arXiv subjects

Miaomiao Zhang

Publications and source records attributed to Miaomiao Zhang.

At least 91 records · Page 5Linked to original sources

arXiv2020

PAC Model Checking of Black-Box Continuous-Time Dynamical Systems

In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning. The black-box dynamical systems are the ones, for which no model is given but whose states changing continuously through time within a finite time interval can be observed at some discrete time instants for a given input. The new model checking approach is termed as PAC model checking due to incorporation of learned models with correctness guarantees expressed using the terms error probability and confidence. Based on the error probability and confidence level, our approach provides statistically formal guarantees that the time-evolving trajectories of the black-box dynamical system over finite time horizons fall within the range of the learned model plus a bounded interval, contributing to insights on the reachability of the black-box system and thus on the satisfiability of its safety requirements. The learned model together with the bounded interval is obtained by scenario optimization, which boils down to a linear programming problem. Three examples demonstrate the performance of our approach.

eess.SY↗

arXiv2020

Local Molecular Gas toward the Aquila Rift Region

We present the results of a ~250 square degrees CO mapping (+26d 4.1x10^5 Msun in the whole region. In fact, the mass of the molecular gas is dominated by the W40 giant molecular cloud (GMC) at ~474 pc (~1.4x10^5 Msun) and the GMC complex G036.0+01.0 at ~560-670 pc (~2.0x10^5 Msun), while the MCs at ~220-260pc have gas masses of 10^2-10^3 Msun. Interestingly, an ~80pc long filamentary MC G044.0-02.5 at a distance of ~404 pc shows a systematic velocity gradient along and perpendicular to the major axis of the filament. The HI gas with the enhanced emission has the similar spatial morphologies and velocity features compared to the corresponding CO structure, indicating that the large-scale converging HI flows are probably responsible for the formation of the MC. Meanwhile, the long filamentary MC consists of many sub-filaments with the lengths ranging from ~0.5 pc to several pc, as well as prevalent networks of filaments in other large-scale local MCs.

astro-ph.SR↗

arXiv2020

On the Applicability of Registration Uncertainty

Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately calculated registration uncertainty and misplace unwarranted confidence in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters. The majority of existing research focuses on trying out different summary statistics as well as a means to exploit them. Distinctively, in this paper, we study two rarely examined topics: (1) whether those summary statistics of the transformation distribution most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, Ut, and label uncertainty Ul. The conventional way of using Ut to quantify Ul is inappropriate and can be misleading. By a real data experiment, we also share a potentially critical finding that making use of the registration uncertainty may not always be an improvement.

cs.CV↗

arXiv2020

DeepFLASH: An Efficient Network for Learning-based Medical Image Registration

This paper presents DeepFLASH, a novel network with efficient training and inference for learning-based medical image registration. In contrast to existing approaches that learn spatial transformations from training data in the high dimensional imaging space, we develop a new registration network entirely in a low dimensional bandlimited space. This dramatically reduces the computational cost and memory footprint of an expensive training and inference. To achieve this goal, we first introduce complex-valued operations and representations of neural architectures that provide key components for learning-based registration models. We then construct an explicit loss function of transformation fields fully characterized in a bandlimited space with much fewer parameterizations. Experimental results show that our method is significantly faster than the state-of-the-art deep learning based image registration methods, while producing equally accurate alignment. We demonstrate our algorithm in two different applications of image registration: 2D synthetic data and 3D real brain magnetic resonance (MR) images. Our code is available at https://github.com/jw4hv/deepflash.

eess.IV↗

arXiv2020

Learning One-Clock Timed Automata

We present an algorithm for active learning of deterministic timed automata with a single clock. The algorithm is within the framework of Angluin's $L^*$ algorithm and inspired by existing work on the active learning of symbolic automata. Due to the need of guessing for each transition whether it resets the clock, the algorithm is of exponential complexity in the size of the learned automata. Before presenting this algorithm, we propose a simpler version where the teacher is assumed to be smart in the sense of being able to provide the reset information. We show that this simpler setting yields a polynomial complexity of the learning process. Both of the algorithms are implemented and evaluated on a collection of randomly generated examples. We furthermore demonstrate the simpler algorithm on the functional specification of the TCP protocol.

cs.FL↗

arXiv2019

Deep PSF photometric catalog of the VVV survey data

The Vista Variables in the Via Lactea (VVV) survey has performed a multi-epoch near-infrared imaging of the inner Galactic plane. High-fidelity photometric catalogs are needed to utilize the data. We aim at producing a deep, point-spread-function (PSF) photometric catalog for the VVV survey J, H, and Ks band data. Specifically, we aim at taking advantage of all the epochs of the survey to reach high limiting magnitudes. We develop an automatic PSF-fitting pipeline based on the DaoPHOT algorithm and perform photometry on the stacked VVV images in J, H, and Ks bands. We present a PSF photometric catalog in the Vega system that contains about 926 million sources in the J, H, and Ks filters. About 10% of the sources are flagged as possible spurious detections. The 5 sigma limiting magnitudes of the sources with high reliability are about 20.8, 19.5, and 18.7 mag in the J, H, and Ks band, respectively, depending on the local crowding condition. Our photometric catalog reaches on average about one magnitude deeper than the previously released PSF DoPHOT photometric catalog. It also includes less spurious detections. There are significant differences in the brightnesses of faint sources between our catalog and the previously released one. The likely origin of these differences is in the different photometric algorithms that are utilized; it is not straightforward to assess which catalog is more accurate in which situations. Our new catalog is beneficial especially for science goals that require high limiting magnitudes; our catalog reaches such in fields that have a relatively uniform source number density. Overall, the limiting magnitudes and completeness are different in the fields with different crowding conditions.

astro-ph.GA↗

arXiv2019

Mixture Probabilistic Principal Geodesic Analysis

Dimensionality reduction on Riemannian manifolds is challenging due to the complex nonlinear data structures. While probabilistic principal geodesic analysis~(PPGA) has been proposed to generalize conventional principal component analysis (PCA) onto manifolds, its effectiveness is limited to data with a single modality. In this paper, we present a novel Gaussian latent variable model that provides a unique way to integrate multiple PGA models into a maximum-likelihood framework. This leads to a well-defined mixture model of probabilistic principal geodesic analysis (MPPGA) on sub-populations, where parameters of the principal subspaces are automatically estimated by employing an Expectation Maximization algorithm. We further develop a mixture Bayesian PGA (MBPGA) model that automatically reduces data dimensionality by suppressing irrelevant principal geodesics. We demonstrate the advantages of our model in the contexts of clustering and statistical shape analysis, using synthetic sphere data, real corpus callosum, and mandible data from human brain magnetic resonance~(MR) and CT images.

cs.LG↗

arXiv2019

Plug-and-Play Priors for Reconstruction-based Placental Image Registration

This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.

eess.IV↗

arXiv2019

CO~($J=1-0$) Observations toward Filamentary Molecular Clouds in the Galactic Region with $l = [169\arcdeg.75, 174\arcdeg.75], b = [-0\arcdeg.75, 0\arcdeg.5]$

We present observations of the CO isotopologues ($^{12}$CO, $^{13}$CO, and C$^{18}$O) toward the Galactic region with $169\arcdeg.75 \leqslant l \leqslant 174\arcdeg.75$ and $-0\arcdeg.75 \leqslant b \leqslant 0\arcdeg.5$, using the Purple Mountain Observatory 13.7~m millimeter-wavelength telescope. Based on the $^{13}$CO~($J = 1-0$) data, we find five molecular clouds within the velocity range between $-$25 and 8~km~s$^{-1}$ that are all characterized by conspicuous filamentary structures. We have identified eight filaments with a length of 6.38--28.45~pc, a mean H$_2$ column density of 0.70$\times$10$^{21}$--6.53$\times$10$^{21}$~cm$^{-2}$, and a line mass of 20.24--161.91~$M_\sun$ pc$^{-1}$, assuming a distance of $\sim$1.7~kpc. Gaussian fittings to the inner parts of the radial density profiles lead to a mean FWHM width of 1.13$\pm$0.01~pc. The velocity structures of most filaments present continuous distributions with slight velocity gradients. We find that turbulence is the dominant internal pressure to support the fragmentation of filaments instead of thermal pressure. Most filaments have virial parameters smaller than 2; thus, they are gravitationally bound. Four filaments have an LTE line mass close to the virial line mass. We further extract dense clumps using the $^{13}$CO data and find that 64$\%$ of the clumps are associated with the filaments. According to the complementary IR data, most filaments have associated Class~II young stellar objects. Class~I objects are mainly found to be located in the filaments with a virial parameter close to 1. Within two virialized filaments, $^{12}$CO outflows have been detected, indicating ongoing star-forming activity therein.

astro-ph.GA↗

arXiv2019

A Large-field J=1-0 Survey of CO and Its Isotopologues Toward the Cassiopeia A Supernova Remnant

We have conducted a large-field simultaneous survey of $^{12}$CO, $^{13}$CO, and C$^{18}$O $J=1-0$ emission toward the Cassiopeia A (Cas A) supernova remnant (SNR), which covers a sky area of $3.5^{\circ}\times3.1^{\circ}$. The Cas giant molecular cloud (GMC) mainly consists of three individual clouds with masses on the order of $10^4-10^5\ M_{\odot}$. The total mass derived from the $\rm{^{13}CO}$ emission of the GMC is 2.1$\times10^{5}\ M_{\odot}$ and is 9.5$\times10^5\ M_{\odot}$ from the $\rm{^{12}CO}$ emission. Two regions with broadened (6$-$7 km s$^{-1}$) or asymmetric $^{12}$CO line profiles are found in the vicinity (within a 10$'\times10'$ region) of the Cas A SNR, indicating possible interactions between the SNR and the GMC. Using the GAUSSCLUMPS algorithm, 547 $^{13}$CO clumps are identified in the GMC, 54$\%$ of which are supercritical (i.e. $α_{\rm{vir}}<2$). The mass spectrum of the molecular clumps follows a power-law distribution with an exponent of $-2.20$. The pixel-by-pixel column density of the GMC can be fitted with a log-normal probability distribution function (N-PDF). The median column density of molecular hydrogen in the GMC is $1.6\times10^{21}$ cm$^{-2}$ and half the mass of the GMC is contained in regions with H$_2$ column density lower than $3\times10^{21}$ cm$^{-2}$, which is well below the threshold of star formation. The distribution of the YSO candidates in the region shows no agglomeration.

astro-ph.GA↗

arXiv2019

Temporal Registration in Application to In-utero MRI Time Series

We present a robust method to correct for motion in volumetric in-utero MRI time series. Time-course analysis for in-utero volumetric MRI time series often suffers from substantial and unpredictable fetal motion. Registration provides voxel correspondences between images and is commonly employed for motion correction. Current registration methods often fail when aligning images that are substantially different from a template (reference image). To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We evaluate the utility of the temporal model in the context of in-utero MRI time series alignment by examining the accuracy of propagated segmentation label maps. Our results suggest that the proposed model captures accurately the temporal dynamics of transformations in in-utero MRI time series.

cs.CV↗

arXiv2018

A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction

With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage became a bottleneck in ultrasound system design. To reduce the amount of sampled channel data, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples.

eess.SP↗

arXiv2018

Star-forming content of the giant molecular filaments in the Milky Way

Observations have discovered numerous giant molecular filaments (GMFs) in the Milky Way. However, their role in the Galactic star formation and Galaxy-scale evolution of dense gas is still unknown. We investigate systematically the star-forming content of all currently known GMFs. This allows us to estimate the star formation rates (SFRs) of the GMFs and to establish relationships between the SFRs and the GMF properties. We identify and classify the young stellar object (YSO) population of each GMF using multi-wavelength photometry from near- to far-infrared. We estimate the total SFRs assuming a universal and fully sampled initial mass function and luminosity function. We uniformly estimate the physical properties of 57 GMFs. The GMFs show correlations between the 13CO line width, mass, and size, similar to Larson's relations. We identify 36 394 infrared excess sources in 57 GMFs and obtain SFRs for 46 GMFs. The median SFR surface density (Sigma_SFR) and star formation efficiency (SFE) of GMFs are 0.62 Msun Myr-1 pc-2 and 1%, similar to the nearby star-forming clouds. The star formation rate per free-fall time of GMFs is between 0.002-0.05 with the median value of 0.02. We also find a strong correlation between SFR and dense gas mass that is defined as gas mass above a visual extinction of 7 mag, which suggests that the SFRs of the GMFs scale similarly with dense gas as those of nearby molecular clouds. We also find a strong correlation between the mean SFR per unit length and dense gas mass per unit length. The origin of this scaling remains unknown, calling for further studies that can link the structure of GMFs to their SF activity and explore the differences between GMFs and other molecular clouds

astro-ph.GA↗

arXiv2018

A large-scale survey of CO and its isotopologues toward the Rosette molecular cloud

Using the PMO-13.7m millimeter telescope at Delingha in China, we have conducted a large-scale simultaneous survey of $^{12}$CO, $^{13}$CO, and C$^{18}$O J=1-0 emission toward the Rosette molecular cloud (RMC) region with a sky coverage of 3.5 $\times$ 2.5 deg$^2$. The majority of the emission in the region comes from the RMC complex with velocities lying in the range from -2 km s$^{-1}$ to 20.5 km s$^{-1}$. Beyond this velocity range, 73 molecular clumps are identified with kinematic distances from 2.4 kpc to 11 kpc. Based on the spatial and velocity distribution, nine individual clouds, C1-C9, have been identified for the RMC complex. It appears that the C3 cloud is different from other clouds in the RMC complex in view of its characteristic velocity, excitation temperature, and velocity dispersion. Most of the young stellar clusters in the region are located in positions of both high column density and high excitation temperature. Seven new molecular filaments are discovered in the RMC complex. Evidence for cloud-cloud collision is found in the region of young stellar clusters REFL9 and PouF, showing that these young stellar clusters probably result from a cloud-cloud collision. The abundance ratios of $^{13}$CO to C$^{18}$O in the region have a mean value of 13.7 which is 2.5 times larger than the solar system value, showing that UV photons from the nearby OB clusters have strong influence on the chemistry of clouds in the RMC complex.

astro-ph.GA↗

arXiv2018

Structure and Fragmentation of a high line-mass filament: Nessie

An increasing number of hundred-parsec scale, high line-mass filaments have been detected in the Galaxy. Their evolutionary path, including fragmentation towards star formation, is virtually unknown. We characterize the fragmentation within the Nessie filament, covering size-scales between $\sim$ 0.1-100 pc. We also connect the small-scale fragments to the star-forming potential of the cloud. We combine near-infrared data from the VVV survey with mid-infrared GLIMPSE data to derive a high-resolution dust extinction map and apply a wavelet decomposition technique on it to analyze the fragmentation characteristics of the cloud, which are compared with predictions from fragmentation models. We compare the detected objects to those identified in $\sim$ 10 times coarser resolution from ATLASGAL data. We present a high-resolution extinction map of Nessie. We estimate the mean line-mass of Nessie to be $\sim$ 627 M$_\odot$/pc and the distance to be $\sim$ 3.5 kpc. We find that Nessie shows fragmentation at multiple size scales. The nearest-neighbour separations of the fragments at all scales are within a factor of 2 of the Jeans' length at that scale. However, the relationship between the mean densities of the fragments and their separations is significantly shallower than expected for Jeans' fragmentation. The relationship is similar to the one predicted for a filament that exhibits a Larson-like scaling between size-scale and velocity dispersion; such a scaling may result from turbulent support. Based on the number of YSOs in Nessie, we estimate that the star formation rate is $\sim$ 371 M$_\odot$/Myr; similar values result if using the number of dense cores, or the amount of dense gas, as the proxy of star formation. The star formation efficiency is 0.017. These numbers indicate that Nessie's star-forming content is comparable to the Solar neighborhood giant molecular clouds like Orion A.

astro-ph.GA↗

arXiv2018

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery.

cs.CV↗

arXiv2017

CO~($J = 1-0$) Observations of a Filamentary Molecular Cloud in the Galactic Region Centered at $l = 150\arcdeg, b = 3.5\arcdeg$

We present large-field (4.25~$\times$~3.75 deg$^2$) mapping observations toward the Galactic region centered at $l = 150\arcdeg, b = 3.5\arcdeg$ in the $J = 1-0$ emission line of CO isotopologues ($^{12}$CO, $^{13}$CO, and C$^{18}$O), using the 13.7 m millimeter-wavelength telescope of the Purple Mountain Observatory. Based on the $^{13}$CO observations, we reveal a filamentary cloud in the Local Arm at a velocity range of $-$0.5 to 6.5~km~s$^{-1}$. This molecular cloud contains 1 main filament and 11 sub-filaments, showing the so-called "ridge-nest" structure. The main filament and three sub-filaments are also detected in the C$^{18}$O line. The velocity structures of most identified filaments display continuous distribution with slight velocity gradients. The measured median excitation temperature, line width, length, width, and linear mass of the filaments are $\sim$9.28~K, 0.85~km~s$^{-1}$, 7.30~pc, 0.79~pc, and 17.92~$M_\sun$~pc$^{-1}$, respectively, assuming a distance of 400~pc. We find that the four filaments detected in the C$^{18}$O line are thermally supercritical, and two of them are in the virialized state, and thus tend to be gravitationally bound. We identify in total 146 $^{13}$CO clumps in the cloud, about 77$\%$ of the clumps are distributed along the filaments. About 56$\%$ of the virialized clumps are found to be associated with the supercritical filaments. Three young stellar object (YSO) candidates are also identified in the supercritical filaments, based on the complementary infrared (IR) data. These results indicate that the supercritical filaments, especially the virialized filaments, may contain star-forming activities.

astro-ph.GA↗

arXiv2016

SSHMT: Semi-supervised Hierarchical Merge Tree for Electron Microscopy Image Segmentation

Region-based methods have proven necessary for improving segmentation accuracy of neuronal structures in electron microscopy (EM) images. Most region-based segmentation methods use a scoring function to determine region merging. Such functions are usually learned with supervised algorithms that demand considerable ground truth data, which are costly to collect. We propose a semi-supervised approach that reduces this demand. Based on a merge tree structure, we develop a differentiable unsupervised loss term that enforces consistent predictions from the learned function. We then propose a Bayesian model that combines the supervised and the unsupervised information for probabilistic learning. The experimental results on three EM data sets demonstrate that by using a subset of only 3% to 7% of the entire ground truth data, our approach consistently performs close to the state-of-the-art supervised method with the full labeled data set, and significantly outperforms the supervised method with the same labeled subset.

cs.CV↗