# Myung Cho

## Contact Details

NameMyung Cho |
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## Pubs By Year |
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## Pub CategoriesComputer Science - Information Theory (13) Mathematics - Information Theory (13) Mathematics - Optimization and Control (4) Statistics - Machine Learning (3) Physics - Materials Science (2) Computer Science - Learning (2) Computer Science - Distributed; Parallel; and Cluster Computing (1) Instrumentation and Methods for Astrophysics (1) |

## Publications Authored By Myung Cho

With explosion of data size and limited storage space at a single location, data are often distributed at different locations. We thus face the challenge of performing large-scale machine learning from these distributed data through communication networks. In this paper, we study how the network communication constraints will impact the convergence speed of distributed machine learning optimization algorithms. Read More

**Authors:**Yeong Jae Shin, Yoonkoo Kim, Sung-Jin Kang, Ho-Hyun Nahm, Pattukkannu Murugavel, Jeong Rae Kim, Myung Rae Cho, Lingfei Wang, Sang Mo Yang, Jong-Gul Yoon, Jin-Seok Chung, Miyoung Kim, Hua Zhou, Seo Hyoung Chang, Tae Won Noh

**Category:**Physics - Materials Science

The atomic-scale synthesis of artificial oxide heterostructures offers new opportunities to create novel states that do not occur in nature. The main challenge related to synthesizing these structures is obtaining atomically sharp interfaces with designed termination sequences. Here, we demonstrate that the oxygen pressure (PO2) during growth plays an important role in controlling the interfacial terminations of SrRuO3/BaTiO3/SrRuO3 (SRO/BTO/SRO) ferroelectric capacitors. Read More

Phaseless super-resolution refers to the problem of superresolving a signal from only its low-frequency Fourier magnitude measurements. In this paper, we consider the phaseless super-resolution problem of recovering a sum of sparse Dirac delta functions which can be located anywhere in the continuous time-domain. For such signals in the continuous domain, we propose a novel Semidefinite Programming (SDP) based signal recovery method to achieve the phaseless superresolution. Read More

In this paper, we study the problem of determining $k$ anomalous random variables that have different probability distributions from the rest $(n-k)$ random variables. Instead of sampling each individual random variable separately as in the conventional hypothesis testing, we propose to perform hypothesis testing using mixed observations that are functions of multiple random variables. We characterize the error exponents for correctly identifying the $k$ anomalous random variables under fixed time-invariant mixed observations, random time-varying mixed observations, and deterministic time-varying mixed observations. Read More

The null space condition for $\ell_1$ minimization in compressed sensing is a necessary and sufficient condition on the sensing matrices under which a sparse signal can be uniquely recovered from the observation data via $\ell_1$ minimization. However, verifying the null space condition is known to be computationally challenging. Most of the existing methods can provide only upper and lower bounds on the proportion parameter that characterizes the null space condition. Read More

We propose novel algorithms that enhance the performance of recovering unknown continuous-valued frequencies from undersampled signals. Our iterative reweighted frequency recovery algorithms employ the support knowledge gained from earlier steps of our algorithms as block prior information to enhance frequency recovery. Our methods improve the performance of the atomic norm minimization which is a useful heuristic in recovering continuous-valued frequency contents. Read More

We address the problem of super-resolution frequency recovery using prior knowledge of the structure of a spectrally sparse, undersampled signal. In many applications of interest, some structure information about the signal spectrum is often known. The prior information might be simply knowing precisely some signal frequencies or the likelihood of a particular frequency component in the signal. Read More

**Authors:**Markus Hartung, Tom Hayward, Les Saddlemyer, Lisa Poyneer, Andrew Cardwell, Chas Cavedoni, Myung Cho, Jeffrey K. Chilcote, Paul Collins, Darren Dillon, Ramon Galvez, Gaston Gausachs, Stephen Goodsell, Andres Guesalaga, Pascal Hibon, James Larkin, Bruce Macintosh, Dave Palmer, Naru Sadakuni, Dmitry Savransky, Andrew Serio, Fredrik Rantakyro, Kent Wallace

The Gemini Planet Imager (GPI) entered on-sky commissioning and had its first-light at the Gemini South (GS) telescope in November 2013. GPI is an extreme adaptive optics (XAO), high-contrast imager and integral-field spectrograph dedicated to the direct detection of hot exo-planets down to a Jupiter mass. The performance of the apodized pupil Lyot coronagraph depends critically upon the residual wavefront error (design goal of 60 nm RMS with 5 mas RMS tip/tilt), and therefore is most sensitive to vibration (internal or external) of Gemini's instrument suite. Read More

We address the problem of super-resolution line spectrum estimation of an undersampled signal with block prior information. The component frequencies of the signal are assumed to take arbitrary continuous values in known frequency blocks. We formulate a general semidefinite program to recover these continuous-valued frequencies using theories of positive trigonometric polynomials. Read More

Mechanical exfoliation of bulk crystals has been widely used to obtain thin topological insulator (TI) flakes for device fabrication. However, such a process produces only micro-sized flakes that are highly irregular in shape and thickness. In this work, we developed a process to transfer the entire area of TI Bi2Se3 thin films grown epitaxially on Al2O3 and SiO2 to arbitrary substrates, maintaining their pristine morphology and crystallinity. Read More

Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In particular, atomic norm minimization was proposed in \cite{tang2012csotg} to recover $1$-dimensional spectrally sparse signal. However, in spite of existing research efforts \cite{chi2013compressive}, it was still an open problem how to formulate an equivalent positive semidefinite program for atomic norm minimization in recovering signals with $d$-dimensional ($d\geq 2$) off-the-grid frequencies. Read More

Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In this paper, we extend off-the-grid CS to applications where some prior information about spectrally sparse signal is known. We specifically consider cases where a few contributing frequencies or poles, but not their amplitudes or phases, are known a priori. Read More

In compressed sensing problems, $\ell_1$ minimization or Basis Pursuit was known to have the best provable phase transition performance of recoverable sparsity among polynomial-time algorithms. It is of great theoretical and practical interest to find alternative polynomial-time algorithms which perform better than $\ell_1$ minimization. \cite{Icassp reweighted l_1}, \cite{Isit reweighted l_1}, \cite{XuScaingLaw} and \cite{iterativereweightedjournal} have shown that a two-stage re-weighted $\ell_1$ minimization algorithm can boost the phase transition performance for signals whose nonzero elements follow an amplitude probability density function (pdf) $f(\cdot)$ whose $t$-th derivative $f^{t}(0) \neq 0$ for some integer $t \geq 0$. Read More

In this paper, we propose new efficient algorithms to verify the null space condition in compressed sensing (CS). Given an $(n-m) \times n$ ($m>0$) CS matrix $A$ and a positive $k$, we are interested in computing $\displaystyle \alpha_k = \max_{\{z: Az=0,z\neq 0\}}\max_{\{K: |K|\leq k\}}$ ${\|z_K \|_{1}}{\|z\|_{1}}$, where $K$ represents subsets of $\{1,2,.. Read More

In this paper, we consider robust system identification under sparse outliers and random noises. In our problem, system parameters are observed through a Toeplitz matrix. All observations are subject to random noises and a few are corrupted with outliers. Read More

In this paper, we consider robust system identification under sparse outliers and random noises. In this problem, system parameters are observed through a Toeplitz matrix. All observations are subject to random noises and a few are corrupted with outliers. Read More