Searcharxiv⌕ Search

arXiv subjects

Xing Gao

Publications and source records attributed to Xing Gao.

At least 145 records · Page 8Linked to original sources
arXiv2021

Photometric study and absolute parameters estimation of six totally eclipsing contact binaries

High precision CCD observations of six totally eclipsing contact binaries were presented and analyzed. It is found that only one target is an A-type contact binary (V429 Cam), while the others are W-type contact ones. By analyzing the times of light minima, we discovered that two of them exhibit secular period increase while three manifest long-term period decrease. For V1033 Her, a cyclic variation superimposed on the long-term increase was discovered. By comparing the Gaia distances with those calculated by the absolute parameters of 173 contact binaries, we found that Gaia distance can be applied to estimate absolute parameters for most contact binaries. The absolute parameters of our six targets were estimated by using their Gaia distances. The evolutionary status of contact binaries was studied, we found that the A- and W- subtype contact binaries may have different formation channels. The relationship between the spectroscopic and photometric mass ratios for 101 contact binaries was presented. It is discovered that the photometric mass ratios are in good agreement with the spectroscopic ones for almost all the totally eclipsing systems, which is corresponding to the results derived by Pribulla et al. and Terrell & Wilson.

astro-ph.SR↗
arXiv2021

Compatible structures on unary binary nonsymmetric operads with quadratic and cubic relations

Various compatibility conditions among replicated copies of operations in a given algebraic structure have appeared in broad contexts in recent years. Taking an uniform approach, this paper gives an operadic study of compatibility conditions for nonsymmetric operads with unary and binary operations, and homogeneous quadratic and cubic relations. This generalizes the previous studies for binary quadratic operads. We consider three compatibility conditions, namely the linear compatibility, matching compatibility and total compatibility, with increasingly strict restraints among the replicated copies. The linear compatibility is in Koszul dual to the total compatibility, while the matching compatibility is self dual. Further, each compatibility can be expressed in terms of either one or both of the two Manin square products.

math.CT↗
arXiv2021

Spatial-Temporal Transformer Networks for Traffic Flow Forecasting

Traffic forecasting has emerged as a core component of intelligent transportation systems. However, timely accurate traffic forecasting, especially long-term forecasting, still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. In this paper, we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies with self-attention mechanism to capture realtime traffic conditions as well as the directionality of traffic flows. Furthermore, different spatial dependency patterns can be jointly modeled with multi-heads attention mechanism to consider diverse relationships related to different factors (e.g. similarity, connectivity and covariance). On the other hand, the temporal transformer is utilized to model long-range bidirectional temporal dependencies across multiple time steps. Finally, they are composed as a block to jointly model the spatial-temporal dependencies for accurate traffic prediction. Compared to existing works, the proposed model enables fast and scalable training over a long range spatial-temporal dependencies. Experiment results demonstrate that the proposed model achieves competitive results compared with the state-of-the-arts, especially forecasting long-term traffic flows on real-world PeMS-Bay and PeMSD7(M) datasets.

eess.SP↗
arXiv2021

The first photometric analysis of the totally eclipsing contact binary V811 Cep

The first photometric analysis of V811 Cep was carried out. The first complete light curves of V, R and I bands are given. The analysis was carried out by Wilson-Devinney (W-D) program, and the results show that V811 Cep is a median-contact binary ($f=33.9(\pm4.9)\%$) with a mass ratio of 0.285. It is a W-subtype contact binary, that is, the component with less mass is hotter than the component with more mass, and the light curves are asymmetric (O'Connell effect), which can be explained by the existence of a hot spot on the component with less mass. The orbital inclination is $i=88.3^{\circ}$, indicating that it is a totally eclipsing binary, so the parameters obtained are reliable. Through the O-C analyzing, it is found that the orbital period decreases at the rate of $\dot{P}=-3.90(\pm0.06)\times 10^{-7}d \cdot yr^{-1}$, which indicates that the mass transfer occurs from the more massive component to the less massive one.

astro-ph.SR↗
arXiv2020

Commutative matching Rota-Baxter operators, shuffle products with decorations and matching Zinbiel algebras

The Rota-Baxter algebra and the shuffle product are both algebraic structures arising from integral operators and integral equations. Free commutative Rota-Baxter algebras provide an algebraic framework for integral equations with the simple Riemann integral operator. The Zinbiel algebras form a category in which the shuffle product algebra is the free object. Motivated by algebraic structures underlying integral equations involving multiple integral operators and kernels, we study commutative matching Rota-Baxter algebras and construct the free objects making use of the shuffle product with multiple decorations. We also construct free commutative matching Rota-Baxter algebras in a relative context, to emulate the action of the integral operators on the coefficient functions in an integral equation. We finally show that free commutative \match Rota-Baxter algebras give the free matching Zinbiel algebra, generalizing the characterization of the shuffle product algebra as the free Zinbiel algebra obtained by Loday.

math.RA↗
arXiv2020

Photometric and Spectroscopic Study of Flares on Ross 15

We conducted photometric and spectroscopic observations for Ross 15 in order to further study the flare properties of this less observed flare star. A total of 28 B-band flares are detected in 128 hours of photometric observations, leading to a total flare rate of 0.22(+-0.04) hour^-1, more accurate than that provided by previous work. We give the energy range of the B-band flare (10^29.5 - 10^31.5 erg) and the FFD for the star. Within the same energy range, the FFD are lower than that of GJ 1243 (M4) and YZ CMi (M4.5), roughly in the middle of those of three M5-type stars and higher than the average FFDs of spectral types >= M6. We performed, for the first time to Ross 15, simultaneous high-cadence spectroscopic and photometric observations, resulting in detection of the most energetic flare in our sample. The intensity enhancements of the continuum and Balmer lines with significant correlations between them are detected during the flare, which is same with that of the other deeply studied flare stars of the similar spectral type.

astro-ph.SR↗
arXiv2020

A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices

Mobile authentication using behavioral biometrics has been an active area of research. Existing research relies on building machine learning classifiers to recognize an individual's unique patterns. However, these classifiers are not powerful enough to learn the discriminative features. When implemented on the mobile devices, they face new challenges from the behavioral dynamics, data privacy and side-channel leaks. To address these challenges, we present a new framework to incorporate training on battery-powered mobile devices, so private data never leaves the device and training can be flexibly scheduled to adapt the behavioral patterns at runtime. We re-formulate the classification problem into deep metric learning to improve the discriminative power and design an effective countermeasure to thwart side-channel leaks by embedding a noise signature in the sensing signals without sacrificing too much usability. The experiments demonstrate authentication accuracy over 95% on three public datasets, a sheer 15% gain from multi-class classification with less data and robustness against brute-force and side-channel attacks with 99% and 90% success, respectively. We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. Finally, we profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos and slightly higher energy than playing games.

cs.LG↗
arXiv2020

GJ 3470 c: A Saturn-like Exoplanet Candidate in the Habitable Zone of GJ 3470

We report the discovery of a new exoplanet candidate orbiting the star GJ 3470. A total of three transits were detected by OKSky Observatory: the first one on December 23, 2019, the second one on February 27, 2020, and the third one on May 3, 2020. We estimate an average transit depth of 0.84 percent and duration of 1 hour and 2 minutes. Based on this parameter, we calculate a radius of 9.2 Earth radii, which would correspond to the size of a Saturn-like exoplanet. We also estimate an orbital period of 66 days that places the exoplanet inside the habitable zone, near the orbital distance at Earths equivalent radiation. Another twelve potential transits that do not belong to GJ 3470 b are also reported. Despite our candidate for GJ 3470 c still has to be confirmed by the scientific community, the discovery represents a turning point in exoplanet research for being the first candidate discovered through an international project managed by amateur astronomers.

astro-ph.EP↗
arXiv2020

Graph Pooling with Node Proximity for Hierarchical Representation Learning

Graph neural networks have attracted wide attentions to enable representation learning of graph data in recent works. In complement to graph convolution operators, graph pooling is crucial for extracting hierarchical representation of graph data. However, most recent graph pooling methods still fail to efficiently exploit the geometry of graph data. In this paper, we propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Node proximity is obtained by harmonizing the kernel representation of topology information and node features. Implicit structure-aware kernel representation of topology information allows efficient graph pooling without explicit eigendecomposition of the graph Laplacian. Similarities of node signals are adaptively evaluated with the combination of the affine transformation and kernel trick using the Gaussian RBF function. Experimental results demonstrate that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.

cs.LG↗
arXiv2020

The Unusual Eruption of the Extragalactic Classical Nova M31N 2017-09a

M31N 2017-09a is a classical nova and was observed for some 160 days following its initial eruption, during which time it underwent a number of bright secondary outbursts. The light-curve is characterized by continual variation with excursions of at least 0.5 magnitudes on a daily time-scale. The lower envelope of the eruption suggests that a single power-law can describe the decline rate. The eruption is relatively long with $t_2 = 111$, and $t_3 = 153$ days.

astro-ph.SR↗
arXiv2020

The first photometric investigation of the contact binary OQ Cam

The first charge-coupled device photometric light curves in B, V, R${_c}$, and I${_c}$ bands of the short-period contact binary star OQ Cam are presented. Through analyzing the light curves with the Wilson-Devinney code, it is found that OQ Cam is a W-type shallow contact binary star with a contact degree of f =20.2%. Based on the O-C analyzing, the orbit period displays a long term increasing with a rate of $dP/dt=4.40\times10{^{-7}} day \cdot year{^{-1}}$. The increasing in orbit period can be interpreted by mass transfer from the less massive component to the more massive one. As the orbit period increasing, OQ Cam would evolve from the present shallow contact configuration to a none contact stage. So it may be a potential candidate to confirm the thermal relaxation oscillation theory.

astro-ph.SR↗
arXiv2020

Free Rota-Baxter family algebras and Free (tri)dendriform family algebras

In this paper, we first construct the free Rota-Baxter family algebra generated by some set $X$ in terms of typed angularly $X$-decorated planar rooted trees. As an application, we obtain a new construction of the free Rota-Baxter algebra only in terms of angularly decorated planar rooted trees (not forests), which is quite different from the known construction via angularly decorated planar rooted forests by K. Ebrahimi-Fard and L. Guo. We then embed the free dendriform (resp. tridendriform) family algebra into the free Rota-Baxter family algebra of weight zero (resp. one). Finally, we prove that the free Rota-Baxter family algebra is the universal enveloping algebra of the free (tri)dendriform family algebra.

math.RA↗
arXiv2020

The first light curve modeling and orbital period change investigation of nine contact binaries around the short period cut-off

In this paper, we present the first light curve synthesis and orbital period change analysis of nine contact binaries around the short period limit. It is found that all these systems are W-subtype contact binaries. One of them is a medium contact system while the others are shallow contact ones. Four of them manifest obvious O'Connell effect explained by a dark spot or hot spot on one of the component stars. Third light was detected in three systems. By investigating orbital period variations, we found that four of the targets display a secular period decrease while the others exhibit a long-term period increase. The secular period decrease is more likely caused by angular momentum loss while the long-term period increase is due to mass transfer from the less massive component to the more massive one. Based on the statistic of 19 ultrashort period contact binaries with known orbital period changes, we found that seven of them display long-term decrease (three of them also exhibit cyclic variations), ten of them manifest long-term increase while two of them only show cyclic variation and that most of them are shallow contact binaries supporting the long timescale angular momentum loss theory suggested by Stepien. For the three deep contact systems, we found that they are probably triple systems. The tertiary companion plays an essential role during their formation and evolution.

astro-ph.SR↗
arXiv2019

iPool -- Information-based Pooling in Hierarchical Graph Neural Networks

With the advent of data science, the analysis of network or graph data has become a very timely research problem. A variety of recent works have been proposed to generalize neural networks to graphs, either from a spectral graph theory or a spatial perspective. The majority of these works however focus on adapting the convolution operator to graph representation. At the same time, the pooling operator also plays an important role in distilling multiscale and hierarchical representations but it has been mostly overlooked so far. In this paper, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs. With the argument that informative nodes dominantly characterize graph signals, we propose a criterion to evaluate the amount of information of each node given its neighbors, and theoretically demonstrate its relationship to neighborhood conditional entropy. This new criterion determines how nodes are selected and coarsened graphs are constructed in the pooling layer. The resulting hierarchical structure yields an effective isomorphism-invariant representation of networked data in arbitrary topologies. The proposed strategy is evaluated in terms of graph classification on a collection of public graph datasets, including bioinformatics and social networks, and achieves state-of-the-art performance on most of the datasets.

cs.LG↗
arXiv2019

Reynolds algebras and their free objects from bracketed words and rooted trees

The study of Reynolds algebras has its origin in the well-known work of O. Reynolds on fluid dynamics in 1895 and has since found broad applications. It also has close relationship with important linear operators such as algebra endomorphisms, derivations and Rota-Baxter operators. Many years ago G.~Birkhoff suggested an algebraic study of Reynolds operators, including the corresponding free algebras. We carry out such a study in this paper. We first provide examples and properties of Reynolds operators, including a multi-variant generalization of the Reynolds identity. We then construct the free Reynolds algebra on a set. For this purpose, we identify a set of bracketed words called Reynolds words which serves as the linear basis of the free Reynolds algebra. A combinatorial interpretation of Reynolds words is given in terms of rooted trees without super crowns. The closure of the Reynolds words under concatenation gives the algebra structure on the space spanned by Reynolds words. Then a linear operator is defined on this algebra such that the Reynolds identity and the desired universal property are satisfied.

math.RA↗
arXiv2019

Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning

Dynamic multi-objective optimization problems (DMOPs) remain a challenge to be settled, because of conflicting objective functions change over time. In recent years, transfer learning has been proven to be a kind of effective approach in solving DMOPs. In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA). The algorithm aims to generate an excellent initial population to accelerate the evolutionary process and improve the evolutionary performance in solving DMOPs. When an environmental change is detected, a regression transfer learning prediction model is constructed by reusing the historical population, which can predict objective values. Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary process. We compare the proposed algorithm with three state-of-the-art algorithms on benchmark functions. Experimental results indicate that the proposed algorithm can significantly enhance the performance of static multi-objective optimization algorithms and is competitive in convergence and diversity.

cs.NE↗
arXiv2019

Online Bagging for Anytime Transfer Learning

Transfer learning techniques have been widely used in the reality that it is difficult to obtain sufficient labeled data in the target domain, but a large amount of auxiliary data can be obtained in the relevant source domain. But most of the existing methods are based on offline data. In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially. In this paper, We are committed to applying the ensemble approach to solving the problem of online transfer learning so that it can be used in anytime setting. More specifically, we propose a novel online transfer learning framework, which applies the idea of online bagging methods to anytime transfer learning problems, and constructs strong classifiers through online iterations of the usefulness of multiple weak classifiers. Further, our algorithm also provides two extension schemes to reduce the impact of negative transfer. Experiments on three real data sets show that the effectiveness of our proposed algorithms.

cs.LG↗