arXiv subjects

Chi Wang

Publications and source records attributed to Chi Wang.

At least 145 records · Page 8Linked to original sources

arXiv2019

Towards Extracting Highlights From Recorded Live Videos: An Implicit Crowdsourcing Approach

Live streaming platforms need to store a lot of recorded live videos on a daily basis. An important problem is how to automatically extract highlights (i.e., attractive short video clips) from these massive, long recorded live videos. One approach is to directly apply a highlight extraction algorithm to video content. However, algorithmic approaches are either domain-specific, which require experts to spend a long time to design, or resource-intensive, which require a lot of training data and/or computing resources. In this paper, we propose Lightor, a novel implicit crowdsourcing approach to overcome these limitations. The key insight is to collect users' natural interactions with a live streaming platform, and then leverage them to detect highlights. Lightor consists of two major components. Highlight Initializer collects time-stamped chat messages from a live video and then uses them to predict approximate highlight positions. Highlight Extractor keeps track of how users interact with these approximate highlight positions and then refines these positions iteratively. We find that the collected user chat and interaction data are very noisy, and propose effective techniques to deal with noise. Lightor can be easily deployed into existing live streaming platforms, or be implemented as a web browser extension. We recruit hundreds of users from Amazon Mechanical Turk, and evaluate the performance of Lightor using two popular games in Twitch. The results show that Lightor can achieve high extraction precision with a small set of training data and low computing resources.

cs.DB↗

arXiv2019

Machine Learning Approach for Solar Wind Categorization

Solar wind classification is conducive to understand the physical processes ongoing at the Sun and solar wind evolution in the interplanetary space, and furthermore, it is helpful for early warning of space weather events. With rapid developments in the field of artificial intelligence, machine learning approaches are increasingly being used for pattern recognition. In this study, an approach from machine learning perspectives is developed to automatically classify the solar wind at 1 AU into four types: coronal-hole-origin, streamer-belt-origin, sector-reversal-region-origin, and ejecta. By exhaustive enumeration, an eight-dimensional scheme ($B_T$, $N_P$, $T_P$, $V_P$, $N_{αp}$, $T_{exp}/T_P$, $S_p$, and $M_f$) is found to perform the best among 8191 combinations of 13 solar wind parameters. 10 popular supervised machine learning models, namely $k$ Nearest Neighbors (KNN), Support Vector Machines with linear and Radial Basic Function kernels, Decision Tree, Random Forest, Adaptive Boosting, Neural Network, Gaussian Naive Bayes, Quadratic Discriminant Analysis, and Extreme Gradient Boosting, are applied to the labeled solar wind data sets. Among them, KNN classifier obtains the highest overall classification accuracy, 92.8%. It significantly improves the accuracy by 9.6% over existing manual schemes. No solar wind composition measurements are needed, permitting our classification scheme to be applied to most solar wind spacecraft data. Besides, two application examples indicate that solar wind classification is helpful for the risk evaluation of predicted magnetic storms and surface charging of geosynchronous spacecrafts.

physics.space-ph↗

arXiv2019

Hour-scale persistent negative anomaly of atmospheric electrostatic field near the epicenter before earthquake

Although earthquake prediction is a big challenge in the world, some simple observational tools can capture many physical signals and demonstrate that an earthquake (EQ) may be forthcoming in short period. Many researchers have studied the significant variation of atmospheric electrostatic field related to the forthcoming earthquake. However, until now, there is not a compelling physical mechanism which can explain why atmospheric electrostatic abnormal signal could appear just before an earthquake. Here we present a precursor signal and propose a brief physical interpretation. Under fair air conditions, if the near-surface atmospheric electrostatic field Ez (oriented down when it is positive) presents a very stable negative anomaly (from -100 V/m to -5000 V/m), it will forebode that an earthquake (seismicity from 3-8) would take place in the next several to tens of hours within a distance less than 100 km. We name this prediction technique as "DC Ez Determination"(DED). In addition, the mechanism of such abnormal quasi-static electric field before a forthcoming earthquake has been proposed here: (1) Radon gas releases from the rock clefts near the epicenter during seismogenic process. (2) α particles are produced due to the radioactive decay of Radon gas in the air. (3) α particle ionizing radiation creates more positive particles in the air, which is much more effective than that β and γ particles produced during Radon radioactive decay. (4) The new positive particles change formal positive atmospheric electric field (Ez) into stably negative near the earth-surface. (5) The closer the instrument is to the epicenter, the more likely it is to observe the negative Ez signal related to the earthquake. It is recommended to establish an instrument network to capture the reliable precursor and make some warnings before the earthquake disaster.

physics.geo-ph↗

arXiv2019

NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization

We study the problem of large-scale network embedding, which aims to learn latent representations for network mining applications. Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization of such matrix generates more powerful embeddings than existing methods. However, directly constructing and factorizing this matrix---which is dense---is prohibitively expensive in terms of both time and space, making it not scalable for large networks. In this work, we present the algorithm of large-scale network embedding as sparse matrix factorization (NetSMF). NetSMF leverages theories from spectral sparsification to efficiently sparsify the aforementioned dense matrix, enabling significantly improved efficiency in embedding learning. The sparsified matrix is spectrally close to the original dense one with a theoretically bounded approximation error, which helps maintain the representation power of the learned embeddings. We conduct experiments on networks of various scales and types. Results show that among both popular benchmarks and factorization based methods, NetSMF is the only method that achieves both high efficiency and effectiveness. We show that NetSMF requires only 24 hours to generate effective embeddings for a large-scale academic collaboration network with tens of millions of nodes, while it would cost DeepWalk months and is computationally infeasible for the dense matrix factorization solution. The source code of NetSMF is publicly available (https://github.com/xptree/NetSMF).

cs.SI↗

arXiv2018

ABC: Efficient Selection of Machine Learning Configuration on Large Dataset

A machine learning configuration refers to a combination of preprocessor, learner, and hyperparameters. Given a set of configurations and a large dataset randomly split into training and testing set, we study how to efficiently select the best configuration with approximately the highest testing accuracy when trained from the training set. To guarantee small accuracy loss, we develop a solution using confidence interval (CI)-based progressive sampling and pruning strategy. Compared to using full data to find the exact best configuration, our solution achieves more than two orders of magnitude speedup, while the returned top configuration has identical or close test accuracy.

cs.LG↗

arXiv2017

Modulation of Solar Wind Energy Flux Input on Global Tropical Cyclone Activity

Studies on Sun-climate connection have been carried out for several decades, and almost all of them focused on the effects of solar total irradiation energy. As the second major terrestrial energy source from outer space, the solar wind energy flux exhibits more significant long-term variations. However, its link to the global climate change is rarely concerned and remain a mystery. As a fundamental and important aspect of the Earth's weather and climate system, tropical cyclone activity has been causing more and more attentions. Here we investigate the possible modulation of the total energy flux input from the solar wind into the Earth's magnetosphere on the global tropical cyclone activity during 1963--2012. From a global perspective, the accumulated cyclone energy increases gradually since 1963 and start to decrease after 1994. Compare to the previously frequently used parameters, e,g., the sunspot number, the total solar irradiation, the solar F10.7 irradiation, the tropical sea surface temperature, and the south oscillation index, the total solar wind energy flux input exhibits a better correlation with the global tropical cyclone activity. Furthermore, the tropical cyclones seem to be more intense with higher geomagnetic activities. A plausible modulation mechanism is thus proposed to link the terrestrial weather phenomenon to the seemly-unrelated solar wind energy input.

physics.space-ph↗

arXiv2017

Evolution of Alfvénic fluctuations inside an interplanetary coronal mass ejection and their contributions to local plasma heating: Joint observations from 1.0 AU to 5.4 AU

Directly tracking an interplanetary coronal mass ejection (ICME) by widely separated spacecrafts is a great challenge. However, such an event could provide us a good opportunity to study the evolution of embedded Alfvénic fluctuations (AFs) inside ICME and their contributions to local plasma heating directly. In this study, an ICME observed by Wind at 1.0 au on March 4-6 1998 is tracked to the location of Ulysess at 5.4 au. AFs are commonly found inside the ICME at 1.0 au, with an occurrence rate of 21.7% and at broadband frequencies from 4$\times 10^{-4}$ to 5$\times 10^{-2}$ Hz. When the ICME propagates to 5.4 au, the Aflvénicity decreases significantly, and AFs are rare and only found at few localized frequencies with the occurrence rate decreasing to 3.0%. At the same time, the magnetic field intensity at the AF-rich region has an extra magnetic dissipation except ICME expansion effect. The energetics of the ICME at different radial distance is also investigated here. Under similar magnetic field intensity situations at 1.0 au, the turbulence cascade rate at the AF-rich region is much larger than the value at the AF-lack region. Moreover, it can maintain as the decrease of magnetic field intensity if there is lack of AFs. However, when there exists many AFs, it reduces significantly as the AFs disappear. The turbulence cascade dissipation rate within the ICME is inferred to be 1622.3 $J\cdot kg^{-1}\cdot s^{-1}$, which satisfies the requirement of local ICME plasma heating rate, 1653.2 $J\cdot kg^{-1}\cdot s^{-1}$. We suggest that AF dissipation is responsible for extra magnetic dissipation and local plasma heating inside ICME.

physics.space-ph↗

arXiv2017

Offline software for the DAMPE experiment

A software system has been developed for the DArk Matter Particle Explorer (DAMPE) mission, a satellite-based experiment. The DAMPE software is mainly written in C++ and steered using Python script. This article presents an overview of the DAMPE offline software, including the major architecture design and specific implementation for simulation, calibration and reconstruction. The whole system has been successfully applied to DAMPE data analysis, based on which some results from simulation and beam test experiments are obtained and presented.

astro-ph.IM↗

arXiv2016

Plasma heating inside ICMEs by Alfvenic fluctuations dissipation

Nonlinear cascade of low-frequency Alfvenic fluctuations (AFs) is regarded as one candidate of the energy sources to heat plasma during the non-adiabatic expansion of interplanetary coronal mass ejections (ICMEs). However, AFs inside ICMEs were seldom reported in the literature. In this study, we investigate AFs inside ICMEs using observations from Voyager 2 between 1 and 6 au. It is found that AFs with high degree of Alfvenicity frequently occurred inside ICMEs, for almost all the identified ICMEs (30 out of 33 ICMEs), and 12.6% of ICME time interval. As ICMEs expand and move outward, the percentage of AF duration decays linearly in general. The occurrence rate of AFs inside ICMEs is much less than that in ambient solar wind, especially within 4 au. AFs inside ICMEs are more frequently presented in the center and at the boundaries of ICMEs. In addition, the proton temperature inside ICME has a similar distribution. These findings suggest significant contribution of AFs on local plasma heating inside ICMEs.

physics.space-ph↗

arXiv2016

Properties of post-shock solar wind deduced from geomagnetic indices responses after sudden impulses

Interplanetary (IP) shock plays a key role in causing the global dynamic changes of the geospace environment. For the perspective of Solar-Terrestrial relationship, it will be of great importance to estimate the properties of post-shock solar wind simply and accurately. Motivated by this, we performed a statistical analysis of IP shocks during 1998-2008, focusing on the significantly different responses of two well-used geomagnetic indices (SYMH and AL) to the passive of two types of IP shocks. For the IP shocks with northward IMF (91 cases), the SYMH index keeps on the high level after the sudden impulses (SI) for a long time. Meanwhile, the change of AL index is relative small, with an mean value of only -29 nT. However, for the IP shocks with southward IMF (92 cases), the SYMH index suddenly decreases at a certain rate after SI, and the change of AL index is much significant, of -316 nT. Furthermore, the change rate of SYMH index after SI is found to be linearly correlated with the post-shock reconnection E-field (E$_{KL}$). Based on these facts, an inversion model of post-shock IMF orientation and E$_{KL}$ is developed. The model validity is also confirmed by studying 68 IP shocks in the period of 2009-2013. The inversion accuracy of IMF orientation is 88.24%, and the inversion efficiency of E$_{KL}$ is as high as 78%.

physics.space-ph↗

arXiv2016

Weighted SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

Introduction It has been demonstrated that a pathway-based feature selection method which incorporates biological information within pathways into the process of feature selection usually outperform a gene-based feature selection algorithm in terms of predictive accuracy, stability, and biological interpretation. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method. Results and Discussion In SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to which this gene belongs, but ignoring the topology information among pathways. In this study, we propose a weighted version of the SAMGSR algorithm by constructing weights based on the connectivity among genes and then incorporating these weights in the test statistic. Conclusions Using both simulated and real-world data, we evaluate the performance of the proposed SAMGSR extension and demonstrate that gene connectivity is indeed informative for feature selection.

stat.ME↗

arXiv2016

Temperature Dependence Calibration and Correction of the DAMPE BGO Electromagnetic Calorimeter

A BGO electromagnetic calorimeter (ECAL) is built for the DArk Matter Particle Explorer (DAMPE) mission. The effect of temperature on the BGO ECAL was investigated with a thermal vacuum experiment. The light output of a BGO crystal depends on temperature significantly. The temperature coefficient of each BGO crystal bar has been calibrated, and a correction method is also presented in this paper.

physics.ins-det↗

arXiv2016

The calibration and electron energy reconstruction of the BGO ECAL of the DAMPE detector

The DArk Matter Particle Explorer (DAMPE) is a space experiment designed to search for dark matter indirectly by measuring the spectra of photons, electrons, and positrons up to 10 TeV. The BGO electromagnetic calorimeter (ECAL) is its main sub-detector for energy measurement. In this paper, the instrumentation and development of the BGO ECAL is briefly described. The calibration on the ground, including the pedestal, minimum ionizing particle (MIP) peak, dynode ratio, and attenuation length with the cosmic rays and beam particles is discussed in detail. Also, the energy reconstruction results of the electrons from the beam test are presented.

physics.ins-det↗

arXiv2015

On Sun-to-Earth Propagation of Coronal Mass Ejections: 2. Slow Events and Comparison with Others

As a follow-up study on Sun-to-Earth propagation of fast coronal mass ejections (CMEs), we examine the Sun-to-Earth characteristics of slow CMEs combining heliospheric imaging and in situ observations. Three events of particular interest, the 2010 June 16, 2011 March 25 and 2012 September 25 CMEs, are selected for this study. We compare slow CMEs with fast and intermediate-speed events, and obtain key results complementing the attempt of \citet{liu13} to create a general picture of CME Sun-to-Earth propagation: (1) the Sun-to-Earth propagation of a typical slow CME can be approximately described by two phases, a gradual acceleration out to about 20-30 solar radii, followed by a nearly invariant speed around the average solar wind level, (2) comparison between different types of CMEs indicates that faster CMEs tend to accelerate and decelerate more rapidly and have shorter cessation distances for the acceleration and deceleration, (3) both intermediate-speed and slow CMEs would have a speed comparable to the average solar wind level before reaching 1 AU, (4) slow CMEs have a high potential to interact with other solar wind structures in the Sun-Earth space due to their slow motion, providing critical ingredients to enhance space weather, and (5) the slow CMEs studied here lack strong magnetic fields at the Earth but tend to preserve a flux-rope structure with axis generally perpendicular to the radial direction from the Sun. We also suggest a "best" strategy for the application of a triangulation concept in determining CME Sun-to-Earth kinematics, which helps to clarify confusions about CME geometry assumptions in the triangulation and to improve CME analysis and observations.

astro-ph.SR↗

arXiv2015

Feature selection for longitudinal microarray data by adapting a pathway analysis method

Introduction: Feature selection and gene set analysis are of increasing interest in bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be used to conduct feature selection. Here we adapt the gene set analysis method, significance analysis of microarray gene set reduction (SAMGSR), for feature selection, and propose two extensions-simple SAMGSR and two-level SAMGSR to identify relevant features for longitudinal microarray data. Results and Discussion: When applied to a real-world application, both simple and two-level SAMGSR work comparably well. Using simulated data, we further demonstrate that both SAMGSR extensions have the ability to identify the true relevant genes. If the relevant genes are not highly correlated with the irrelevant ones, the final models given by the two SAMGSR extensions are parsimonious as well. Conclusions: By adapting SAMGSR for feature selection and applying the proposed algorithms on a longitudinal gene expression dataset, we demonstrate that a gene set analysis method can be used for the purpose of feature selection. We believe this work paves the way for more research to bridge feature selection and gene set analysis with the development of novel algorithms.

stat.ME↗

arXiv2015

A study of energy correction for the electron beam data in the BGO ECAL of the DAMPE

The DArk Matter Particle Explorer (DAMPE) is an orbital experiment aiming at searching for dark matter indirectly by measuring the spectra of photons, electrons and positrons originating from deep space. The BGO electromagnetic calorimeter is one of the key sub-detectors of the DAMPE, which is designed for high energy measurement with a large dynamic range from 5 GeV to 10 TeV. In this paper, some methods for energy correction are discussed and tried, in order to reconstruct the primary energy of the incident electrons. Different methods are chosen for the appropriate energy ranges. The results of Geant4 simulation and beam test data (at CERN) are presented.

physics.ins-det↗

arXiv2015

No evidence of histology subtype-specific prognostic signatures among lung adenocarcinoma and squamous cell carcinoma patients at early stages

Background Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. Among NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes. It has been demonstrated that there exist fundamental differences in the underlying mechanisms between them, which motivated us to postulate there might exist specific genes relevant to prognosis of each histology subtype. Results In this article, we propose a simple filterer feature selection algorithm with a Cox regression model as the base. Applying this method to a real-world microarray data, no evidence has been found to support the existence of histology-specific prognostic gene signature. Nevertheless, a 31-gene prognostic gene signature for the early-stage AC and SCC samples is obtained, which provides comparable performance when compared with other relevant signatures. Conclusions Our proposal is conceptually simple and straightforward to implement. Therefore, it is expected that other researchers, especially those with less statistical knowledge and experience, can adapt this method readily to test their own research hypotheses.

stat.AP↗

arXiv2015

In situ Evidence of Breaking the Ion Frozen-in Condition via the Non-gyrotropic Pressure Effect in Magnetic Reconnection

For magnetic reconnection to proceed, the frozen-in condition for both ion fluid and electron fluid in a localized diffusion region must be violated by inertial effects, thermal pressure effects, or inter-species collisions. It has been unclear which underlying effects unfreeze ion fluid in the diffusion region. By analyzing in-situ THEMIS spacecraft measurements at the dayside magnetopause, we present clear evidence that the off-diagonal components of the ion pressure tensor is mainly responsible for breaking the ion frozen-in condition in reconnection. The off-diagonal pressure tensor, which corresponds to a nongyrotropic pressure effect, is a fluid manifestation of ion demagnetization in the diffusion region. From the perspective of the ion momentum equation, the reported non-gyrotropic ion pressure tensor is a fundamental aspect in specifying the reconnection electric field that controls how quickly reconnection proceeds.

physics.plasm-ph↗