arXiv subjects

Cheng Cheng

Publications and source records attributed to Cheng Cheng.

At least 181 records · Page 10Linked to original sources

arXiv2021

Short-and-Sparse Deconvolution Via Rank-One Constrained Optimization (ROCO)

Short-and-sparse deconvolution (SaSD) aims to recover a short kernel and a long and sparse signal from their convolution. In the literature, formulations of blind deconvolution is either a convex programming via a matrix lifting of convolution, or a bilinear Lasso. Optimization solvers are typically based on bilinear factorizations. In this paper, we formulate SaSD as a non-convex optimization with a rank-one matrix constraint, hence referred to as Rank-One Constrained Optimization (ROCO). The solver is based on alternating direction method of multipliers (ADMM). It operates on the full rank-one matrix rather than bilinear factorizations. Closed form updates are derived for the efficiency of ADMM. Simulations include both synthetic data and real images. Results show substantial improvements in recovery accuracy (at least 19dB in PSNR for real images) and comparable runtime compared with benchmark algorithms based on bilinear factorization.

eess.SP↗

arXiv2021

Polynomial graph filter of multiple shifts and distributed implementation of inverse filtering

Polynomial graph filters and their inverses play important roles in graph signal processing. An advantage of polynomial graph filters is that they can be implemented in a distributed manner, which involves data transmission between adjacent vertices only. The challenge arisen in the inverse filtering is that a direct implementation may suffer from high computational burden, as the inverse graph filter usually has full bandwidth even if the original filter has small bandwidth. In this paper, we consider distributed implementation of the inverse filtering procedure for a polynomial graph filter of multiple shifts, and we propose two iterative approximation algorithms that can be implemented in a distributed network, where each vertex is equipped with systems for limited data storage, computation power and data exchanging facility to its adjacent vertices. We also demonstrate the effectiveness of the proposed iterative approximation algorithms to implement the inverse filtering procedure and their satisfactory performance to denoise time-varying graph signals and a data set of US hourly temperature at 218 locations.

cs.IT↗

arXiv2021

An IFU View of the Active Galactic Nuclei in MaNGA Galaxy Pairs

The role of active galactic nuclei (AGNs) during galaxy interactions and how they influence the star formation in the system are still under debate. We use a sample of 1156 galaxies in galaxy pairs or mergers (hereafter `pairs') from the MaNGA survey. This pair sample is selected by the velocity offset, projected separation, and morphology, and is further classified into four cases along the merger sequence based on morphological signatures. We then identify a total of 61 (5.5%) AGNs in pairs based on the emission-line diagnostics. No evolution of the AGN fraction is found, either along the merger sequence or compared to isolated galaxies (5.0%). We observe a higher fraction of passive galaxies in galaxy pairs, especially in the pre-merging cases, and associate the higher fraction to their environmental dependence. The isolated AGN and AGN in pairs show similar distributions in their global stellar mass, star formation rate (SFR), and central [OIII] surface brightness. AGNs in pairs show radial profiles of increasing specific SFR and declining Dn4000 from center to outskirts, and no significant difference from the isolated AGNs. This is clearly different from star-forming galaxies (SFGs) in our pair sample, which show enhanced central star formation, as reported before. AGNs in pairs have lower Balmer decrements at outer regions, possibly indicating less dust attenuation. Our findings suggest that AGNs likely follow an inside-out quenching and the merger impact on the star formation in AGNs is less prominent than in SFGs.

astro-ph.GA↗

arXiv2021

Dictionary Learning Using Rank-One Atomic Decomposition (ROAD)

Dictionary learning aims at seeking a dictionary under which the training data can be sparsely represented. Methods in the literature typically formulate the dictionary learning problem as an optimization w.r.t. 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 Atomic Decomposition (ROAD) formulation where dictionary learning is cast as an optimization w.r.t. a single variable which is a set of rank one matrices. The resulting algorithm is hence single-stage. Compared with two-stage algorithms, ROAD minimizes the sparsity of the coefficients whilst keeping the data consistency constraint throughout the whole learning process. An alternating direction method of multipliers (ADMM) is derived to solve the optimization problem and the lower bound of the penalty parameter is computed to guarantees a global convergence despite non-convexity of the optimization formulation. From practical point of view, ROAD reduces the number of tuning parameters required in other benchmark algorithms. Numerical tests demonstrate that ROAD outperforms other benchmark algorithms for both synthetic data and real data, especially when the number of training samples is small.

eess.SP↗

arXiv2021

Dictionary Learning with Convex Update (ROMD)

Dictionary learning aims to find a dictionary under which the training data can be sparsely represented, and it is usually achieved by iteratively applying two stages: sparse coding and dictionary update. Typical methods for dictionary update focuses on refining both dictionary atoms and their corresponding sparse coefficients by using the sparsity patterns obtained from sparse coding stage, and hence it is a non-convex bilinear inverse problem. In this paper, we propose a Rank-One Matrix Decomposition (ROMD) algorithm to recast this challenge into a convex problem by resolving these two variables into a set of rank-one matrices. Different from methods in the literature, ROMD updates the whole dictionary at a time using convex programming. The advantages hence include both convergence guarantees for dictionary update and faster convergence of the whole dictionary learning. The performance of ROMD is compared with other benchmark dictionary learning algorithms. The results show the improvement of ROMD in recovery accuracy, especially in the cases of high sparsity level and fewer observation data.

eess.SP↗

arXiv2021

DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement

Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color distortion, unknown noise, detail loss and halo artifacts. In this paper, we propose a Degradation-Aware Deep Retinex Network (denoted as DA-DRN) for low-light image enhancement and tackle the above degradation. Based on Retinex Theory, the decomposition net in our model can decompose low-light images into reflectance and illumination maps and deal with the degradation in the reflectance during the decomposition phase directly. We propose a Degradation-Aware Module (DA Module) which can guide the training process of the decomposer and enable the decomposer to be a restorer during the training phase without additional computational cost in the test phase. DA Module can achieve the purpose of noise removal while preserving detail information into the illumination map as well as tackle color distortion and halo artifacts. We introduce Perceptual Loss to train the enhancement network to generate the brightness-improved illumination maps which are more consistent with human visual perception. We train and evaluate the performance of our proposed model over the LOL real-world and LOL synthetic datasets, and we also test our model over several other frequently used datasets without Ground-Truth (LIME, DICM, MEF and NPE datasets). We conduct extensive experiments to demonstrate that our approach achieves a promising effect with good rubustness and generalization and outperforms many other state-of-the-art methods qualitatively and quantitatively. Our method only takes 7 ms to process an image with 600x400 resolution on a TITAN Xp GPU.

eess.IV↗

arXiv2021

100% renewable electricity in Japan

Japan has committed to carbon neutrality by 2050. Emissions from the electricity sector amount to 42% of the total. Solar photovoltaics (PV) and wind comprise three quarters of global net capacity additions because of low and falling prices. This provides an opportunity for Japan to make large reductions in emissions while also reducing its dependence on energy imports. This study shows that Japan has 14 times more solar and offshore wind resources than needed to supply 100% renewable electricity. A 40 year hourly energy balance model is presented of Japan's electricity system using historical data. Pumped hydro energy storage, high voltage interconnection and dispatchable capacity (hydro, biomass and hydrogen energy) are included to balance variable generation and demand. Differential evolution is used to find the least-cost solution under various constraints. The levelized cost of electricity is found to be USD 86 per MWh for a PV-dominated system, and USD 110 per MWh for a wind-dominated system. These costs can be compared with the average system prices on the spot market in Japan of USD 102 per MWh. In summary, Japan can be self-sufficient for electricity supply at competitive costs.

physics.soc-ph↗

arXiv2021

Searching for low-redshift faint galaxies with MMT/Hectospec

We present redshifts for 2753 low-redshift galaxies between $0.03 \lesssim z_{\rm spec}\lesssim0.5$ with 18 $\leq$ $r$ $\leq$ 22 obtained with Hectospec at the Multi-Mirror Telescope (MMT). The observations targeted the XMM-LSS, ELAIS-N1 and DEEP2-3 fields, each of which covers $\sim$ 1 deg$^2$. These fields are also part of the recently completed CFHT Large Area U-band Deep Survey (CLAUDS) and on-going Hyper Suprime-Cam deep fields surveys. The efficiency of our technique for selecting low-redshift galaxies is confirmed by the redshift distribution of our sources. In addition to redshifts, these high S/N spectra are used to measure ages, metallicities, and nuclear activity levels. In combination with the photometric catalogue in $u$, $g$, $r$, $i$, $z$, $y$ down to 27 AB mag, we are able to study the galaxy population down to stellar masses of $\sim$ 10$^8 M_\odot$ . This paper presents the observational strategy, the reduction procedure and properties of the galaxy sample.

astro-ph.GA↗

arXiv2021

Spectral energy distribution similarity of the local galaxies and the 3.6um selected galaxies from the Spitzer Extended Deep Survey

The Spitzer Extended Deep Survey (SEDS) as a deep and wide mid-infrared (MIR) survey project provides a sample of 500000+ sources spreading 1.46 square degree and a depth of 26 AB mag (3$σ$). Combining with the previous available data, we build a PSF-matched multi-wavelength photometry catalog from u band to 8$μ$m. We fit the SEDS galaxies spectral energy distributions by the local galaxy templates. The results show that the SEDS galaxy can be fitted well, indicating the high redshift galaxy ($z \sim 1$) shares the same templates with the local galaxies. This study would facilitate the further study of the galaxy luminosity and high redshift mass function.

astro-ph.GA↗

arXiv2021

BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration

Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color bias, unknown noise, detail loss and halo artifacts. In this paper, we propose a very fast deep learning framework called Bringing the Lightness (denoted as BLNet) that consists of two U-Nets with a series of well-designed loss functions to tackle all of the above degradations. Based on Retinex Theory, the decomposition net in our model can decompose low-light images into reflectance and illumination and remove noise in the reflectance during the decomposition phase. We propose a Noise and Color Bias Control module (NCBC Module) that contains a convolutional neural network and two loss functions (noise loss and color loss). This module is only used to calculate the loss functions during the training phase, so our method is very fast during the test phase. This module can smooth the reflectance to achieve the purpose of noise removal while preserving details and edge information and controlling color bias. We propose a network that can be trained to learn the mapping between low-light and normal-light illumination and enhance the brightness of images taken in low-light illumination. We train and evaluate the performance of our proposed model over the real-world Low-Light (LOL) dataset), and we also test our model over several other frequently used datasets (LIME, DICM and MEF datasets). We conduct extensive experiments to demonstrate that our approach achieves a promising effect with good rubustness and generalization and outperforms many other state-of-the-art methods qualitatively and quantitatively. Our method achieves high speed because we use loss functions instead of introducing additional denoisers for noise removal and color correction. The code and model are available at https://github.com/weixinxu666/BLNet.

eess.IV↗

arXiv2021

SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection. Due to the lack of ground truth labels, practitioners often have to build a large number of unsupervised, heterogeneous models (i.e., different algorithms with varying hyperparameters) for further combination and analysis, rather than relying on a single model. How to accelerate the training and scoring on new-coming samples by outlyingness (referred as prediction throughout the paper) with a large number of unsupervised, heterogeneous OD models? In this study, we propose a modular acceleration system, called SUOD, to address it. The proposed system focuses on three complementary acceleration aspects (data reduction for high-dimensional data, approximation for costly models, and taskload imbalance optimization for distributed environment), while maintaining performance accuracy. Extensive experiments on more than 20 benchmark datasets demonstrate SUOD's effectiveness in heterogeneous OD acceleration, along with a real-world deployment case on fraudulent claim analysis at IQVIA, a leading healthcare firm. We open-source SUOD for reproducibility and accessibility.

cs.LG↗

arXiv2020

Unpacking the Drop in COVID-19 Case Fatality Rates: A Study of National and Florida Line-Level Data

Since the COVID-19 pandemic first reached the United States, the case fatality rate has fallen precipitously. Several possible explanations have been floated, including greater detection of mild cases due to expanded testing, shifts in age distribution among the infected, lags between confirmed cases and reported deaths, improvements in treatment, mutations in the virus, and decreased viral load as a result of mask-wearing. Using both Florida line-level data and recently released (but incomplete) national line level data from April 1, 2020 to November 1, 2020 on cases, hospitalizations, and deaths--each stratified by age--we unpack the drop in case fatality rate (CFR). Under the hypothesis that improvements in treatment efficacy should correspond to decreases in hospitalization fatality rate (HFR), we find that improvements in the national data do not always match the story told by Florida data. In the national data, treatment improvements between the first wave and the second wave appear substantial, but modest when compared to the drop in aggregate CFR. By contrast, possibly due to constrained resources in a much larger second peak, Florida data suggests comparatively little difference between the first and second wave, with HFR slightly increasing in every age group. However, by November 1st, both Florida and national data suggest significant decreases in age-stratified HFR since April 1st. By accounting for several confounding factors, our analysis shows how age-stratified HFR can provide a more realistic picture of treatment improvements than CFR. One key limitation of our analysis is that the national line-level data remains incomplete and plagued by artifacts. Our analysis highlights the crucial role that this data can play but also the pressing need for public, complete, and high-quality age-stratified line-level data for both cases, hospitalizations, and deaths for all states.

stat.AP↗

arXiv2020

Diversity Dependent Uncertainty Management for Hydrocarbon Stimulation in Uncertain Heterogeneous Reservoir with Improved Efficiency

The history of oil and gas well stimulation through hydraulic fracturing is characterized by a pursuit of optimal designs tailored to reservoir properties. However, as with many engineering systems, the impact of variability and uncertainty (in this case of reservoir properties) is not accounted in these deterministic approaches. We propose an expansion of the principle of design diversity wherein the focus becomes developing optimal portfolio combinations of multiple designs rather than repeated application of a single design. This approach can substantially increase efficiency and decrease risk when designing systems where outcome desirability is definable as a smoothly-varying efficiency and where risk is related to the variance of outcomes tied to uncertain variability of the design environment. For the case of oil/gas wells, we demonstrate diverse portfolios of designs can improve stimulation efficiency by nearly a factor of four and cut risk associated with variability and uncertainty of outcomes by over 80%. Hydraulic fracturing optimization is therefore fundamentally dependent upon uncertainty, and a change of approach toward the pursuit of design diversity can have a profound impact on efficiency and risk, and in turn cost and environmental impacts, in oil and gas development.

math.OC↗

arXiv2020

Distributed algorithms to determine eigenvectors of matrices on spatially distributed networks

Eigenvectors of matrices on a network have been used for understanding spectral clustering and influence of a vertex. For matrices with small geodesic-width, we propose a distributed iterative algorithm in this letter to find eigenvectors associated with their given eigenvalues. We also consider the implementation of the proposed algorithm at the vertex/agent level in a spatially distributed network.

cs.DC↗

arXiv2020

UV and NIR size of the HI selected low surface brightness galaxies

How does the low surface brightness galaxies (LSBGs) form stars and assemble the stellar mass is one of the most important questions to understand the LSBG population. We select a sample of 381 HI bright LSBGs with both Far Ultraviolet (FUV) and Near Infrared (NIR) observation to investigate the star formation rate (SFR) and stellar mass scales, and the growth mode. We measure the UV and NIR radius of our sample, which represent the star-forming and stellar mass distribution scales. We also compare the UV and H band radius-stellar mass relation with the archive data, to identify the SFR and stellar mass structure difference between the LSBG population and other galaxies. Since galaxy HI mass has a tight correlation with the HI radius, we can also compare the HI and UV radii to understand the distribution of the HI gas and star formation activities. Our results show that most of the HI selected LSBGs have extended star formation structure. The stellar mass distribution of LSBGs may have a similar structure as the disk galaxies at the same stellar mass bins, while the star-forming activity of LSBGs happens at a larger radius than the high surface density galaxies, which may help to select the LSBG sample from the wide-field deep u band image survey. The HI also distributed at a larger radius, implying a steeper (or no) Kennicutt-Schmidt relation for LSBGs.

astro-ph.GA↗

arXiv2020

A kpc-scale resolved study of unobscured and obscured star-formation activity in normal galaxies at z = 1.5 and 2.2 from ALMA and HiZELS

We present Atacama Large Millimeter/Submillimeter Array (ALMA) continuum observations of a sample of nine star-forming galaxies at redshifts 1.47 and 2.23 selected from the High-$z$ Emission Line Survey (HiZELS). Four galaxies in our sample are detected at high significance by ALMA at a resolution of 0.25'' at rest-frame 355 $μ$m. Together with the previously observed H$α$ emission, from adaptive optics-assisted integral-field-unit spectroscopy (0.15'' resolution), and F606W and F140W imaging from the Hubble Space Telescope (0.2'' resolution), we study the star-formation activity, stellar and dust mass in these high-redshift galaxies at $\sim$kpc-scale resolution. We find that ALMA detection rates are higher for more massive galaxies ($M_*>10^{10.5}$ M$_\odot$) and higher [N {\sc ii}]/H$α$ ratios ($>0.25$, a proxy for gas-phase metallicity). The dust extends out to a radius of 8 kpc, with a smooth structure, even for those galaxies presenting clumpy H$α$ morphologies. The half-light radii ($R_{\rm dust}$) derived for the detected galaxies are of the order $\sim$4.5 kpc, more than twice the size of submillimetre-selected galaxies at a similar redshift. Our global star-formation rate estimates -- from far-IR and extinction-corrected H$α$ luminosities -- are in good agreement. However, the different morphologies of the different phases of the interstellar medium suggest complex extinction properties of the high-redshift normal galaxies.

astro-ph.GA↗

arXiv2020

Design Diversity for Improving Efficiency and Reducing Risk in Oil and Gas Well Stimulation under Uncertain Reservoir Conditions

Hydraulic fracturing stimulates fracture swarm in reservoir formation though pressurized injection fluid. However restricted by the availability of formation data, the variability embraced by reservoir keeps uncertain, driving unstable gas recovery along with low resource efficiency, being responsible for resource scarcity, contaminated water, and injection-induced earthquake. Resource efficiency is qualified though new determined energy efficiency, a scale of recovery and associated environmental footprint. To maximize energy efficiency while minimize its' variation, we issue picked designs at reservoir conditions dependent optimal probabilities, assembling high efficiency portfolios and low risk portfolios for portfolio combination, which balance the variation and efficiency at optimal by adjusting the proportion of each portfolio. Relative to regular design for one well, the optimal portfolio combination applied in multiple wells receive remarkable variation reduction meanwhile substantial energy efficiency increase, in response to the call of more recovery per unit investment and less environment cost per unit nature gas extracted.

q-fin.PM↗

arXiv2020

A simulation-based evaluation of a Cargo-Hitching service for E-commerce using mobility-on-demand vehicles

Time-sensitive parcel deliveries, shipments requested for delivery in a day or less, are an increasingly important research subject. It is challenging to deal with these deliveries from a carrier perspective since it entails additional planning constraints, preventing an efficient consolidation of deliveries which is possible when demand is well known in advance. Furthermore, such time-sensitive deliveries are requested to a wider spatial scope than retail centers, including homes and offices. Therefore, an increase in such deliveries is considered to exacerbate negative externalities such as congestion and emissions. One of the solutions is to leverage spare capacity in passenger transport modes. This concept is often denominated as cargo-hitching. While there are various possible system designs, it is crucial that such solution does not deteriorate the quality of service of passenger trips. This research aims to evaluate the use of Mobility-On-Demand services to perform same-day parcel deliveries. For this purpose, we use SimMobility, a high-resolution agent-based simulation platform of passenger and freight flows, applied in Singapore. E-commerce demand carrier data are used to characterize simulated parcel delivery demand. Operational scenarios that aim to minimize the adverse effect of fulfilling deliveries with Mobility-On-Demand vehicles on Mobility-On-Demand passenger flows (fulfillment, wait and travel times) are explored. Results indicate that the Mobility-On-Demand services have potential to fulfill a considerable amount of parcel deliveries and decrease freight vehicle traffic and total vehicle-kilometers-travelled without compromising the quality of Mobility On-Demand for passenger travel.

cs.MA↗