Novel nonvolatile memory with multibit storage based on a ZnO nanowire transistor.

We demonstrate a room temperature processed ferroelectric (FE) nonvolatile memory based on a ZnO nanowire (NW) FET where the NW channel is coated with FE nanoparticles. A single device exhibits excellent memory characteristics with the large modulation in channel conductance between ON and OFF states exceeding 10(4), a long retention time of over 4 × 10(4) s, and multibit memory storage ability. Our findings provide a viable way to create new functional high-density nonvolatile memory devices compatible with simple processing techniques at low temperature for flexible devices made on plastic substrates.

Multimode radiation from an unflanged, semi-infinite circular duct with uniform flow.

Multimode sound radiation from an unflanged, semi-infinite, rigid-walled circular duct with uniform subsonic mean flow everywhere is investigated theoretically. The multimode directivity depends on the amplitude and directivity function of each individual cut-on mode. The amplitude of each mode is expressed as a function of cut-on ratio for a uniform distribution of incoherent monopoles, a uniform distribution of incoherent axial dipoles, and for equal power per mode. The directivity function of each mode is obtained by applying a Lorentz transformation to the zero-flow directivity function, which is given by a Wiener-Hopf solution. This exact numerical result is compared to an analytic solution, valid in the high-frequency limit, for multimode directivity with uniform flow. The high-frequency asymptotic solution is derived assuming total transmission of power at the open end of the duct, and gives the multimode directivity function with flow in the forward arc for a general family of mode amplitude distribution functions. At high frequencies the agreement between the exact and asymptotic solutions is shown to be excellent.

Modified Kanerva model. Results for real time word recognition

This paper describes results obtained using the modified Kanerva model to perform word recognition in continuous speech after being trained on the multi-speaker Alvey 'Hotel' speech corpus. Theoretical discoveries have recently enabled us to increase the speed of execution of part of the model by two orders of magnitude over that previously reported by Prager & Fallside. The memory required for the operation of the model has been similarly reduced. The recognition accuracy reaches 95% without syntactic constraints when tested on different data from seven trained speakers. Real time simulation of a model with 9,734 active units is now possible in both training and recognition modes using the Alvey PARSIFAL transputer array. The modified Kanerva model is a static network consisting of a fixed nonlinear mapping (location matching) followed by a single layer of conventional adaptive links. A section of preprocessed speech is transformed by the non-linear mapping to a high dimensional representation. From this intermediate representation a simple linear mapping is able to perform complex pattern discrimination to form the output, indicating the nature of the speech features present in the input window.

Feasibility of AlGaInAs lasers for high speed uncooled communication systems

The feasibility of using AlGaInAs lasers for high-speed modulation at high temperatures was evaluated and compared with performance of GaInAsP devices. Both drift-diffusion and rate equation simulation were involved so that the temperature dependence of material parameters was found in terms of overall dynamic performance. Differential gain was estimated by means of drift-diffusion simulations.

Identification and effectiveness of self-help groups in Cambodia

The CGIAR Research Program on Aquatic Agricultural Systems (AAS) seeks to reduce poverty and improve food security for the millions of small-scale fishers and farmers who depend on the world’s floodplains, deltas and coasts. AAS combines more conventional approaches for introducing and scaling technical innovations, such as applied research and training, with approaches that foster innovation and promote institutional and policy change. Specifically, AAS utilizes participatory action research with communities to identify technology and policy solutions that best meet community long-term needs. One of the themes identified under AAS is the role of self-help groups in increasing livelihood resilience of agriculture and fisheries communities. As AAS establishes a hub of operations in Cambodia, AAS and Oxfam America are cooperating to investigate the potential of community-based self-help groups as a strategy for AAS implementation. As part of this cooperation, Oxfam America undertook this consultancy to analyze and describe the role, efficiency and effectiveness of the various types of self-help groups in Cambodia. This report gives an overview of this program which aims to conduct a field-based study to identify the types, main characteristics and effectiveness of self-help groups, with a particular focus on livelihood resilience of agriculture and fisheries communities.

Natural selection on EPAS1 (HIF2 alpha) associated with low hemoglobin concentration in Tibetan highlanders

By impairing both function and survival, the severe reduction in oxygen availability associated with high-altitude environments is likely to act as an agent of natural selection. We used genomic and candidate gene approaches to search for evidence of such genetic selection. First, a genome-wide allelic differentiation scan (GWADS) comparing indigenous highlanders of the Tibetan Plateau (3,200 3,500 m) with closely related lowland Han revealed a genome-wide significant divergence across eight SNPs located near EPAS1. This gene encodes the transcription factor HIF2 alpha, which stimulates production of red blood cells and thus increases the concentration of hemoglobin in blood. Second, in a separate cohort of Tibetans residing at 4,200 m, we identified 31 EPAS1 SNPs in high linkage disequilibrium that correlated significantly with hemoglobin concentration. The sex-adjusted hemoglobin concentration was, on average, 0.8 g/dL lower in the major allele homozygotes compared with the heterozygotes. These findings were replicated in a third cohort of Tibetans residing at 4,300 m. The alleles associating with lower hemoglobin concentrations were correlated with the signal from the GWADS study and were observed at greatly elevated frequencies in the Tibetan cohorts compared with the Han. High hemoglobin concentrations are a cardinal feature of chronic mountain sickness offering one plausible mechanism for selection. Alternatively, as EPAS1 is pleiotropic in its effects, selection may have operated on some other aspect of the phenotype. Whichever of these explanations is correct, the evidence for genetic selection at the EPAS1 locus from the GWADS study is supported by the replicated studies associating function with the allelic variants.

Water quality assessment of Sonbolrood River by using hylsenhof index

This research investigates the quality of sonbolrood river by using Hylsenhof HFBI indicators and identified Macroinvertebrates invertebrates community in the family level. This study took place during 1388-1389 with four sampling season in four stations respectively in the forests of Kalyj kheyl village in Savadkuh (first station), industrial area of Islamabad (second Station), earth dam of Sonbolrood (third station) and the Place crosses Sonbolrood with Babolrood river (fourth Station). Macroinvertebrates invertebrates collected by quantitative sampler of Sorbr and they were isolated in laboratory by loop and they were identified in the family level. Generally, Macroinvertebrates of Sonbolrood river were formed three branches: Arthropods and flat worms and mollusks, including 3 tiers, 6 orders and 14 families that showed the maximum diversity and density in autumn and the least diversity and density in summer at all stations, also the third and fourth stations respectively were highest and lowest diversity and density. The water quality of Sonbolrood river based on the water quality Guide(Hylsenhof) is evaluated with excellent condition for all stations except third station. Sonbolrood river with having high slope, rocky and sandy bed, with self-refining act, completely is a proper ecosystem for aquatic organisms, but it is done due to increased organic matter and sewage factory located in industrial zone in the third station and then the increased water pollution caused by nurturing the water warm fish in the earth dam of Sonbolrood. (because of this, the water quality at third station based on the water quality Guide(Hylsenhof) are evaluated in a fairly good condition) and adding domestic sewages of adjacent villages like Seyedkola village and Shirdarkola caused increased pollution and increased trophy of Macroinvertebrates that are resistant to pollution and affect upon Macroinvertebrates community.

Derivative Kernels for Noise Robust ASR

Recently there has been interest in combined gen- erative/discriminative classifiers. In these classifiers features for the discriminative models are derived from generative kernels. One advantage of using generative kernels is that systematic approaches exist how to introduce complex dependencies beyond conditional independence assumptions. Furthermore, by using generative kernels model-based compensation/adaptation tech- niques can be applied to make discriminative models robust to noise/speaker conditions. This paper extends previous work with combined generative/discriminative classifiers in several directions. First, it introduces derivative kernels based on context- dependent generative models. Second, it describes how derivative kernels can be incorporated in continuous discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high- dimensional features of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task.

Derivative kernels for noise robust ASR

Recently there has been interest in combining generative and discriminative classifiers. In these classifiers features for the discriminative models are derived from the generative kernels. One advantage of using generative kernels is that systematic approaches exist to introduce complex dependencies into the feature-space. Furthermore, as the features are based on generative models standard model-based compensation and adaptation techniques can be applied to make discriminative models robust to noise and speaker conditions. This paper extends previous work in this framework in several directions. First, it introduces derivative kernels based on context-dependent generative models. Second, it describes how derivative kernels can be incorporated in structured discriminative models. Third, it addresses the issues associated with large number of classes and parameters when context-dependent models and high-dimensional feature-spaces of derivative kernels are used. The approach is evaluated on two noise-corrupted tasks: small vocabulary AURORA 2 and medium-to-large vocabulary AURORA 4 task. © 2011 IEEE.

Gaussian Process Vine Copulas for Multivariate Dependence

Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

High-density remote plasma sputtering of high-dielectric-constant amorphous hafnium oxide films

Hafnium oxide (HfOx) is a high dielectric constant (k) oxide which has been identified as being suitable for use as the gate dielectric in thin film transistors (TFTs). Amorphous materials are preferred for a gate dielectric, but it has been an ongoing challenge to produce amorphous HfOx while maintaining a high dielectric constant. A technique called high target utilization sputtering (HiTUS) is demonstrated to be capable of depositing high-k amorphous HfOx thin films at room temperature. The plasma is generated in a remote chamber, allowing higher rate deposition of films with minimal ion damage. Compared to a conventional sputtering system, the HiTUS technique allows finer control of the thin film microstructure. Using a conventional reactive rf magnetron sputtering technique, monoclinic nanocrystalline HfOx thin films have been deposited at a rate of ∼1.6nmmin-1 at room temperature, with a resistivity of 1013Ωcm, a breakdown strength of 3.5MVcm-1 and a dielectric constant of ∼18.2. By comparison, using the HiTUS process, amorphous HfOx (x=2.1) thin films which appear to have a cubic-like short-range order have been deposited at a high deposition rate of ∼25nmmin-1 with a high resistivity of 1014Ωcm, a breakdown strength of 3MVcm-1 and a high dielectric constant of ∼30. Two key conditions must be satisfied in the HiTUS system for high-k HfOx to be produced. Firstly, the correct oxygen flow rate is required for a given sputtering rate from the metallic target. Secondly, there must be an absence of energetic oxygen ion bombardment to maintain an amorphous microstructure and a high flux of medium energy species emitted from the metallic sputtering target to induce a cubic-like short range order. This HfOx is very attractive as a dielectric material for large-area electronic applications on flexible substrates. A remote plasma sputtering process (high target utilization sputtering, HiTUS) has been used to deposit amorphous hafnium oxide with a very high dielectric constant (∼30). X-ray diffraction shows that this material has a microstructure in which the atoms have a cubic-like short-range order, whereas radio frequency (rf) magnetron sputtering produced a monoclinic polycrystalline microstructure. This is correlated to the difference in the energetics of remote plasma and rf magnetron sputtering processes. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linear regression under fixed-rank constraints: A Riemannian approach

In this paper, we tackle the problem of learning a linear regression model whose parameter is a fixed-rank matrix. We study the Riemannian manifold geometry of the set of fixed-rank matrices and develop efficient line-search algorithms. The proposed algorithms have many applications, scale to high-dimensional problems, enjoy local convergence properties and confer a geometric basis to recent contributions on learning fixed-rank matrices. Numerical experiments on benchmarks suggest that the proposed algorithms compete with the state-of-the-art, and that manifold optimization offers a versatile framework for the design of rank-constrained machine learning algorithms. Copyright 2011 by the author(s)/owner(s).

Regression on fixed-rank positive semidefinite matrices: A Riemannian approach

The paper addresses the problem of learning a regression model parameterized by a fixed-rank positive semidefinite matrix. The focus is on the nonlinear nature of the search space and on scalability to high-dimensional problems. The mathematical developments rely on the theory of gradient descent algorithms adapted to the Riemannian geometry that underlies the set of fixedrank positive semidefinite matrices. In contrast with previous contributions in the literature, no restrictions are imposed on the range space of the learned matrix. The resulting algorithms maintain a linear complexity in the problem size and enjoy important invariance properties. We apply the proposed algorithms to the problem of learning a distance function parameterized by a positive semidefinite matrix. Good performance is observed on classical benchmarks. © 2011 Gilles Meyer, Silvere Bonnabel and Rodolphe Sepulchre.

HTGR fuel element depletion benchmark: Stage three results

Recently, a new numerical benchmark exercise for High Temperature Gas Cooled Reactor (HTGR) fuel depletion was defined. The purpose of this benchmark is to provide a comparison basis for different codes and methods applied to the burnup analysis of HTGRs. The benchmark specifications include three different models: (1) an infinite lattice of tristructural isotropic (TRISO) fuel particles, (2) an infinite lattice of fuel pebbles, and (3) a prismatic fuel including fuel and coolant channels. In this paper, we present the results of the third stage of the benchmark obtained with MCNP based depletion code BGCore and deterministic lattice code HELIOS 1.9. The depletion calculations were performed for three-dimensional model of prismatic fuel with explicitly described TRISO particles as well as for two-dimensional model, in which double heterogeneity of the TRISO particles was eliminated using reactivity equivalent physical transformation (RPT). Generally, good agreement in the results of the calculations obtained using different methods and codes was observed.

Narrow line high power picosecond pulse generation in a multicontact distributed feedback laser using modified Q switching

High power bandwidth-limited picosecond pulses with peak powers in excess of 200 mW have been generated using multi-contact distributed feedback laser diodes for the first time. The pulses have widths typically less than 10 ps, time-bandwidth products of as little as 0·24, and can be generated on demand at generator limited repetition rates of up to 140 MHz.