arXiv subjects

Jian Huang

Publications and source records attributed to Jian Huang.

At least 199 records · Page 11Linked to original sources

arXiv2019

A Semi-Smooth Newton Algorithm for High-Dimensional Nonconvex Sparse Learning

The smoothly clipped absolute deviation (SCAD) and the minimax concave penalty (MCP) penalized regression models are two important and widely used nonconvex sparse learning tools that can handle variable selection and parameter estimation simultaneously, and thus have potential applications in various fields such as mining biological data in high-throughput biomedical studies. Theoretically, these two models enjoy the oracle property even in the high-dimensional settings, where the number of predictors $p$ may be much larger than the number of observations $n$. However, numerically, it is quite challenging to develop fast and stable algorithms due to their non-convexity and non-smoothness. In this paper we develop a fast algorithm for SCAD and MCP penalized learning problems. First, we show that the global minimizers of both models are roots of the nonsmooth equations. Then, a semi-smooth Newton (SSN) algorithm is employed to solve the equations. We prove that the SSN algorithm converges locally and superlinearly to the Karush-Kuhn-Tucker (KKT) points. Computational complexity analysis shows that the cost of the SSN algorithm per iteration is $O(np)$. Combined with the warm-start technique, the SSN algorithm can be very efficient and accurate. Simulation studies and a real data example suggest that our SSN algorithm, with comparable solution accuracy with the coordinate descent (CD) and the difference of convex (DC) proximal Newton algorithms, is more computationally efficient.

stat.CO↗

arXiv2019

Demonstration of Si based InAs/GaSb type-II superlattice p-i-n photodetector

In this paper, mid-wave infrared photodetection based on an InAs/GaSb type-II superlattice p-i-n photodetector grown directly on Si substrate is demonstrated and characterized. Excitation power dependence on integrated intensity from the photoluminescence measurements reveals a power coefficient of P~I0.74, indicating that defects related process is playing an important role in the predominant recombination channel for photogenerated carriers. At 70 K, the device exhibits a dark current density of 2.3 A/cm2 under -0.1 V bias. Arrhenius analysis of dark current shows activation energies much less than half of the active layer bandgap, which suggests that the device is mainly limited by surface leakage and defect-assisted tunneling, consistent with the photoluminescence analysis. The detector shows 50% cutoff wavelength at ~5.5 um at 70 K under bias of -0.1 V. The corresponding peak responsivity and specific detectivity are 1.2 A/W and 1.3*10e9 cm*Hz1/2/W, respectively. Based on these optoelectronics characterization results, reduction of defects by optimizing the III/V-Si interface, and suppression of surface leakage channels are argued to be the main factors for performance improvement in this Si-based T2SL detector towards low cost, large-format MWIR detection system on Si photonics platform.

physics.app-ph↗

arXiv2019

Deep levels analysis in wavelength extended InGaAsBi photodetector

InP based dilute Bismide InGaAsBi material is emerging as a promising candidate for extending short wavelength infrared detection. One critical factor to limit the performance of these InGaAsBi photodiodes is dark current caused by defects within the material. In this work, low frequency noise spectroscopy (LFNS) and temperature varied photoluminescence was used to characterize the defect levels in the devices. Three deep levels located at Ec -0.33 eV, Ev +0.14 eV, and Ec -0.51 eV were identified from the LFNS spectra, which are consistent with emission peak energy found by photoluminescence spectra of InGaAsBi.

physics.app-ph↗

arXiv2019

Two-micron wavelength high speed photodiode with InGaAs/GaAsSb type-II multiple quantum wells absorber

Current optical communication system operating at 1.55 μm wavelength band may not be able to continually satisfy the growing demand on the data capacity within the next few years. Opening a new spectral window at around 2 μm wavelength with recently developed hollow-core photonic band gap fiber and thulium-doped fiber amplifier is a promising solution to increase the transmission capacity due to the low loss and wide bandwidth properties of these components at this wavelength. However, as a key component, the already demonstrated high speed photodetectors at 2 μm wavelength are still not comparable with those at 1.55 μm wavelength band, which chokes the feasibility of the new spectral window. In this work, we, for the first time, demonstrated a high speed uni-traveling carrier photodiode for 2 μm applications with InGaAs/GaAsSb type-II multiple quantum wells as the absorption region, which is lattice matched to InP. The device shows a 3dB bandwidth of 25 GHz at -3 V bias voltage and is, to the best of our knowledge, the fastest photodiodes among all group III-V and group IV photodetectors working in 2 μm wavelength range.

physics.app-ph↗

arXiv2019

Integration of Nonlinear Disturbance Observer within Proxy-based Sliding Mode Control for Pneumatic Muscle Actuators

This paper presents an integration of nonlinear disturbance observer within proxy-based sliding mode control (IDO-PSMC) approach for Pneumatic Muscle Actuators (PMAs). Due to the nonlinearities, uncertainties, hysteresis, and time-varying characteristics of the PMA, the model parameters are difficult to be identified accurately, which results in unmeasurable uncertainties and disturbances of the system. To solve this problem, a novel design of proxy-based sliding mode controller (PSMC) combined with a nonlinear disturbance observer (DO) is used for the tracking control of the PMA. Our approach combines both the merits of the PSMC and the DO so that it is effective in both reducing the ``chattering" phenomenon and improving the system robustness. A constrained Firefly Algorithm is used to search for the optimal control parameters. Based on the Lyapunov theorem, the states of the PMA are shown to be globally uniformly ultimately bounded. Extensive experiments were conducted to verify the superior performance of our approach, in multiple tracking scenarios.

eess.SY↗

arXiv2019

Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression

Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing. This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously. Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches. Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries.

cs.LG↗

arXiv2019

A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery

In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations. We develop a novel algorithm of primal-dual active set type for a class of nonconvex sparsity-promoting penalties, including $\ell^0$, bridge, smoothly clipped absolute deviation, capped $\ell^1$ and minimax concavity penalty. First we establish the existence of a global minimizer for the related optimization problems. Then we derive a novel necessary optimality condition for the global minimizer using the associated thresholding operator. The solutions to the optimality system are coordinate-wise minimizers, and under minor conditions, they are also local minimizers. Upon introducing the dual variable, the active set can be determined using the primal and dual variables together. Further, this relation lends itself to an iterative algorithm of active set type which at each step involves first updating the primal variable only on the active set and then updating the dual variable explicitly. When combined with a continuation strategy on the regularization parameter, the primal dual active set method is shown to converge globally to the underlying regression target under certain regularity conditions. Extensive numerical experiments with both simulated and real data demonstrate its superior performance in efficiency and accuracy compared with the existing sparse recovery methods.

math.OC↗

arXiv2019

Towards Commercializing Vanadium Dioxide Films: Investigation of the Impact of Different Interface on the Deterioration Process for Largely Extended Service Life

Long term stability is the most pressing issue that impedes commercialization of Vanadium Dioxide (VO2) based functional films, which show a gradual loss of relative phase transition performance, especially in humid conditions when serving as smart windows. Here, we investigated the impact of different interface on the deterioration process of VO2 films and proposed a novel encapsulation structure for largely extended service life. Hydrophobic and stable hafnium dioxide (HfO2) layers have been incorporated with VO2 films for encapsulated surfaces and cross sections. With modified thickness and structure of HfO2 layers, the degradation process of VO2 can be effectively suppressed. The proposed films can retain stable phase transition performances under high relative humidity (90%) and temperature (60 Celsius) over 100 days, which is equal to about 16 years in the real environment. Improving the stability of VO2 materials is a necessary step towards commercializing production of high performance films for long term use.

physics.app-ph↗

arXiv2018

Angular Distributions of Thomson Scattering in Combined Laser and Magnetic fields

Angular distributions of Thomson scattering are researched in the combined fields with a circularly polarization of laser field and a strong uniform magnetic field. The trajectories of the electron and the dependence of it on the initial phase are also given. It is found that the angular distributions with respect to the azimuthal angle show twofold symmetry whatever the laser intensity, the order of harmonics, the resonance parameter, and the initial axial momentum are. On the other hand, the radiation with respect to the polar angle is mainly distributed in two regions which are roughly symmetric of the laser propagation. In addition, the larger the laser intensity, the resonance parameters, the initial axial momentum are, the closer radiation is to the laser propagation direction. Besides, a new possibility of X-ray production is indicated. That is to say, with appropriate choice of laser and electron parameters, the high frequency part of the Thomson scattering radiation can reach the frequency range of X-ray (10^17 Hz - 10^18 Hz).

physics.plasm-ph↗

arXiv2018

An efficient numerical method for a time-fractional diffusion equation

A reaction-diffusion problem with a Caputo time derivative is considered. An integral discretization scheme on a graded mesh along with a decomposition of the exact solution is proposed. The truncation error estimate of the discretization scheme is derived by using the remainder formula of the linear interpolation and some inequality estimate techniques. It is proved that the scheme is second-order convergent by applying a difference analogue of Gronwall's inequality, which exhibits an enhancement in the convergence rate compared with the L1 schemes. Numerical experiments are presented to support the theoretical result.

math.NA↗

arXiv2018

SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties

We propose a semismooth Newton algorithm for pathwise optimization (SNAP) for the LASSO and Enet in sparse, high-dimensional linear regression. SNAP is derived from a suitable formulation of the KKT conditions based on Newton derivatives. It solves the semismooth KKT equations efficiently by actively and continuously seeking the support of the regression coefficients along the solution path with warm start. At each knot in the path, SNAP converges locally superlinearly for the Enet criterion and achieves an optimal local convergence rate for the LASSO criterion, i.e., SNAP converges in one step at the cost of two matrix-vector multiplication per iteration. Under certain regularity conditions on the design matrix and the minimum magnitude of the nonzero elements of the target regression coefficients, we show that SNAP hits a solution with the same signs as the regression coefficients and achieves a sharp estimation error bound in finite steps with high probability. The computational complexity of SNAP is shown to be the same as that of LARS and coordinate descent algorithms per iteration. Simulation studies and real data analysis support our theoretical results and demonstrate that SNAP is faster and accurate than LARS and coordinate descent algorithms.

stat.ML↗

arXiv2018

Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition

Automatic emotion recognition is a challenging task. In this paper, we present our effort for the audio-video based sub-challenge of the Emotion Recognition in the Wild (EmotiW) 2018 challenge, which requires participants to assign a single emotion label to the video clip from the six universal emotions (Anger, Disgust, Fear, Happiness, Sad and Surprise) and Neutral. The proposed multimodal emotion recognition system takes audio, video and text information into account. Except for handcraft features, we also extract bottleneck features from deep neutral networks (DNNs) via transfer learning. Both temporal classifiers and non-temporal classifiers are evaluated to obtain the best unimodal emotion classification result. Then possibilities are extracted and passed into the Beam Search Fusion (BS-Fusion). We test our method in the EmotiW 2018 challenge and we gain promising results. Compared with the baseline system, there is a significant improvement. We achieve 60.34% accuracy on the testing dataset, which is only 1.5% lower than the winner. It shows that our method is very competitive.

cs.CV↗

arXiv2018

Active Learning for Regression Using Greedy Sampling

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.

cs.LG↗

arXiv2018

REMI: Regression with marginal information and its application in genome-wide association studies

In this study, we consider the problem of variable selection and estimation in high-dimensional linear regression models when the complete data are not accessible, but only certain marginal information or summary statistics are available. This problem is motivated from the Genome-wide association studies (GWAS) that have been widely used to identify risk variants underlying complex human traits/diseases. With a large number of completed GWAS, statistical methods using summary statistics become more and more important because of restricted accessibility to individual-level data sets. Theoretically guaranteed methods are highly demanding to advance the statistical inference with a large amount of available marginal information. Here we propose an $\ell_1$ penalized approach, REMI, to estimate high dimensional regression coefficients with marginal information and external reference samples. We establish an upper bound on the error of the REMI estimator, which has the same order as that of the minimax error bound of Lasso with complete individual-level data. In particular, when marginal information is obtained from a large number of samples together with a small number of reference samples, REMI yields good estimation and prediction results, and outperforms the Lasso because the sample size of accessible individual-level data can be limited. Through simulation studies and real data analysis of the NFBC1966 GWAS data set, we demonstrate that REMI can be widely applicable. The developed R package and the codes to reproduce all the results are available at https://github.com/gordonliu810822/REMI

stat.AP↗

arXiv2017

Robust Decoding from 1-Bit Compressive Sampling with Least Squares

In 1-bit compressive sensing (1-bit CS) where target signal is coded into a binary measurement, one goal is to recover the signal from noisy and quantized samples. Mathematically, the 1-bit CS model reads: $y = η\odot\textrm{sign} (Ψx^* + ε)$, where $x^{*}\in \mathcal{R}^{n}, y\in \mathcal{R}^{m}$, $Ψ\in \mathcal{R}^{m\times n}$, and $ε$ is the random error before quantization and $η\in \mathcal{R}^{n}$ is a random vector modeling the sign flips. Due to the presence of nonlinearity, noise and sign flips, it is quite challenging to decode from the 1-bit CS. In this paper, we consider least squares approach under the over-determined and under-determined settings. For $m>n$, we show that, up to a constant $c$, with high probability, the least squares solution $x_{\textrm{ls}}$ approximates $ x^*$ with precision $δ$ as long as $m \geq\widetilde{\mathcal{O}}(\frac{n}{δ^2})$. For $m< n$, we prove that, up to a constant $c$, with high probability, the $\ell_1$-regularized least-squares solution $x_{\ell_1}$ lies in the ball with center $x^*$ and radius $δ$ provided that $m \geq \mathcal{O}( \frac{s\log n}{δ^2})$ and $\|x^*\|_0 := s < m$. We introduce a Newton type method, the so-called primal and dual active set (PDAS) algorithm, to solve the nonsmooth optimization problem. The PDAS possesses the property of one-step convergence. It only requires to solve a small least squares problem on the active set. Therefore, the PDAS is extremely efficient for recovering sparse signals through continuation. We propose a novel regularization parameter selection rule which does not introduce any extra computational overhead. Extensive numerical experiments are presented to illustrate the robustness of our proposed model and the efficiency of our algorithm.

math.NA↗

arXiv2017

LPG: a four-groups probabilistic approach to leveraging pleiotropy in genome-wide association studies

To date, genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants among a variety of traits/diseases, shedding a light on the genetic architecture of complex diseases. Polygenicity of complex diseases, which refers to the phenomenon that a vast number of risk variants collectively contribute to the heritability of complex diseases with modest individual effects, have been widely accepted. This imposes a major challenge towards fully characterizing the genetic bases of complex diseases. An immediate implication of polygenicity is that a much larger sample size is required to detect risk variants with weak/moderate effects. Meanwhile, accumulating evidence suggests that different complex diseases can share genetic risk variants, a phenomenon known as pleiotropy. In this study, we propose a statistical framework for Leveraging Pleiotropic effects in large-scale GWAS data (LPG). LPG utilizes a variational Bayesian expectation-maximization (VBEM) algorithm, making it computationally efficient and scalable for genome-wide scale analysis. To demon- strate the advantage of LPG over existing methods that do not leverage pleiotropy, we conducted extensive simulation studies and also applied LPG to analyze three au- toimmune disorders (Crohn's disease, rheumatoid arthritis and Type 1 diabetes). The results indicate that LPG can improve the power of prioritization of risk variants and accuracy of risk prediction by leveraging pleiotropy. The software is available at http- s://github.com/Shufeyangyi2015310117/LPG.

stat.ME↗

arXiv2017

A Space-Efficient Method for Navigable Ensemble Analysis and Visualization

Scientists increasingly rely on simulation runs of complex models in lieu of cost-prohibitive or infeasible experimentation. The data output of many controlled simulation runs, the ensemble, is used to verify correctness and quantify uncertainty. However, due to their size and complexity, ensembles are difficult to visually analyze because the working set often exceeds strict memory limitations. We present a navigable ensemble analysis tool, NEA, for interactive exploration of ensembles. NEA's pre-processing component takes advantage of the data similarity characteristics of ensembles to represent the data in a new, spatially-efficient data structure which does not require fully reconstructing the original data at visualization time. This data structure allows a fine degree of control in working set management, which enables interactive ensemble exploration while fitting within memory limitations. Scientists can also gain new insights from the data-similarity analysis in the pre-processing component.

cs.HC↗

arXiv2017

Visual Progression Analysis of Student Records Data

University curriculum, both on a campus level and on a per-major level, are affected in a complex way by many decisions of many administrators and faculty over time. As universities across the United States share an urgency to significantly improve student success and success retention, there is a pressing need to better understand how the student population is progressing through the curriculum, and how to provide better supporting infrastructure and refine the curriculum for the purpose of improving student outcomes. This work has developed a visual knowledge discovery system called eCamp that pulls together a variety of populationscale data products, including student grades, major descriptions, and graduation records. These datasets were previously disconnected and only available to and maintained by independent campus offices. The framework models and analyzes the multi-level relationships hidden within these data products, and visualizes the student flow patterns through individual majors as well as through a hierarchy of majors. These results support analytical tasks involving student outcomes, student retention, and curriculum design. It is shown how eCamp has revealed student progression information that was previously unavailable.

cs.CY↗