An Infinite Latent Attribute Model for Network Data

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes then depends only on their cluster assignment. Currently available models can be classified by whether clusters are disjoint or are allowed to overlap. These models can explain a "flat" clustering structure. Hierarchical Bayesian models provide a natural approach to capture more complex dependencies. We propose a model in which objects are characterised by a latent feature vector. Each feature is itself partitioned into disjoint groups (subclusters), corresponding to a second layer of hierarchy. In experimental comparisons, the model achieves significantly improved predictive performance on social and biological link prediction tasks. The results indicate that models with a single layer hierarchy over-simplify real networks.

Augmented attribute representations

We propose a new learning method to infer a mid-level feature representation that combines the advantage of semantic attribute representations with the higher expressive power of non-semantic features. The idea lies in augmenting an existing attribute-based representation with additional dimensions for which an autoencoder model is coupled with a large-margin principle. This construction allows a smooth transition between the zero-shot regime with no training example, the unsupervised regime with training examples but without class labels, and the supervised regime with training examples and with class labels. The resulting optimization problem can be solved efficiently, because several of the necessity steps have closed-form solutions. Through extensive experiments we show that the augmented representation achieves better results in terms of object categorization accuracy than the semantic representation alone. © 2012 Springer-Verlag.

Scanning gate microscopy of current-annealed single layer graphene

We have used scanning gate microscopy to explore the local conductivity of a current-annealed graphene flake. A map of the local neutrality point (NP) after annealing at low current density exhibits micron-sized inhomogeneities. Broadening of the local e-h transition is also correlated with the inhomogeneity of the NP. Annealing at higher current density reduces the NP inhomogeneity, but we still observe some asymmetry in the e-h conduction. We attribute this to a hole-doped domain close to one of the metal contacts combined with underlying striations in the local NP. © 2010 American Institute of Physics.

Influence of ion energy and substrate temperature on the optical and electronic properties of tetrahedral amorphous carbon (ta-C) films

The properties of amorphous carbon (a-C) deposited using a filtered cathodic vacuum arc as a function of the ion energy and substrate temperature are reported. The sp3 fraction was found to strongly depend on the ion energy, giving a highly sp3 bonded a-C denoted as tetrahedral amorphous carbon (ta-C) at ion energies around 100 eV. The optical band gap was found to follow similar trends to other diamondlike carbon films, varying almost linearly with sp2 fraction. The dependence of the electronic properties are discussed in terms of models of the electronic structure of a-C. The structure of ta-C was also strongly dependent on the deposition temperature, changing sharply to sp2 above a transition temperature, T1, of ≈200°C. Furthermore, T1 was found to decrease with increasing ion energy. Most film properties, such as compressive stress and plasmon energy, were correlated to the sp3 fraction. However, the optical and electrical properties were found to undergo a more gradual transition with the deposition temperature which we attribute to the medium range order of sp2 sites. We attribute the variation in film properties with the deposition temperature to diffusion of interstitials to the surface above T1 due to thermal activation, leading to the relaxation of density in context of a growth model. © 1997 American Institute of Physics.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty.

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models.

How to achieve high mobility thin film transistors by direct deposition of silicon using 13.56 MHz RF PECVD?

CMOS nanocrystalline silicon thin film transistors with high field effect mobility are reported. The transistors were directly deposited by radio-frequency plasma enhanced chemical vapor deposition at 150°C The transistors show maximum field effect mobility of 450 cm2/V-s for electrons and 100 cm2/V-s for holes at room temperature. We attribute the high mobilities to a reduction of the oxygen content, which acts as an accidental donor. Indeed, secondary ion mass spectrometry measurements show that the impurity concentration in the nanocrystalline Si layer is comparable to, or lower than, the defect density in the material, which is already low thanks to hydrogen passivation.

Risk-sensitive optimal feedback control accounts for sensorimotor behavior under uncertainty

Many aspects of human motor behavior can be understood using optimality principles such as optimal feedback control. However, these proposed optimal control models are risk-neutral; that is, they are indifferent to the variability of the movement cost. Here, we propose the use of a risk-sensitive optimal controller that incorporates movement cost variance either as an added cost (risk-averse controller) or as an added value (risk-seeking controller) to model human motor behavior in the face of uncertainty. We use a sensorimotor task to test the hypothesis that subjects are risk-sensitive. Subjects controlled a virtual ball undergoing Brownian motion towards a target. Subjects were required to minimize an explicit cost, in points, that was a combination of the final positional error of the ball and the integrated control cost. By testing subjects on different levels of Brownian motion noise and relative weighting of the position and control cost, we could distinguish between risk-sensitive and risk-neutral control. We show that subjects change their movement strategy pessimistically in the face of increased uncertainty in accord with the predictions of a risk-averse optimal controller. Our results suggest that risk-sensitivity is a fundamental attribute that needs to be incorporated into optimal feedback control models. © 2010 Nagengast et al.

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.

Using mesoscopic models to design strong and tough biomimetic polymer networks

Using computational modeling, we investigate the mechanical properties of polymeric materials composed of coiled chains, or "globules", which encompass a folded secondary structure and are cross-linked by labile bonds to form a macroscopic network. In the presence of an applied force, the globules can unfold into linear chains and thereby dissipate energy as the network is deformed; the latter attribute can contribute to the toughness of the material. Our goal is to determine how to tailor the labile intra- and intermolecular bonds within the network to produce material exhibiting both toughness and strength. Herein, we use the lattice spring model (LSM) to simulate the globules and the cross-linked network. We also utilize our modified Hierarchical Bell model (MHBM) to simulate the rupture and reforming of N parallel bonds. By applying a tensile deformation, we demonstrate that the mechanical properties of the system are sensitive to the values of N in and N out, the respective values of N for the intra- and intermolecular bonds. We find that the strength of the material is mainly controlled by the value of N out, with the higher value of N out providing a stronger material. We also find that, if N in is smaller than N out, the globules can unfold under the tensile load before the sample fractures and, in this manner, can increase the ductility of the sample. Our results provide effective strategies for exploiting relatively weak, labile interactions (e.g., hydrogen bonding or the thiol/disulfide exchange reaction) in both the intra- and intermolecular bonds to tailor the macroscopic performance of the materials. © 2011 American Chemical Society.

Strong carrier lifetime enhancement in GaAs nanowires coated with semiconducting polymer.

The ultrafast charge carrier dynamics in GaAs/conjugated polymer type II heterojunctions are investigated using time-resolved photoluminescence spectroscopy at 10 K. By probing the photoluminescence at the band edge of GaAs, we observe strong carrier lifetime enhancement for nanowires blended with semiconducting polymers. The enhancement is found to depend crucially on the ionization potential of the polymers with respect to the Fermi energy level at the surface of the GaAs nanowires. We attribute these effects to electron doping by the polymer which reduces the unsaturated surface-state density in GaAs. We find that when the surface of nanowires is terminated by native oxide, the electron injection across the interface is greatly reduced and such surface doping is absent. Our results suggest that surface engineering via π-conjugated polymers can substantially improve the carrier lifetime in nanowire hybrid heterojunctions with applications in photovoltaics and nanoscale photodetectors.

In-plane critical current density measurements of straight and curved tracks in RABiTS-MOD coated conductors

Depending on the temperature and the magnitude and orientation of an external magnetic field, the critical current density, J c , of a coated conductor can be limited either by the properties of the grain boundaries or by those of the grains. In order to ascertain what governs J c under different conditions, we have measured straight and curved tracks, patterned into RABiTS-MOD samples, while a magnetic field was swept in the plane of the films. Significantly different results were obtained at different field and temperature ranges, which we were able to attribute to J c being limited by either grain boundaries or grains.

Nanoscale plasmonic memristor with optical readout functionality.

We experimentally demonstrate for the first time a nanoscale resistive random access memory (RRAM) electronic device integrated with a plasmonic waveguide providing the functionality of optical readout. The device fabrication is based on silicon on insulator CMOS compatible approach of local oxidation of silicon, which enables the realization of RRAM and low optical loss channel photonic waveguide at the same fabrication step. This plasmonic device operates at telecom wavelength of 1.55 μm and can be used to optically read the logic state of a memory by measuring two distinct levels of optical transmission. The experimental characterization of the device shows optical bistable behavior between these levels of transmission in addition to well-defined hysteresis. We attribute the changes in the optical transmission to the creation of a nanoscale absorbing and scattering metallic filament in the amorphous silicon layer, where the plasmonic mode resides.