Structural tunability and switchable exciton emission in inorganic-organic hybrids with mixed halides

Room-temperature tunable excitonic photoluminescence is demonstrated in alloy-tuned layered Inorganic-Organic (IO) hybrids, (C12H 25NH3)2PbI4(1-y)Br4y (y = 0 to 1). These perovskite IO hybrids adopt structures with alternating stacks of low-dimensional inorganic and organic layers, considered to be naturally self-assembled multiple quantum wells. These systems resemble stacked monolayer 2D semiconductors since no interlayer coupling exists. Thin films of IO hybrids exhibit sharp and strong photoluminescence (PL) at room-temperature due to stable excitons formed within the low-dimensional inorganic layers. Systematic variation in the observed exciton PL from 510 nm to 350 nm as the alloy composition is changed, is attributed to the structural readjustment of crystal packing upon increase of the Br content in the Pb-I inorganic network. The energy separation between exciton absorption and PL is attributed to the modified exciton density of states and diffusion of excitons from relatively higher energy states corresponding to bromine rich sites towards the lower energy iodine sites. Apart from compositional fluctuations, these excitons show remarkable reversible flips at temperature-induced phase transitions. All the results are successfully correlated with thermal and structural studies. Such structural engineering flexibility in these hybrids allows selective tuning of desirable exciton properties within suitable operating temperature ranges. Such wide-range PL tunability and reversible exciton switching in these novel IO hybrids paves the way to potential applications in new generation of optoelectronic devices. © 2013 AIP Publishing LLC.

Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials.

The trajectory of the somatic membrane potential of a cortical neuron exactly reflects the computations performed on its afferent inputs. However, the spikes of such a neuron are a very low-dimensional and discrete projection of this continually evolving signal. We explored the possibility that the neuron's efferent synapses perform the critical computational step of estimating the membrane potential trajectory from the spikes. We found that short-term changes in synaptic efficacy can be interpreted as implementing an optimal estimator of this trajectory. Short-term depression arose when presynaptic spiking was sufficiently intense as to reduce the uncertainty associated with the estimate; short-term facilitation reflected structural features of the statistics of the presynaptic neuron such as up and down states. Our analysis provides a unifying account of a powerful, but puzzling, form of plasticity.

Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

Applications of the dynamic mode decomposition

The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of an unforced and harmonically forced jet. The algorithm relies on the reconstruction of a low-dimensional inter-snapshot map from the available flow field data. The spectral decomposition of this map results in an eigenvalue and eigenvector representation (referred to as dynamic modes) of the underlying fluid behavior contained in the processed flow fields. This dynamic mode decomposition allows the breakdown of a fluid process into dynamically revelant and coherent structures and thus aids in the characterization and quantification of physical mechanisms in fluid flow. © 2010 Springer-Verlag.

Semantic Discriminant mapping for classification and browsing of remote sensing textures and objects

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks. © 2005 IEEE.

Altering the ordering and disordering of a triangular nanographene at room temperature.

Molecular self-organization has the potential to serve as an efficient and versatile tool for the spontaneous creation of low-dimensional nanostructures on surfaces. We demonstrate how the subtle balance between intermolecular interactions and molecule-surface interactions can be altered by modifying the environment or through manipulation by means of the tip in a scanning tunnelling microscope (STM) at room temperature. We show how this leads to the distinctive ordering and disordering of a triangular nanographene molecule, the trizigzag-hexa-peri-hexabenzocoronenes-phenyl-6 (trizigzagHBC-Ph6), on two different surfaces: graphite and Au(111). The assembly of submonolayer films on graphite reveals a sixfold packing symmetry under UHV conditions, whereas at the graphite-phenyloctane interface, they reorganize into a fourfold packing symmetry, mediated by the solvent molecules. On Au(111) under UHV conditions in the multilayer films we investigated, although disorder prevails with the molecules being randomly distributed, their packing behaviour can be altered by the scanning motion of the tip. The asymmetric diode-like current-voltage characteristics of the molecules are retained when deposited on both substrates. This paper highlights the importance of the surrounding medium and any external stimulus in influencing the molecular organization process, and offers a unique approach for controlling the assembly of molecules at a desired location on a substrate.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.

Copula-based Kernel Dependency Measures

The paper presents a new copula based method for measuring dependence between random variables. Our approach extends the Maximum Mean Discrepancy to the copula of the joint distribution. We prove that this approach has several advantageous properties. Similarly to Shannon mutual information, the proposed dependence measure is invariant to any strictly increasing transformation of the marginal variables. This is important in many applications, for example in feature selection. The estimator is consistent, robust to outliers, and uses rank statistics only. We derive upper bounds on the convergence rate and propose independence tests too. We illustrate the theoretical contributions through a series of experiments in feature selection and low-dimensional embedding of distributions.

Collective motion, sensor networks, and ocean sampling

This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored © 2006 IEEE.

Novel nanostructured carbon nanotube electron sources

In this chapter, we present a review of our continuing efforts toward the development of discrete, low-dimensional nanostructured carbon-based electron emitters. Carbon nanotubes and nanofibers, herein referred to simply as CNTs, are one-dimensional carbon allotropes formed from cylindrically rolled and nested graphene sheets, have diameters between 1 and 500 nm and lengths of up to several millimeters, and are perfect candidates for field emission (FE) applications. By virtue of their extremely strong sp2 C-C bonding, intrinsic to the graphene hexagonal lattice, CNTs have demonstrated impressive chemical inertness, unprecedented thermal stabilities, significant resistance to electromigration, and exceptionally high axial current carrying capacities, even at elevated temperatures. These near ideal cold cathode electron emitters have incredibly high electric field enhancing aspect ratios combined with virtual point sources of the order of a few nanometers in size. The correct integration and judicious development of suitable FE platforms based on these extraordinary molecules is critical and will ultimately enable enhanced technologies. This chapter will review some of the more recent platforms, devices and structures developed by our group, as well as our contributions towards the development of industry-scalable technologies for ultra-high-resolution electron microscopy, portable x-ray sources, and flexible environmental lighting technologies. © 2012 by Pan Stanford Publishing Pte. Ltd. All rights reserved.

Linear dimensionality reduction: Survey, insights, and generalizations

© 2015 John P. Cunningham and Zoubin Ghahramani. Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of interest, such as covariance, dynamical structure, correlation between data sets, input-output relationships, and margin between data classes. Methods have been developed with a variety of names and motivations in many fields, and perhaps as a result the connections between all these methods have not been highlighted. Here we survey methods from this disparate literature as optimization programs over matrix manifolds. We discuss principal component analysis, factor analysis, linear multidimensional scaling, Fisher's linear discriminant analysis, canonical correlations analysis, maximum autocorrelation factors, slow feature analysis, sufficient dimensionality reduction, undercomplete independent component analysis, linear regression, distance metric learning, and more. This optimization framework gives insight to some rarely discussed shortcomings of well-known methods, such as the suboptimality of certain eigenvector solutions. Modern techniques for optimization over matrix manifolds enable a generic linear dimensionality reduction solver, which accepts as input data and an objective to be optimized, and returns, as output, an optimal low-dimensional projection of the data. This simple optimization framework further allows straightforward generalizations and novel variants of classical methods, which we demonstrate here by creating an orthogonal-projection canonical correlations analysis. More broadly, this survey and generic solver suggest that linear dimensionality reduction can move toward becoming a blackbox, objective-agnostic numerical technology.

Uncooled DBR laser directly modulated at 3.125 Gb/s as athermal transmitter for low-cost WDM systems

An uncooled three-section tunable distributed Bragg reflector laser is demonstrated as an athermal transmitter for low-cost uncooled wavelength-division-multiplexing (WDM) systems with tight channel spacing. A ±0.02-nm thermal wavelength drift is achieved under continuous-wave operation up to 70 °C. Dynamic sidemode suppression ratio of greater than 35 dB is consistently obtained under 3.125-Gb/s direct modulation over a 20 °C-70 °C temperature range, with wavelength variation of as low as ±0.2 nm. This indicates that more than an order of magnitude reduction in coarse WDM channel spacing is possible using this source. © 2005 IEEE.