Une méthode d’optimisation discrète pour les filtres cascade à capacités commutées

A discrete optimization method using a noninteraction property between the different sections of cascade switched-capacitor filter is described. The optimization, realized with a reduced set of intermediate parameters caracterizing each section, yields to a non minimum capacitive area structure, but which has very accurate capacitor ratios and which could be realized with minimum unit capacitors. This method may be applied to switched-current filters (aspect ratios of transistor discretization). An eighth order band-pass Cauer filter and a fifth order low-pass Cauer filter show the effectiveness of this method.     

Identification of leek-moth and diamondback-moth frass volatiles that stimulate parasitoid,Diadromus pulchellus

Acrolepiopsis assectella andPlutella xylostella frass volatiles, trapped on Tenax GC, were examined by capillary gas chromatography. In both moths, the same three disulfides, dimethyl, dipropyl, and methyl propyl, were the most abundant substances, but in different proportions. The synthetic disulfides elicited the same behavioral response by the parasitoid,Diadromus pulchellus as frass. The plant origin of these substances is discussed.     

Kernel methods for software effort estimation

Analogy based estimation (ABE) generates an effort estimate for a new software project through adaptation of similar past projects (a.k.a. analogies). Majority of ABE methods follow uniform weighting in adaptation procedure. In this research we investigated non-uniform weighting through kernel density estimation. After an extensive experimentation of 19 datasets, 3 evaluation criteria, 5 kernels, 5 bandwidth values and a total of 2090 ABE variants, we found that: (1) non-uniform weighting through kernel methods cannot outperform uniform weighting ABE and (2) kernel type and bandwidth parameters do not produce a definite effect on estimation performance. In summary simple ABE approaches are able to perform better than much more complex approaches. Hence,—provided that similar experimental settings are adopted—we discourage the use of kernel methods as a weighting strategy in ABE.     

Empirical evaluation of computational fear contagion models in crowd dispersions

In social psychology, emotional contagion describes the widely observed phenomenon of one person’s emotions being influenced by surrounding people’s emotions. While the overall effect is agreed upon, the underlying mechanism of the spread of emotions has seen little quantification and application to computational agents despite extensive evidence of its impacts in everyday life. In this paper, we examine computational models of emotional contagion by implementing two models (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) and Durupinar, From audiences to mobs: Crowd simulation with psychological factors, PhD dissertation, Bilkent University, 2010) that draw from two separate lines of contagion research: thermodynamics-based and epidemiological-based. We first perform sensitivity tests on each model in an evacuation simulation, ESCAPES, showing both models to be reasonably robust to parameter variations with certain exceptions. We then compare their ability to reproduce a real crowd panic scene in simulation, showing that the thermodynamics-style model (Bosse et al., European council on modeling and simulation, pp. 212–218, 2009) produces superior results due to the ill-suited contagion mechanism at the core of epidemiological models. We also identify that a graduated effect of fear and proximity-based contagion effects are key to producing the superior results. We then reproduce the methodology on a second video, showing that the same results hold, implying generality of the conclusions reached in the first scene.     

An MDA approach to knowledge engineering

This paper proposes an MDA approach to knowledge engineering, centered on the CommonKADS knowledge model. The latter corresponds to the CIM level of MDA whereas PRR, which represents production rules and rulesets, corresponds to the PIM level. The paper explores the mapping between CommonKADS knowledge models and production rules and rulesets based on PRR. Mapping CommonKADS knowledge models into PRR is very useful, due to the fact that the CIM level remains relatively unexplored, despite its key role in MDA. This motivates our choice to focus on the CIM and PIM levels. Furthermore, the mapping between PIM and PSM (i.e. the implementation of production rules in specific rule-based systems) constitutes less of an issue. To map CommonKADS knowledge models into PRR production rules and rulesets, we propose and illustrate a set of transformations. To ease these transformations, we start by grouping elements of the CommonKADS knowledge models into so-called “inference groups”. We propose and illustrate an algorithm that defines these inference groups automatically. The definition of transformations between models (CIM to PIM levels) requires a specific metamodel for CommonKADS as well as a dedicated metamodel for PRR. Unlike PRR, there is no published CommonKADS metamodel. This paper proposes a comprehensive CommonKADS knowledge metamodel. We describe and discuss an example, applying the whole approach.     

Multi-Level Composition for Software Federations

Reusability is the holly grail of software engineers. But reusability requires powerful composition mechanisms since the pieces to compose have no reason to match perfectly. Unfortunately, the composition mechanisms available today, mostly method call and component assembly are rather primitive. This paper shows what is the current state of the art in software composition, showing that there is little composition flexibility at code level, even using workflow approaches.The approach presented here claims that composition requires reasoning at different levels of abstraction. Workflow support is first decoupled from real tools, using abstract tool modeling. Contracts have been included to increase the independence between process and tools. Then, we have introduced conceptual abstractions linked by contract to real tools. Finally, we show that it is possible to define composition at the abstract level.The resulting system shows very high adaptability capabilities. Experience shows, however, that to be practical, this approach requires adapted framework and specialized tools. This paper shows the experience gained in implementing many versions of such a framework. The current system is currently in industrial use.     

Combining FDI and AI approaches within causal-model-based diagnosis.

This paper presents a model-based diagnostic method designed in the context of process supervision. It has been inspired by both artificial intelligence and control theory. AI contributes tools for qualitative modeling, including causal modeling, whose aim is to split a complex process into elementary submodels. Control theory, within the framework of fault detection and isolation (FDI), provides numerical models for generating and testing residuals, and for taking into account inaccuracies in the model, unknown disturbances and noise. Consistency-based reasoning provides a logical foundation for diagnostic reasoning and clarifies fundamental assumptions, such as single fault and exoneration. The diagnostic method presented in the paper benefits from the advantages of all these approaches. Causal modeling enables the method to focus on sufficient relations for fault isolation, which avoids combinatorial explosion. Moreover, it allows the model to be modified easily without changing any aspect of the diagnostic algorithm. The numerical submodels that are used to detect inconsistency benefit from the precise quantitative analysis of the FDI approach. The FDI models are studied in order to link this method with DX component-oriented reasoning. The recursive on-line use of this algorithm is explained and the concept of local exoneration is introduced.     

The Innate Immune Glycoprotein Lactoferrin Represses the Helicobacter pylori cag Type IV Secretion System

Chronic infection with Helicobacter pylori increases risk of gastric diseases including gastric cancer. Despite development of a robust immune response, H. pylori persists in the gastric niche. Progression of gastric inflammation to serious disease outcomes is associated with infection with H. pylori strains which encode the cag Type IV Secretion System (cag T4SS). The cag T4SS is responsible for translocating the oncogenic protein CagA into host cells and inducing pro-inflammatory and carcinogenic signaling cascades. Our previous work demonstrated that nutrient iron modulates the activity of the T4SS and biogenesis of T4SS pili. In response to H. pylori infection, the host produces a variety of antimicrobial molecules, including the iron-binding glycoprotein, lactoferrin. Our work shows that apo-lactoferrin exerts antimicrobial activity against H. pylori under iron-limited conditions, while holo-lactoferrin enhances bacterial growth. Culturing H. pylori in the presence of holo-lactoferrin prior to co-culture with gastric epithelial cells, results in repression of the cag T4SS activity. Concomitantly, a decrease in biogenesis of cag T4SS pili at the host-pathogen interface was observed under these culture conditions by high-resolution electron microscopy analyses. Taken together, these results indicate that acquisition of alternate sources of nutrient iron plays a role in regulating the pro-inflammatory activity of a bacterial secretion system and present novel therapeutic targets for the treatment of H. pylori-related disease.