arXiv subjects

Cheng Cheng

Publications and source records attributed to Cheng Cheng.

At least 163 records · Page 9Linked to original sources

arXiv2023

JWST's PEARLS: dust attenuation and gravitational lensing in the backlit-galaxy system VV 191

We derive the spatial and wavelength behavior of dust attenuation in the multiple-armed spiral galaxy VV191b using backlighting by the superimposed elliptical system VV191a in a pair with an exceptionally favorable geometry for this measurement. Imaging using JWST and HST spans the wavelength range 0.3-4.5 microns with high angular resolution, tracing the dust in detail from 0.6 to 1.5 microns. Distinct dust lanes continue well beyond the bright spiral arms, and trace a complex web, with a very sharp radial cutoff near 1.7 Petrosian radii. We present attenuation profiles and coverage statistics in each band at radii 14-21 kpc. We derive the attenuation law with wavelength; the data both within and between the dust lanes clearly favor a stronger reddening behavior (R ~ 2.0 between 0.6 and 0.9 microns, approaching unity by 1.5 microns) than found for starbursts and star-forming regions of galaxies. Power-law extinction behavior lambda^(-beta) gives beta=2.1 from 0.6-0.9 microns. R decreases at increasing wavelengths (R~1.1 between 0.9 and 1.5 microns), while beta steepens to 2.5. Mixing regions of different column density flattens the wavelength behavior, so these results suggest a different grain population than in our vicinity. The NIRCam images reveal a lens arc and counterimage from a background galaxy at z~1, spanning 90 degrees azimuthally at 2.8" from the foreground elliptical galaxy nucleus, and an additional weakly-lensed galaxy. The lens model and imaging data give a mass/light ratio 7.6 in solar units within the Einstein radius 2.0 kpc.

astro-ph.GA↗

arXiv2022

JWST's PEARLS: A JWST/NIRCam view of ALMA sources

We report the results of James Webb Space Telescope/NIRCam observations of 19 (sub)millimeter (submm/mm) sources detected by the Atacama Large Millimeter Array (ALMA). The accurate ALMA positions allowed unambiguous identifications of their NIRCam counterparts. Taking gravitational lensing into account, these represent 16 distinct galaxies in three fields and constitute the largest sample of its kind to date. The counterparts' spectral energy distributions from rest-frame ultraviolet to near infrared provide photometric redshifts ($1 10^{10.5}$ Msol), which are similar to sub-millimeter galaxy (SMG) hosts studied previously. However, our sample is fainter in submm/mm than the classic SMG samples are, and our sources exhibit a wider range of properties. They have dust-embedded star-formation rates as low as 10 Msol yr$^{-1}$, and the sources populate both the star-forming main sequence and the quiescent categories. The deep NIRCam data allow us to study the rest-frame near-IR morphologies. Excluding two multiply imaged systems and one quasar, the majority of the remaining sources are disk-like and show either little or no disturbance. This suggests that secular growth is a potential route for the assembly of high-mass disk galaxies. While a few hosts have large disks, the majority have small disks (median half-mass radius of 1.6 kpc). At this time, it is unclear whether this is due to the prevalence of small disks at these redshifts or some unknown selection effects of deep ALMA observations. A larger sample of ALMA sources with NIRCam observations will be able to address this question.

astro-ph.GA↗

arXiv2022

Webb's PEARLS: Bright 1.5--2.0 micron Dropouts in the Spitzer/IRAC Dark Field

Using the first epoch of four-band NIRCam observations obtained by the James Webb Space Telescope (JWST) Prime Extragalactic Areas for Reionization and Lensing Science Program in the Spitzer IRAC Dark Field, we search for F150W and F200W dropouts. In 14.2 arcmin^2, we have found eight F150W dropouts and eight F200W dropouts, all brighter than 27.5 mag (the brightest being ~24 mag) in the band to the red side of the break. As they are detected in multiple bands, these must be real objects. Their nature, however, is unclear, and characterizing their properties is important for realizing the full potential of JWST. If the observed color decrements are due to the Lyman break, these objects should be at z >~ 11.7 and z >~ 15.4, respectively. The color diagnostics show that at least four F150W dropouts are far away from the usual contaminators encountered in dropout searches (red galaxies at much lower redshifts or brown dwarf stars). While the diagnostics of the F200W dropouts are less certain due to the limited number of passbands, at least one of them is likely not a known type of contaminant, and the rest are consistent with either high-redshift galaxies with evolved stellar populations or old galaxies at z ~ 3 to 8. If a significant fraction of our dropouts are indeed at z ~ 12, we have to face the severe problem of explaining their high luminosities and number densities. Spectroscopic identifications of such objects are urgently needed.

astro-ph.GA↗

arXiv2022

JWST's PEARLS: Prime Extragalactic Areas for Reionization and Lensing Science: Project Overview and First Results

We give an overview and describe the rationale, methods, and first results from NIRCam images of the JWST "Prime Extragalactic Areas for Reionization and Lensing Science" ("PEARLS") project. PEARLS uses up to eight NIRCam filters to survey several prime extragalactic survey areas: two fields at the North Ecliptic Pole (NEP); seven gravitationally lensing clusters; two high redshift proto-clusters; and the iconic backlit VV 191 galaxy system to map its dust attenuation. PEARLS also includes NIRISS spectra for one of the NEP fields and NIRSpec spectra of two high-redshift quasars. The main goal of PEARLS is to study the epoch of galaxy assembly, AGN growth, and First Light. Five fields, the JWST NEP Time-Domain Field (TDF), IRAC Dark Field (IDF), and three lensing clusters, will be observed in up to four epochs over a year. The cadence and sensitivity of the imaging data are ideally suited to find faint variable objects such as weak AGN, high-redshift supernovae, and cluster caustic transits. Both NEP fields have sightlines through our Galaxy, providing significant numbers of very faint brown dwarfs whose proper motions can be studied. Observations from the first spoke in the NEP TDF are public. This paper presents our first PEARLS observations, their NIRCam data reduction and analysis, our first object catalogs, the 0.9-4.5 $μ$m galaxy counts and Integrated Galaxy Light. We assess the JWST sky brightness in 13 NIRCam filters, yielding our first constraints to diffuse light at 0.9-4.5 μm. PEARLS is designed to be of lasting benefit to the community.

astro-ph.CO↗

arXiv2022

First Batch of Candidate Galaxies at Redshifts 11 to 20 Revealed by the James Webb Space Telescope Early Release Observations

On July 13, 2022, NASA released to the whole world the data obtained by the James Webb Space Telescope (JWST) Early Release Observations (ERO). These are the first set of science-grade data from this long-awaited facility, marking the beginning of a new era in astronomy. In the study of the early universe, JWST will allow us to push far beyond z ~ 11, the redshift boundary previously imposed by the 1.7 um red cut-off of the Hubble Space Telescope (HST). In contrast, JWST's NIRCam reaches 5 um. Among the JWST ERO targets there is a nearby galaxy cluster SMACS 0723-73, which is a massive cluster and has been long recognized as a potential "cosmic telescope" in amplifying background galaxies. The ERO six-band NIRCam observations on this target have covered an additional flanking field not boosted by gravitational lensing, which also sees far beyond HST. Here we report the result from our search of candidate objects at z > 11 using these ERO data. In total, there are 87 such objects identified by using the standard "dropout" technique. These objects are all detected in multiple bands and therefore cannot be spurious. For most of them, their multi-band colors are inconsistent with known types of contaminants. If the detected dropout signature is interpreted as the expected Lyman-break, it implies that these objects are at z ~ 11--20. The large number of such candidate objects at such high redshifts is not expected from the previously favored predictions and demands further investigations. JWST spectroscopy on such objects will be critical.

astro-ph.GA↗

arXiv2022

Large-scale motions and self-similar structures in compressible turbulent channel flows

In this work, we study the scale characteristics of the log- and outer-region motions and structures in subsonic and supersonic turbulence. To this end, a series of direct numerical simulations of the compressible turbulent channel flow at medium Reynolds numbers are performed. Based on this database, the streamwise and spanwise length scales of the outer-region motions are investigated by the two-point correlations and the one-dimensional spectra. The energy distribution among the multi-scale structures in the outer region is found to be dominated by the semilocal friction-Reynolds-number effects rather than the Mach-number effects. This conclusion not only holds for the velocity fluctuations but also the fluctuations of the thermodynamic variables. Besides, the streamwise and spanwise length scales of the outer motions do not alter significantly when the flow passes the sound barrier as reported by a previous experimental study (Bross et al., J. Fluid Mech., vol. 911, 2021, A2). On the other hand, the self-similar structures populating the logarithmic region are investigated by adopting a linear coherence spectrum. The streamwise/wall-normal aspect ratio of the self-similar wall-attached structures of the streamwise velocity and temperature fluctuations is approximately 15.5, and the counterpart of density and pressure fluctuations is 1.8. The present study confirms the existence of self-similar structures in compressible wall turbulence and assesses their geometrical characteristics.

physics.flu-dyn↗

arXiv2022

Graph Fourier transforms on directed product graphs

Graph Fourier transform (GFT) is one of the fundamental tools in graph signal processing to decompose graph signals into different frequency components and to represent graph signals with strong correlation by different modes of variation effectively. The GFT on undirected graphs has been well studied and several approaches have been proposed to define GFTs on directed graphs. In this paper, based on the singular value decompositions of some graph Laplacians, we propose two GFTs on the Cartesian product graph of two directed graphs. We show that the proposed GFTs could represent spatial-temporal data sets on directed networks with strong correlation efficiently, and in the undirected graph setting they are essentially the joint GFT in the literature. In this paper, we also consider the bandlimiting procedure in the spectral domain of the proposed GFTs, and demonstrate its performance to denoise the temperature data set in the region of Brest (France) on January 2014.

cs.IT↗

arXiv2022

Likelihood-free Inference with Mixture Density Network

In this work, we propose using the mixture density network (MDN) to estimate cosmological parameters. We test the MDN method by constraining parameters of the $Λ$CDM and $w$CDM models using Type Ia supernovae and the power spectra of the cosmic microwave background. We find that the MDN method can achieve the same level of accuracy as the Markov Chain Monte Carlo method, with a slight difference of $\mathcal{O}(10^{-2}σ)$. Furthermore, the MDN method can provide accurate parameter estimates with $\mathcal{O}(10^3)$ forward simulation samples, which are useful for complex and resource-consuming cosmological models. This method can process either one data set or multiple data sets to achieve joint constraints on parameters, extendable for any parameter estimation of complicated models in a wider scientific field. Thus, the MDN provides an alternative way for likelihood-free inference of parameters.

astro-ph.CO↗

arXiv2022

A General End-to-end Diagnosis Framework for Manufacturing Systems

The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on ten representative datasets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical corner stone in smart manufacturing.

cs.LG↗

arXiv2022

A deep learning-based remaining useful life prediction approach for bearings

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $ε$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions.

cs.LG↗

arXiv2022

Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19

With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival models predicting severe COVID-19 progression. In this endeavor, we face a tradeoff between more accurate models relying on many features and less accurate models relying on a few features aligned with clinician intuition. Complicating matters, many EHR features tend to be under-coded, degrading the accuracy of smaller models. In this study, we develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features; and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor. Learned concepts boost performance over the corresponding features (C-index 0.858 vs. 0.844) and demonstrate improvements over (i) when evaluated out-of-sample (subsequent time periods). Our models outperform previous works (C-index 0.844-0.872 vs. 0.598-0.810).

cs.LG↗

arXiv2022

Properties of host galaxies of submillimeter sources as revealed by JWST Early Release Observations in SMACS J0723.3-7327

Using the 0.9--4.4~$μ$m imaging data from the James Webb Space Telescope (JWST) Early Release Observation in the SMACS J0723.3-7327 galaxy cluster field, we discuss the properties of three submillimeter galaxies (SMGs) detected by the Atacama Large Millimeter Array. These sources are magnified by 1.4--2.1$\times$due to gravitational lensing. This is the first time that SMG host galaxies are resolved in the rest-frame near-infrared (NIR). One source was previous undetected by HST, while the remaining two are disk galaxies with Sérsic indices of $\sim 0.9$ and star formation rates on or just below the star formation "main sequence". Their submillimeter emission originates from the inner parts of the hosts, suggesting that their dust contents are concentrated towards the center. The host half-light radii measured in the rest-frame NIR are $\sim$1.5$\times$ smaller than those measured in the rest-frame optical, consistent with a concentrated dust distribution. The more severe extinction that optical light suffers towards the center makes it seemingly less concentrated. Therefore, we expect that the optically-based determination of the stellar mass distribution within host galaxies could still be severely biased by dust. Interestingly, these two disk galaxies are dramatically different in their outer regions, with one being star forming and the other being quiescent. Upcoming JWST observations of statistically significant samples of SMGs will allow us to understand the correlation between the dusty star forming regions and their hosts.

astro-ph.GA↗

arXiv2022

Streamwise inclination angle of wall-attached eddies in turbulent channel flows

We develop a new methodology to assess the streamwise inclination angles (SIAs) of the wall-attached eddies populating the logarithmic region with a given wall-normal height. To remove the influences originating from other scales on the SIA estimated via two-point correlation, the footprints of the targeted eddies in the vicinity of the wall and the corresponding streamwise velocity fluctuations carried by them are isolated simultaneously, by coupling the spectral stochastic estimation with the attached-eddy hypothesis. Datasets produced with direct numerical simulations spanning $Re_τ \sim O(10^2)-O(10^3)$ are dissected to study the Reynolds-number effect. The present results show, for the first time, that the SIAs of attached eddies are Reynolds-number dependent in low and medium Reynolds numbers and tend to saturate at $45^{\circ}$ as the Reynolds number increases. The mean SIA reported by vast previous experimental studies are demonstrated to be the outcomes of the additive effect contributed by multi-scale attached eddies. These findings clarify the long-term debate and perfect the picture of the attached-eddy model.

physics.flu-dyn↗

arXiv2022

FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity

AdaBelief, one of the current best optimizers, demonstrates superior generalization ability compared to the popular Adam algorithm by viewing the exponential moving average of observed gradients. AdaBelief is theoretically appealing in that it has a data-dependent $O(\sqrt{T})$ regret bound when objective functions are convex, where $T$ is a time horizon. It remains however an open problem whether the convergence rate can be further improved without sacrificing its generalization ability. %on how to exploit strong convexity to further improve the convergence rate of AdaBelief. To this end, we make a first attempt in this work and design a novel optimization algorithm called FastAdaBelief that aims to exploit its strong convexity in order to achieve an even faster convergence rate. In particular, by adjusting the step size that better considers strong convexity and prevents fluctuation, our proposed FastAdaBelief demonstrates excellent generalization ability as well as superior convergence. As an important theoretical contribution, we prove that FastAdaBelief attains a data-dependant $O(\log T)$ regret bound, which is substantially lower than AdaBelief. On the empirical side, we validate our theoretical analysis with extensive experiments in both scenarios of strong and non-strong convexity on three popular baseline models. Experimental results are very encouraging: FastAdaBelief converges the quickest in comparison to all mainstream algorithms while maintaining an excellent generalization ability, in cases of both strong or non-strong convexity. FastAdaBelief is thus posited as a new benchmark model for the research community.

cs.LG↗

arXiv2022

Consistency between the attached-eddy model and the inner-outer interaction model: a study of streamwise wall-shear stress fluctuations in a turbulent channel flow

The inner-outer interaction model (Marusic, Mathis & Hutchins, Science, vol. 329, 2010, 193-196) and the attached-eddy model (Townsend, Cambridge University Press, 1976) are two fundamental models describing the multi-scale turbulence interactions and the organization of energy-containing motions in the logarithmic region of high-Reynolds number wall-bounded turbulence, respectively. In this paper, by coupling the additive description with the attached-eddy model, the generation process of streamwise wall-shear fluctuations, resulting from wall-attached eddies, is portrayed. Then, by resorting to the inner-outer interaction model, the streamwise wall-shear stress fluctuations generated by attached eddies in a turbulent channel flow are isolated. Direct comparison between the statistics from these two models demonstrates that they are consistent to and complement each other. Meanwhile, we further show that the superpositions of attached eddies follow an additive process strictly by verifying the validity of the strong and extended self similarity. Moreover, we propose a Gaussian model to characterize the instantaneous distribution of streamwise wall-shear stress, resulting from the attached-eddy superpositions. These findings are important for developing an advanced reduced-order wall model.

physics.flu-dyn↗

arXiv2022

Graph Fourier transform based on singular value decomposition of directed Laplacian

Graph Fourier transform (GFT) is a fundamental concept in graph signal processing. In this paper, based on singular value decomposition of Laplacian, we introduce a novel definition of GFT on directed graphs, and use singular values of Laplacian to carry the notion of graph frequencies. % of the proposed GFT. The proposed GFT is consistent with the conventional GFT in the undirected graph setting, and on directed circulant graphs, the proposed GFT is the classical discrete Fourier transform, up to some rotation, permutation and phase adjustment. We show that frequencies and frequency components of the proposed GFT can be evaluated by solving some constrained minimization problems with low computational cost. Numerical demonstrations indicate that the proposed GFT could represent graph signals with different modes of variation efficiently.

eess.SP↗

arXiv2022

Wiener filters on graphs and distributed polynomial approximation algorithms

In this paper, we consider Wiener filters to reconstruct deterministic and (wide-band) stationary graph signals from their observations corrupted by random noises, and we propose distributed algorithms to implement Wiener filters and inverse filters on networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents. The proposed distributed polynomial approximation algorithm is an exponential convergent quasi-Newton method based on Jacobi polynomial approximation and Chebyshev interpolation polynomial approximation to analytic functions on a cube. Our numerical simulations show that Wiener filtering procedure performs better on denoising (wide-band) stationary signals than the Tikhonov regularization approach does, and that the proposed polynomial approximation algorithms converge faster than the Chebyshev polynomial approximation algorithm and gradient decent algorithm do in the implementation of an inverse filtering procedure associated with a polynomial filter of commutative graph shifts.

eess.SP↗

arXiv2022

OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

Deep learning frameworks such as TensorFlow and PyTorch provide a productive interface for expressing and training a deep neural network (DNN) model on a single device or using data parallelism. Still, they may not be flexible or efficient enough in training emerging large models on distributed devices, which require more sophisticated parallelism beyond data parallelism. Plugins or wrappers have been developed to strengthen these frameworks for model or pipeline parallelism, but they complicate the usage and implementation of distributed deep learning. Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model. SBP enables much easier programming of data parallelism and model parallelism than existing frameworks, and the actor model provides a succinct runtime mechanism to manage the complex dependencies imposed by resource constraints, data movement and computation in distributed deep learning. We demonstrate the general applicability and efficiency of OneFlow for training various large DNN models with case studies and extensive experiments. The results show that OneFlow outperforms many well-known customized libraries built on top of the state-of-the-art frameworks. The code of OneFlow is available at: https://github.com/Oneflow-Inc/oneflow.

cs.DC↗