an introspective API such as Ruby, Python, CLOS, Java and C#.

Caregiver Responses to the Language Mixing of a Young Trilingual

The situation once described by Hoffmann (1985), in which children grow up exposed to three languages from an early age, is a reality for an increasing number of families. In Europe – as elsewhere – greater mobility is leading to greater numbers of mixed-language couples (Piller 2002), and, by extension, multilingual families. For such families, questions concerning the acquisition and maintenance of three or more languages in a natural environment are of direct relevance. Researchers in bilingualism have already pointed out the importance of social context for the acquisition of two languages in childhood, focusing in particular on the quantity and quality of exposure to the languages (De Houwer 1990; Döpke 1992; Okita 2002; Lanza 2004) or the prestige of the languages (Lambert 1977). In this paper, I will make use of the insights gained by such researchers and test them in a trilingual setting. The paper will focus mainly on one aspect, namely the conversational style of parents and caretakers. The data come from research being carried out in Switzerland and consist of 33 interviews with multilingual families, as well as case studies of two trilingual children. The findings attest to the importance of conversational style, but at the same time indicate that a number of further factors are also of great significance.

Simulating Cortical Development as a Self Constructing Process: A Novel Multi-Scale Approach Combining Molecular and Physical Aspects

Current models of embryological development focus on intracellular processes such as gene expression and protein networks, rather than on the complex relationship between subcellular processes and the collective cellular organization these processes support. We have explored this collective behavior in the context of neocortical development, by modeling the expansion of a small number of progenitor cells into a laminated cortex with layer and cell type specific projections. The developmental process is steered by a formal language analogous to genomic instructions, and takes place in a physically realistic three-dimensional environment. A common genome inserted into individual cells control their individual behaviors, and thereby gives rise to collective developmental sequences in a biologically plausible manner. The simulation begins with a single progenitor cell containing the artificial genome. This progenitor then gives rise through a lineage of offspring to distinct populations of neuronal precursors that migrate to form the cortical laminae. The precursors differentiate by extending dendrites and axons, which reproduce the experimentally determined branching patterns of a number of different neuronal cell types observed in the cat visual cortex. This result is the first comprehensive demonstration of the principles of self-construction whereby the cortical architecture develops. In addition, our model makes several testable predictions concerning cell migration and branching mechanisms.

Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

The adsorption of biomolecules to multi-walled carbon nanotubes is influenced by both pulmonary surfactant lipids and surface chemistry

Background During production and processing of multi-walled carbon nanotubes (MWCNTs), they may be inhaled and may enter the pulmonary circulation. It is essential that interactions with involved body fluids like the pulmonary surfactant, the blood and others are investigated, particularly as these interactions could lead to coating of the tubes and may affect their chemical and physical characteristics. The aim of this study was to characterize the possible coatings of different functionalized MWCNTs in a cell free environment. Results To simulate the first contact in the lung, the tubes were coated with pulmonary surfactant and subsequently bound lipids were characterized. The further coating in the blood circulation was simulated by incubating the tubes in blood plasma. MWCNTs were amino (NH2)- and carboxyl (-COOH)-modified, in order to investigate the influence on the bound lipid and protein patterns. It was shown that surfactant lipids bind unspecifically to different functionalized MWCNTs, in contrast to the blood plasma proteins which showed characteristic binding patterns. Patterns of bound surfactant lipids were altered after a subsequent incubation in blood plasma. In addition, it was found that bound plasma protein patterns were altered when MWCNTs were previously coated with pulmonary surfactant. Conclusions A pulmonary surfactant coating and the functionalization of MWCNTs have both the potential to alter the MWCNTs blood plasma protein coating and to determine their properties and behaviour in biological systems.

Using Smalltalk as a Reflective Executable Meta-Language

Object-oriented meta-languages such as MOF or EMOF are often used to specify domain specific languages. However, these meta-languages lack the ability to describe behavior or operational semantics. Several approaches used a subset of Java mixed with OCL as executable meta-languages. In this paper, we report our experience of using Smalltalk as an executable and integrated meta-language. We validated this approach in incrementally building over the last decade, Moose, a meta-described reengineering environment. The reflective capabilities of Smalltalk support a uniform way of letting the base developer focus on his tasks while at the same time allowing him to meta-describe his domain model. The advantage of our this approach is that the developer uses the same tools and environment

Interactional behaviour as a marker for screening patients with environment-related complaints

BACKGROUND: Adequate assessment of symptoms of patients suffering from environmental illnesses requires appropriate procedures such as psychological and psychiatric diagnostics, medical screening and a thorough analysis of noxious environmental factors. The Basel pilot research project established a multi-methodological assessment procedure that meets these criteria. However, an exhaustive three-fold analysis is very costly in terms of both equipment and personnel, and hence the need for a heuristic approach and pre-screening persists. METHOD: The three-fold diagnostic approach was preceded by a structured psychodynamic interview; the findings were used to construct a new profile of the patient's interactional behaviour (IB) in conjunction with the interviewer's countertransference. The extent to which this new profile could predict the results of the multi-method assessment was then assessed. RESULTS: A low level of IB on the part of the patient significantly predicted the degree of stress and the extent of the psychiatric diagnosis, including personality disorders. A negative IB was associated with negative personality traits. Furthermore, a high level of IB implied more medical, but not more environmental, findings which could plausibly be related to the patient's complaints. CONCLUSIONS: Assessment of patients' IB in conjunction with one's own countertransference is very helpful as a preliminary heuristic approach and may lead to consequences for treatment and therapy. Therefore, the training provided for experts who deal with patients suffering from environment-related complaints should place more specific emphasis on assessing patients' behaviour and on incorporating information gathered from countertransference. Nevertheless, an interdisciplinary assessment including medical, psychological/psychiatric, and environmental expertise remains mandatory for adequate and satisfactory diagnosis of patients with environment-related complaints.