LARS: A learning algorithm for rewriting systems

Whereas there is a number of methods and algorithms to learn regular languages, moving up the Chomsky hierarchy is proving to be a challenging task. Indeed, several theoretical barriers make the class of context-free languages hard to learn. To tackle these barriers, we choose to change the way we represent these languages. Among the formalisms that allow the definition of classes of languages, the one of string-rewriting systems (SRS) has outstanding properties. We introduce a new type of SRS’s, called Delimited SRS (DSRS), that are expressive enough to define, in a uniform way, a noteworthy and non trivial class of languages that contains all the regular languages, , , the parenthesis languages of Dyck, the language of Lukasiewicz, and many others. Moreover, DSRS’s constitute an efficient (often linear) parsing device for strings, and are thus promising candidates in forthcoming applications of grammatical inference. In this paper, we pioneer the problem of their learnability. We propose a novel and sound algorithm (called LARS) which identifies a large subclass of them in polynomial time (but not data). We illustrate the execution of our algorithm through several examples, discuss the position of the class in the Chomsky hierarchy and finally raise some open questions and research directions. This work was supported in part by the IST Program of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors’ views. Editor: Georgios Paliouras and Yasubumi Sakakibara     

The road to chaos by time-asymmetric Hebbian learning in recurrent neural networks

This letter aims at studying the impact of iterative Hebbian learning algorithms on the recurrent neural network's underlying dynamics. First, an iterative supervised learning algorithm is discussed. An essential improvement of this algorithm consists of indexing the attractor information items by means of external stimuli rather than by using only initial conditions, as Hopfield originally proposed. Modifying the stimuli mainly results in a change of the entire internal dynamics, leading to an enlargement of the set of attractors and potential memory bags. The impact of the learning on the network's dynamics is the following: the more information to be stored as limit cycle attractors of the neural network, the more chaos prevails as the background dynamical regime of the network. In fact, the background chaos spreads widely and adopts a very unstructured shape similar to white noise. Next, we introduce a new form of supervised learning that is more plausible from a biological point of view: the network has to learn to react to an external stimulus by cycling through a sequence that is no longer specified a priori. Based on its spontaneous dynamics, the network decides "on its own" the dynamical patterns to be associated with the stimuli. Compared with classical supervised learning, huge enhancements in storing capacity and computational cost have been observed. Moreover, this new form of supervised learning, by being more "respectful" of the network intrinsic dynamics, maintains much more structure in the obtained chaos. It is still possible to observe the traces of the learned attractors in the chaotic regime. This complex but still very informative regime is referred to as "frustrated chaos."     

Dialogue in context, towards a referential approach in collective learning

In this article, we present research in the making of a collective work environment within the framework of a distance education course. We base our theoretical and methodological standpoints on examples of dialogical discourses recorded within the framework of this CSCL system called Symba. In fact, the results of previous research lead us to rethink our vision of the study of collaborative moments between participants in a computer-supported human learning environment that proposes several communication tools. Redefining the methodological process aiming at finding and understanding these rich learning moments is also necessary. We intend to describe “socio-technical” instances during which these collaboration phases appear. More generally speaking, our aim is to draw up both new theoretical and methodological perspectives that would be reusable in CSCL environments; in view of the nature of these two perspectives, and the diversity of the domain knowledge (sociology, cognitivism, linguistics, philosophy, statistics, etc.) brought to bear in the study of the environment in question, our approach constitutes a trans-disciplinary reassessment of the uses of the communication tools—and the study thereof—proposed.

The mechanical properties and tensile failure mechanism of a high strength polymer modified Portland cement

The mechanical properties of one of the new high strength polymer modified hydraulic cements have been investigated. An important parameter for the material is the amount of polymer present, and the properties are found to be dependent upon the degree of drying. For example, in the wet state, polymer content has little effect upon ultimate flexural strength, but does cause nonlinearity in the stress-strain behaviour. Although increasing polymer content causes a reduction in the initial tangent modulus, it is shown that retardation of hydration may account for this. In the dry state, increasing polymer content leads to increasing flexural strength, fracture toughness and failure strain, but leaves initial elastic modulus relatively unchanged. It is concluded that removal of pores is not the principal strengthening mechanism since strength increases are consequent upon water removal from the microstructure in the presence of the polymer. A fibrillar pull-out model is proposed to explain the observed behaviour of both wet and dry material and ordinary Portland cement paste, which shows good correlation with experimental results. The principal effect of the polymer is to act as an adhesive at the interface between interacting CSH fibrils.     

Fair,biased, and self-balancing merge operators: Their specification and implementation in Concurrent Prolog

The problem of allowing a dynamically changing set of processes fair access to a shared resource is considered, in the context of communication-stream based systems. It is argued that fair binary merge operators alone cannot solve this problem satisfactorily. Two solutions are proposed. One employs binary merge operators with a programmable bias; the other binary and ternary fair merge operators capable of self-balancing, using the concept of 2–3 trees. A Concurrent Prolog implementation of these operators is described. The implementation of the self-balancing merge operators illustrates the expressive power of incomplete messages, a programming technique that supports messages that contain communication channels as arguments. In the course of implementing the self-balancing merge operator, it was necessary to develop a distributed variant of the 2–3 tree deletion algorithm.     

Lysosomal proteinases in muscle tissue and leukocytes of meat animals

Carboxypeptidases A and B, cathepsins B, D and E, dipeptidyl aminopeptidase 1 and collagenase were extracted from bovine and porcine leukocyte lysosomes and diaphragm muscle lysosomes with 4% n-butyl alcohol in dry ice and various chemical properties were studied. The enzymes were classified by their specific activities with different substrates, pH optima, energies of activation and the influence of inhibitors and stimulators. Cathepsins D and E were the most active proteases studied and all enzymes were more active in leukocytes than in sediments from muscle tissue. The properties of these enzymes, compared with enzymes from other sources, and their possible involvement in meat tenderness are discussed.     

Wafer-level fabrication process for fully encapsulated micro-supercapacitors with high specific energy

In this paper a wafer-level process is proposed to fully integrate carbon-based micro-supercapacitor onto silicon substrate. This process relies on the deposition of a paste containing carbon, PVDF and acetone into cavities etched in silicon. After electrolyte deposition in a controlled atmosphere, a wafer-level encapsulation is realized. Cyclic voltammetry performed on non-encapsulated micro-components showed specific energy of 257 mJ cm⁻² for 336 μm deep cavities. The specific encapsulation process developed was tested separately and proved to be efficient in terms of resistance to organic electrolytes and mechanical strength.     

An intrinsic dimensionality estimator from near-neighbor information

The intrinsic dimensionality of a set of patterns is important in determining an appropriate number of features for representing the data and whether a reasonable two- or three-dimensional representation of the data exists. We propose an intuitively appealing, noniterative estimator for intrinsic dimensionality which is based on nearneighbor information. We give plausible arguments supporting the consistency of this estimator. The method works well in identifying the true dimensionality for a variety of artificial data sets and is fairly insensitive to the number of samples and to the algorithmic parameters. Comparisons between this new method and the global eigenvalue approach demonstrate the utility of our estimator.