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.

Positive Correlations in Tunneling through coupled Quantum Dots

Due to the Fermi-Dirac statistics of electrons the temporal correlations of tunneling events in a double barrier setup are typically negative. Here, we investigate the shot noise behavior of a system of two capacitively coupled quantum dot states by means of a Master equation model. In an asymmetric setup positive correlations in the tunneling current can arise due to the bunching of tunneling events. The underlying mechanism will be discussed in detail in terms of the current-current correlation function and the frequency-dependent Fano factor.

Cooperative attitude synchronization in satellite swarms: A consensus approach

The present paper considers the problem of autonomous synchronization of attitudes in a swarm of spacecraft. Building upon our recent results on consensus on manifolds, we model the spacecraft as particles on SO(3) and drive these particles to a common point in SO(3). Unlike the Euler angle or quaternion descriptions, this model suffers no singularities nor double-points. Our approach is fully cooperative and autonomous: we use no leader nor external reference. We present two types of control laws, in terms of applied control torques, that globally drive the swarm towards attitude synchronization: one that requires tree-like or all-to-all inter-satellite communication (most efficient) and one that works with nearly arbitrary communication (most robust).

A Grassman-Rayleigh quotient iteration for computing invariant subspaces

The classical Rayleigh Quotient Iteration (RQI) computes a 1-dimensional invariant subspace of a symmetric matrix A with cubic convergence. We propose a generalization of the RQI which computes a p-dimensional invariant subspace of A. The geometry of the algorithm on the Grassmann manifold Gr(p,n) is developed to show cubic convergence and to draw connections with recently proposed Newton algorithms on Riemannian manifolds.

Application of fast oxygen sensors for investigations into air-path dynamics and EGR distribution in a diesel engine

The control of NOX emissions by exhaust gas recirculation (EGR) is of widespread application. However, despite dramatic improvements in all aspects of engine control, the subtle mixing processes that determine the cylinder-to-cylinder distribution of the recirculated gas often results in a mal-distribution that is still an issue for the engine designer and calibrator. In this paper we demonstrate the application of a relatively straightforward technique for the measurement of the absolute and relative dilution quantity in both steady state and transient operation. This was achieved by the use of oxygen sensors based on standard UEGO (universal exhaust gas oxygen) sensors but packaged so as to give good frequency response (∼ 10 ms time constant) and be completely insensitivity to the sample pressure and temperature. Measurements can be made at almost any location of interest, for example exhaust and inlet manifolds as well as EGR path(s), with virtually no flow disturbance. At the same time, the measurements yield insights into air-path dynamics. We argue that "dilution", as indicated by the deviation of the oxygen concentration from that of air, is a more appropriate parameter than EGR rate in the context of NOX control, especially for diesel engines. Experimental results are presented for the EGR distribution in a current production light duty 4-cylinder diesel engine in which significant differences were found in the proportion of the recirculated gas that reached each cylinder. Even the individual inlet runners of the cylinders exhibited very different dilution rates - differences of nearly 50% were observed at some conditions. An application of such data may be in the improvement of calibration and validation of CFD and other modelling techniques. Copyright © 2014 SAE International.

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.

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.

Electron tunneling through a planar single barrier in HgTe quantum wells with inverted band structures

We present a theoretical study on the electron tunneling through a single barrier created in a two-dimensional electron gas (2DEG) and quantum spin Hall (QSH) bar in a HgTe/CdTe quantum well with inverted band structures. For the 2DEG, the transmission shows the Fabry-Perot resonances for the interband tunneling process and is blocked when the incident energy lies in the bulk gap of the barrier region. For the QSH bar, the transmission gap is reduced to the edge gap caused by the finite size effect. Instead, transmission dips appear due to the interference between the edge states and the bound states originated from the bulk states. Such a Fano-like resonance leads to a sharp dip in the transmission which can be observed experimentally.

Coulomb blockade double-dot Aharonov-Bohm interferometer: Giant fluctuations

Electron transport through two parallel quantum dots is a kind of solid-state realization of double path interference We demonstrate that the inter-clot Coulomb correlation and quantum coherence would result in strong current fluctuations with a divergent Fano factor at zero frequency. We also provide physical interpretation for this surprising result, which displays its generic feature and allows us to recover this phenomenon in more complicated systems. (C) 2009 Elsevier B.V. All rights reserved.

Kondo effect in a triangular triple quantum dots ring with three terminals

For a triangular triple quantum dots (TTQDs) ring with three terminals, when lowering one of the dot-lead coupling to realize the left-right (L-R) reflection symmetry coupling, the internal C-upsilon of the TTQDs is well preserved in the absence of many-body effect for the symmetric distribution of the dot-lead coupling on the molecular orbits. In the presence of Kondo effect, the decrement of one of the dot-lead couplings suppresses the inter-dot hopping. This happens especially for the coupled quantum dot (QD), which decouples with the other two ones gradually to form a localized state near the Fermi level As a result, the internal dynamic symmetry of the TTQDs ring is reduced to L-R reflection symmetry, and simultaneously, the linear conductance is lifted for the new forming molecular orbit near the Fermi level

instability of standing wave, global existence and blowup for the klein-gordon-zakharov system with different-degree nonlinearities

This paper discusses the Klein–Gordon–Zakharov system with different-degree nonlinearities in two and three space dimensions. Firstly, we prove the existence of standing wave with ground state by applying an intricate variational argument. Next, by introducing an auxiliary functional and an equivalent minimization problem, we obtain two invariant manifolds under the solution flow generated by the Cauchy problem to the aforementioned Klein–Gordon–Zakharov system. Furthermore, by constructing a type of constrained variational problem, utilizing the above two invariant manifolds as well as applying potential well argument and concavity method, we derive a sharp threshold for global existence and blowup. Then, combining the above results, we obtain two conclusions of how small the initial data are for the solution to exist globally by using dilation transformation. Finally, we prove a modified instability of standing wave to the system under study.

DISORDER POINT FOR 2D ELECTRONS IN A HALF-FILLED LANDAU-LEVEL

Short-range correlations of two-dimensional electrons in a strong magnetic field are shown to be triangular in nature well below half-filling, but honeycomb well above half-filling. The half-filling point is thus proposed, and qualitatively confirmed by three-body correlation calculations, to be a new type of disorder point where short-range correlations change character. A wavefunction study also suggests that nodes become unbound at half-filling. Evidence for incompressibility but deformability of the half-filling state earlier suggested by Fano, Ortolani and Tosatti, is also presented and found to be in agreement with recent experiments.

Doubly excited 2s2p P-1,3(1) resonances in photoionization of helium

The multi-configuration Dirac Fock (MCDF) method is implemented to study doubly excited 2s2p P-1,3(1) resonances of the helium atom and the interference between photoionization and photo excitation autoionization processes. In order to reproduce the total photoionization sprectra, the excited energies from the ground 1s(2) S-1(0) state to the doubly excited 2s2p P-1,3(1) states and the relevant Auger decay rates and widths are calculated in detail. Further more, the interference profile determined by the so-called Fano parameters q and rho(2) is also reproduced. Good agreement is found between the present results and other available theoretical and experimental results. This indeed shows a promising way to investigate the Fano resonances in photoionization of atoms within the MCDF scheme, although there are some discrepancies in the present calculations of the 2s2p P-3(1) state.

Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

Single molecule dynamics and statistical fluctuations of gene regulatory networks: A repressilator

The authors developed a time dependent method to study the single molecule dynamics of a simple gene regulatory network: a repressilator with three genes mutually repressing each other. They quantitatively characterize the time evolution dynamics of the repressilator. Furthermore, they study purely dynamical issues such as statistical fluctuations and noise evolution. They illustrated some important features of the biological network such as monostability, spirals, and limit cycle oscillation. Explicit time dependent Fano factors which describe noise evolution and show statistical fluctuations out of equilibrium can be significant and far from the Poisson distribution. They explore the phase space and the interrelationships among fluctuations, order, amplitude, and period of oscillations of the repressilators. The authors found that repressilators follow ordered limit cycle orbits and are more likely to appear in the lower fluctuating regions. The amplitude of the repressilators increases as the suppressing of the genes decreases and production of proteins increases. The oscillation period of the repressilators decreases as the suppressing of the genes decreases and production of proteins increases.