A note on finding all homogeneous set sandwiches

A module is a set of vertices H of a graph G=(V,E) such that each vertex of V?H is either adjacent to all vertices of H or to none of them. A homogeneous set is a nontrivial module. A graph G_s=(V,E_s) is a sandwich for a pair of graphs G_t=(V,E_t) and G=(V,E) if E_t⊆E_s⊆E. In a recent paper, Tang et al. [Inform. Process. Lett. 77 (2001) 17-22] described an O(Δn²) algorithm for testing the existence of a homogeneous set in sandwich graphs of G_t=(V,E_t) and G=(V,E) and then extended it to an enumerative algorithm computing all these possible homogeneous sets. In this paper, we invalidate this latter algorithm by proving there are possibly exponentially many such sets, even if we restrict our attention to strong modules. We then give a correct characterization of a homogeneous set of a sandwich graph.     

Experience with a Hybrid Processor: K-Means Clustering

We discuss hardware/software co-processing on a hybrid processor for a compute- and data-intensive multispectral imaging algorithm, k-means clustering. The experiments are performed on two models of the Altera Excalibur board, the first using the soft IP core 32-bit NIOS 1.1 RISC processor, and the second with the hard IP core ARM processor. In our experiments, we compare performance of the sequential k-means algorithm with three different accelerated versions. We consider granularity and synchronization issues when mapping an algorithm to a hybrid processor. Our results show that speedup of 11.8X is achieved by migrating computation to the Excalibur ARM hardware/software as compared to software only on a Gigahertz Pentium III. Speedup on the Excalibur NIOS is limited by the communication cost of transferring data from external memory through the processor to the customized circuits. This limitation is overcome on the Excalibur ARM, in which dual-port memories, accessible to both the processor and configurable logic, have the biggest performance impact of all the techniques studied.     

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.     

Robust sound onset detection using leaky integrate-and-fire neurons with depressing synapses

A biologically inspired technique for detecting onsets in sound is presented. Outputs from a cochlea-like filter are spike coded, in a way similar to the auditory nerve (AN). These AN-like spikes are presented to a leaky integrate-and-fire neuron through a depressing synapse. Onsets are detected with essentially zero latency relative to these AN spikes. Onset detection results for a tone burst, musical sounds and the DARPA/NIST TIMIT speech corpus are presented.     

Image processing of electron micrographs of deformed filaments

Electron micrographs of filaments are frequently unsuitable for image processing because they are not perfectly straight. Even minor departures from straightness result in appreciable deterioration of the Fourier transform and analysis of periodicities and subsequent image processing becomes difficult. A procedure is described for recovering the projection of a straight filament from that of an imperfectly straight filament.     

Compressive and Tensile Behaviours of PLLA Matrix Composites Reinforced with Randomly Dispersed Flax Fibres

Nowadays, the ecological footprint of a material is becoming tremendously important. The Poly l-Lactide Acid (PLLA) matrix composites reinforced by randomly scattered flax fibres have mechanical properties similar to polyester/glass composites [1], lower environmental impacts and can be compost at the end of their lives. In this study, the mechanical characterization of biocomposites has been pushed further with the determination of the compressive and tensile properties. Furthermore, the mechanical properties of single flax fibres have been measured and implemented in a micro-mechanical estimation of the composite elastic modulus. Tensile and compressive stiffness determined by the mechanical analyses show very good correlations with the mathematical estimation.     

Integrated Computational Materials Engineering (ICME) Approach to Design of Novel Microstructures for Ti-Alloys

In this overview, we present integrated CAPHAD and phase-field modeling with critical experiments to explore a newly discovered, nonconventional, solid–solid phase transformation pathway based on the so-called pseudo-spinodal mechanism. We show that this new transformation pathway offers a new design strategy for Ti alloys with extremely fine and uniform α + β microstructures that could potentially have highly attractive balances of mechanical properties. To broaden the processing window for such a mechanism to operate, we also explore a different nonconventional transformation pathway that involves precursory phase separation. In addition, the variant selection process during the β → α transformation leading to macrozones is investigated and the results could shed light on how to control processing conditions to avoid or reduce microtexture at both the individual β grain level and the overall polycrystalline sample level.     

Non-targeted analysis of tea by hydrophilic interaction liquid chromatography and high resolution mass spectrometry

Tea is the second most consumed beverage in the world and its consumption has been associated with numerous potential health benefits. Factors such as fermentation methods, geographical origin and season can affect the primary and secondary metabolite composition of tea. In this study, a hydrophilic interaction liquid chromatography (HILIC) method coupled to high resolution mass spectrometry in both positive and negative ionisation modes was developed and optimised. The method when combined with principal component analysis to analyse three different types of tea, successfully distinguished samples into different categories, and provided evidence of the metabolites which differed between them. The accurate mass and high resolution attributes of the mass spectrometric data were utilised and relative quantification data were extracted post-data acquisition on 18 amino acids, showing significant differences in amino acid concentrations between tea types and countries. This study highlights the potential of HILIC chromatography combined with non-targeted mass spectrometric methods to provide a comprehensive understanding of polar metabolites in plant extracts.     

In situ injection of THF to trigger gas hydrate crystallization: Application to the evaluation of a kinetic hydrate promoter

This paper investigates an original method to efficiently trigger gas hydrate crystallization. This method consists of an in situ injection of a small amount of THF into an aqueous phase in contact with a gas-hydrate-former phase at pressure and temperature conditions inside the hydrate metastable zone. In the presence of a CO₂–CH₄ gas mixture, our results show that the THF injection induces immediate crystallization of a first hydrate containing THF. This triggers the formation of the CO₂–CH₄ binary hydrate as proven by the pressure and temperature reached at equilibrium. This experimental method, which “cancels out” the stochasticity of the hydrate crystallization, was used to evaluate the effect of the anionic surfactant SDS at different concentrations, on the formation kinetics of the CO₂–CH₄ hydrate. The results are discussed and compared with those published in a recent article (Ricaurte et al., 2013), where THF was not injected but present in the aqueous phase from the beginning and at much higher concentrations.