2,706 research outputs found
Principal component analysis for second-order stationary vector time series
We extend the principal component analysis (PCA) to second-order stationary
vector time series in the sense that we seek for a contemporaneous linear
transformation for a -variate time series such that the transformed series
is segmented into several lower-dimensional subseries, and those subseries are
uncorrelated with each other both contemporaneously and serially. Therefore
those lower-dimensional series can be analysed separately as far as the linear
dynamic structure is concerned. Technically it boils down to an eigenanalysis
for a positive definite matrix. When is large, an additional step is
required to perform a permutation in terms of either maximum cross-correlations
or FDR based on multiple tests. The asymptotic theory is established for both
fixed and diverging when the sample size tends to infinity.
Numerical experiments with both simulated and real data sets indicate that the
proposed method is an effective initial step in analysing multiple time series
data, which leads to substantial dimension reduction in modelling and
forecasting high-dimensional linear dynamical structures. Unlike PCA for
independent data, there is no guarantee that the required linear transformation
exists. When it does not, the proposed method provides an approximate
segmentation which leads to the advantages in, for example, forecasting for
future values. The method can also be adapted to segment multiple volatility
processes.Comment: The original title dated back to October 2014 is "Segmenting Multiple
Time Series by Contemporaneous Linear Transformation: PCA for Time Series
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
We consider a multivariate time series model which represents a high
dimensional vector process as a sum of three terms: a linear regression of some
observed regressors, a linear combination of some latent and serially
correlated factors, and a vector white noise. We investigate the inference
without imposing stationary conditions on the target multivariate time series,
the regressors and the underlying factors. Furthermore we deal with the
endogeneity that there exist correlations between the observed regressors and
the unobserved factors. We also consider the model with nonlinear regression
term which can be approximated by a linear regression function with a large
number of regressors. The convergence rates for the estimators of regression
coefficients, the number of factors, factor loading space and factors are
established under the settings when the dimension of time series and the number
of regressors may both tend to infinity together with the sample size. The
proposed method is illustrated with both simulated and real data examples
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Learning accurate cross-domain preference mappings in the absence of
overlapped users/items has presented a persistent challenge in Non-overlapping
Cross-domain Recommendation (NOCDR). Despite the efforts made in previous
studies to address NOCDR, several limitations still exist. Specifically, 1)
while some approaches substitute overlapping users/items with overlapping
behaviors, they cannot handle NOCDR scenarios where such auxiliary information
is unavailable; 2) often, cross-domain preference mapping is modeled by
learning deterministic explicit representation matchings between sampled users
in two domains. However, this can be biased due to individual preferences and
thus fails to incorporate preference continuity and universality of the general
population. In light of this, we assume that despite the scattered nature of
user behaviors, there exists a consistent latent preference distribution shared
among common people. Modeling such distributions further allows us to capture
the continuity in user behaviors within each domain and discover preference
invariance across domains. To this end, we propose a Distributional
domain-invariant Preference Matching method for non-overlapping Cross-Domain
Recommendation (DPMCDR). For each domain, we hierarchically approximate a
posterior of domain-level preference distribution with empirical evidence
derived from user-item interactions. Next, we aim to build distributional
implicit matchings between the domain-level preferences of two domains. This
process involves mapping them to a shared latent space and seeking a consensus
on domain-invariant preference by minimizing the distance between their
distributional representations therein. In this way, we can identify the
alignment of two non-overlapping domains if they exhibit similar patterns of
domain-invariant preference.Comment: 9 pages, 5 figures, full research paper accepted by ICDM 202
Adaptive and Robust Methods of Reconstruction (ARMOR) for Thermoacoustic Tomography
In this paper, we present new adaptive and robust methods of reconstruction (ARMOR) for thermoacoustic tomography (TAT), and study their performances for breast cancer detection. TAT is an emerging medical imaging technique that combines the merits of high contrast due to electromagnetic or laser stimulation and high resolution offered by thermal acoustic imaging. The current image reconstruction methods used for TAT, such as the delay-and-sum (DAS) approach, are data-independent and suffer from low-resolution, high sidelobe levels, and poor interference rejection capabilities. The data-adaptive ARMOR can have much better resolution and much better interference rejection capabilities than their data-independent counterparts. By allowing certain uncertainties, ARMOR can be used to mitigate the amplitude and phase distortion problems encountered in TAT. The excellent performance of ARMOR is demonstrated using both simulated and experimentally measured data
Phorbol ester induced ex vivo expansion of rigorously-defined phenotypic but not functional human cord blood hematopoietic stem cells: a cautionary tale demonstrating that phenotype does not always recapitulate stem cell function
Dynamical modulation of solar flare electron acceleration due to plasmoid-shock interactions in the looptop region
A fast-mode shock can form in the front of reconnection outflows and has been
suggested as a promising site for particle acceleration in solar flares. Recent
development of magnetic reconnection has shown that numerous plasmoids can be
produced in a large-scale current layer. Here we investigate the dynamical
modulation of electron acceleration in the looptop region when plasmoids
intermittently arrive at the shock by combining magnetohydrodynamics
simulations with a particle kinetic model. As plasmoids interact with the
shock, the looptop region exhibits various compressible structures that
modulate the production of energetic electrons. The energetic electron
population varies rapidly in both time and space. The number of 510 keV
electrons correlates well with the area with compression, while that of 50
keV electrons shows good correlation with strong compression area but only
moderate correlation with shock parameters. We further examine the impacts of
the first plasmoid, which marks the transition from a quasi-steady shock front
to a distorted and dynamical shock. The number of energetic electrons is
reduced by at 1525 keV and nearly 40\% for 2550 keV, while
the number of 510 keV electrons increases. In addition, the electron energy
spectrum above 10 keV evolves softer with time. We also find double or even
multiple distinct sources can develop in the looptop region when the plasmoids
move across the shock. Our simulations have strong implications to the
interpretation of nonthermal looptop sources, as well as the commonly observed
fast temporal variations in flare emissions, including the quasi-periodic
pulsations.Comment: accepted for publication in ApJ
Giant Magneto-Optical Sch\"{a}fer-Hubert Effect in Two-Dimensional van der Waals Antiferromagnets \textit{M}PS (\textit{M}=Mn, Fe, Ni)
The recent discovery of long-range magnetic order in atomically thin films
has triggered particular interest in two-dimensional (2D) van der Waals (vdW)
magnetic materials. In this paper, we perform a systematic theoretical study of
the magneto-optical Sch\"{a}fer-Hubert effect (MOSHE) in 2D vdW
antiferromagnetic \textit{M}PS (\textit{M} = Mn, Fe, Ni) with multifold
intralayer and interlayer magnetic orders. The formula for evaluating the MOSHE
in 2D magnets is derived by considering the influence of a non-magnetic
substrate. The MOSHE of monolayer and bilayer \textit{M}PS are considerably
large (), originating from the strong anisotropy of in-plane
optical conductivity. The Sch\"{a}fer-Hubert rotation angles are surprisingly
insensitive to the orientations of the N\'{e}el vector, while the
Sch\"{a}fer-Hubert ellipticities are identified to be a good criterion to
distinguish different interlayer magnetic orders. Our work establishes a
theoretical framework for exploring novel 2D vdW magnets and facilitates the
promising applications of the 2D \textit{M}PS family in antiferromagnetic
nanophotonic devices
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