131 research outputs found
Convex recovery of a structured signal from independent random linear measurements
This chapter develops a theoretical analysis of the convex programming method
for recovering a structured signal from independent random linear measurements.
This technique delivers bounds for the sampling complexity that are similar
with recent results for standard Gaussian measurements, but the argument
applies to a much wider class of measurement ensembles. To demonstrate the
power of this approach, the paper presents a short analysis of phase retrieval
by trace-norm minimization. The key technical tool is a framework, due to
Mendelson and coauthors, for bounding a nonnegative empirical process.Comment: 18 pages, 1 figure. To appear in "Sampling Theory, a Renaissance."
v2: minor corrections. v3: updated citations and increased emphasis on
Mendelson's contribution
Moderated Network Models
Pairwise network models such as the Gaussian Graphical Model (GGM) are a
powerful and intuitive way to analyze dependencies in multivariate data. A key
assumption of the GGM is that each pairwise interaction is independent of the
values of all other variables. However, in psychological research this is often
implausible. In this paper, we extend the GGM by allowing each pairwise
interaction between two variables to be moderated by (a subset of) all other
variables in the model, and thereby introduce a Moderated Network Model (MNM).
We show how to construct the MNM and propose an L1-regularized nodewise
regression approach to estimate it. We provide performance results in a
simulation study and show that MNMs outperform the split-sample based methods
Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting
moderation effects. Finally, we provide a fully reproducible tutorial on how to
estimate MNMs with the R-package mgm and discuss possible issues with model
misspecification
Network Psychometrics
This chapter provides a general introduction of network modeling in
psychometrics. The chapter starts with an introduction to the statistical model
formulation of pairwise Markov random fields (PMRF), followed by an
introduction of the PMRF suitable for binary data: the Ising model. The Ising
model is a model used in ferromagnetism to explain phase transitions in a field
of particles. Following the description of the Ising model in statistical
physics, the chapter continues to show that the Ising model is closely related
to models used in psychometrics. The Ising model can be shown to be equivalent
to certain kinds of logistic regression models, loglinear models and
multi-dimensional item response theory (MIRT) models. The equivalence between
the Ising model and the MIRT model puts standard psychometrics in a new light
and leads to a strikingly different interpretation of well-known latent
variable models. The chapter gives an overview of methods that can be used to
estimate the Ising model, and concludes with a discussion on the interpretation
of latent variables given the equivalence between the Ising model and MIRT.Comment: In Irwing, P., Hughes, D., and Booth, T. (2018). The Wiley Handbook
of Psychometric Testing, 2 Volume Set: A Multidisciplinary Reference on
Survey, Scale and Test Development. New York: Wile
Variable-Range Hopping of Spin Polarons: Magnetoresistance in a Modified Mott Regime
We analize electrical conductivity controlled by hopping of bound spin
polarons in disordered solids with wide distributions of electron energies and
polaron shifts (barriers). By means of percolation theory and Monte Carlo
simulations we have shown that in such materials at low temperatures, when
hopping occurs in the vicinity of the Fermi level, a hard polaron gap does not
manifest itself in the transport properties. This happens because as
temperature decreases the hopping polaron trades the decreasing electron and
polaron barriers for increasing hopping distance. As a result, in the absence
of the Coulomb correlation effects, in this variable-range variable-barrier
hopping regime, the electrical resistivity as a function of temperature obeys a
non-activation law, which differs from the standard Mott law
The Gaussian graphical model in cross-sectional and time-series data
We discuss the Gaussian graphical model (GGM; an undirected network of
partial correlation coefficients) and detail its utility as an exploratory data
analysis tool. The GGM shows which variables predict one-another, allows for
sparse modeling of covariance structures, and may highlight potential causal
relationships between observed variables. We describe the utility in 3 kinds of
psychological datasets: datasets in which consecutive cases are assumed
independent (e.g., cross-sectional data), temporally ordered datasets (e.g., n
= 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In
time-series analysis, the GGM can be used to model the residual structure of a
vector-autoregression analysis (VAR), also termed graphical VAR. Two network
models can then be obtained: a temporal network and a contemporaneous network.
When analyzing data from multiple subjects, a GGM can also be formed on the
covariance structure of stationary means---the between-subjects network. We
discuss the interpretation of these models and propose estimation methods to
obtain these networks, which we implement in the R packages graphicalVAR and
mlVAR. The methods are showcased in two empirical examples, and simulation
studies on these methods are included in the supplementary materials.Comment: Accepted pending revision in Multivariate Behavioral Researc
Thermal Properties of Carbon Nanotube–Copper Composites for Thermal Management Applications
Carbon nanotube–copper (CNT/Cu) composites have been successfully synthesized by means of a novel particles-compositing process followed by spark plasma sintering (SPS) technique. The thermal conductivity of the composites was measured by a laser flash technique and theoretical analyzed using an effective medium approach. The experimental results showed that the thermal conductivity unusually decreased after the incorporation of CNTs. Theoretical analyses revealed that the interfacial thermal resistance between the CNTs and the Cu matrix plays a crucial role in determining the thermal conductivity of bulk composites, and only small interfacial thermal resistance can induce a significant degradation in thermal conductivity for CNT/Cu composites. The influence of sintering condition on the thermal conductivity depended on the combined effects of multiple factors, i.e. porosity, CNTs distribution and CNT kinks or twists. The composites sintered at 600°C for 5 min under 50 MPa showed the maximum thermal conductivity. CNT/Cu composites are considered to be a promising material for thermal management applications
Deciphering the stem cell machinery as a basis for understanding the molecular mechanism underlying reprogramming
Stem cells provide fascinating prospects for biomedical applications by combining the ability to renew themselves and to differentiate into specialized cell types. Since the first isolation of embryonic stem (ES) cells about 30 years ago, there has been a series of groundbreaking discoveries that have the potential to revolutionize modern life science. For a long time, embryos or germ cell-derived cells were thought to be the only source of pluripotency—a dogma that has been challenged during the last decade. Several findings revealed that cell differentiation from (stem) cells to mature cells is not in fact an irreversible process. The molecular mechanism underlying cellular reprogramming is poorly understood thus far. Identifying how pluripotency maintenance takes place in ES cells can help us to understand how pluripotency induction is regulated. Here, we review recent advances in the field of stem cell regulation focusing on key transcription factors and their functional interplay with non-coding RNAs
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