arXiv subjects

Ying Zhu

Publications and source records attributed to Ying Zhu.

At least 55 records · Page 3Linked to original sources

arXiv2022

Testing wave turbulence theory for Gross-Pitaevskii system

We test the predictions of the theory of weak wave turbulence by performing numerical simulations of the Gross-Pitaevskii equation (GPE) and the associated wave-kinetic equation (WKE). We consider an initial state localized in Fourier space, and we confront the solutions of the WKE obtained numerically with GPE data for both the wave-action spectrum and the probability density functions (PDFs) of the Fourier mode intensities. We find that the temporal evolution of the GPE data is accurately predicted by the WKE, with no adjustable parameters, for about two nonlinear kinetic times. Qualitative agreement between the GPE and the WKE persists also for longer times with some quantitative deviations that may be attributed to the combination of breakdown of the theoretical assumptions underlying the WKE as well as numerical issues. Furthermore, we study how the wave statistics evolves toward Gaussianity in a time scale of the order of the kinetic time.The excellent agreement between direct numerical simulations of the GPE and the WKE provides a new and solid ground to the theory of weak wave turbulence.

physics.flu-dyn↗

arXiv2022

SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech

Voice-activated systems are integrated into a variety of desktop, mobile, and Internet-of-Things (IoT) devices. However, voice spoofing attacks, such as impersonation and replay attacks, in which malicious attackers synthesize the voice of a victim or simply replay it, have brought growing security concerns. Existing speaker verification techniques distinguish individual speakers via the spectrographic features extracted from an audible frequency range of voice commands. However, they often have high error rates and/or long delays. In this paper, we explore a new direction of human voice research by scrutinizing the unique characteristics of human speech at the ultrasound frequency band. Our research indicates that the high-frequency ultrasound components (e.g. speech fricatives) from 20 to 48 kHz can significantly enhance the security and accuracy of speaker verification. We propose a speaker verification system, SUPERVOICE that uses a two-stream DNN architecture with a feature fusion mechanism to generate distinctive speaker models. To test the system, we create a speech dataset with 12 hours of audio (8,950 voice samples) from 127 participants. In addition, we create a second spoofed voice dataset to evaluate its security. In order to balance between controlled recordings and real-world applications, the audio recordings are collected from two quiet rooms by 8 different recording devices, including 7 smartphones and an ultrasound microphone. Our evaluation shows that SUPERVOICE achieves 0.58% equal error rate in the speaker verification task, it only takes 120 ms for testing an incoming utterance, outperforming all existing speaker verification systems. Moreover, within 91 ms processing time, SUPERVOICE achieves 0% equal error rate in detecting replay attacks launched by 5 different loudspeakers.

cs.SD↗

arXiv2021

Time-free solution to independent set problem using P systems with active membranes

Membrane computing is a branch of natural computingwhich abstracts fromthe structure and the functioning of living cells. The computation models obtained in the field of membrane computing are usually called P systems. P systems have been used to solve computationally hard problems efficiently on the assumption that the execution of each rule is completed in exactly one time-unit (a global clock is assumed for timing and synchronizing the execution of rules). However, in biological reality, different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, with this biological reality, we give a time-free solution to independent set problemusing P systems with active membranes, which solve the problem independent of the execution time of the involved rules.

cs.CC↗

arXiv2021

Omitted variable bias of Lasso-based inference methods: A finite sample analysis

We study the finite sample behavior of Lasso-based inference methods such as post double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso not selecting relevant controls. This phenomenon can occur even when the coefficients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern high-dimensional OLS-based methods and provide practical guidance.

math.ST↗

arXiv2021

Enhancing Goos-Hänchen shift based on magnetic dipole quasi-bound states in the continuum in all-dielectric metasurfaces

Metasurface-mediated bound states in the continuum (BIC) provides a versatile platform for light manipulation at subwavelength dimension with diverging radiative quality factor and extreme optical localization. In this work, we employ magnetic dipole quasi-BIC resonance in asymmetric silicon nanobar metasurfaces to realize giant Goos-Hänchen (GH) shift enhancement by more than three orders of wavelength. In sharp contrast to GH shift based on the Brewster dip or transmission-type resonance, the maximum GH shift here is located at the reflection peak with unity reflectance, which can be conveniently detected in the experiment. By adjusting the asymmetric parameter of metasurfaces, the $Q$-factor and GH shift can be modulated accordingly. More interestingly, it is found that GH shift exhibits an inverse quadratic dependence on the asymmetric parameter. Furthermore, we design an ultrasensitive environmental refractive index sensor based on the quasi-BIC enhanced GH shift, with a maximum sensitivity of 1.5$\times$10$^{7}$ $μ$m/RIU. Our work not only reveals the essential role of BIC in engineering the basic optical phenomena, but also suggests the way for pushing the performance limits of optical communication devices, information storage, wavelength division de/multiplexers, and ultrasensitive sensors.

physics.optics↗

arXiv2021

Visualization -- a vital decision driving tool for enterprises

This report documents the results found through surveys and interviews on how visualizations help the employees in their workspace. The objectives of this study were to get in-depth knowledge on what prepares an employee to have the right skill set in constructing an informative visualization as well as the tools and techniques that they use on their daily basis for analysis and visualization purposes. Using the results gathered, we sorted the information in many different ways for analysis and came to conclusions ranging from corporation-based strategies to individualized employee and position preferences.

cs.HC↗

arXiv2021

Classes of ODE solutions: smoothness, covering numbers, implications for noisy function fitting, and the curse of smoothness phenomenon

Many numerical methods for recovering ODE solutions from data rely on approximating the solutions using basis functions or kernel functions under a least square criterion. The accuracy of this approach hinges on the smoothness of the solutions. This paper provides a theoretical foundation for these methods by establishing novel results on the smoothness and covering numbers of ODE solution classes (as a measure of their "size"). Our results provide answers to "how do the degree of smoothness and the "size" of a class of ODEs affect the "size" of the associated class of solutions?" We show that: (1) for $y^{'}=f\left(y\right)$ and $y^{'}=f\left(x,\,y\right)$, if the absolute values of all $k$th ($k\leqβ+1$) order derivatives of $f$ are bounded by $1$, then the solution can end up with the $(k+1)$th derivative whose magnitude grows factorially fast in $k$ -- "a curse of smoothness"; (2) our upper bounds for the covering numbers of the $(β+2)-$degree smooth solution classes are greater than those of the "standard" $(β+2)-$degree smooth class of univariate functions; (3) the mean squared error of least squares fitting for noisy recovery has a convergence rate no larger than $\left(\frac{1}{n}\right)^{\frac{2\left(β+2\right)}{2\left(β+2\right)+1}}$ if $n=Ω\left(\left(β\sqrt{\log\left(β\vee1\right)}\right)^{4β+10}\right)$, and under this condition, the rate $\left(\frac{1}{n}\right)^{\frac{2\left(β+2\right)}{2\left(β+2\right)+1}}$ is minimax optimal in the case of $y^{'}=f\left(x,\,y\right)$; (4) more generally, for the higher order Picard type ODEs, $y^{\left(m\right)}=f\left(x,\,y,\,y^{'},\,...,y^{\left(m-1\right)}\right)$, the covering number of the solution class is bounded from above by the product of the covering number of the class $\mathcal{F}$ that $f$ ranges over and the covering number of the set where initial values lie.

math.ST↗

arXiv2019

Statistical inference and feasibility determination: a nonasymptotic approach

We develop non-asymptotically justified methods for hypothesis testing about the $p-$dimensional coefficients $θ^{*}$ in (possibly nonlinear) regression models. Given a function $h:\,\mathbb{R}^{p}\mapsto\mathbb{R}^{m}$, we consider the null hypothesis $H_{0}:\,h(θ^{*})\inΩ$ against the alternative hypothesis $H_{1}:\,h(θ^{*})\notinΩ$, where $Ω$ is a nonempty closed subset of $\mathbb{R}^{m}$ and $h$ can be nonlinear in $θ^{*}$. Our (nonasymptotic) control on the Type I and Type II errors holds for fixed $n$ and does not rely on well-behaved estimation error or prediction error; in particular, when the number of restrictions in $H_{0}$ is large relative to $p-n$, we show it is possible to bypass the sparsity assumption on $θ^{*}$ (for both Type I and Type II error control), regularization on the estimates of $θ^{*}$, and other inherent challenges in an inverse problem. We also demonstrate an interesting link between our framework and Farkas' lemma (in math programming) under uncertainty, which points to some potential applications of our method outside traditional hypothesis testing.

math.ST↗

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

Adaptive operation strategy for voltage stability enhancement in active DMFCs

An adaptive operation strategy for on-demand control of active direct methanol fuel cells (DMFCs) is proposed as an alternative method to enhance the voltage stability. A simplified semi-empirical model is firstly developed to describe I-V relationships based on uniform-designed experiments. It is then embedded into multi-objective optimizations to construct the adaptive operation strategy. Experimental studies are implemented on different DMFC systems to validate the proposed semi-empirical model, control strategy and system response to operational adjustments. Numerical simulations are also performed to investigate the underlying mechanisms of the proposed adaptive operation strategy. The results show that the adaptive operation strategy provides possibilities for voltage stability enhancement without the sacrifice of energy conversion efficiency. The adaptive operations are also found to be able to extend the range of operating current density or to decrease the voltage deviation according to one's requirements. Moreover, the response of DMFCs to operational adjustments is quick, which further validates the effectiveness and feasibility of the adaptive operation strategy in practical applications. The proposed strategy contributes to a guideline for the better control of output voltage from operating DMFC systems.

physics.app-ph↗

arXiv2017

A Partially Supervised Bayesian Image Classification Model with Applications in Diagnosis of Sentinel Lymph Node Metastases in Breast Cancer

A method has been developed for the analysis of images of sentinel lymph nodes generated by a spectral scanning device. The aim is to classify the nodes, excised during surgery for breast cancer, as normal or metastatic. The data from one node constitute spectra at 86 wavelengths for each pixel of a 20*20 grid. For the analysis, the spectra are reduced to scores on two factors, one derived externally from a linear discriminant analysis using spectra taken manually from known normal and metastatic tissue, and one derived from the node under investigation to capture variability orthogonal to the external factor. Then a three-group mixture model (normal, metastatic, non-nodal background) using multivariate t distributions is fitted to the scores, with external data being used to specify informative prior distributions for the parameters of the three distributions. A Markov random field prior imposes smoothness on the image generated by the model. Finally, the node is classified as metastatic if any one pixel in this smoothed image is classified as metastatic. The model parameters were tuned on a training set of nodes, and then the tuned model was tested on a separate validation set of nodes, achieving satisfactory sensitivity and specificity. The aim in developing the analysis was to allow flexibility in the way each node is modelled whilst still using external information. The Bayesian framework employed is ideal for this.

stat.AP↗

arXiv2017

High Dimensional Inference in Partially Linear Models

We propose two semiparametric versions of the debiased Lasso procedure for the model $Y_i = X_iβ_0 + g_0(Z_i) + ε_i$, where $β_0$ is high dimensional but sparse (exactly or approximately). Both versions are shown to have the same asymptotic normal distribution and do not require the minimal signal condition for statistical inference of any component in $β_0$. Our method also works when $Z_i$ is high dimensional provided that the function classes $E(X_{ij} |Z_i)$s and $E(Y_i|Z_i)$ belong to exhibit certain sparsity features, e.g., a sparse additive decomposition structure. We further develop a simultaneous hypothesis testing procedure based on multiplier bootstrap. Our testing method automatically takes into account of the dependence structure within the debiased estimates, and allows the number of tested components to be exponentially high.

math.ST↗

arXiv2017

Decorative Plasmonic Surfaces

Low-profile patterned plasmonic surfaces are synergized with a broad class of silicon microstructures to greatly enhance near-field nanoscale imaging, sensing, and energy harvesting coupled with far-field free-space detection. This concept has a clear impact on several key areas of interest for the MEMS community, including but not limited to ultra-compact microsystems for sensitive detection of small number of target molecules, and surface devices for optical data storage, micro-imaging and displaying. In this paper, we review the current state-of-the-art in plasmonic theory as well as derive design guidance for plasmonic integration with microsystems, fabrication techniques, and selected applications in biosensing, including refractive-index based label-free biosensing, plasmonic integrated lab-on-chip systems, plasmonic near-field scanning optical microscopy and plasmonics on-chip systems for cellular imaging. This paradigm enables low-profile conformal surfaces on microdevices, rather than bulk material or coatings, which provide clear advantages for physical, chemical and biological-related sensing, imaging, and light harvesting, in addition to easier realization, enhanced flexibility, and tunability.

physics.optics↗

arXiv2017

Filtering Signal Processes in Molecular Multimedia Memristors

To obtain precisely controllable, robust as well as reproduceable memristor for efficient neuromorphic computing still very challenging. Molecular tailoring aims at obtaining the much more flexibly tuning plasticity has recently generated significant interest as new paradigms toward the realization of novel memristor-based synapses. Herein, inspired by the deliberate oxygen transport carried by the hemoglobin in our blood circulation, we report a novel molecular-regulated electronic/ionic semiconducting (MEIS) platform ITO/MTPP/Al2O3-x/Al with a series of metallophorphyrins (MTPPs) to delicately regulate the ionic migration for robust molecular multimedia memristors. The stable pinched hysteresis resulted from the coordination-regulated ionic migration was verified by different device structures, operation modes, as well as the characterizations of scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy (STEM-EDX) and X-ray photoelectron spectroscopy (XPS). Metal coordination-dependent device parameters such as potential and depression as well as retention curves further support the correlation between the coordination and stimulating flux-dependent memristive behaviors. In the 5,10,15,20-tetraphenyl-21H,23H-porphyrin zinc(II) (ZnTPP) synapse, we implement versatile emulations, mainly including transition from short-term memory (STM) to long-term-memory (LTM), learning experience and activity-dependent learning-memory process in integrated neuromorphic configurations based on the biological Hebbian rules, and develop the fresh Spike-Amplitude-Dependent-Plasticity (SADP) with the applications of signal filtering and habituation and sensitization which are beyond the prevalent Hebbian rules.

cond-mat.mes-hall↗

arXiv2014

High-Dimensional Semiparametric Selection Models: Estimation Theory with an Application to the Retail Gasoline Market

This paper proposes a multi-stage projection-based Lasso procedure for the semiparametric sample selection model in high-dimensional settings under a weak nonparametric restriction on the selection correction. In particular, the number of regressors in the main equation, p, and the number of regressors in the selection equation, d, can grow with and exceed the sample size n. The analysis considers the exact sparsity case and the approximate sparsity case. The main theoretical results are finite-sample bounds from which sufficient scaling conditions on the sample size for estimation consistency and variable-selection consistency are established. Statistical efficiency of the proposed estimators is studied via lower bounds on minimax risks and the result shows that, for a family of models with exactly sparse structure on the coefficient vector in the main equation, one of the proposed estimators attains the smallest estimation error up to the (n,d,p)-scaling among a class of procedures in worst-case scenarios. Inference procedures for the coefficients of the main equation, one based on a pivotal Dantzig selector to construct non-asymptotic confidence sets and one based on a post-selection strategy, are discussed. Other theoretical contributions include establishing the non-asymptotic counterpart of the familiar asymptotic oracle results from previous literature: the estimator of the coefficients in the main equation behaves as if the unknown nonparametric component were known, provided the nonparametric component is sufficiently smooth. Small-sample performance of the high-dimensional multi-stage estimation procedure is evaluated by Monte-Carlo simulations and illustrated with an empirical application to the retail gasoline market in the Greater Saint Louis area.

math.ST↗

arXiv2013

Sparse Linear Models and Two-Stage Estimation in High-Dimensional Settings with Possibly Many Endogenous Regressors

This paper explores the validity of the two-stage estimation procedure for sparse linear models in high-dimensional settings with possibly many endogenous regressors. In particular, the number of endogenous regressors in the main equation and the instruments in the first-stage equations can grow with and exceed the sample size n. The analysis concerns the exact sparsity case, i.e., the maximum number of non-zero components in the vectors of parameters in the first-stage equations, k1, and the number of non-zero components in the vector of parameters in the second-stage equation, k2, are allowed to grow with n but slowly compared to n. I consider the high-dimensional version of the two-stage least square estimator where one obtains the fitted regressors from the first-stage regression by a least square estimator with l_1-regularization (the Lasso or Dantzig selector) when the first-stage regression concerns a large number of instruments relative to n, and then construct a similar estimator using these fitted regressors in the second-stage regression. The main theoretical results of this paper are non-asymptotic bounds from which I establish sufficient scaling conditions on the sample size for estimation consistency in l_2-norm and variable-selection consistency. A technical issue regarding the so-called "restricted eigenvalue (RE) condition" for estimation consistency and the "mutual incoherence (MI) condition" for selection consistency arises in the two-stage estimation from allowing the number of regressors in the main equation to exceed n and this paper provides analysis to verify these RE and MI conditions. Depending on the underlying assumptions, the upper bounds on the l_2-error and the sample size required to obtain these consistency results differ by factors involving k1 and/or k2. Simulations are conducted to gain insight on the finite sample performance.

math.ST↗

arXiv2012

How to freeze drops with powder

This document accompanies fluid dyanmics video entry V83911 for APS DFD 2012 meeting. In this video, we present experiments on how drop oscillations can be "frozen" using hydrophobic powders.

physics.flu-dyn↗

arXiv2006

Detection of Markov Random Fields on Two-Dimensional Intersymbol Interference Channels

We present a novel iterative algorithm for detection of binary Markov random fields (MRFs) corrupted by two-dimensional (2D) intersymbol interference (ISI) and additive white Gaussian noise (AWGN). We assume a first-order binary MRF as a simple model for correlated images. We assume a 2D digital storage channel, where the MRF is interleaved before being written and then read by a 2D transducer; such channels occur in recently proposed optical disk storage systems. The detection algorithm is a concatenation of two soft-input/soft-output (SISO) detectors: an iterative row-column soft-decision feedback (IRCSDF) ISI detector, and a MRF detector. The MRF detector is a SISO version of the stochastic relaxation algorithm by Geman and Geman in IEEE Trans. Pattern Anal. and Mach. Intell., Nov. 1984. On the 2 x 2 averaging-mask ISI channel, at a bit error rate (BER) of 10^{-5}, the concatenated algorithm achieves SNR savings of between 0.5 and 2.0 dB over the IRCSDF detector alone; the savings increase as the MRFs become more correlated, or as the SNR decreases. The algorithm is also fairly robust to mismatches between the assumed and actual MRF parameters.

cs.IT↗