arXiv subjects

Xi Luo

Publications and source records attributed to Xi Luo.

64 records · Page 4Linked to original sources

arXiv2016

Noncommutative Chern-Simons theory and exotic geometry emerging from the lowest Landau level

We relate the collective dynamic internal geometric degrees of freedom to the gauge fluctuations in $ν=1/m$(m odd) fractional quantum Hall effects. In this way, in the lowest Landau level, a highly nontrivial quantum geometry in two-dimensional guiding center space emerges from these internal geometric modes. Using the Dirac bracket method, we find that this quantum geometric field theory is a topological non-commutative Chern-Simons theory.Topological indices, such as the guiding center angular momentum (also called the shift) and the guiding center spin, which characterize the fractional quantum Hall (FQH) states besides the filling factor, are naturally defined. A noncommutative K-matrix Chern-Simons theory is proposed as a generalization to a large class of Abelian FQH topological orders.

cond-mat.str-el↗

arXiv2016

Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators

In many scientific studies, it becomes increasingly important to delineate the causal pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as products of coefficients. However, it becomes unstable to fit such models with high dimensional mediators, especially for a general setting where all the mediators are causally dependent but the exact causal relationships between them are unknown. This paper proposes a sparse mediation model using a regularized SEM approach, where sparsity here means that a small number of mediators have nonzero mediation effects between a treatment and an outcome. To address the model selection challenge, we innovate by introducing a new penalty called Pathway Lasso. This penalty function is a convex relaxation of the non-convex product function, and it enables a computationally tractable optimization criterion to estimate and select many pathway effects simultaneously. We develop a fast ADMM-type algorithm to compute the model parameters, and we show that the iterative updates can be expressed in closed form. On both simulated data and a real fMRI dataset, the proposed approach yields higher pathway selection accuracy and lower estimation bias than other competing methods.

stat.ML↗

arXiv2016

Continuous-wave and Transient Characteristics of Phosphorene Microwave Transistors

Few-layer phosphorene MOSFETs with 0.3-um-long gate and 15-nm-thick Al2O3 gate insulator was found to exhibit a forward-current cutoff frequency of 2 GHz and a maximum oscillation frequency of 8 GHz after de-embedding for the parasitic capacitance associated mainly with the relatively large probe pads. The gate lag and drain lag of the transistor was found to be on the order of 1 us or less, which is consistent with the lack of hysteresis, carrier freeze-out or persistent photoconductivity in DC characteristics. These results confirm that the phosphorene MOSFET can be a viable microwave transistor for both small-signal and large-signal applications.

cond-mat.mes-hall↗

arXiv2015

Phosphorene FETs: Promising transistors based on a few layers of phosphorus atoms

This paper reviews the emergence and progress of phosphorene FETs, all within about a year. In such a short time, back-gated FETs evolved into top-gated FETs, gate length was reduced to the sub-micron range, passivation by high-k dielectrics or hexagonal boron nitride was demonstrated with temporal, thermal and mechanical stability, ohmic contact was achieved down to cryogenic temperatures, and cutoff frequencies was pushed above 10 GHz. These and other attractive characteristics of phosphorene promise the phosphorene FET to be a viable candidate for current-generation flexible electronics as well as future-generation ultra-thin-body low-power-consumption high-speed and high-frequency transistors.

cond-mat.mes-hall↗

arXiv2015

Collective modes in a Dirac insulator with short range interactions

We study a Haldane model with nearest neighbor interactions. We find one-dimensional like collective modes arising due to the interplay of pseudo-spin and valley degrees of freedom. In the large band gap or moderate interaction limit, these excitations are low energy modes lying in the band gap. The dispersion relations are qualitatively different in trivial insulator phase and Chern insulator phase, thus can be used to identify the topology of the Haldane model with the bulk property. We also discuss how to detect these modes in cold atom systems. An abelian gauge theory will emerge when a physical current-current interaction is introduced to the Haldane model or the Kane-Mele model.

cond-mat.str-el↗

arXiv2014

Temporal and Thermal Stability of Al2O3-passivated Phosphorene MOSFETs

This letter evaluates temporal and thermal stability of a state-of-the-art few-layer phosphorene MOSFET with Al2O3 surface passivation and Ti/Au top gate. As fabricated, the phosphorene MOSFET was stable in atmosphere for at least 100 h. With annealing at 200°C in dry nitrogen for 1 h, its drain current increased by an order of magnitude to approximately 100 mA/mm, which could be attributed to the reduction of trapped charge in Al2O3 and/or Schottky barrier at the source and drain contacts. Thereafter, the drain current was stable between -50°C and 150°C up to at least 1000 h. These promising results suggest that environmental protection of phosphorene should not be a major concern, and passivation of phosphorene should focus on its effect on electronic control and transport as in conventional silicon MOSFETs. With cutoff frequencies approaching the gigahertz range, the present phosphorene MOSFET, although far from being optimized, can meet the frequency and stability requirements of most flexible electronics for which phosphorene is intrinsically advantageous due to its corrugated lattice structure.

cond-mat.mtrl-sci↗

arXiv2014

A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRI

Brain networks has attracted the interests of many neuroscientists. From functional MRI (fMRI) data, statistical tools have been developed to recover brain networks. However, the dimensionality of whole-brain fMRI, usually in hundreds of thousands, challenges the applicability of these methods. We develop a hierarchical graphical model (HGM) to remediate this difficulty. This model introduces a hidden layer of networks based on sparse Gaussian graphical models, and the observed data are sampled from individual network nodes. In fMRI, the network layer models the underlying signals of different brain functional units, and how these units directly interact with each other. The introduction of this hierarchical structure not only provides a formal and interpretable approach, but also enables efficient computation for inferring big networks with hundreds of thousands of nodes. Based on the conditional convexity of our formulation, we develop an alternating update algorithm to compute the HGM model parameters simultaneously. The effectiveness of this approach is demonstrated on simulated data and a real dataset from a stop/go fMRI experiment.

stat.ME↗

arXiv2013

Recovering Model Structures from Large Low Rank and Sparse Covariance Matrix Estimation

Many popular statistical models, such as factor and random effects models, give arise a certain type of covariance structures that is a summation of low rank and sparse matrices. This paper introduces a penalized approximation framework to recover such model structures from large covariance matrix estimation. We propose an estimator based on minimizing a non-likelihood loss with separable non-smooth penalty functions. This estimator is shown to recover exactly the rank and sparsity patterns of these two components, and thus partially recovers the model structures. Convergence rates under various matrix norms are also presented. To compute this estimator, we further develop a first-order iterative algorithm to solve a convex optimization problem that contains separa- ble non-smooth functions, and the algorithm is shown to produce a solution within O(1/t^2) of the optimal, after any finite t iterations. Numerical performance is illustrated using simulated data and stock portfolio selection on S&P 100.

stat.ME↗

arXiv2013

Phylogenetic analysis of gene expression

Phylogenetic analyses of gene expression have great potential for addressing a wide range of questions. These analyses will, for example, identify genes that have evolutionary shifts in expression that are correlated with evolutionary changes in morphological, physiological, and developmental characters of interest. This will provide entirely new opportunities to identify genes related to particular phenotypes. There are, however, three key challenges that must be addressed for such studies to realize their potential. First, gene expression data must be measured from multiple species, some of which may be field collected, and parameterized in such a way that they can be compared across species. Second, it will be necessary to develop phylogenetic comparative methods suitable for large multidimensional datasets. In most phylogenetic comparative studies to date, the number n of independent observations (independent contrasts) has been greater than the number p of variables (characters). The behavior of comparative methods for these classic n>p problems are now well understood under a wide variety of conditions. In gene expression studies, and studies based on other high-throughput tools, the number n of samples is dwarfed by the number p of variables. The estimated covariance matrices will be singular, complicating their analysis and interpretation, and prone to spurious results. Third, new approaches are needed to investigate the expression of the many genes whose phylogenies are not congruent with species phylogenies due to gene loss, gene duplication, and incomplete lineage sorting. Here we outline general project design considerations for phylogenetic analyses of gene expression, and suggest solutions to these three categories of challenges. These topics are relevant to high-throughput phenotypic data well beyond gene expression.

q-bio.PE↗

arXiv2011

A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation

A constrained L1 minimization method is proposed for estimating a sparse inverse covariance matrix based on a sample of $n$ iid $p$-variate random variables. The resulting estimator is shown to enjoy a number of desirable properties. In particular, it is shown that the rate of convergence between the estimator and the true $s$-sparse precision matrix under the spectral norm is $s\sqrt{\log p/n}$ when the population distribution has either exponential-type tails or polynomial-type tails. Convergence rates under the elementwise $L_{\infty}$ norm and Frobenius norm are also presented. In addition, graphical model selection is considered. The procedure is easily implementable by linear programming. Numerical performance of the estimator is investigated using both simulated and real data. In particular, the procedure is applied to analyze a breast cancer dataset. The procedure performs favorably in comparison to existing methods.

stat.ME↗