arXiv subjects

Bin Yu

Publications and source records attributed to Bin Yu.

At least 217 records · Page 12Linked to original sources

arXiv2011

Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models

Functional MRI (fMRI) has become the most common method for investigating the human brain. However, fMRI data present some complications for statistical analysis and modeling. One recently developed approach to these data focuses on estimation of computational encoding models that describe how stimuli are transformed into brain activity measured in individual voxels. Here we aim at building encoding models for fMRI signals recorded in the primary visual cortex of the human brain. We use residual analyses to reveal systematic nonlinearity across voxels not taken into account by previous models. We then show how a sparse nonparametric method [J. Roy. Statist. Soc. Ser. B 71 (2009b) 1009-1030] can be used together with correlation screening to estimate nonlinear encoding models effectively. Our approach produces encoding models that predict about 25% more accurately than models estimated using other methods [Nature 452 (2008a) 352--355]. The estimated nonlinearity impacts the inferred properties of individual voxels, and it has a plausible biological interpretation. One benefit of quantitative encoding models is that estimated models can be used to decode brain activity, in order to identify which specific image was seen by an observer. Encoding models estimated by our approach also improve such image identification by about 12% when the correct image is one of 11,500 possible images.

stat.AP↗

arXiv2011

Chemical Vapor Deposition-Assembled Graphene Field-Effect Transistor on Hexagonal Boron Nitride

We investigate key electrical properties of monolayer graphene assembled by chemical-vapor-deposition (CVD) as impacted by supporting substrate material. Graphene field-effect transistors (GFETs) were fabricated with carbon channel placing directly on hexagonal boron nitride (h-BN) and SiO2, respectively. Small-signal transconductance (gm) and effective carrier mobility (μeff) are improved by 8.5 and 4 times on h-BN, respectively, as compared with that on SiO2. Compared with GFET with exfoliated graphene on SiO2, gm and μeff measured from device with CVD graphene on h-BN substrate exhibits comparable values. The experiment demonstrates the potential of employing h-BN as a platform material for large-area carbon electronics.

cond-mat.mtrl-sci↗

arXiv2011

Highly Conductive 3D Nano-Carbon: Stacked Multilayer Graphene System with Interlayer Decoupling

We investigate electrical conduction and breakdown behavior of 3D nano-carbon-stacked multilayer graphene (s-MLG) system with complete interlayer decoupling. The s-MLG is prepared by transferring and stacking large-area CVD-grown graphene monolayers, followed by wire patterning and plasma etching. Raman spectroscopy was used to confirm the layer number. The D-band peak indicates low defect level in the samples. Electrical current stressing induced doping is performed to shift the charge-neutrality Dirac point and decrease the graphene/metal contact resistance, improving the overall electrical conduction. Breakdown experiments show the current-carrying capacity of s-MLG is largely enhanced as compared with that of monolayer graphene.

cond-mat.mtrl-sci↗

arXiv2011

Local Electrical Stress-Induced Doping and Formation of 2D Monolayer Graphene P-N Junction

We demonstrated doping in 2D monolayer graphene via local electrical stressing. The doping, confirmed by the resistance-voltage transfer characteristics of the graphene system, is observed to continuously tunable from N-type to P-type as the electrical stressing level (voltage) increases. Two major physical mechanisms are proposed to interpret the observed phenomena: modifications of surface chemistry for N-type doping (at low-level stressing) and thermally-activated charge transfer from graphene to SiO2 substrate for P-type doping (at high-level stressing). The formation of P-N junction on 2D graphene monolayer is demonstrated with complementary doping based on locally applied electrical stressing.

cond-mat.mtrl-sci↗

arXiv2011

Lyapunov graphs of nonsingular Smale flows on $S^{1}\times S^{2}$

In this paper, following J. Franks' work on Lyapunov graphs of nonsingular Smale flows on $S^3$, we study Lyapunov graphs of nonsingular Smale flows on $S^1 \times S^2$. More precisely, we determine necessary and sufficient conditions on an abstract Lyapunov graph to be associated with a nonsingular Smale flow on $S^1 \times S^2$. We also study the singular type vertices in Lyapunov graphs of nonsingular Smale flows on 3-manifolds.

math.DS↗

arXiv2011

The Templates of Nonsingular Smale Flows on Three Manifolds

In this paper, we first discuss some connections between template theory and the description of basic sets of Smale flows on 3-manifolds due to F. Béguin and C. Bonatti. The main tools we use are symbolic dynamics, template moves and some combinatorial surgeries. Second, we obtain some relationship between the surgeries and the number of $S^1 \times S^2$ factors of $M$ for a nonsingular Smale flow on a given closed orientable 3-manifold $M$. Besides these, we also prove that any template $T$ can model a basic set $Λ$ of a nonsingular Smale flow on $nS^1 \times S^2$ for some positive integer $n$.

math.DS↗

arXiv2011

Multicolor Graphene Nanoribbon/Semiconductor Nanowire Heterojunction Light-Emitting Diodes

We report novel graphene nanoribbon (GNR)/semiconductor nanowire (SNW) heterojunction light-emitting diodes (LEDs) for the first time. The GNR and SNW have a face-to-face contact structure, which has the merit of bigger active region. ZnO, CdS, and CdSe NWs were employed in our case. At forward biases, the GNR/SNW heterjunction LEDs could emit light with wavelengths varying from ultraviolet (380 nm) to green (513 nm) to red (705 nm), which were determined by the band-gaps of the involved SNWs. The mechanism of light emitting for the GNR/SNW heterojunction LED was discussed. Our approach can easily be extended to other semiconductor nano-materials. Moreover, our achievement opens the door to next-generation display technologies, including portable, "see-through", and conformable products.

cond-mat.mes-hall↗

arXiv2011

Electronic Transport in Monolayer Graphene with Extreme Physical Deformation: ab Initio Density Functional Calculation

Electronic transport properties of monolayer graphene with extreme physical bending up to 90o angle are studied using ab Initio first-principle calculations. The importance of key structural parameters including step height, curvature radius and bending angle are discussed how they modify the transport properties of the deformed graphene sheet comparing to the corresponding flat ones. The local density of state reveals that energy state modification caused by the physical bending is highly localized. It is observed that the transport properties of bent graphene with a wide range of geometrical configurations are insensitive to the structural deformation in the low-energy transmission spectra, even in the extreme case of bending. The results support that graphene, with its superb electromechanical robustness, could serve as a viable material platform in a spectrum of applications such as photovoltaics, flexible electronics, OLED, and 3D electronic chips.

quant-ph↗

arXiv2011

Remembering Leo

I do not remember when was the first time that I met Leo, but I have a clear memory of going to Leo's office on the 4th floor of Evans Hall to talk to him in my second year in Berkeley's Ph.D. program in 1986. The details of the conversation are not retained but a visual image of his clean and orderly office remains, in a stark contrast to a high entropy state of the same office now being used by myself.

stat.AP↗

arXiv2010

A Simple and Scalable Graphene Patterning Method and Its Application in CdSe Nanobelt/Graphene Schottky Junction Solar Cells

We develop a simple and scalable graphene patterning method using electron-beam or ultraviolet lithography followed by a lift-off process. This method, with the merits of: high pattern resolution and high alignment accuracy, free from additional etching or harsh process, universal to arbitrary substrates, compatible to Si microelectronic technology, can be easily applied to diverse graphene-based devices, especially in array-based applications, where large-scale graphene patterns are desired. We have applied this method to fabricate CdSe nanobelt (NB)/graphene Schottky junction solar cells, which have potential application in integrated nano-optoelectronic systems. Typical as-fabricated solar cell shows excellent photovoltaic behavior with an open-circuit voltage of ~ 0.51 V, a short-circuit current density of ~ 5.75 mA/cm2, and an energy conversion efficiency of ~1.25%. We attribute the high performance of the cell to the as-patterned high-performance graphene, which can form an ideal Schottky contact with CdSe NB. Our results suggest both the developed graphene patterning method and the as-fabricated CdSe nanobelt (NB)/graphene Schottky junction solar cells have reachable application prospect.

cond-mat.mes-hall↗

arXiv2010

The Lasso under Heteroscedasticity

The performance of the Lasso is well understood under the assumptions of the standard linear model with homoscedastic noise. However, in several applications, the standard model does not describe the important features of the data. This paper examines how the Lasso performs on a non-standard model that is motivated by medical imaging applications. In these applications, the variance of the noise scales linearly with the expectation of the observation. Like all heteroscedastic models, the noise terms in this Poisson-like model are \textit{not} independent of the design matrix. More specifically, this paper studies the sign consistency of the Lasso under a sparse Poisson-like model. In addition to studying sufficient conditions for the sign consistency of the Lasso estimate, this paper also gives necessary conditions for sign consistency. Both sets of conditions are comparable to results for the homoscedastic model, showing that when a measure of the signal to noise ratio is large, the Lasso performs well on both Poisson-like data and homoscedastic data. Simulations reveal that the Lasso performs equally well in terms of model selection performance on both Poisson-like data and homoscedastic data (with properly scaled noise variance), across a range of parameterizations. Taken as a whole, these results suggest that the Lasso is robust to the Poisson-like heteroscedastic noise.

stat.ML↗

arXiv2010

Regular level sets of Lyapunov graphs of nonsingular Smale flows on 3-manifolds

In this paper, we first discuss the regular level set of a nonsingular Smale flow (NSF) on a 3-manifold. The main result about this topic is that a 3-manifold $M$ admits an NSF flow which has a regular level set homeomorphic to $(n+1)T^{2}$ $(n\in \mathbb{Z}, n\geq 0)$ if and only if $M=M'\sharp n S^{1}\times S^{2}$. Then we discuss how to realize a template as a basic set of an NSF on a 3-manifold. We focus on the connection between the genus of the template $T$ and the topological structure of the realizing 3-manifold $M$.

math.GT↗

arXiv2009

Data spectroscopy: Eigenspaces of convolution operators and clustering

This paper focuses on obtaining clustering information about a distribution from its i.i.d. samples. We develop theoretical results to understand and use clustering information contained in the eigenvectors of data adjacency matrices based on a radial kernel function with a sufficiently fast tail decay. In particular, we provide population analyses to gain insights into which eigenvectors should be used and when the clustering information for the distribution can be recovered from the sample. We learn that a fixed number of top eigenvectors might at the same time contain redundant clustering information and miss relevant clustering information. We use this insight to design the data spectroscopic clustering (DaSpec) algorithm that utilizes properly selected eigenvectors to determine the number of clusters automatically and to group the data accordingly. Our findings extend the intuitions underlying existing spectral techniques such as spectral clustering and Kernel Principal Components Analysis, and provide new understanding into their usability and modes of failure. Simulation studies and experiments on real-world data are conducted to show the potential of our algorithm. In particular, DaSpec is found to handle unbalanced groups and recover clusters of different shapes better than the competing methods.

stat.ML↗

arXiv2009

Minimax rates of estimation for high-dimensional linear regression over $\ell_q$-balls

Consider the standard linear regression model $\y = \Xmat \betastar + w$, where $\y \in \real^\numobs$ is an observation vector, $\Xmat \in \real^{\numobs \times \pdim}$ is a design matrix, $\betastar \in \real^\pdim$ is the unknown regression vector, and $w \sim \mathcal{N}(0, σ^2 I)$ is additive Gaussian noise. This paper studies the minimax rates of convergence for estimation of $\betastar$ for $\ell_\rpar$-losses and in the $\ell_2$-prediction loss, assuming that $\betastar$ belongs to an $\ell_{\qpar}$-ball $\Ballq(\myrad)$ for some $\qpar \in [0,1]$. We show that under suitable regularity conditions on the design matrix $\Xmat$, the minimax error in $\ell_2$-loss and $\ell_2$-prediction loss scales as $\Rq \big(\frac{\log \pdim}{n}\big)^{1-\frac{\qpar}{2}}$. In addition, we provide lower bounds on minimax risks in $\ell_{\rpar}$-norms, for all $\rpar \in [1, +\infty], \rpar \neq \qpar$. Our proofs of the lower bounds are information-theoretic in nature, based on Fano's inequality and results on the metric entropy of the balls $\Ballq(\myrad)$, whereas our proofs of the upper bounds are direct and constructive, involving direct analysis of least-squares over $\ell_{\qpar}$-balls. For the special case $q = 0$, a comparison with $\ell_2$-risks achieved by computationally efficient $\ell_1$-relaxations reveals that although such methods can achieve the minimax rates up to constant factors, they require slightly stronger assumptions on the design matrix $\Xmat$ than algorithms involving least-squares over the $\ell_0$-ball.

math.ST↗

arXiv2009

The composite absolute penalties family for grouped and hierarchical variable selection

Extracting useful information from high-dimensional data is an important focus of today's statistical research and practice. Penalized loss function minimization has been shown to be effective for this task both theoretically and empirically. With the virtues of both regularization and sparsity, the $L_1$-penalized squared error minimization method Lasso has been popular in regression models and beyond. In this paper, we combine different norms including $L_1$ to form an intelligent penalty in order to add side information to the fitting of a regression or classification model to obtain reasonable estimates. Specifically, we introduce the Composite Absolute Penalties (CAP) family, which allows given grouping and hierarchical relationships between the predictors to be expressed. CAP penalties are built by defining groups and combining the properties of norm penalties at the across-group and within-group levels. Grouped selection occurs for nonoverlapping groups. Hierarchical variable selection is reached by defining groups with particular overlapping patterns. We propose using the BLASSO and cross-validation to compute CAP estimates in general. For a subfamily of CAP estimates involving only the $L_1$ and $L_{\infty}$ norms, we introduce the iCAP algorithm to trace the entire regularization path for the grouped selection problem. Within this subfamily, unbiased estimates of the degrees of freedom (df) are derived so that the regularization parameter is selected without cross-validation. CAP is shown to improve on the predictive performance of the LASSO in a series of simulated experiments, including cases with $p\gg n$ and possibly mis-specified groupings. When the complexity of a model is properly calculated, iCAP is seen to be parsimonious in the experiments.

math.ST↗

arXiv2009

Asymptotic distribution and sparsistency for l1-penalized parametric M-estimators with applications to linear SVM and logistic regression

Since its early use in least squares regression problems, the l1-penalization framework for variable selection has been employed in conjunction with a wide range of loss functions encompassing regression, classification and survival analysis. While a well developed theory exists for the l1-penalized least squares estimates, few results concern the behavior of l1-penalized estimates for general loss functions. In this paper, we derive two results concerning penalized estimates for a wide array of penalty and loss functions. Our first result characterizes the asymptotic distribution of penalized parametric M-estimators under mild conditions on the loss and penalty functions in the classical setting (fixed-p-large-n). Our second result explicits necessary and sufficient generalized irrepresentability (GI) conditions for l1-penalized parametric M-estimates to consistently select the components of a model (sparsistency) as well as their sign (sign consistency). In general, the GI conditions depend on the Hessian of the risk function at the true value of the unknown parameter. Under Gaussian predictors, we obtain a set of conditions under which the GI conditions can be re-expressed solely in terms of the second moment of the predictors. We apply our theory to contrast l1-penalized SVM and logistic regression classifiers and find conditions under which they have the same behavior in terms of their model selection consistency (sparsistency and sign consistency). Finally, we provide simulation evidence for the theory based on these classification examples.

math.ST↗

arXiv2009

Genus two Smale-Williams solenoids in 3-manifolds

Using alternating Heegaard diagrams, we construct some 3-manifolds which admit diffeomorphisms such that the non-wandering sets of the diffeomorphisms are composed of Smale-Williams solenoid attractors and repellers, an interesting example is the truncated-cube space. In addition, we prove that if the nonwandering set of the diffeomorphism consists of genus two Smale-Williams solenoids, then the Heegaard genus of the closed manifold is at most two.

math.GT↗

arXiv2009

Lasso-type recovery of sparse representations for high-dimensional data

The Lasso is an attractive technique for regularization and variable selection for high-dimensional data, where the number of predictor variables $p_n$ is potentially much larger than the number of samples $n$. However, it was recently discovered that the sparsity pattern of the Lasso estimator can only be asymptotically identical to the true sparsity pattern if the design matrix satisfies the so-called irrepresentable condition. The latter condition can easily be violated in the presence of highly correlated variables. Here we examine the behavior of the Lasso estimators if the irrepresentable condition is relaxed. Even though the Lasso cannot recover the correct sparsity pattern, we show that the estimator is still consistent in the $\ell_2$-norm sense for fixed designs under conditions on (a) the number $s_n$ of nonzero components of the vector $β_n$ and (b) the minimal singular values of design matrices that are induced by selecting small subsets of variables. Furthermore, a rate of convergence result is obtained on the $\ell_2$ error with an appropriate choice of the smoothing parameter. The rate is shown to be optimal under the condition of bounded maximal and minimal sparse eigenvalues. Our results imply that, with high probability, all important variables are selected. The set of selected variables is a meaningful reduction on the original set of variables. Finally, our results are illustrated with the detection of closely adjacent frequencies, a problem encountered in astrophysics.

math.ST↗