arXiv subjects

Cheng Cheng

Publications and source records attributed to Cheng Cheng.

At least 199 records · Page 11Linked to original sources

arXiv2020

A Large Massive Quiescent Galaxy Sample at z~1.2

In this paper we present a simple color-magnitude selection and obtain a large sample of 33,893 massive quiescent galaxies at intermediate redshifts (1 10.5, with a median of log(M*/M_sun)=11.0. A small fraction of our galaxies is also covered by the HST CANDELS. Morphological analysis in the observed H band shows that the majority of this subsample are early-type galaxies. As massive early-type galaxies trace the high density regions in the large scale structure in the universe, our study provides a quick and simple way to obtain a statistical significant sample of massive galaxies in a relative narrow redshift range. Our sample is 7-20 times larger at the massive end (log(M*/M_sun)>10.5) than any existing samples obtained in previous surveys. This is a pioneer study, and the technique introduced here can be applied to future wide-field survey to study large scale structure, and to identify high density region and clusters.

astro-ph.GA↗

arXiv2020

ALMA [N ıı] 205 μm Imaging Spectroscopy of the Lensed Submillimeter galaxy ID 141 at redshift 4.24

We present the Atacama Large Millimeter/submillimeter Array (ALMA) observation of the Sub-millimeter galaxy (SMG) ID 141 at z=4.24 in the [N II] 205 $μ$m line (hereafter [N II]) and the underlying continuum at (rest-frame) 197.6 $μ$m. Benefiting from lensing magnification by a galaxy pair at z=0.595, ID 141 is one of the brightest z$>4$ SMGs. At the angular resolutions of $\sim1.2"$ to $1.5"$ ($1" \sim6.9$ kpc), our observation clearly separates, and moderately resolves the two lensed images in both continuum and line emission at $\rm S/N>5$ . Our continuum-based lensing model implies an averaged amplification factor of $\sim5.8$ and reveals that the de-lensed continuum image has the Sérsic index $\simeq 0.95$ and the Sérsic radius of $\sim0.18" (\sim 1.24$ kpc). Furthermore, the reconstructed [N II] velocity field in the source plane is dominated by a rotation component with a maximum velocity of $\sim 300$ km/s at large radii, indicating a dark matter halo mass of $\sim 10^{12}M_{\odot}$. This, together with the reconstructed velocity dispersion field being smooth and modest in value ($<100$ km/s) over much of the outer parts of the galaxy, favours the interpretation of ID 141 being a disk galaxy dynamically supported by rotation. The observed [N II]/CO (7-6) and [N II]/[C II] 158 $μ$m line luminosity ratios, which are consistent with the corresponding line ratio vs. far-infrared color correlation from local luminous infrared galaxies, imply a de-lensed star formation rate of ($1.8\pm 0.6)\times10^3M_\odot$/yr and provide an independent estimate on the size of the star-forming region $0.7^{+0.3}_{-0.3}$ kpc in radius.

astro-ph.GA↗

arXiv2020

VALES VII: Molecular and ionized gas properties in pressure balanced interstellar medium of starburst galaxies at z ~ 0.15

Context. Spatially resolved observations of the ionized and molecular gas are critical for understanding the physical processes that govern the interstellar medium (ISM) in galaxies. Aims. To study the morpho-kinematic properties of the ionized and molecular gas in three dusty starburst galaxies at $z = 0.12-0.17$ to explore the relation between molecular ISM gas phase dynamics and the star-formation activity. Methods. We analyse $\sim$kpc-scale ALMA CO(1--0) and seeing limited SINFONI Paschen-$α$ observations. We use a dynamical mass model, which accounts for beam-smearing effects, to constrain the CO-to-H$_2$ conversion factor. Results. One starburst galaxy shows irregular morphology which may indicate a major merger, while the other two systems show disc-like morpho-kinematics. The two disc-like starbursts show molecular gas velocity dispersion values comparable with that seen in local LIRG/ULIRGs, but in an ISM with molecular gas fraction and surface density values consistent to that reported for local star-forming galaxies. These molecular gas velocity dispersion values can be explained by assuming vertical pressure equilibrium. The star-formation activity is correlated with the molecular gas content suggesting depletion times of the order of $\sim 0.1-1$ Gyr. The star formation rate surface density ($Σ_{\rm SFR}$) correlates with the ISM pressure set by self-gravity ($P_{\rm grav}$) following a power law with an exponent close to 0.8. Conclusions. In dusty disc-like starburst galaxies, our data support the scenario in which the molecular gas velocity dispersion values are driven by the ISM pressure set by self-gravity, responsible to maintain the vertical pressure balance. The correlation between $Σ_{\rm SFR}$ and $P_{\rm grav}$ suggests that, in these dusty starbursts galaxies, the star formation activity arises as a consequence of the ISM pressure balance.

astro-ph.GA↗

arXiv2020

Pixel-Face: A Large-Scale, High-Resolution Benchmark for 3D Face Reconstruction

3D face reconstruction is a fundamental task that can facilitate numerous applications such as robust facial analysis and augmented reality. It is also a challenging task due to the lack of high-quality datasets that can fuel current deep learning-based methods. However, existing datasets are limited in quantity, realisticity and diversity. To circumvent these hurdles, we introduce Pixel-Face, a large-scale, high-resolution and diverse 3D face dataset with massive annotations. Specifically, Pixel-Face contains 855 subjects aging from 18 to 80. Each subject has more than 20 samples with various expressions. Each sample is composed of high-resolution multi-view RGB images and 3D meshes with various expressions. Moreover, we collect precise landmarks annotation and 3D registration result for each data. To demonstrate the advantages of Pixel-Face, we re-parameterize the 3D Morphable Model (3DMM) into Pixel-3DM using the collected data. We show that the obtained Pixel-3DM is better in modeling a wide range of face shapes and expressions. We also carefully benchmark existing 3D face reconstruction methods on our dataset. Moreover, Pixel-Face serves as an effective training source. We observe that the performance of current face reconstruction models significantly improves both on existing benchmarks and Pixel-Face after being fine-tuned using our newly collected data. Extensive experiments demonstrate the effectiveness of Pixel-3DM and the usefulness of Pixel-Face.

cs.CV↗

arXiv2020

Adaptive support driven Bayesian reweighted algorithm for sparse signal recovery

Sparse learning has been widely studied to capture critical information from enormous data sources in the filed of system identification. Often, it is essential to understand internal working mechanisms of unknown systems (e.g. biological networks) in addition to input-output relationships. For this purpose, various feature selection techniques have been developed. For example, sparse Bayesian learning (SBL) was proposed to learn major features from a dictionary of basis functions, which makes identified models interpretable. Reweighted L1-regularization algorithms are often applied in SBL to solve optimization problems. However, they are expensive in both computation and memory aspects, thus not suitable for large-scale problems. This paper proposes an adaptive support driven Bayesian reweighted (ASDBR) algorithm for sparse signal recovery. A restart strategy based on shrinkage-thresholding is developed to conduct adaptive support estimate, which can effectively reduce computation burden and memory demands. Moreover, ASDBR accurately extracts major features and excludes redundant information from large datasets. Numerical experiments demonstrate the proposed algorithm outperforms state-of-the-art methods.

eess.SP↗

arXiv2020

Preconditioned Gradient Descent Algorithm for Inverse Filtering on Spatially Distributed Networks

Graph filters and their inverses have been widely used in denoising, smoothing, sampling, interpolating and learning. Implementation of an inverse filtering procedure on spatially distributed networks (SDNs) is a remarkable challenge, as each agent on an SDN is equipped with a data processing subsystem with limited capacity and a communication subsystem with confined range due to engineering limitations. In this letter, we introduce a preconditioned gradient descent algorithm to implement the inverse filtering procedure associated with a graph filter having small geodesic-width. The proposed algorithm converges exponentially, and it can be implemented at vertex level and applied to time-varying inverse filtering on SDNs.

cs.IT↗

arXiv2020

A zero-carbon, reliable and affordable energy future in Australia

Australia has one of the highest per capita consumption of energy and emissions of greenhouse gases in the world. It is also the global leader in rapid per capita annual deployment of new solar and wind energy, which is causing the country's emissions to decline. Australia is located at low-moderate latitudes along with three quarters of the global population. These factors make the Australian experience globally significant. In this study, we model a fully decarbonised electricity system together with complete electrification of heating, transport and industry in Australia leading to an 80% reduction in greenhouse gas emissions. An energy supply-demand balance is simulated based on long-term (10 years), high-resolution (half-hourly) meteorological and energy demand data. A significant feature of this model is that short-term off-river energy storage and distributed energy storage are utilised to support the large-scale integration of variable solar and wind energy. The results show that high levels of energy reliability and affordability can be effectively achieved through a synergy of flexible energy sources; interconnection of electricity grids over large areas; response from demand-side participation; and mass energy storage. This strategy represents a rapid and generic pathway towards zero-carbon energy futures within the Sunbelt.

eess.SY↗

arXiv2020

A micromechanics-informed phase field model for brittle fracture accounting for the unilateral constraint

We propose a new direction-dependent model for the unilateral constraint involved in the phase field approach to fracture and also in the continuous damage mechanics models. The construction of this phase field model is informed by micromechanical modeling through the homogenization theory, where the representative volume element (RVE) has a planar crack in the center. The proposed model is made closely match the response of the RVE, including the frictionless self-contact condition. This homogenization approach allows to identify a direction-dependent phase field model with the tension-compression split obtained from cracked microstructures. One important feature of the proposed model is that unlike most other models, the material degradation is consistently determined without artificial assumptions or ad hoc parameters with no physical interpretation, thus, a more realistic modeling is resulted. With standard tests such as uniaxial loadings, three-point bending, simple shear, and through-crack tests, the proposed model predicts reasonable crack paths. Moreover, with the RVE response as a benchmark, the proposed model gives rise to an accurate stress-strain curve under shear loads, more accurate than most existing models.

cs.CE↗

arXiv2020

Stable Phase Retrieval from Locally Stable and Conditionally Connected Measurements

This paper is concerned with stable phase retrieval for a family of phase retrieval models we name "locally stable and conditionally connected" (LSCC) measurement schemes. For every signal $f$, we associate a corresponding weighted graph $G_f$, defined by the LSCC measurement scheme, and show that the phase retrievability of the signal $f$ is determined by the connectivity of $G_f$. We then characterize the phase retrieval stability of the signal $f$ by two measures that are commonly used in graph theory to quantify graph connectivity: the Cheeger constant of $G_f$ for real valued signals, and the algebraic connectivity of $G_f$ for complex valued signals. We use our results to study the stability of two phase retrieval models that can be cast as LSCC measurement schemes, and focus on understanding for which signals the "curse of dimensionality" can be avoided. The first model we discuss is a finite-dimensional model for locally supported measurements such as the windowed Fourier transform. For signals "without large holes", we show the stability constant exhibits only a mild polynomial growth in the dimension, in stark contrast with the exponential growth which uniform stability constants tend to suffer from; more precisely, in $R^d$ the constant grows proportionally to $d^{1/2}$, while in $C^d$ it grows proportionally to $d$. We also show the growth of the constant in the complex case cannot be reduced, suggesting that complex phase retrieval is substantially more difficult than real phase retrieval. The second model we consider is an infinite-dimensional phase retrieval problem in a principal shift invariant space. We show that despite the infinite dimensionality of this model, signals with monotone exponential decay will have a finite stability constant. In contrast, the stability bound provided by our results will be infinite if the signal's decay is polynomial.

cs.IT↗

arXiv2020

The atomic gas of star-forming galaxies at z$\sim$0.05 as revealed by the Five-hundred-meter Aperture Spherical Radio Telescope

We report new HI observations of four z$\sim$0.05 star-forming galaxies undertaken during the commissioning phase of the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). FAST is the largest single-dish telescope with a 500 meter aperture and a 19-Beam receiver. Exploiting the unprecedented sensitivity provided by FAST, we aim to study the atomic gas, via the HI 21cm emission line, in low-$z$ star-forming galaxies taken from the Valparaíso ALMA/APEX Line Emission Survey (VALES) project. Together with previous ALMA CO($J=1-0$) observations, the HI data provides crucial information to measure the gas mass and dynamics. As a pilot HI survey, we targeted four local star-forming galaxies at $z\sim0.05$. In particular, one of them has already been detected in HI by the Arecibo Legacy Fast ALFA survey (ALFALFA), allowing a careful comparison. We use an ON-OFF observing approach that allowed us to reach an rms of 0.7mJy/beam at a 1.7km/s velocity resolution within only 20 minutes ON-target integration time. We demonstrate the great capabilities of the FAST 19-beam receiver for pushing the detectability of the HI emission line of extra-galactic sources. The HI emission line detected by FAST shows good consistency with the previous ALFALFA results. Our observations are put in context with previous multi-wavelength data to reveal the physical properties of these low-$z$ galaxies. We find that the CO($J=1-0$) and HI emission line profiles are similar. The dynamical mass estimated from the HI data is an order of magnitude higher than the baryon mass and the dynamical mass derived from the CO observations, implying that the mass probed by dynamics of HI is dominated by the dark matter halo. In one case, a target shows an excess of CO($J=1-0$) in the line centre, which can be explained by an enhanced CO($J=1-0$) emission induced by a nuclear starburst showing high velocity dispersion.

astro-ph.GA↗

arXiv2020

Predicting Mortality Risk in Viral and Unspecified Pneumonia to Assist Clinicians with COVID-19 ECMO Planning

Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death. Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life. While early planning and surgical cannulation for ECMO can increase survival, clinicians report the lack of a risk score hinders these efforts. In this work, we leverage machine learning techniques to develop the PEER score, used to highlight critically ill patients with viral or unspecified pneumonia at high risk of mortality or decompensation in a subpopulation eligible for ECMO. The PEER score is validated on two large, publicly available critical care databases and predicts mortality at least as well as other existing risk scores. Stratifying our cohorts into low-risk and high-risk groups, we find that the high-risk group also has a higher proportion of decompensation indicators such as vasopressor and ventilator use. Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO.

stat.AP↗

arXiv2020

Stellar Mass and stellar Mass-to-light ratio-Color relations for Low Surface Brightness Galaxies

We estimate the stellar mass for a sample of low surface brightness galaxies (LSBGs) by fitting their multiband spectral energy distributions (SEDs) to the stellar population synthesis (SPS) model. The derived stellar masses (log M*/Msun) span from 7.1 to 11.1, with a mean of log M*/Msun=8.5, which is lower than that for normal galaxies. The stellar mass-to-light ratio (M/L) in each band varies little with absolute magnitude, but increases with higher M*. This trend of M/L with M* is even stronger in bluer bands. In addition, the M/L for our LSBGs slightly declines from the r band to the longer wavelength bands. The log M/L_j (j=g,r,i,and z) have relatively tight relations with optical colors of g-r and g-i. Compared with several representative M/L-color relations (MLCRs) from literature, our MLCRs based on LSBG data are consistently among those literature MLCRs previously defined on diverse galaxy samples, and the existing minor differences between the MLCRs are more caused by the differences in the SED model ingredients including initial mass function, star formation history, and stellar population model, and the line fitting techniques, galaxy samples, and photometric zero-point as well, rather than the galaxy surface brightness itself which distinguishes LSBGs from HSBGs. Our LSBGs would be very likely to follow those representative MLCRs previously defined on diverse galaxy populations, if those main ingredients were taken into account.

astro-ph.GA↗

arXiv2020

Disk evolution of the M87's nucleus observed in 2008

We report the discovery of year-scale X-ray variation in the nuclear region of the M87 by reanalyzing the 8 Chandra observations from 2007 to 2008. The X-ray spectra are fitted and decomposed into the disk and flaring components. This year-scale X-ray variability can be explained quite well by a simple clumpy accretion model. We conclude that the central super-massive black hole of the M87 was accreting a cloud of $\sim0.5$M$_\odot$ at that time.

astro-ph.HE↗

arXiv2020

Ensemble emotion recognizing with multiple modal physiological signals

Physiological signals that provide the objective repression of human affective states are attracted increasing attention in the emotion recognition field. However, the single signal is difficult to obtain completely and accurately description for emotion. Multiple physiological signals fusing models, building the uniform classification model by means of consistent and complementary information from different emotions to improve recognition performance. Original fusing models usually choose the particular classification method to recognition, which is ignoring different distribution of multiple signals. Aiming above problems, in this work, we propose an emotion classification model through multiple modal physiological signals for different emotions. Features are extracted from EEG, EMG, EOG signals for characterizing emotional state on valence and arousal levels. For characterization, four bands filtering theta, beta, alpha, gamma for signal preprocessing are adopted and three Hjorth parameters are computing as features. To improve classification performance, an ensemble classifier is built. Experiments are conducted on the benchmark DEAP datasets. For the two-class task, the best result on arousal is 94.42\%, the best result on valence is 94.02\%, respectively. For the four-class task, the highest average classification accuracy is 90.74, and it shows good stability. The influence of different peripheral physiological signals for results is also analyzed in this paper.

cs.LG↗

arXiv2019

UV and NIR size of the low-mass field galaxies: the UV compact galaxies

Most of the massive star-forming galaxies are found to have `inside-out' stellar mass growth modes, which means the inner parts of the galaxies mainly consist of the older stellar population, while the star forming in the outskirt of the galaxy is still ongoing. The high-resolution HST images from Hubble Deep UV Legacy Survey (HDUV) and Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) projects with the unprecedented depth in both F275W and F160W bands are the perfect data sets to study the forming and formed stellar distribution directly. We selected the low redshift ($0.05 < z_{\rm spec} < 0.3$) galaxy sample from the GOODS-North field where the HST F275W and F160W images are available. Then we measured the half light radius in F275W and F160W bands, which are the indicators of the star formation and stellar mass. By comparing the F275W and F160W half light radius, we find the massive galaxies are mainly follow the `inside-out' growth which is consistent with the previous results. Moreover, the HST F275W and F160W images reveal that some of the low-mass galaxies ($<10^8M_\odot$) have the `outside-in' growth mode: their images show a compact UV morphology, implying an ongoing star formation in the galaxy centre, the stars in the outskirts of the galaxies are already formed. The two modes transit smoothly at stellar mass range about $10^{8-9}M_\odot$ with a large scatter. We also try to identify the possible neighbour massive galaxies from the SDSS data, which represent the massive galaxy sample. We find that all of the spec-z selected galaxies have no massive galaxy nearby. Thus the `outside-in' mode we find in the low-mass galaxies are not likely originated from the environment.

astro-ph.GA↗

arXiv2019

Combining Machine Learning Models using combo Library

Model combination, often regarded as a key sub-field of ensemble learning, has been widely used in both academic research and industry applications. To facilitate this process, we propose and implement an easy-to-use Python toolkit, combo, to aggregate models and scores under various scenarios, including classification, clustering, and anomaly detection. In a nutshell, combo provides a unified and consistent way to combine both raw and pretrained models from popular machine learning libraries, e.g., scikit-learn, XGBoost, and LightGBM. With accessibility and robustness in mind, combo is designed with detailed documentation, interactive examples, continuous integration, code coverage, and maintainability check; it can be installed easily through Python Package Index (PyPI) or https://github.com/yzhao062/combo.

cs.LG↗

arXiv2019

Dictionary Learning Using Rank-One Projection (ROP)

Dictionary learning aims to find a dictionary that can sparsely represent the training data. Methods in the literature typically formulate the dictionary learning problem as an optimisation with respect to two variables, i.e., dictionary and sparse coefficients, and solve it by alternating between two stages: sparse coding and dictionary update. The key contribution of this work is a Rank-One Projection (ROP) formulation where dictionary learning is cast as an optimisation with respect to a single variable which is a set of rank one matrices. The resulting algorithm is hence single staged. An alternating direction method of multipliers (ADMM) is derived to solve the optimisation problem and guarantees a global convergence despite non-convexity of the optimisation formulation. Also ROP reduces the number of tuning parameters required in other benchmark algorithms. Numerical tests demonstrate that ROP outperforms other benchmarks for both synthetic and real data especially when the sample number is small.

eess.SP↗

arXiv2019

Knowledge Distillation in Document Retrieval

Complex deep learning models now achieve state of the art performance for many document retrieval tasks. The best models process the query or claim jointly with the document. However for fast scalable search it is desirable to have document embeddings which are independent of the claim. In this paper we show that knowledge distillation can be used to encourage a model that generates claim independent document encodings to mimic the behavior of a more complex model which generates claim dependent encodings. We explore this approach in document retrieval for a fact extraction and verification task. We show that by using the soft labels from a complex cross attention teacher model, the performance of claim independent student LSTM or CNN models is improved across all the ranking metrics. The student models we use are 12x faster in runtime and 20x smaller in number of parameters than the teacher

cs.IR↗