Band-gap bowing and p-type doping of (Zn, Mg, Be)O wide-gap semiconductor alloys: a first-principles study

Using a first-principles band-structure method and a special quasirandom structure (SQS) approach, we systematically calculate the band gap bowing parameters and p-type doping properties of (Zn, Mg, Be)O related random ternary and quaternary alloys. We show that the bowing parameters for ZnBeO and MgBeO alloys are large and dependent on composition. This is due to the size difference and chemical mismatch between Be and Zn(Mg) atoms. We also demonstrate that adding a small amount of Be into MgO reduces the band gap indicating that the bowing parameter is larger than the band-gap difference. We select an ideal N atom with lower p atomic energy level as dopant to perform p-type doping of ZnBeO and ZnMgBeO alloys. For N doped in ZnBeO alloy, we show that the acceptor transition energies become shallower as the number of the nearest neighbor Be atoms increases. This is thought to be because of the reduction of p-d repulsion. The N-O acceptor transition energies are deep in the ZnMgBeO quaternary alloy lattice-matched to GaN substrate due to the lower valence band maximum. These decrease slightly as there are more nearest neighbor Mg atoms surrounding the N dopant. The important natural valence band alignment between ZnO, MgO, BeO, ZnBeO, and ZnMgBeO quaternary alloy is also investigated.

Mutual passivation of donors and isovalent nitrogen in GaAs

We study the mutual passivation of shallow donor and isovalent N in GaAs. We find that all the donor impurities, Si-Ga, Ge-Ga, S-As, and Se-As, bind to N in GaAsN, which has a large N-induced band-gap reduction relative to GaAs. For a group-IV impurity such as Si, the formation of the nearest-neighbor Si-Ga-N-As defect complex creates a deep donor level below the conduction band minimum (CBM). The coupling between this defect level with the CBM pushes the CBM upwards, thus restoring the GaAs band gap; the lowering of the defect level relative to the isolated Si-Ga shallow donor level is responsible for the increased electrical resistivity. Therefore, Si and N mutually passivate each other's electrical and optical activities in GaAs. For a group-VI shallow donor such as S, the binding between S-As and N-As does not form a direct bond; therefore, no mutual passivation exists in the GaAs(S+N) system.

High-frequency hydrogen-related infrared modes in silicon grown in a hydrogen atmosphere

High-frequency vibrational modes have been observed at liquid-helium temperature in silicon samples grown in a H-2 or D-2 atmosphere. The highest-frequency ones are due to the overtones and combination modes of SiH fundamentals. Others are CH modes due to (C,H) complexes, but the simultaneous presence of NH modes due to (N,H) complexes cannot be excluded. The present results seem to show also the existence of centers including both SiH and CH or NH bonds. One sharp mode at 4349 cm-l is related to a weak SiH fundamental at 2210 cm(-1). The related center is ascribed to a vacancy fully decorated with hydrogen with a nearest-neighbor C atom. [S0163-1829(99)00911-X].

移动对象在空间网络数据库上的kNN查询

中国计算机学会

Optical response in a quantum dot superlattice nanoring under a lateral electric field

The optical absorption of a GaAs/AlGaAs quantum dot superlattice nanoring (QDSLNR) under a lateral dc electric field and with magnetic flux threading the ring is investigated. This structure and configuration provides a unique opportunity to study the optical response of a superlattice under an inhomogeneous electric field, which is not easily realized for general quantum well superlattices (QWSLs) but naturally realized for QDSLNRs under a homogeneous lateral electric field. It has been shown that a lateral dc electric field gives rise to a substantial change of the optical absorption spectra. Under a low field, the excitonic optical absorption is dominated by a 1s exciton. And with the electric field increasing, the optical absorption undergoes a transition from 1s excitonic absorption to 0 excitronic WSL absorption. (The number of 0, and -1 and +1 below are WSLs index.) The -1 and the +1 WSLs corresponding to the maximum effective field can also be identified. Due to the inhomogeneity of the electric field, the peaks of the -1 and the +1 WSLs are diminished and between them there exist rich and complicated structures. This is in contrast to the general QWSLs under a homogenous electric field. The complicated structures can be understood by considering the inhomogeneity of the electric field along the ring, which results in the nearest-neighbor transition, the next-nearest-neighbor transition, etc., have a different value repectively, at different sites along the ring. This may give rise to multiple WSLs. We have also shown that the line shape of the optical absorption is not sensitive to the threading magnetic flux. The threading magnetic flux only gives rise to a slight diamagnetic shift. Thus the enhancement of the sensitivity to the flux allowing for observation of the excitonic Aharanov-Bohm effect in the plain nanoring is not expected in QDSLNRs.

Ab initio calculations for the neutral and charged O vacancy in sapphire

The energetics, lattice relaxation, and the defect-induced states of st single O vacancy in alpha-Al2O3 are studied by means of supercell total-energy calculations using a first-principles method based on density-functional theory. The supercell model with 120 atoms in a hexagonal lattice is sufficiently large to give realistic results for an isolated single vacancy (square). Self-consistent calculations are performed for each assumed configuration of lattice relaxation involving the nearest-neighbor Al atoms and the next-nearest-neighbor O atoms of the vacancy site. Total-energy data thus accumulated are used to construct an energy hypersurface. A theoretical zero-temperature vacancy formation energy of 5.83 eV is obtained. Our results show a large relaxation of Al (O) atoms away from the vacancy site by about 16% (8%) of the original Al-square (O-square) distances. The relaxation of the neighboring Al atoms has a much weaker energy dependence than the O atoms. The O vacancy introduces a deep and doubly occupied defect level, or an F center in the gap, and three unoccupied defect levels near the conduction band edge, the positions of the latter are sensitive to the degree of relaxation. The defect state wave functions are found to be not so localized, but extend up to the boundary of the supercell. Defect-induced levels are also found in the valence-band region below the O 2s and the O 2p bands. Also investigated is the case of a singly occupied defect level (an F+ center). This is done by reducing both the total number of electrons in the supercell and the background positive charge by one electron in the self-consistent electronic structure calculations. The optical transitions between the occupied and excited states of the: F and F+ centers are also investigated and found to be anisotropic in agreement with optical data.

New parameters of many-body potentials: studying the thermal and mechanical properties of noble metals

New parameters of nearest-neighbor EAM (1N-EAM), n-th neighbor EAM (NN-EAM), and the second-moment approximation to the tight-binding (TB-SMA) potentials are obtained by fitting experimental data at different temperatures. In comparison with the available many-body potentials, our results suggest that the 1N-EAM potential with the new parameters is the best description of atomic interactions in studying the thermal expansion of noble metals. For mechanical properties, it is suggested that the elastic constants should be calculated in the experimental zero-stress states for all three potentials. Furthermore, for NNEAM and TB-SMA potentials, the calculated results approach the experimental data as the range of the atomic interaction increases from the first-neighbor to the sixth-neighbor distance.

DECODE: a new method for discovering clusters of different densities in spatial data

National Key Basic Research and Development Program of China [2006CB701305]; State Key Laboratory of Resource and Environment Information System [088RA400SA]; Chinese Academy of Sciences

MULTIVARIATE CLASSIFICATION BASED ON METAL CONTENTS IN HUMAN SENILE CATARACT LENSES

Multivariate classification methods were used to evaluate data on the concentrations of eight metals in human senile lenses measured by atomic absorption spectrometry. Principal components analysis and hierarchical clustering separated senile cataract lenses, nuclei from cataract lenses, and normal lenses into three classes on the basis of the eight elements. Stepwise discriminant analysis was applied to give discriminant functions with five selected variables. Results provided by the linear learning machine method were also satisfactory; the k-nearest neighbour method was less useful.

Fast Pose Estimation with Parameter Sensitive Hashing

Example-based methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and high-dimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly becme prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task. Our algorithm extends a recently developed method for locality-sensitive hashing, which finds approximate neighbors in time sublinear in the number of examples. This method depends critically on the choice of hash functions; we show how to find the set of hash functions that are optimally relevant to a particular estimation problem. Experiments demonstrate that the resulting algorithm, which we call Parameter-Sensitive Hashing, can rapidly and accurately estimate the articulated pose of human figures from a large database of example images.

Aiding classification of gene expression data with feature selection: a comparative study

C. Shang and Q. Shen. Aiding classification of gene expression data with feature selection: a comparative study. Computational Intelligence Research, 1(1):68-76.

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Geometric Generalizations of the Power of Two Choices

A well-known paradigm for load balancing in distributed systems is the``power of two choices,''whereby an item is stored at the less loaded of two (or more) random alternative servers. We investigate the power of two choices in natural settings for distributed computing where items and servers reside in a geometric space and each item is associated with the server that is its nearest neighbor. This is in fact the backdrop for distributed hash tables such as Chord, where the geometric space is determined by clockwise distance on a one-dimensional ring. Theoretically, we consider the following load balancing problem. Suppose that servers are initially hashed uniformly at random to points in the space. Sequentially, each item then considers d candidate insertion points also chosen uniformly at random from the space,and selects the insertion point whose associated server has the least load. For the one-dimensional ring, and for Euclidean distance on the two-dimensional torus, we demonstrate that when n data items are hashed to n servers,the maximum load at any server is log log n / log d + O(1) with high probability. While our results match the well-known bounds in the standard setting in which each server is selected equiprobably, our applications do not have this feature, since the sizes of the nearest-neighbor regions around servers are non-uniform. Therefore, the novelty in our methods lies in developing appropriate tail bounds on the distribution of nearest-neighbor region sizes and in adapting previous arguments to this more general setting. In addition, we provide simulation results demonstrating the load balance that results as the system size scales into the millions.

Learning Euclidean Embeddings for Indexing and Classification

BoostMap is a recently proposed method for efficient approximate nearest neighbor retrieval in arbitrary non-Euclidean spaces with computationally expensive and possibly non-metric distance measures. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. The key idea is formulating embedding construction as a machine learning task, where AdaBoost is used to combine simple, 1D embeddings into a multidimensional embedding that preserves a large amount of the proximity structure of the original space. This paper demonstrates that, using the machine learning formulation of BoostMap, we can optimize embeddings for indexing and classification, in ways that are not possible with existing alternatives for constructive embeddings, and without additional costs in retrieval time. First, we show how to construct embeddings that are query-sensitive, in the sense that they yield a different distance measure for different queries, so as to improve nearest neighbor retrieval accuracy for each query. Second, we show how to optimize embeddings for nearest neighbor classification tasks, by tuning them to approximate a parameter space distance measure, instead of the original feature-based distance measure.

Query-Sensitive Embeddings

A common problem in many types of databases is retrieving the most similar matches to a query object. Finding those matches in a large database can be too slow to be practical, especially in domains where objects are compared using computationally expensive similarity (or distance) measures. This paper proposes a novel method for approximate nearest neighbor retrieval in such spaces. Our method is embedding-based, meaning that it constructs a function that maps objects into a real vector space. The mapping preserves a large amount of the proximity structure of the original space, and it can be used to rapidly obtain a short list of likely matches to the query. The main novelty of our method is that it constructs, together with the embedding, a query-sensitive distance measure that should be used when measuring distances in the vector space. The term "query-sensitive" means that the distance measure changes depending on the current query object. We report experiments with an image database of handwritten digits, and a time-series database. In both cases, the proposed method outperforms existing state-of-the-art embedding methods, meaning that it provides significantly better trade-offs between efficiency and retrieval accuracy.