A high-stability fiber amplifier source for the fiber opticgyroscope

We demonstrate an erbium-doped fiber amplifier (EDFA) source for the fiber gyroscope with an overall mean wavelength stability of four parts per million. This source, which amplifies the gyroscope signal before detection, is shown to be more stable than a similar single-pass source in a standard configuration. The effects of varying the fiber temperature, pump power, pump wavelength, gyroscope feedback power and signal polarization state on the output mean wavelength are individually explored. Experimental results are in excellent agreement with simulations     

Destabilizing interactions between the partners of a bifunctional fusion protein

Hybrid MalE–GVP is a bifunctional protein in vitro sinceit binds maltose as protein MalE of Escherichia coli and sinceit is dimeric and specifically binds single-stranded DNA asprotein GVP of phage M13. The oxidation rate of a unique cysteineresidue was used to compare the stabilities of GVP in its freeand hybrid forms, under conditions where MalE was either foldedor unfolded by a denaturing agent. The results showed that boththe covalent link and tertiary non-covalent interactions betweenMalE and GVP destabilized GVP in MalE–GVP. To test whetherGVP had identical structures in its free and hybrid forms, mutationswere used as local conformational probes. The effects of thesemutations on the capabilities of MalE–GVP to dimerizeand to bind single-stranded DNA were assayed in vitro. Theywere compatible with the effects of the same mutations on theglobal activity of free GVP in vivo and with the effects thatcould be predicted from the known data on free GVP, in particularits crystal structure. Thus, one partner of a hybrid proteincan be destabilized by the other partner while maintaining itsstructural and functional characteristics.     

Mechanism of hydrogen adsorption/absorption at thin Pd layers on Au(1 1 1)

Hydrogen adsorption and absorption at thin palladium deposits of 0.8-10 monolayers (ML) on Au(1 1 1) was studied in 0.1 M H₂SO₄ and HClO₄ using cyclic voltammetry, ac voltammetry, and impedance spectroscopy in the absence and in the presence of poison, crystal violet. Hydrogen adsorption on palladium is more reversible in sulfuric acid than in perchloric acid but it occurs at potentials 30 mV more positive in latter. The charge-transfer resistance exhibits a minimum at ∼0.27 V versus RHE and decreases with increasing in Pd deposit thickness in both acids. Adsorption capacitance at 0.8 ML Pd reaches maximum at the same potential. At other deposits the pseudo-capacitance starts to increase at lower overpotentials indicating the beginning of absorption, even at 2 ML Pd. The double layer capacitance is similar for all the deposits in sulfuric acid and it has a sharp maximum at 0.27 V versus RHE. In perchloric acid a broad maximum is observed. Crystal violet inhibits hydrogen adsorption but makes hydrogen absorption more reversible. The results suggest a fast direct hydrogen absorption mechanism that proceeds in parallel with slower hydrogen adsorption and indirect absorption.     

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.     

Robust skeletonization using the discrete λ-medial axis

Medial axes and skeletons are notoriously sensitive to contour irregularities. This lack of stability is a serious problem for applications in e.g. shape analysis and recognition. In 2005, Chazal and Lieutier introduced the λ-medial axis as a new concept for computing the medial axis of a shape subject to single parameter filtering. The λ-medial axis is stable under small shape perturbations, as proved by these authors. In this article, a discrete λ-medial axis (DLMA) is introduced and compared with the recently introduced integer medial axis (GIMA). We show that DLMA provides measurably better results than GIMA, with regard to stability and sensibility to rotations. We give efficient algorithms to compute the DLMA, and we also introduce a variant of the DLMA which may be computed in linear-time.     

The minimum information about failures for solving non-local tasks in message-passing systems

We first define the basic notions of local and non-local tasks for distributed systems. Intuitively, a task is local if, in a system with no failures, each process can compute its output value locally by applying some local function on its own input value (so the output value of each process depends only on the process’ own input value, not on the input values of the other processes); a task is nonlocal otherwise. All the interesting distributed tasks, including all those that have been investigated in the literature (e.g., consensus, set agreement, renaming, atomic commit, etc.) are non-local. In this paper we consider non-local tasks and determine the minimum information about failures that is necessary to solve such tasks in message-passing distributed systems. As part of this work, we also introduces weak set agreement—a natural weakening of set agreement—and show that, in some precise sense, it is the weakest nonlocal task in message-passing systems.     

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."     

NX210c Peptide Promotes Glutamatergic Receptor-Mediated Synaptic Transmission and Signaling in the Mouse Central Nervous System

NX210c is a disease-modifying dodecapeptide derived from the subcommissural organ-spondin that is under preclinical and clinical development for the treatment of neurological disorders. Here, using whole-cell patch-clamp recordings, we demonstrate that NX210c increased α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)- and GluN2A-containing N-methyl-D-aspartate receptor (GluN2A-NMDAR)-mediated excitatory postsynaptic currents in the brain. Accordingly, using extracellular field excitatory postsynaptic potential recordings, an enhancement of synaptic transmission was shown in the presence of NX210c in two different neuronal circuits. Furthermore, the modulation of synaptic transmission and GluN2A-NMDAR-driven signaling by NX210c restored memory in mice chronically treated with the NMDAR antagonist phencyclidine. Overall, by promoting glutamatergic receptor-related neurotransmission and signaling, NX210c represents an innovative therapeutic opportunity for patients suffering from CNS disorders, injuries, and states with crippling synaptic dysfunctions.