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Liang Zhao

Publications and source records attributed to Liang Zhao.

At least 487 records · Page 27Linked to original sources
arXiv2016

A Statistical Study of the Average Iron Charge Distributions inside Magnetic Clouds for Solar Cycle 23

Magnetic clouds (MCs) are the interplanetary counterpart of coronal magnetic flux ropes. They can provide valuable information to reveal the flux rope characteristics at their eruption stage in the corona, which are unable to be explored in situ at present. In this paper, we make a comprehensive survey of the average iron charge state ( Fe) distributions inside 96 MCs for solar cycle 23 using ACE (Advanced Composition Explorer) data. As the Fe in the solar wind are typically around 9+ to 11+, the Fe charge state is defined as high when the Fe is larger than 12+, which implies the existence of a considerable amount of Fe ions with high charge states (e.g., \geq 16+). The statistical results show that the Fe distributions of 92 (~ 96%) MCs can be classified into four groups with different characteristics. In group A (11 MCs), the Fe shows a bimodal distribution with both peaks higher than 12+. Group B (4 MCs) presents a unimodal distribution of Fe with its peak higher than 12+. In groups C (29 MCs) and D (48 MCs), the Fe remains higher and lower than 12+ throughout ACE passage through the MC, respectively. Possible explanations to these distributions are discussed.

astro-ph.SR↗
arXiv2016

EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System

EMBERS is an anticipatory intelligence system forecasting population-level events in multiple countries of Latin America. A deployed system from 2012, EMBERS has been generating alerts 24x7 by ingesting a broad range of data sources including news, blogs, tweets, machine coded events, currency rates, and food prices. In this paper, we describe our experiences operating EMBERS continuously for nearly 4 years, with specific attention to the discoveries it has enabled, correct as well as missed forecasts, and lessons learnt from participating in a forecasting tournament including our perspectives on the limits of forecasting and ethical considerations.

cs.CY↗
arXiv2016

New Studies of Randomized Augmentation and Additive Preprocessing

1. A standard Gaussian random matrix has full rank with probability 1 and is well-conditioned with a probability quite close to 1 and converging to 1 fast as the matrix deviates from square shape and becomes more rectangular. 2. If we append sufficiently many standard Gaussian random rows or columns to any normalized matrix A, then the augmented matrix has full rank with probability 1 and is well-conditioned with a probability close to 1, even if the matrix A is rank deficient or ill-conditioned. 3. We specify and prove these properties of augmentation and extend them to additive preprocessing, that is, to adding a product of two rectangular Gaussian matrices. 4. By applying our randomization techniques to a matrix that has numerical rank r, we accelerate the known algorithms for the approximation of its leading and trailing singular spaces associated with its r largest and with all its remaining singular values, respectively. 5. Our algorithms use much fewer random parameters and run much faster when various random sparse and structured preprocessors replace Gaussian. Empirically the outputs of the resulting algorithms is as accurate as the outputs under Gaussian preprocessing. 6. Our novel duality techniques provides formal support, so far missing, for these empirical observations and opens door to derandomization of our preprocessing and to further acceleration and simplification of our algorithms by using more efficient sparse and structured preprocessors. 7. Our techniques and our progress can be applied to various other fundamental matrix computations such as the celebrated low-rank approximation of a matrix by means of random sampling.

math.NA↗
arXiv2015

Time Series Clustering via Community Detection in Networks

In this paper, we propose a technique for time series clustering using community detection in complex networks. Firstly, we present a method to transform a set of time series into a network using different distance functions, where each time series is represented by a vertex and the most similar ones are connected. Then, we apply community detection algorithms to identify groups of strongly connected vertices (called a community) and, consequently, identify time series clusters. Still in this paper, we make a comprehensive analysis on the influence of various combinations of time series distance functions, network generation methods and community detection techniques on clustering results. Experimental study shows that the proposed network-based approach achieves better results than various classic or up-to-date clustering techniques under consideration. Statistical tests confirm that the proposed method outperforms some classic clustering algorithms, such as $k$-medoids, diana, median-linkage and centroid-linkage in various data sets. Interestingly, the proposed method can effectively detect shape patterns presented in time series due to the topological structure of the underlying network constructed in the clustering process. At the same time, other techniques fail to identify such patterns. Moreover, the proposed method is robust enough to group time series presenting similar pattern but with time shifts and/or amplitude variations. In summary, the main point of the proposed method is the transformation of time series from time-space domain to topological domain. Therefore, we hope that our approach contributes not only for time series clustering, but also for general time series analysis tasks.

stat.ML↗
arXiv2015

Modeling of Breaching Due to Overtopping Flow and Waves Based on Coupled Flow and Sediment Transport

Breaching of earthen or sandy dams/dunes by overtopping flow and waves is a complicated process with strong, unsteady flow, high sediment transport, and rapid bed changes in which the interactions between flow and morphology should not be ignored. This study presents a depth-averaged two-dimensional (2D) coupled flow and sediment transport model to investigate the flow and breaching processes with and without waves. Bed change and variable flow density are included in the flow continuity and momentum equations to consider the impacts of sediment transport. The model adopts the non-equilibrium approach for total-load sediment transport and specifies different repose angles to handle non-cohesive embankment slope avalanching. The equations are solved using an explicit finite volume method on a rectangular grid with the improved Godunov-type central upwind scheme and the nonnegative reconstruction of the water depth method to handle mixed-regime flows near the breach. The model has been tested against two sets of experimental data which show that it well simulates the flow characteristics, bed changes, and sediment transport. It is then applied to analyze flow and morphologic changes by overtopping flow with and without waves. The simulated bed change and breach cross-section shape show a significant difference if waves are considered. Erosion by flow without waves mainly occurs at the breach and is dominated by vertical erosion at the initial stage followed by the lateral erosion. With waves, the flow overtops the entire length of the dune to cause faster erosion along the entire length. Erosion mainly takes place at the upper layer at the initial stage and gradually accelerates as the height of the dune reduces and flow discharge increases, which indicates the simulated results with waves shall be further verified by physical experimental evidence.

physics.flu-dyn↗
arXiv2015

Strongly enhanced charge-density-wave order in monolayer NbSe$_2$

Two-dimensional (2D) atomic materials possess very different properties from their bulk counterparts. While changes in the single-particle electronic properties have been extensively investigated, modifications in the many-body collective phenomena in the exact 2D limit, where interaction effects are strongly enhanced, remain mysterious. Here we report a combined optical and electrical transport study on the many-body collective-order phase diagram of 2D NbSe$_2$. Both the charge density wave (CDW) and the superconducting phase have been observed down to the monolayer limit. While the superconducting transition temperature ($T_C$) decreases with lowering the layer thickness, the newly observed CDW transition temperature ($T_{\mathrm{CDW}}$) increases drastically from 33 K in the bulk to 145 K in the monolayers. Such highly unusual enhancement of CDWs in atomically thin samples can be understood as a result of significantly enhanced electron-phonon interactions in 2D NbSe$_2$, which cause a crossover from the weak coupling to the strong coupling limit. This is supported by the large blueshift of the collective amplitude vibrations observed in our experiment.

cond-mat.mtrl-sci↗
arXiv2015

Existence of solutions for a higher order Kirchhoff type problem with exponential critical growth

The higher order Kirchhoff type equation $$\int_{\mathbb{R}^{2m}}(|\nabla^m u|^2 +\sum_{γ=0}^{m-1}a_γ(x)|\nabla^γu|^2)dx \left((-Δ)^m u+\sum_{γ=0}^{m-1}(-1)^γ\nabla^γ\cdot(a_γ(x)\nabla^γu)\right) =\frac{f(x,u)}{|x|^β}+εh(x)\ \ \text{in}\ \ \mathbb{R}^{2m}$$ is considered in this paper. We assume that the nonlinearity of the equation has exponential critical growth and prove that, for a positive $ε$ which is small enough, there are two distinct nontrivial solutions to the equation. When $ε=0$, we also prove that the equation has a nontrivial mountain-pass type solution.

math.AP↗
arXiv2015

Polynomial Root Isolation by Means of Root Radii Approximation

Univariate polynomial root-finding is a classical subject, still important for modern computing. Frequently one seeks just the real roots of a real coefficient polynomial. They can be approximated at a low computational cost if the polynomial has no nonreal roots, but for high degree polynomials, nonreal roots are typically much more numerous than the real ones. The challenge is known for long time, and the subject has been intensively studied. The Boolean cost bounds for the refinement of the simple and isolated real roots have been decreased to nearly optimal, but the success has been more limited at the stage of the isolation of real roots. We obtain substantial progress by applying the algorithm of of 1982 by Schoenhage for the approximation of the root radii, that is, the distances of the roots to the origin. Namely we isolate the simple and well-conditioned real roots of a polynomial at the Boolean cost dominated by the nearly optimal bounds for the refinement of such roots. We also extend our algorithm to the isolation of complex, possibly multiple, roots and root clusters staying within the same (nearly optimal) asymptotic Boolean cost bound. Our numerical tests with benchmark polynomials performed with the IEEE standard double precision show that our nearly optimal real root-finder is practically promising. Our techniques are simple, and their power and application range may increase in combination with the known efficient methods.

math.NA↗
arXiv2015

Mortality cohort effect detection and measurement based on differential geometry

This paper analyzes mortality cohort effect of birth year and develops an approach to identify and measure cohort effects in mortality data set. The approach is based on differential geometry and leads to an explicit result which can describe how strong the cohort effect is in any period. This quantitative measurement provides a possibility to compare cohort effects among different countries or groups. The paper also suggests to use coefficient of variation as a measurement of the whole cohort effect for one country. Data of several countries are taken as examples to explain our approach and results.

stat.AP↗
arXiv2014

A feasible roadmap for developing volumetric probability atlas of localized prostate cancer

A statistical volumetric model, showing the probability map of localized prostate cancer within the host anatomical structure, has been developed from 90 optically-imaged surgical specimens. This master model permits an accurate characterization of prostate cancer distribution patterns and an atlas-informed biopsy sampling strategy. The model is constructed by mapping individual prostate models onto a site model, together with localized tumors. An accurate multi-object non-rigid warping scheme is developed based on a mixture of principal-axis registrations. We report our evaluation and pilot studies on the effectiveness of the method and its application to optimizing needle biopsy strategies.

q-bio.QM↗
arXiv2014

Pattern Recognition in a Ring of Delayed Phase Oscillators

We show that a ring of phase oscillators coupled with transmission delays can be used as a pattern recognition system. The introduced model encodes patterns as stable periodic orbits. We present a detailed analysis of the underlying dynamics. In particular, we show that the system possesses a multitude of periodic solutions, prove stability results and present a bifurcation analysis. Furthermore, we show successful recognition results using artificial patterns and speech recordings.

math.DS↗
arXiv2014

Tightly bound excitons in monolayer WSe2

Exciton binding energy and excited states in monolayers of tungsten diselenide (WSe2) are investigated using the combined linear absorption and two-photon photoluminescence excitation spectroscopy. The exciton binding energy is determined to be 0.37eV, which is about an order of magnitude larger than that in III-V semiconductor quantum wells and renders the exciton excited states observable even at room temperature. The exciton excitation spectrum with both experimentally determined one- and two-photon active states is distinct from the simple two-dimensional (2D) hydrogenic model. This result reveals significantly reduced and nonlocal dielectric screening of Coulomb interactions in 2D semiconductors. The observed large exciton binding energy will also have a significant impact on next-generation photonics and optoelectronics applications based on 2D atomic crystals.

cond-mat.mtrl-sci↗
arXiv2014

Spatial Neural Networks and their Functional Samples: Similarities and Differences

Models of neural networks have proven their utility in the development of learning algorithms in computer science and in the theoretical study of brain dynamics in computational neuroscience. We propose in this paper a spatial neural network model to analyze the important class of functional networks, which are commonly employed in computational studies of clinical brain imaging time series. We developed a simulation framework inspired by multichannel brain surface recordings (more specifically, EEG -- electroencephalogram) in order to link the mesoscopic network dynamics (represented by sampled functional networks) and the microscopic network structure (represented by an integrate-and-fire neural network located in a 3D space -- hence the term spatial neural network). Functional networks are obtained by computing pairwise correlations between time-series of mesoscopic electric potential dynamics, which allows the construction of a graph where each node represents one time-series. The spatial neural network model is central in this study in the sense that it allowed us to characterize sampled functional networks in terms of what features they are able to reproduce from the underlying spatial network. Our modeling approach shows that, in specific conditions of sample size and edge density, it is possible to precisely estimate several network measurements of spatial networks by just observing functional samples.

cs.NE↗
arXiv2014

'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators

We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.

cs.SI↗
arXiv2013

High Level Pattern Classification via Tourist Walks in Networks

Complex networks refer to large-scale graphs with nontrivial connection patterns. The salient and interesting features that the complex network study offer in comparison to graph theory are the emphasis on the dynamical properties of the networks and the ability of inherently uncovering pattern formation of the vertices. In this paper, we present a hybrid data classification technique combining a low level and a high level classifier. The low level term can be equipped with any traditional classification techniques, which realize the classification task considering only physical features (e.g., geometrical or statistical features) of the input data. On the other hand, the high level term has the ability of detecting data patterns with semantic meanings. In this way, the classification is realized by means of the extraction of the underlying network's features constructed from the input data. As a result, the high level classification process measures the compliance of the test instances with the pattern formation of the training data. Out of various high level perspectives that can be utilized to capture semantic meaning, we utilize the dynamical features that are generated from a tourist walker in a networked environment. Specifically, a weighted combination of transient and cycle lengths generated by the tourist walk is employed for that end. Interestingly, our study shows that the proposed technique is able to further improve the already optimized performance of traditional classification techniques.

cs.AI↗
arXiv2011

On the Inclusion Relation of Reproducing Kernel Hilbert Spaces

To help understand various reproducing kernels used in applied sciences, we investigate the inclusion relation of two reproducing kernel Hilbert spaces. Characterizations in terms of feature maps of the corresponding reproducing kernels are established. A full table of inclusion relations among widely-used translation invariant kernels is given. Concrete examples for Hilbert-Schmidt kernels are presented as well. We also discuss the preservation of such a relation under various operations of reproducing kernels. Finally, we briefly discuss the special inclusion with a norm equivalence.

math.FA↗
arXiv2010

Comparison of Two Solar Minima: Narrower Streamer Stalk Region and Conserved Open Magnetic Flux in the Region Outside of Streamer Stalk

To explore the difference between the most two recent solar minima, we analyze the in-situ ACE and ULYSSES observations and examine the distributions of the three types of solar wind (streamer-stalk-associated wind, wind from outside the streamer stalk that can be associated, in part, with coronal holes, and interplanetary coronal mass ejections). We use the taxonomy provided by Zhao et al. (2009) to identify the three types of solar wind. We then map the in-situ observations to the 2.5 solar radii surface. With the aid of the potential-field-source surface model (PFSS), we calculate the normal distance from the solar wind "foot point" to the local helisopheric current sheet on that surface. We find that the source region of the streamer stalk wind is narrower compared to the previous minimum. The area outside the streamer stalk is accordingly larger, but the magnetic field strength is observed to be lower, with the result that the total amount of the magnetic open flux from the outside of streamer stalk region is conserved in the two successive solar minima. The implications of the conservation of open magnetic flux for models of the behavior of the solar magnetic field are discussed.

astro-ph.SR↗