arXiv subjects

Hui Zhang

Publications and source records attributed to Hui Zhang.

At least 451 records · Page 25Linked to original sources

arXiv2019

High-resolution Infrared Spectrograph for Exoplanet Characterization with the Keck and Thirty Meter Telescopes

HISPEC (High-resolution Infrared Spectrograph for Exoplanet Characterization) is a proposed diffraction-limited spectrograph for the W.M. Keck Observatory, and a pathfinder for the MODHIS facility project (Multi-Object Diffraction-limited High-resolution Infrared Spectrograph) on the Thirty Meter Telescope. HISPEC/MODHIS builds on diffraction-limited spectrograph designs which rely on adaptively corrected single-mode fiber feeds. Seeing-limited high-resolution spectrographs, by virtue of the conservation of beam etendue, grow in volume following a D^3 power law (D is the telescope diameter), and are subject to daunting challenges associated with their large size. Diffraction-limited spectrographs fed by single mode fibers are decoupled from the telescope input, and are orders of magnitude more compact and have intrinsically stable line spread functions. Their efficiency is directly proportional to the performance of the adaptive optics (AO) system. AO technologies have matured rapidly over the past two decades and are baselined for future extremely large telescopes. HISPEC/MODHIS will take R>100,000 spectra of a few objects in a 10" field-of-view sampled at the diffraction limit (~10-50 mas), simultaneously from 0.95 to 2.4 microns (y-K). The scientific scope ranges from exoplanet infrared precision radial velocities, spectroscopy of transiting, close-in, and directly imaged exoplanets (atmospheric composition and dynamics, RM effect, spin measurements, Doppler imaging), brown dwarf characterization, stellar physics/chemistry, proto-planetary disk kinematics/composition, Solar system, extragalactic science, and cosmology. HISPEC/MODHIS features a compact, cost-effective design optimized to fully exploit the existing Keck-AO and future TMT-NFIRAOS infrastructures and boost the scientific reach of Keck Observatory and TMT soon after first light.

astro-ph.IM↗

arXiv2019

DELTA: A DEep learning based Language Technology plAtform

In this paper we present DELTA, a deep learning based language technology platform. DELTA is an end-to-end platform designed to solve industry level natural language and speech processing problems. It integrates most popular neural network models for training as well as comprehensive deployment tools for production. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. We demonstrate the reliable performance with DELTA on several natural language processing and speech tasks, including text classification, named entity recognition, natural language inference, speech recognition, speaker verification, etc. DELTA has been used for developing several state-of-the-art algorithms for publications and delivering real production to serve millions of users.

cs.CL↗

arXiv2019

Mesonic Condensation in Isospin Matter under Rotation

We investigate the mesonic condensation in isospin matter under rotation. Using the two-flavor NJL effective model under the presence of global rotation, we demonstrate two important effects of the rotation on its phase structure: a rotational suppression of the scalar-channel condensates, in particular the pion condensation region; and a rotational enhancement of the rho condensation region with vector-channel condensate. A new phase diagram for isospin matter under rotation is mapped out on the $ω-μ_I$ plane where three distinctive phases, corresponding to $σ$, $π$, $ρ$ dominated regions respectively, are separated by a second-order line at low isospin chemical potential and a first-order line at high rotation which are further connected at a tri-critical point.

hep-ph↗

arXiv2019

DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter

The Long Short-Term Memory (LSTM) neural network based data association algorithm named as DeepDA for multi-target tracking in clutters is proposed to deal with the NP-hard combinatorial optimization problem in this paper. Different from the classical data association methods involving complex models and accurate prior knowledge on clutter density, filter covariance or associated gating etc, data-driven deep learning methods have been extensively researched for this topic. Firstly, data association mathematical problem for multitarget tracking on unknown target number, missed detection and clutter, which is beyond one-to-one mapping between observations and targets is redefined formally. Subsequently, an LSTM network is designed to learn the measurement-to-track association probability from radar noisy measurements and exist tracks. Moreover, an LSTM-based data-driven deep neural network after a supervised training through the BPTT and RMSprop optimization method can get the association probability directly. Experimental results on simulated data show a significant performance on association ratio, target ID switching and time-consuming for tracking multiple targets even they are crossing each other in the complicated clutter environment.

cs.LG↗

arXiv2019

TESS Spots a Compact System of Super-Earths around the Naked-Eye Star HR 858

Transiting Exoplanet Survey Satellite (TESS) observations have revealed a compact multi-planet system around the sixth-magnitude star HR 858 (TIC 178155732, TOI 396), located 32 parsecs away. Three planets, each about twice the size of Earth, transit this slightly-evolved, late F-type star, which is also a member of a visual binary. Two of the planets may be in mean motion resonance. We analyze the TESS observations, using novel methods to model and remove instrumental systematic errors, and combine these data with follow-up observations taken from a suite of ground-based telescopes to characterize the planetary system. The HR 858 planets are enticing targets for precise radial velocity observations, secondary eclipse spectroscopy, and measurements of the Rossiter-McLaughlin effect.

astro-ph.EP↗

arXiv2019

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination

The intra-axonal water exchange time τi, a parameter associated with axonal permeability, could be an important biomarker for understanding demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI is sensitive to changes in permeability, however, the parameter has remained elusive due to the intractability of the mathematical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters, and recently, a theoretical framework using a random forest (RF) suggests this is a promising approach. In this study, we adopt such an RF approach and experimentally investigate its suitability as a biomarker for demyelinating pathologies through direct comparison with histology. For this, we use an in-vivo cuprizone (CPZ) mouse model of demyelination with available ex-vivo electron microscopy (EM) data. We test our model on noise-free simulations and find very strong correlations between the predicted and ground truth parameters. For realistic noise levels as in our in-vivo data, the performance is affected, however, the parameters are still well estimated. We apply our RF model on in-vivo data from 8 CPZ and 8 wild-type (WT) mice and validate the RF estimates using histology. We find a strong correlation between the in-vivo RF estimates of τi and the EM measurements of myelin thickness ({ρ_τ}i = 0.82), and between RF estimates and EM measurements of intra-axonal volume fraction ({ρ_f} = 0.98). When comparing τi in CPZ and WT mice we find a statistically significant decrease in the corpus callosum of the CPZ compared to the WT mice, in line with our expectations that τi is lower in regions where the myelin sheath is damaged. Overall, these results demonstrate the suitability of machine learning compartment models with permeability as a potential biomarker for demyelinating pathologies.

physics.med-ph↗

arXiv2019

A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting

Robustness against noise is critical for keyword spotting (KWS) in real-world environments. To improve the robustness, a speech enhancement front-end is involved. Instead of treating the speech enhancement as a separated preprocessing before the KWS system, in this study, a pre-trained speech enhancement front-end and a convolutional neural networks (CNNs) based KWS system are concatenated, where a feature transformation block is used to transform the output from the enhancement front-end into the KWS system's input. The whole model is trained jointly, thus the linguistic and other useful information from the KWS system can be back-propagated to the enhancement front-end to improve its performance. To fit the small-footprint device, a novel convolution recurrent network is proposed, which needs fewer parameters and computation and does not degrade performance. Furthermore, by changing the input features from the power spectrogram to Mel-spectrogram, less computation and better performance are obtained. our experimental results demonstrate that the proposed method significantly improves the KWS system with respect to noise robustness.

cs.SD↗

arXiv2019

Complexity of Dependencies in Bounded Domains, Armstrong Codes, and Generalizations

The study of Armstrong codes is motivated by the problem of understanding complexities of dependencies in relational database systems, where attributes have bounded domains. A $(q,k,n)$-Armstrong code is a $q$-ary code of length $n$ with minimum Hamming distance $n-k+1$, and for any set of $k-1$ coordinates there exist two codewords that agree exactly there. Let $f(q,k)$ be the maximum $n$ for which such a code exists. In this paper, $f(q,3)=3q-1$ is determined for all $q\geq 5$ with three possible exceptions. This disproves a conjecture of Sali. Further, we introduce generalized Armstrong codes for branching, or $(s,t)$-dependencies, construct several classes of optimal Armstrong codes and establish lower bounds for the maximum length $n$ in this more general setting.

math.CO↗

arXiv2019

A stabilized semi-implicit Fourier spectral method for nonlinear space-fractional reaction-diffusion equations

The reaction-diffusion model can generate a wide variety of spatial patterns, which has been widely applied in chemistry, biology, and physics, even used to explain self-regulated pattern formation in the developing animal embryo. In this work, a second-order stabilized semi-implicit time-stepping Fourier spectral method is presented for the reaction-diffusion systems of equations with space described by the fractional Laplacian. We adopt the temporal-spatial error splitting argument to illustrate that the proposed method is stable without imposing the CFL condition, and we prove an optimal L2-error estimate. We also analyze the linear stability of the stabilized semi-implicit method and obtain a practical criterion to choose the time step size to guarantee the stability of the semi-implicit method. Our approach is illustrated by solving several problems of practical interest, including the fractional Allen-Cahn, Gray-Scott and FitzHugh-Nagumo models, together with an analysis of the properties of these systems in terms of the fractional power of the underlying Laplacian operator, which are quite different from the patterns of the corresponding integer-order model.

math.NA↗

arXiv2019

Inertial Bregman Proximal Gradient Algorithm For Nonconvex Problem with Smooth Adaptable Property

In this paper we study the problems of minimizing the sum of two nonconvex functions: one is differentiable and satisfies smooth adaptable property. The smooth adaptable property, also named relatively smooth condition, is weaker than the globally gradient Lipschitz continuity. We analyze an inertial version of the Bregman Proximal Gradient (BPG) algorithm and prove its stationary convergence. Besides, we prove a sublinear convergence of the inertial algorithm. Moreover, if the objective function satisfies Kurdyka--Łojasiewicz (KL) property, its global convergence to a critical point of the objective function can be also guaranteed.

math.OC↗

arXiv2019

Pulsar Candidate Selection Using Ensemble Networks for FAST Drift-Scan Survey

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope (FAST) Survey (CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system (PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks (CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual--GPU and 24--core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches.

astro-ph.IM↗

arXiv2019

A New Exact Worst-Case Linear Convergence Rate of the Proximal Gradient Method

In this note, we establish a new exact worst-case linear convergence rate of the proximal gradient method in terms of the proximal gradient norm, which complements the recent results in [1] and implies a refined descent lemma.descent lemma. Based on the new lemma, we improve the linear convergence rate of the objective function accuracy under the Polyak-Lojasiewicz inequality.

math.OC↗

arXiv2019

Training GANs with Centripetal Acceleration

Training generative adversarial networks (GANs) often suffers from cyclic behaviors of iterates. Based on a simple intuition that the direction of centripetal acceleration of an object moving in uniform circular motion is toward the center of the circle, we present the Simultaneous Centripetal Acceleration (SCA) method and the Alternating Centripetal Acceleration (ACA) method to alleviate the cyclic behaviors. Under suitable conditions, gradient descent methods with either SCA or ACA are shown to be linearly convergent for bilinear games. Numerical experiments are conducted by applying ACA to existing gradient-based algorithms in a GAN setup scenario, which demonstrate the superiority of ACA.

cs.LG↗

arXiv2019

Grassmannian Codes with New Distance Measures for Network Coding

Grassmannian codes are known to be useful in error-correction for random network coding. Recently, they were used to prove that vector network codes outperform scalar linear network codes, on multicast networks, with respect to the alphabet size. The multicast networks which were used for this purpose are generalized combination networks. In both the scalar and the vector network coding solutions, the subspace distance is used as the distance measure for the codes which solve the network coding problem in the generalized combination networks. In this work we show that the subspace distance can be replaced with two other possible distance measures which generalize the subspace distance. These two distance measures are shown to be equivalent under an orthogonal transformation. It is proved that the Grassmannian codes with the new distance measures generalize the Grassmannian codes with the subspace distance and the subspace designs with the strength of the design. Furthermore, optimal Grassmannian codes with the new distance measureshave minimal requirements for network coding solutions of some generalized combination networks. The coding problems related to these two distance measures, especially with respect to network coding, are discussed. Finally, by using these new concepts it is proved that codes in the Hamming scheme form a subfamily of the Grassmannian codes.

cs.IT↗

arXiv2019

Exoplanets in the Antarctic sky. II. 116 Transiting Exoplanet Candidates Found by AST3-II (CHESPA) within the Southern CVZ of TESS

We report first results from the CHinese Exoplanet Searching Program from Antarctica (CHESPA)---a wide-field high-resolution photometric survey for transiting exoplanets carried out using telescopes of the AST3 (Antarctic Survey Telescopes times 3) project. There are now three telescopes (AST3-I, AST3-II, and CSTAR-II) operating at Dome A---the highest point on the Antarctic Plateau---in a fully automatic and remote mode to exploit the superb observing conditions of the site, and its long and uninterrupted polar nights. The search for transiting exoplanets is one of the key projects for AST3. During the Austral winters of 2016 and 2017 we used the AST3-II telescope to survey a set of target fields near the southern ecliptic pole, falling within the continuous viewing zone of the TESS mission \citep{Ricker10}. The first data release of the 2016 data, including images, catalogs and lightcurves of 26578 bright stars ($7.5\le i \le15$) was presented in \citet{Zhang18}. The best precision, as measured by the RMS of the lightcurves at the optimum magnitude of the survey ($i=10$), is around 2\,mmag. We detect 222 objects with plausible transit signals from these data, 116 of which are plausible transiting exoplanet candidates according to their stellar properties as given by the TESS Input Catalog \citep{Stassun17}, Gaia DR2 \citep{Gaia18} and TESS-HERMES spectroscopy \citep{Sharma18}. With the first data release from TESS expected in late 2018, this candidate list will be a timely for improving the rejection of potential false positives.

astro-ph.EP↗

arXiv2018

Exoplanets in the Antarctic Sky. I. The First Data Release of AST3-II (CHESPA) and New Found Variables within the Southern CVZ of TESS

Located at Dome A, the highest point of the Antarctic plateau, the Chinese Kunlun station is considered to be one of the best ground-based photometric sites because of its extremely cold, dry, and stable atmosphere(Saunders et al. 2009). A target can be monitored from there for over 40 days without diurnal interruption during a polar winter. This makes Kunlun station a perfect site to search for short-period transiting exoplanets. Since 2008, an observatory has been built at Kunlun station and three telescopes are working there. Using these telescopes, the AST3 project has been carried out over the last six years with a search for transiting exoplanets as one of its key programs (CHESPA). In the austral winters of 2016 and 2017, a set of target fields in the Southern CVZ of TESS (Ricker et al. 2009) were monitored by the AST3-II telescope. In this paper, we introduce the CHESPA and present the first data release containing photometry of 26,578 bright stars (m_i < 15). The best photometric precision at the optimum magnitude for the survey is around 2 mmag. To demonstrate the data quality, we also present a catalog of 221 variables with a brightness variation greater than 5 mmag from the 2016 data. Among these variables, 179 are newly identified periodic variables not listed in the AAVSO databasea), and 67 are listed in the Candidate Target List(Stassun et al. 2017). These variables will require careful attention to avoid false-positive signals when searching for transiting exoplanets. Dozens of new transiting exoplanet candidates will be also released in a subsequent paper(Zhang et al. 2018b).

astro-ph.IM↗

arXiv2018

Estimation of Inter-Sentiment Correlations Employing Deep Neural Network Models

This paper focuses on sentiment mining and sentiment correlation analysis of web events. Although neural network models have contributed a lot to mining text information, little attention is paid to analysis of the inter-sentiment correlations. This paper fills the gap between sentiment calculation and inter-sentiment correlations. In this paper, the social emotion is divided into six categories: love, joy, anger, sadness, fear, and surprise. Two deep neural network models are presented for sentiment calculation. Three datasets - the titles, the bodies, the comments of news articles - are collected, covering both objective and subjective texts in varying lengths (long and short). From each dataset, three kinds of features are extracted: explicit expression, implicit expression, and alphabet characters. The performance of the two models are analyzed, with respect to each of the three kinds of the features. There is controversial phenomenon on the interpretation of anger (fn) and love (gd). In subjective text, other emotions are easily to be considered as anger. By contrast, in objective news bodies and titles, it is easy to regard text as caused love (gd). It means, journalist may want to arouse emotion love by writing news, but cause anger after the news is published. This result reflects the sentiment complexity and unpredictability.

cs.CL↗

arXiv2018

A power law decay evolution scenario for polluted single white dwarfs

Planetary systems can survive the stellar evolution, as evidenced by the atmospheric metal pollution and dusty disks of single white dwarfs. Recent observations show that 1 to 4 percent of single white dwarfs are accompanied by dusty disks, while the occurrence rate of metal pollution is about 25 to 50 percent . The dusty disks and metal pollution have been associated with accretion of remnant planetary systems around white dwarfs, yet the relation between these two phenomena is still unclear. Here we suggest an evolutionary scenario to link the two observational phenomena. By analyzing a sample of metal polluted white dwarfs, we find that the mass accretion rate onto the white dwarf generally follows a broken power law decay, which matches well with the theoretical prediction, if assuming dust accretion is primarily driven by Poynting-Robertson drag and the dust source is primarily delivered via dynamically falling asteroids perturbed by a Jovian planet. The presence of disks is mainly at the early stage (about 0.1 to 0.7 billion years) of the whole process of metal pollution, which is detectable until about 8 billion years, naturally explaining the fraction (about 2-16 percent) of metal-polluted white dwarfs having dusty disks. The success of this scenario also implies that the configuration of an asteroid belt with an outer gas giant might be common around stars of several solar masses.

astro-ph.EP↗