arXiv subjects

Cheng Li

Publications and source records attributed to Cheng Li.

At least 343 records · Page 19Linked to original sources

arXiv2021

Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

eess.IV↗

arXiv2021

LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding

Document layout comprises both structural and visual (eg. font-sizes) information that is vital but often ignored by machine learning models. The few existing models which do use layout information only consider textual contents, and overlook the existence of contents in other modalities such as images. Additionally, spatial interactions of presented contents in a layout were never really fully exploited. To bridge this gap, we parse a document into content blocks (eg. text, table, image) and propose a novel layout-aware multimodal hierarchical framework, LAMPreT, to model the blocks and the whole document. Our LAMPreT encodes each block with a multimodal transformer in the lower-level and aggregates the block-level representations and connections utilizing a specifically designed transformer at the higher-level. We design hierarchical pretraining objectives where the lower-level model is trained similarly to multimodal grounding models, and the higher-level model is trained with our proposed novel layout-aware objectives. We evaluate the proposed model on two layout-aware tasks -- text block filling and image suggestion and show the effectiveness of our proposed hierarchical architecture as well as pretraining techniques.

cs.CL↗

arXiv2021

Qualification tests of 997 8-inch photomultiplier tubes for the water Cherenkov detector array of the LHAASO experiment

The Large High-Altitude Air Shower Observatory (LHAASO) is being built at Haizi Mountain, Sichuan province of China at an altitude of 4410 meters. One of its main goals is to survey the northern sky for very-high-energy gamma ray sources via its ground-based water Cherenkov detector array (WCDA). 900 8-inch photomultiplier tubes (PMTs) CR365-02-1 from Beijing Hamamatsu Photon Techniques INC. (BHP) are installed in the WCDA, collecting Cherenkov photons produced by air shower particles crossing water. The design of the PMT base with a high dynamic range for CR365-02-1, the PMT batch test system, and the test results of 997 PMTs are presented in this paper.

physics.ins-det↗

arXiv2021

Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification

ICD coding is the international standard for capturing and reporting health conditions and diagnosis for revenue cycle management in healthcare. Manually assigning ICD codes is prone to human error due to the large code vocabulary and the similarities between codes. Since machine learning based approaches require ground truth training data, the inconsistency among human coders is manifested as noise in labeling, which makes the training and evaluation of ICD classifiers difficult in presence of such noise. This paper investigates the characteristics of such noise in manually-assigned ICD-10 codes and furthermore, proposes a method to train robust ICD-10 classifiers in the presence of labeling noise. Our research concluded that the nature of such noise is systematic. Most of the existing methods for handling label noise assume that the noise is completely random and independent of features or labels, which is not the case for ICD data. Therefore, we develop a new method for training robust classifiers in the presence of systematic noise. We first identify ICD-10 codes that human coders tend to misuse or confuse, based on the codes' locations in the ICD-10 hierarchy, the types of the codes, and baseline classifier's prediction behaviors; we then develop a novel training strategy that accounts for such noise. We compared our method with the baseline that does not handle label noise and the baseline methods that assume random noise, and demonstrated that our proposed method outperforms all baselines when evaluated on expert validated labels.

cs.LG↗

arXiv2021

Ion-beam-assisted characterization of quinoline-insoluble particles in nuclear graphite

The irradiation behavior of graphite is essential for its applications in nuclear industry. However, the differences between graphite's behaviors are not well understood because of the very limited knowledge of microstructural differences between graphites. One typical structure, quinoline insoluble (QI) particle, was investigated using IG-110 and NBG-18 graphite. After irradiation, the QI particles on the polished surface were proved to become hillocks and can be easily identified by using a scanning electron microscope (SEM). Thus a method combining ion irradiation and SEM characterization was proposed to study the distribution and concentration of QI particles in graphite. During irradiation, the QI particles were found to evolve into densified spheres which have weak bonding with the surrounding graphite structures, indicating that the densification of QI particles does not contribute obviously to graphite dimensional shrinkage. A much higher concentration of QI particles in NBG-18 than IG-110 was characterized and is suggested to be responsible for the smaller maximum dimensional shrinkage of NBG-18 than IG-110 during irradiation.

physics.app-ph↗

arXiv2021

How to empirically model star formation in dark matter halos: I. Inferences about central galaxies from numerical simulations

We use TNG and EAGLE hydrodynamic simulations to investigate the central galaxy - dark matter halo relations that are needed for a halo-based empirical model of star formation in galaxies. Using a linear dimension reduction algorithm and a model ensemble method, we find that for both star-forming and quenched galaxies, the star formation history (SFH) is tightly related to the halo mass assembly history (MAH). The quenching of a low-mass galaxy is mainly due to the infall-ejection process related to a nearby massive halo, while the quenching of a high-mass galaxy is closely related to the formation of a massive progenitor in its host halo. The classification of star-forming and quenched populations based solely on halo properties contains contamination produced by sample imbalance and overlapping distributions of the two populations. Guided by the results from hydrodynamic simulations, we build an empirical model to predict the SFH of central galaxies based on the MAH of their host halos, and we model the star-forming and quenched populations separately. Our model is based on the idea of adopting star formation templates from hydrodynamic simulations to reduce model complexity. We use various tests to demonstrate that the model can recover star formation histories of individual galaxies, and can statistically reproduce the galaxy bimodal distribution, stellar mass - halo mass and star formation rate - halo mass relations from low to high redshift, and assembly bias. Our study provides a framework of using hydrodynamic simulations to discover, and to motivate the use of, key ingredients to model galaxy formation using halo properties.

astro-ph.GA↗

arXiv2021

A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint

Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning. Nevertheless, the efficiency and performance of the existing registration algorithms can still be improved. In this paper, we propose a novel unsupervised learning-based framework to achieve accurate and efficient multi-contrast MR image registrations. Specifically, an end-to-end coarse-to-fine network architecture consisting of affine and deformable transformations is designed to improve the robustness and achieve end-to-end registration. Furthermore, a dual consistency constraint and a new prior knowledge-based loss function are developed to enhance the registration performances. The proposed method has been evaluated on a clinical dataset containing 555 cases, and encouraging performances have been achieved. Compared to the commonly utilized registration methods, including VoxelMorph, SyN, and LT-Net, the proposed method achieves better registration performance with a Dice score of 0.8397 in identifying stroke lesions. With regards to the registration speed, our method is about 10 times faster than the most competitive method of SyN (Affine) when testing on a CPU. Moreover, we prove that our method can still perform well on more challenging tasks with lacking scanning information data, showing high robustness for the clinical application.

eess.IV↗

arXiv2021

Phenomenology of a Supersymmetric Model Inspired by Inflation

The current challenges in High Energy Physics and Cosmology are to build coherent particle physics models to describe the phenomenology at colliders in the laboratory and the observations in the universe. From these observations, the existence of an inflationary phase in the early universe gives guidance for particle physics models. We study a supersymmetric model which incorporates successfully inflation by a non-minimal coupling to supergravity and shows a unique collider phenomenology. Motivated by experimental data, we set a special emphasis on a new singlet-like state at 97 GeV and single out possible observables for a future linear collider that permit a distinction of the model from a similar scenario without inflation. We define a benchmark scenario that is in agreement with current collider and Dark Matter constraints, and study the influence of the non-minimal coupling on the phenomenology. Measuring the singlet-like state with high precision on the percent level seems to be promising for resolving the models, even though the Standard Model-like Higgs couplings deviate only marginally. However, a hypothetical singlet-like state with couplings of about 20% compared to a Standard Model Higgs at 97 GeV encourages further studies of such footprint scenarios of inflation.

hep-ph↗

arXiv2021

Spatially-Resolved Characterization of Oil-in-Water Emulsion Sprays

This study concerns the effects of pressure, spatial location, and application of oil emulsions on the resulting droplet size, eccentricity, as well as velocity distributions, all of which are crucial information in determining the dispersion dynamics of the droplets during the spray applications. Experiments were conducted with the abovementioned droplets information measured using digital inline holography (DIH). Results show that the volumetric droplet size distributions (VDSD) span widely from sub-200 um to over 2 mm in size. The application of an oil-in-water emulsion results largely in the suppression of smaller droplets, while the VDSD is relatively insensitive to increasing the oil volume fraction beyond a critical level. DIH additionally enables the determination of size-dependent droplet eccentricity and velocity measurements. Interestingly, the application of oil-in-water emulsion generally decreases the eccentricity, more significantly at the center than at the edge of the spray fan. We attribute this decrease to the increase in lamella sheet thickness and thus decrease in characteristic shrinkage rates, consistent with the observation using high-speed shadowgraphs. In all instances, oil-in-water emulsion droplets have higher velocities than equivalent sized water droplets. We attribute this to the earlier action of the spray breakup process in the oil-in-water emulsion, reduced surface energy generation during a breakup (larger droplets), and reduce energy dissipation during breakup with oil-in-water emulsions, leading to increased translational energy after the breakup process. Therefore, it appears that oil-in-water emulsion application simultaneously suppresses small droplet formation and increases droplet velocity, and hence spray penetration in agricultural application.

physics.flu-dyn↗

arXiv2020

Moiré Band Topology in Twisted Bilayer Graphene

Recently twisted bilayer graphene (t-BLG) emerges as a new strongly correlated physical platform near a magic twist angle, which hosts many exciting phenomena such as the Mott-like insulating phases, unconventional superconducting behavior and emergent ferromagnetism. Besides the apparent significance of band flatness, band topology may be another critical element in determining strongly correlated twistronics yet receives much less attention. Here we report compelling evidence for nontrivial noninteracting band topology of t-BLG moiré Dirac bands through a systematic nonlocal transport study, in conjunction with an examination rooted in $K$-theory. The moiré band topology of t-BLG manifests itself as two pronounced nonlocal responses in the electron and hole superlattice gaps. We further show that the nonlocal responses are robust to the interlayer electric field, twist angle, and edge termination, exhibiting a universal scaling law. While an unusual symmetry of t-BLG trivializes Berry curvature, we elucidate that two $Z_2$ invariants characterize the topology of the moiré Dirac bands, validating the topological edge origin of the observed nonlocal responses. Our findings not only provide a new perspective for understanding the emerging strongly correlated phenomena in twisted van der Waals heterostructures, but also suggest a potential strategy to achieve topologically nontrivial metamaterials from topologically trivial quantum materials based on twist engineering.

cond-mat.str-el↗

arXiv2020

Storms and the Depletion of Ammonia in Jupiter: II. Explaining the Juno Observations

Observations of Jupiter's deep atmosphere by the Juno spacecraft have revealed several puzzling facts: The concentration of ammonia is variable down to pressures of tens of bars, and is strongly dependent on latitude. While most latitudes exhibit a low abundance, the Equatorial Zone of Jupiter has an abundance of ammonia that is high and nearly uniform with depth. In parallel, the Equatorial Zone is peculiar for its absence of lightning, which is otherwise prevalent most everywhere else on the planet. We show that a model accounting for the presence of small-scale convection and water storms originating in Jupiter's deep atmosphere accounts for the observations. Where strong thunderstorms are observed on the planet, we estimate that the formation of ammonia-rich hail ('mushballs') and subsequent downdrafts can deplete efficiency the upper atmosphere of its ammonia and transport it efficiently to the deeper levels. In the Equatorial Zone, the absence of thunderstorms shows that this process is not occurring, implying that small-scale convection can maintain a near-homogeneity of this region. A simple model satisfying mass and energy balance accounts for the main features of Juno's MWR observations and successfully reproduces the inverse correlation seen between ammonia abundance and the lightning rate as function of latitude. We predict that in regions where ammonia is depleted, water should also be depleted to great depths. The fact that condensates are not well mixed by convection until far deeper than their condensation level has consequences for our understanding of Jupiter's deep interior and of giant-planet atmospheres in general.

astro-ph.EP↗

arXiv2020

The water abundance in Jupiter's equatorial zone

Oxygen is the most common element after hydrogen and helium in Jupiter's atmosphere, and may have been the primary condensable (as water ice) in the protoplanetary disk. Prior to the Juno mission, in situ measurements of Jupiter's water abundance were obtained from the Galileo Probe, which dropped into a meteorologically anomalous site. The findings of the Galileo Probe were inconclusive because the concentration of water was still increasing when the probe died. Here, we initially report on the water abundance in the equatorial region, from 0 to 4 degrees north latitude, based on 1.25 to 22 GHz data from Juno Microwave radiometer probing approximately 0.7 to 30 bars pressure. Because Juno discovered the deep atmosphere to be surprisingly variable as a function of latitude, it remains to confirm whether the equatorial abundance represents Jupiter's global water abundance. The water abundance at the equatorial region is inferred to be $2.5_{-1.6}^{+2.2}\times10^3$ ppm, or $2.7_{-1.7}^{+2.4}$ times the protosolar oxygen elemental ratio to H (1$σ$ uncertainties). If reflective of the global water abundance, the result suggests that the planetesimals formed Jupiter are unlikely to be water-rich clathrate hydrates.

astro-ph.EP↗

arXiv2020

Keys of a Mission to Uranus or Neptune, the Closest Ice Giants

Uranus and Neptune are the archetypes of "ice giants", a class of planets that may be among the most common in the Galaxy. They hold the keys to understand the atmospheric dynamics and structure of planets with hydrogen atmospheres inside and outside the solar system; however, they are also the last unexplored planets of the Solar System. Their atmospheres are active and storms are believed to be fueled by methane condensation which is both extremely abundant and occurs at low optical depth. This means that mapping temperature and methane abundance as a function of position and depth will inform us on how convection organizes in an atmosphere with no surface and condensates that are heavier than the surrounding air, a general feature of giant planets. Owing to the spatial and temporal variability of these atmospheres, an orbiter is required. A probe would provide a reference atmospheric profile to lift ambiguities inherent to remote observations. It would also measure the abundances of noble gases which can be used to reconstruct the history of planet formation in the Solar System. Finally, mapping the planets' gravity and magnetic fields will be essential to constrain their global composition, atmospheric dynamics, structure and evolution. An exploration of Uranus or Neptune will be essential to understand these planets and will also be key to constrain and analyze data obtained at Jupiter, Saturn, and for numerous exoplanets with hydrogen atmospheres.

astro-ph.IM↗

arXiv2020

Modulating nano-inhomogeneity at electrode-solid electrolyte interfaces for dendrite-proof solid-state batteries and long-life memristors

The penetration of dendrites in ceramic lithium conductors severely constrains the development of solid-state batteries (SSBs) while its nanoscopic origin remain unelucidated. We develop an in-situ nanoscale electrochemical characterization technique to reveal the nanoscopic lithium dendrite growth kinetics and use it as a guiding tool to unlock the design of interfaces for dendrite-proof SSBs. Using Li7La3Zr2O12 (LLZO) as a model system, in-situ nanoscopic dendrite triggering measurements, ex-situ electro-mechanical characterizations, and finite element simulations are carried out which reveal the dominating role of Li+ flux detouring and nano-mechanical inhomogeneity on dendrite penetration. To mitigate such nano-inhomogeneity, an ionic-conductive homogenizing layer based on poly(propylene carbonate) is designed which in-situ reacts with lithium to form a highly conformal interphase at mild conditions. A high critical current density of 1.8mA cm-2 and a low interfacial resistance of 14Ω cm2 is achieved. Practical SSBs based on LiFePO4 cathodes show great cyclic stability without capacity decay over 300 cycles. Beyond this, highly reversible electrochemical dendrite healing behavior in LLZO is discovered using the nano-electrode, based on which a model memristor with a high on/off ratio of ~10^5 is demonstrated for >200 cycles. This work not only provides a novel tool to investigate and design interfaces in SSBs but offers also new opportunities for solid electrolytes beyond energy applications.

physics.app-ph↗

arXiv2020

The Breakdown Scale of HI Bias Linearity

The 21 cm intensity mapping experiments promise to obtain the large-scale distribution of HI gas at the post-reionization epoch. In order to reveal the underlying matter density fluctuations from the HI mapping, it is important to understand how HI gas traces the matter density distribution. Both nonlinear halo clustering and nonlinear effects modulating HI gas in halos may determine the scale below which the HI bias deviates from linearity. We employ three approaches to generate the mock HI density from a large-scale N-body simulation at low redshifts, and demonstrate that the assumption of HI linearity is valid at the scale corresponding to the first peak of baryon acoustic oscillations, but breaks down at $k \gtrsim 0.1\,h\, {\rm Mpc}^{-1}$. The nonlinear effects of halo clustering and HI content modulation counteract each other at small scales, and their competition results in a model-dependent "sweet-spot" redshift near $z$=1 where the HI bias is scale-independent down to small scales. We also find that the linear HI bias scales approximately linearly with redshift for $z\le 3$.

astro-ph.CO↗

arXiv2020

Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching

Many natural language processing and information retrieval problems can be formalized as the task of semantic matching. Existing work in this area has been largely focused on matching between short texts (e.g., question answering), or between a short and a long text (e.g., ad-hoc retrieval). Semantic matching between long-form documents, which has many important applications like news recommendation, related article recommendation and document clustering, is relatively less explored and needs more research effort. In recent years, self-attention based models like Transformers and BERT have achieved state-of-the-art performance in the task of text matching. These models, however, are still limited to short text like a few sentences or one paragraph due to the quadratic computational complexity of self-attention with respect to input text length. In this paper, we address the issue by proposing the Siamese Multi-depth Transformer-based Hierarchical (SMITH) Encoder for long-form document matching. Our model contains several innovations to adapt self-attention models for longer text input. In order to better capture sentence level semantic relations within a document, we pre-train the model with a novel masked sentence block language modeling task in addition to the masked word language modeling task used by BERT. Our experimental results on several benchmark datasets for long-form document matching show that our proposed SMITH model outperforms the previous state-of-the-art models including hierarchical attention, multi-depth attention-based hierarchical recurrent neural network, and BERT. Comparing to BERT based baselines, our model is able to increase maximum input text length from 512 to 2048. We will open source a Wikipedia based benchmark dataset, code and a pre-trained checkpoint to accelerate future research on long-form document matching.

cs.IR↗

arXiv2020

ALMaQUEST -- IV. The ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) Survey

The ALMaQUEST (ALMA-MaNGA QUEnching and STar formation) survey is a program with spatially-resolved $^{12}$CO(1-0) measurements obtained with the Atacama Large Millimeter Array (ALMA) for 46 galaxies selected from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) DR15 optical integral-field spectroscopic survey. The aim of the ALMaQUEST survey is to investigate the dependence of star formation activity on the cold molecular gas content at kpc scales in nearby galaxies. The sample consists of galaxies spanning a wide range in specific star formation rate (sSFR), including starburst (SB), main-sequence (MS), and green valley (GV) galaxies. In this paper, we present the sample selection and characteristics of the ALMA observations, and showcase some of the key results enabled by the combination of spatially-matched stellar populations and gas measurements. Considering the global (aperture-matched) stellar mass, molecular gas mass, and star formation rate of the sample, we find that the sSFR depends on both the star formation efficiency (SFE) and the molecular gas fraction ($f_{\rm H_{2}}$), although the correlation with the latter is slightly weaker. Furthermore, the dependence of sSFR on the molecular gas content (SFE or $f_{\rm H_{2}}$) is stronger than that on either the atomic gas fraction or the molecular-to-atomic gas fraction, albeit with the small HI sample size. On kpc scales, the variations in both SFE and $f_{\rm H_{2}}$ within individual galaxies can be as large as 1-2 dex thereby demonstrating that the availability of spatially-resolved observations is essential to understand the details of both star formation and quenching processes.

astro-ph.GA↗

arXiv2020

Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach

Search engines often follow a two-phase paradigm where in the first stage (the retrieval stage) an initial set of documents is retrieved and in the second stage (the re-ranking stage) the documents are re-ranked to obtain the final result list. While deep neural networks were shown to improve the performance of the re-ranking stage in previous works, there is little literature about using deep neural networks to improve the retrieval stage. In this paper, we study the merits of combining deep neural network models and lexical models for the retrieval stage. A hybrid approach, which leverages both semantic (deep neural network-based) and lexical (keyword matching-based) retrieval models, is proposed. We perform an empirical study, using a publicly available TREC collection, which demonstrates the effectiveness of our approach and sheds light on the different characteristics of the semantic approach, the lexical approach, and their combination.

cs.IR↗