Searcharxiv⌕ Search

arXiv subjects

Xi Zhang

Publications and source records attributed to Xi Zhang.

At least 343 records · Page 19Linked to original sources
arXiv2018

A Tensor-Based Sub-Mode Coordinate Algorithm for Stock Prediction

The investment on the stock market is prone to be affected by the Internet. For the purpose of improving the prediction accuracy, we propose a multi-task stock prediction model that not only considers the stock correlations but also supports multi-source data fusion. Our proposed model first utilizes tensor to integrate the multi-sourced data, including financial Web news, investors' sentiments extracted from the social network and some quantitative data on stocks. In this way, the intrinsic relationships among different information sources can be captured, and meanwhile, multi-sourced information can be complemented to solve the data sparsity problem. Secondly, we propose an improved sub-mode coordinate algorithm (SMC). SMC is based on the stock similarity, aiming to reduce the variance of their subspace in each dimension produced by the tensor decomposition. The algorithm is able to improve the quality of the input features, and thus improves the prediction accuracy. And the paper utilizes the Long Short-Term Memory (LSTM) neural network model to predict the stock fluctuation trends. Finally, the experiments on 78 A-share stocks in CSI 100 and thirteen popular HK stocks in the year 2015 and 2016 are conducted. The results demonstrate the improvement on the prediction accuracy and the effectiveness of the proposed model.

cs.LG↗
arXiv2018

Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask

Using a layered representation for motion estimation has the advantage of being able to cope with discontinuities and occlusions. In this paper, we learn to estimate optical flow by combining a layered motion representation with deep learning. Instead of pre-segmenting the image to layers, the proposed approach automatically generates a layered representation of optical flow using the proposed soft-mask module. The essential components of the soft-mask module are maxout and fuse operations, which enable a disjoint layered representation of optical flow and more accurate flow estimation. We show that by using masks the motion estimate results in a quadratic function of input features in the output layer. The proposed soft-mask module can be added to any existing optical flow estimation networks by replacing their flow output layer. In this work, we use FlowNet as the base network to which we add the soft-mask module. The resulting network is tested on three well-known benchmarks with both supervised and unsupervised flow estimation tasks. Evaluation results show that the proposed network achieve better results compared with the original FlowNet.

cs.CV↗
arXiv2018

Formation of Silicate and Titanium Clouds on Hot Jupiters

We present the first application of a bin-scheme microphysical and vertical transport model to determine the size distribution of titanium and silicate cloud particles in the atmospheres of hot Jupiters. We predict particle size distributions from first principles for a grid of planets at four representative equatorial longitudes, and investigate how observed cloud properties depend on the atmospheric thermal structure and vertical mixing. The predicted size distributions are frequently bimodal and irregular in shape. There is a negative correlation between total cloud mass and equilibrium temperature as well as a positive correlation between total cloud mass and atmospheric mixing. The cloud properties on the east and west limbs show distinct differences that increase with increasing equilibrium temperature. Cloud opacities are roughly constant across a broad wavelength range with the exception of features in the mid-infrared. Forward scattering is found to be important across the same wavelength range. Using the fully resolved size distribution of cloud particles as opposed to a mean particle size has a distinct impact on the resultant cloud opacities. The particle size that contributes the most to the cloud opacity depends strongly on the cloud particle size distribution. We predict that it is unlikely that silicate or titanium clouds are responsible for the optical Rayleigh scattering slope seen in many hot Jupiters. We suggest that cloud opacities in emission may serve as sensitive tracers of the thermal state of a planet's deep interior through the existence or lack of a cold trap in the deep atmosphere.

astro-ph.EP↗
arXiv2018

Exploiting Investors Social Network for Stock Prediction in China's Market

Recent works have shown that social media platforms are able to influence the trends of stock price movements. However, existing works have majorly focused on the U.S. stock market and lacked attention to certain emerging countries such as China, where retail investors dominate the market. In this regard, as retail investors are prone to be influenced by news or other social media, psychological and behavioral features extracted from social media platforms are thought to well predict stock price movements in the China's market. Recent advances in the investor social network in China enables the extraction of such features from web-scale data. In this paper, on the basis of tweets from Xueqiu, a popular Chinese Twitter-like social platform specialized for investors, we analyze features with regard to collective sentiment and perception on stock relatedness and predict stock price movements by employing nonlinear models. The features of interest prove to be effective in our experiments.

cs.CE↗
arXiv2018

Improving Stock Market Prediction via Heterogeneous Information Fusion

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people's sentiments towards the market and stocks, have been proved to play important roles in the stocks' volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users' sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsic relations among the events and the investors' sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model.

cs.SI↗
arXiv2017

Effects of Laser Polarization and Color on Hybrid Laser Wakefield and Direct Acceleration

We demonstrate that hybrid laser wakefield and direct acceleration (LWDA) can be significantly improved by using two laser pulses with different polarizations or frequencies. The improvement entails higher energy and charge of the accelerated electrons, as well as weaker sensitivity to the time delay between the leading (pump) pulse responsible for generating the wakefield, and the trailing laser pulse responsible for direct laser acceleration (DLA). When the two laser pulses have the same frequency, it is shown that it is advantageous to have their polarization states orthogonal to each other. A specific scenario of a frequency-doubled ($λ_{\rm DLA}=λ_{\rm pump}/2$) DLA laser pulse is also considered, and found to be an improvement over the single-frequency ($λ_{\rm DLA} = λ_{\rm pump}$) scenario.

physics.plasm-ph↗
arXiv2017

Lecture video indexing using boosted margin maximizing neural networks

This paper presents a novel approach for lecture video indexing using a boosted deep convolutional neural network system. The indexing is performed by matching high quality slide images, for which text is either known or extracted, to lower resolution video frames with possible noise, perspective distortion, and occlusions. We propose a deep neural network integrated with a boosting framework composed of two sub-networks targeting feature extraction and similarity determination to perform the matching. The trained network is given as input a pair of slide image and a candidate video frame image and produces the similarity between them. A boosting framework is integrated into our proposed network during the training process. Experimental results show that the proposed approach is much more capable of handling occlusion, spatial transformations, and other types of noises when compared with known approaches.

cs.CV↗
arXiv2017

HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval

As the rapid growth of multi-modal data, hashing methods for cross-modal retrieval have received considerable attention. Deep-networks-based cross-modal hashing methods are appealing as they can integrate feature learning and hash coding into end-to-end trainable frameworks. However, it is still challenging to find content similarities between different modalities of data due to the heterogeneity gap. To further address this problem, we propose an adversarial hashing network with attention mechanism to enhance the measurement of content similarities by selectively focusing on informative parts of multi-modal data. The proposed new adversarial network, HashGAN, consists of three building blocks: 1) the feature learning module to obtain feature representations, 2) the generative attention module to generate an attention mask, which is used to obtain the attended (foreground) and the unattended (background) feature representations, 3) the discriminative hash coding module to learn hash functions that preserve the similarities between different modalities. In our framework, the generative module and the discriminative module are trained in an adversarial way: the generator is learned to make the discriminator cannot preserve the similarities of multi-modal data w.r.t. the background feature representations, while the discriminator aims to preserve the similarities of multi-modal data w.r.t. both the foreground and the background feature representations. Extensive evaluations on several benchmark datasets demonstrate that the proposed HashGAN brings substantial improvements over other state-of-the-art cross-modal hashing methods.

cs.CV↗
arXiv2017

Optimizing Joint Data and Power Transfer in Energy Harvesting Multiuser Wireless Networks

Energy harvesting emerges as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we focus on Radio Frequency (RF) energy harvesting for multiuser multicarrier mobile wireless networks. Specifically, we propose joint data and energy transfer optimization frameworks for powering mobile wireless devices through RF energy harvesting. We introduce a power utility that captures the power consumption cost at the base station (BS) and the used power from the users' batteries, and determine optimal power resource allocations that meet data rate requirements of downlink and uplink communications. Two types of harvesting capabilities are considered at each user: harvesting only from dedicated RF signals and hybrid harvesting from both dedicated and ambient RF signals. The developed frameworks increase the end users' battery lifetime at the cost of a slight increase in the BS power consumption. Several evaluation studies are conducted in order to validate our proposed frameworks.

cs.NI↗
arXiv2017

A New Measurement of the Spectral Lag of Gamma-Ray Bursts and its Implications for Spectral Evolution Behaviors

We carry out a systematical study of the spectral lag properties of 50 single-pulsed Gamma-Ray Bursts (GRBs) detected by Fermi/GBM. By dividing the light curves into multiple consecutive energy channels we provide a new measurement of the spectral lag which is independent on energy channel selections. We perform a detailed statistical study of our new measurements. We find two similar power-law energy dependencies of both the pulse arrival time and pulse width. Our new results on the power-law indices would favor the relativistic geometric effects for the origin of spectral lag. However, a complete theoretical framework that can fully account for the diverse energy dependencies of both arrival time and pulse width revealed in this work is still missing. We also study the spectral evolution behaviors of the GRB pulses. We find that the GRB pulse with negligible spectral lag would usually have a shorter pulse duration and would appear to have a "hardness-intensity tracking" (HIT) behavior and the GRB pulse with a significant spectral lag would usually have a longer pulse duration and would appear to have a "hard-to-soft" (HTS) behavior.

astro-ph.HE↗
arXiv2017

Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)

In November 2016 we submitted to arXiv our paper "Automated Inference on Criminality Using Face Images". It generated a great deal of discussions in the Internet and some media outlets. Our work is only intended for pure academic discussions; how it has become a media consumption is a total surprise to us. Although in agreement with our critics on the need and importance of policing AI research for the general good of the society, we are deeply baffled by the ways some of them mispresented our work, in particular the motive and objective of our research.

cs.CV↗
arXiv2017

Cusp Kähler-Ricci flow on compact Kähler manifold

In this paper, by limiting twisted conical Kähler-Ricci flows, we prove the long-time existence and uniqueness of cusp Kähler-Ricci flow on compact Kähler manifold $M$ which carries a smooth hypersurface $D$ such that the twisted canonical bundle $K_M+D$ is ample. Furthermore, we prove that this flow converge to a unique cusp Kähler-Einstein metric.

math.DG↗
arXiv2017

The limit of the Hermitian-Yang-Mills flow on reflexive sheaves

In this paper, we study the asymptotic behavior of the Hermitian-Yang-Mills flow on a reflexive sheaf. We prove that the limiting reflexive sheaf is isomorphic to the double dual of the graded sheaf associated to the Harder-Narasimhan-Seshadri filtration, this answers a question by Bando and Siu.

math.DG↗
arXiv2017

Effects of Bulk Composition on The Atmospheric Dynamics on Close-in Exoplanets

Super Earths and mini Neptunes likely have a wide range of atmospheric compositions, ranging from low-molecular mass atmospheres of H2 to higher molecular atmospheres of water, CO2, N2, or other species. Here, we systematically investigate the effects of atmospheric bulk compositions on temperature and wind distributions for tidally locked sub-Jupiter-sized planets, using an idealized 3D general circulation model (GCM). The bulk composition effects are characterized in the framework of two independent variables: molecular weight and molar heat capacity. The effect of molecular weight dominates. As the molecular weight increases, the atmosphere tends to have a larger day-night temperature contrast, a smaller eastward phase shift in the thermal phase curve and a smaller zonal wind speed. The width of the equatorial super-rotating jet also becomes narrower and the "jet core" region, where the zonal-mean jet speed maximizes, moves to a greater pressure level. The zonal-mean zonal wind is more prone to exhibit a latitudinally alternating pattern in a higher-molecular-weight atmosphere. We also present analytical theories that quantitatively explain the above trends and shed light on the underlying dynamical mechanisms. Those trends might be used to indirectly determine the atmospheric compositions on tidally locked sub-Jupiter-sized planets. The effects of the molar heat capacity are generally small. But if the vertical temperature profile is close to adiabatic, molar heat capacity will play a significant role in controlling the transition from a divergent flow in the upper atmosphere to a jet-dominated flow in the lower atmosphere.

astro-ph.EP↗