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.

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.     

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.     

Modular proofs for key exchange: rigorous optimizations in the Canetti–Krawczyk model

Various optimizations in the Canetti–Krawczyk model for secure protocol design are proven to preserve security. In particular it is shown that multiple authenticators may be safely used together; that certain message components generated by authenticators may be reordered (to be sent at a different time) or replaced with other values with certain precautions; and that protocols may be defined in the ideal world with session identifiers constructed during protocol runs. Consequently protocol designers now have a set of clear rules to optimize and customize their designs without fear of breaking the security proof. In order to obtain the required proofs, we find it necessary to slightly revise the authenticated links part of the Canetti–Krawczyk model. Research funded by Australian Research Council through Discovery Project DP0345775     

Calculations of diffusion and diffusion-limited processes in Ni3Al using accelerated molecular dynamics

We present a systematic microscopic approach to diffusion and diffusion-limited processes in Ni₃Al. These processes have been identified as controlling the deformation of the material under specific circumstances. The embedded atom method calculations are done using kinetic Monte Carlo combined with the Dimer method of finding saddlepoints. We compute the tracer diffusivities as functions of composition and temperature. The comparison with available experiments is good. We find that at temperatures below about 1000 K, the diffusivity is a sharp function of composition, showing a pronounced dip on the Ni-rich side at 76 at.% Ni. This agrees well with experiment, except that the experiments show this structure setting in a temperatures below about 1300 K. We show that the structure arises from the composition dependence of both the vacancy formation energy and pre-exponential of the diffusivity. We also compute the mobility of an anti-phase boundary perpendicular to its plane, and conclude that vacancy-assistance is very plausible. We conclude that the kMC + Dimer method works well for these problems above 700 K but less effectively below, owing to the presence of short-range, low-energy hops that tend to localize the vacancy and lower the efficiency of the calculation.     

Porous methacrylate tissue engineering scaffolds: using carbon dioxide to control porosity and interconnectivity

Porous scaffold structures are used in tissue engineering to provide structural guidance for regenerating tissues. The use of carbon dioxide (CO₂) to create such scaffolds has received some attention in the past but many researchers believe that although CO₂ processing of polymers can lead to porous scaffolds there is limited interconnectivity between the pores. In this study, highly porous (greater than 85%) and well interconnected scaffolds were obtained in which the size, distribution and number of pores could be controlled. This control was achieved by altering the rate of venting from polymer discs saturated with CO₂ under modest temperature and pressure. The polymer used is a blend of poly (ethyl methacrylate) and tetrahydrofurfuryl methacrylate (PEMA/THFMA). This polymer system has shown promise for potential applications in cartilage repair.