A coordination-based approach to elasticity of floppy and stiff random networks

We study the role of connectivity on the linear and nonlinear elastic behavior of amorphous systems using a two-dimensional random network of harmonic springs as a model system. A natural characterization of these systems arises in terms of the network coordination relative to that of an isostatic network $\delta z$; a floppy network has $\delta z<0$, while a stiff network has $\delta z>0$. Under the influence of an externally applied load we observe that the response of both floppy and rigid network are controlled by the same critical point, corresponding to the onset of rigidity. We use numerical simulations to compute the exponents which characterize the shear modulus, the amplitude of non-affine displacements, and the network stiffening as a function of $\delta z$, derive these theoretically and make predictions for the mechanical response of glasses and fibrous networks.

Transparent electrode with a nanostructured coating.

Using single-walled nanotubes as an example, we fabricated transparent conductive coatings and demonstrated a new technique of centrifuge coating as a potential low-waste, solution-based batch process for the fabrication of nanostructured coatings. A theoretical model is developed to account for the sheet resistance exhibited by layered random-network coatings such as nanofilaments and graphene. The model equation is analytical and compact, and allows the correlation of very different scaling regimes reported in the literature to the underlying coating microstructure. Finally, we also show a refined experimental setup to systematically measure the curvature-dependent sheet resistance.

Field emission properties of single-walled carbon nanotubes with a variety of emitter morphologies

Field emission properties of single-walled carbon nanotubes (SWCNTs), which were prepared through alcohol catalytic chemical vapor deposition for 10-60s, were characterized in a diode configuration. Protrusive bundles at the top surface of samples act selectively as emission sites. The number of emission sites was controlled by emitter morphologies combined with texturing of Si substrates. SWCNTs grown on a textured Si substrate exhibited a turn-on field as low as 2.4 V/μm at a field emission current density of 1 μA/cm 2. Uniform spatial luminescence (0.5 cm2) from the rear surface of the anode was revealed for SWCNTs prepared on the textured Si substrate. Deterioration of field emission properties through repetitive measurements was reduced for the textured samples in comparison with vertically aligned SWCNTs and a random network of SWCNTs prepared on flat Si substrates. Emitter morphology resulting in improved field emission properties is a crucial factor for the fabrication of SWCNT-electron sources. Morphologically controlled SWCNTs with promising emitter performance are expected to be practical electron sources. © 2008 The Japan Society of Applied Physics.

Brillouin scattering of cluster-assembled carbon films

The low frequency vibrational spectrum of cluster beam deposited carbon films was studied by Brillouin light scattering. In thin films the values of both bulk modulus and shear modulus has been estimated from the shifts of surface phonon peaks. The values found indicate a mainly sp2 coordinated random network with low density. In thick films a bulk longitudinal phonon peak was detected in a spectral range compatible with the value of the index of refraction and of the elastic constants of thin films. High surface roughness, combined with a rather strong bulk central peak, prevented the observation of surface phonon features. © 1998 Elsevier Science Ltd. All rights reserved.

Failure mechanisms in fibrous scaffolds.

Polymeric fibrous scaffolds have been considered as replacements for load-bearing soft tissues, because of their ability to mimic the microstructure of natural tissues. Poor toughness of fibrous materials results in failure, which is an issue of importance to both engineering and medical practice. The toughness of fibrous materials depends on the ability of the microstructure to develop toughening mechanisms. However, such toughening mechanisms are still not well understood, because the detailed evolution at the microscopic level is difficult to visualize. A novel and simple method was developed, namely, a sample-taping technique, to examine the detailed failure mechanisms of fibrous microstructures. This technique was compared with in situ fracture testing by scanning electron microscopy. Examination of three types of fibrous networks showed that two different failure modes occurred in fibrous scaffolds. For brittle cracking in gelatin electrospun scaffolds, the random network morphology around the crack tip remained during crack propagation. For ductile failure in polycaprolactone electrospun scaffolds and nonwoven fabrics, the random network deformed via fiber rearrangement, and a large number of fiber bundles formed across the region in front of the notch tip. These fiber bundles not only accommodated mechanical strain, but also resisted crack propagation and thus toughened the fibrous scaffolds. Such understanding provides insight for the production of fibrous materials with enhanced toughness.

Failure mechanisms in fibrous scaffolds

Polymeric fibrous scaffolds have been considered as replacements for load-bearing soft tissues, because of their ability to mimic the microstructure of natural tissues. Poor toughness of fibrous materials results in failure, which is an issue of importance to both engineering and medical practice. The toughness of fibrous materials depends on the ability of the microstructure to develop toughening mechanisms. However, such toughening mechanisms are still not well understood, because the detailed evolution at the microscopic level is difficult to visualize. A novel and simple method was developed, namely, a sample-taping technique, to examine the detailed failure mechanisms of fibrous microstructures. This technique was compared with in situ fracture testing by scanning electron microscopy. Examination of three types of fibrous networks showed that two different failure modes occurred in fibrous scaffolds. For brittle cracking in gelatin electrospun scaffolds, the random network morphology around the crack tip remained during crack propagation. For ductile failure in polycaprolactone electrospun scaffolds and nonwoven fabrics, the random network deformed via fiber rearrangement, and a large number of fiber bundles formed across the region in front of the notch tip. These fiber bundles not only accommodated mechanical strain, but also resisted crack propagation and thus toughened the fibrous scaffolds. Such understanding provides insight for the production of fibrous materials with enhanced toughness. © 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Packet error rate and bit error rate non-deterministic relationship in optical network applications

The non-deterministic relationship between Bit Error Rate and Packet Error Rate is demonstrated for an optical media access layer in common use. We show that frequency components of coded, non-random data can cause this relationship. © 2005 Optical Society of America.

Continuous speech recognition for the TIMIT database using neural networks

Four types of neural networks which have previously been established for speech recognition and tested on a small, seven-speaker, 100-sentence database are applied to the TIMIT database. The networks are a recurrent network phoneme recognizer, a modified Kanerva model morph recognizer, a compositional representation phoneme-to-word recognizer, and a modified Kanerva model morph-to-word recognizer. The major result is for the recurrent net, giving a phoneme recognition accuracy of 57% from the si and sx sentences. The Kanerva morph recognizer achieves 66.2% accuracy for a small subset of the sa and sx sentences. The results for the word recognizers are incomplete.

Joint modelling of voicing label and continuous F0 for HMM based speech synthesis

Fundamental frequency, or F0 is critical for high quality speech synthesis in HMM based speech synthesis. Traditionally, F0 values are considered to depend on a binary voicing decision such that they are continuous in voiced regions and undefined in unvoiced regions. Multi-space distribution HMM (MSDHMM) has been used for modelling the discontinuous F0. Recently, a continuous F0 modelling framework has been proposed and shown to be effective, where continuous F0 observations are assumed to always exist and voicing labels are explicitly modelled by an independent stream. In this paper, a refined continuous F0 modelling approach is proposed. Here, F0 values are assumed to be dependent on voicing labels and both are jointly modelled in a single stream. Due to the enforced dependency, the new method can effectively reduce the voicing classification error. Subjective listening tests also demonstrate that the new approach can yield significant improvements on the naturalness of the synthesised speech. A dynamic random unvoiced F0 generation method is also investigated. Experiments show that it has significant effect on the quality of synthesised speech. © 2011 IEEE.

Improving LVCSR system combination using neural network language model cross adaptation

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. The standard approach involves only cross adapting acoustic models. To fully exploit the complimentary features among sub-systems, language model (LM) cross adaptation techniques can be used. Previous research on multi-level n-gram LM cross adaptation is extended to further include the cross adaptation of neural network LMs in this paper. Using this improved LM cross adaptation framework, significant error rate gains of 4.0%-7.1% relative were obtained over acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. Copyright © 2011 ISCA.

Random function priors for exchangeable arrays with applications to graphs and relational data

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Continuous dynamical systems that realize discrete optimization on the hypercube

We study the problem of finding a local minimum of a multilinear function E over the discrete set {0,1}n. The search is achieved by a gradient-like system in [0,1]n with cost function E. Under mild restrictions on the metric, the stable attractors of the gradient-like system are shown to produce solutions of the problem, even when they are not in the vicinity of the discrete set {0,1}n. Moreover, the gradient-like system connects with interior point methods for linear programming and with the analog neural network studied by Vidyasagar (IEEE Trans. Automat. Control 40 (8) (1995) 1359), in the same context. © 2004 Elsevier B.V. All rights reserved.