Bandgap engineering in semiconductor alloy nanomaterials with widely tunable compositions

Over the past decade, tremendous progress has been achieved in the development of nanoscale semiconductor materials with a wide range of bandgaps by alloying different individual semiconductors. These materials include traditional II-VI and III-V semiconductors and their alloys, inorganic and hybrid perovskites, and the newly emerging 2D materials. One important common feature of these materials is that their nanoscale dimensions result in a large tolerance to lattice mismatches within a monolithic structure of varying composition or between the substrate and target material, which enables us to achieve almost arbitrary control of the variation of the alloy composition. As a result, the bandgaps of these alloys can be widely tuned without the detrimental defects that are often unavoidable in bulk materials, which have a much more limited tolerance to lattice mismatches. This class of nanomaterials could have a far-reaching impact on a wide range of photonic applications, including tunable lasers, solid-state lighting, artificial photosynthesis and new solar cells

Speech synthesis, Speech simulation and speech science

Speech synthesis research has been transformed in recent years through the exploitation of speech corpora - both for statistical modelling and as a source of signals for concatenative synthesis. This revolution in methodology and the new techniques it brings calls into question the received wisdom that better computer voice output will come from a better understanding of how humans produce speech. This paper discusses the relationship between this new technology of simulated speech and the traditional aims of speech science. The paper suggests that the goal of speech simulation frees engineers from inadequate linguistic and physiological descriptions of speech. But at the same time, it leaves speech scientists free to return to their proper goal of building a computational model of human speech production

Parametrization of stochastic inputs using generative adversarial networks with application in geology

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude

Coarse-grained simulations of RNA and DNA duplexes

Although RNAs play many cellular functions little is known about the dynamics and thermodynamics of these molecules. In principle, all-atom molecular dynamics simulations can investigate these issues, but with current computer facilities, these simulations have been limited to small RNAs and to short times. HiRe-RNA, a recently proposed high-resolution coarse-grained for RNA that captures many geometric details such as base pairing and stacking, is able to fold RNA molecules to near-native structures in a short computational time. So far it had been applied to simple hairpins, and here we present its application to duplexes of a couple dozen nucleotides and show how with our model and with Replica Exchange Molecular Dynamics (REMD) we can easily predict the correct double helix from a completely random configuration and study the dissociation curve. To show the versatility of our model, we present an application to a double stranded DNA molecule as well. A reconstruction algorithm allows us to obtain full atom structures from the coarse-grained model. Through atomistic Molecular Dynamics (MD) we can compare the dynamics starting from a representative structure of a low temperature replica or from the experimental structure, and show how the two are statistically identical, highlighting the validity of a coarse-grained approach for structured RNAs and DNAs.Comment: 28 pages, 11 figure

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

There is a high prevalence of coronary artery disease (CAD) in patients with left bundle branch block (LBBB); however there are many other causes for this electrocardiographic abnormality. Non-invasive assessment of these patients remains difficult, and all commonly used modalities exhibit several drawbacks. This often leads to these patients undergoing invasive coronary angiography which may not have been necessary. In this review, we examine the uses and limitations of commonly performed non-invasive tests for diagnosis of CAD in patients with LBBB

A design tool for high-resolution high-frequency cascade continuous- time Σ∆ modulators

Event: Microtechnologies for the New Millennium, 2007, Maspalomas, Gran Canaria, SpainThis paper introduces a CAD methodology to assist the de signer in the implementation of continuous-time (CT) cas- cade Σ∆ modulators. The salient features of this methodology ar e: (a) flexible behavioral modeling for optimum accuracy- efficiency trade-offs at different stages of the top-down synthesis process; (b) direct synthesis in the continuous-time domain for minimum circuit complexity and sensitivity; a nd (c) mixed knowledge-based and optimization-based architec- tural exploration and specification transmission for enhanced circuit performance. The applicability of this methodology will be illustrated via the design of a 12 bit 20 MHz CT Σ∆ modulator in a 1.2V 130nm CMOS technology.Ministerio de Ciencia y Educación TEC2004-01752/MICMinisterio de Industria, Turismo y Comercio FIT-330100-2006-134 SPIRIT Projec