26,435 research outputs found
A new mechanism of development and differentiation through slow binding/unbinding of regulatory proteins to the genes
Understanding the differentiation, a biological process from a multipotent
stem or progenitor state to a mature cell is critically important. We develop a
theoretical framework to quantify the underlying potential landscape and
biological paths for cell development and differentiation. We propose a new
mechanism of differentiation and development through binding/unbinding of
regulatory proteins to the gene promoters. We found indeed the differentiated
states can emerge from the slow promoter binding/unbinding processes.
Furthermore, under slow promoter binding/unbinding, we found multiple
meta-stable differentiated states. This can explain the origin of multiple
states observed in the recent experiments. In addition, the kinetic time
quantified by mean first passage transition time for the differentiation and
reprogramming strongly depends on the time scale of the promoter
binding/unbinding processes. We discovered an optimal speed for differentiation
for certain binding/unbinding rates of regulatory proteins to promoters. More
experiments in the future might be able to tell if cells differentiate at at
that optimal speed. In addition, we quantify kinetic pathways for the
differentiation and reprogramming. We found that they are irreversible. This
captures the non-equilibrium dynamics in multipotent stem or progenitor cells.
Such inherent time-asymmetry as a result of irreversibility of state transition
pathways as shown may provide the origin of time arrow for cell development.Comment: 25 pages, 5 figure
An Ensemble EM Algorithm for Bayesian Variable Selection
We study the Bayesian approach to variable selection in the context of linear
regression. Motivated by a recent work by Rockova and George (2014), we propose
an EM algorithm that returns the MAP estimate of the set of relevant variables.
Due to its particular updating scheme, our algorithm can be implemented
efficiently without inverting a large matrix in each iteration and therefore
can scale up with big data. We also show that the MAP estimate returned by our
EM algorithm achieves variable selection consistency even when diverges
with . In practice, our algorithm could get stuck with local modes, a common
problem with EM algorithms. To address this issue, we propose an ensemble EM
algorithm, in which we repeatedly apply the EM algorithm on a subset of the
samples with a subset of the covariates, and then aggregate the variable
selection results across those bootstrap replicates. Empirical studies have
demonstrated the superior performance of the ensemble EM algorithm
A Variational Algorithm for Bayesian Variable Selection
There has been an intense development on the estimation of a sparse
regression coefficient vector in statistics, machine learning and related
fields. In this paper, we focus on the Bayesian approach to this problem, where
sparsity is incorporated by the so-called spike-and-slab prior on the
coefficients. Instead of replying on MCMC for posterior inference, we propose a
fast and scalable algorithm based on variational approximation to the posterior
distribution. The updating scheme employed by our algorithm is different from
the one proposed by Carbonetto and Stephens (2012). Those changes seem crucial
for us to show that our algorithm can achieve asymptotic consistency even when
the feature dimension diverges exponentially fast with the sample size.
Empirical results have demonstrated the effectiveness and efficiency of the
proposed algorithm
Multi-view Reconstructive Preserving Embedding for Dimension Reduction
With the development of feature extraction technique, one sample always can
be represented by multiple features which locate in high-dimensional space.
Multiple features can re ect various perspectives of one same sample, so there
must be compatible and complementary information among the multiple views.
Therefore, it's natural to integrate multiple features together to obtain
better performance. However, most multi-view dimension reduction methods cannot
handle multiple features from nonlinear space with high dimensions. To address
this problem, we propose a novel multi-view dimension reduction method named
Multi-view Reconstructive Preserving Embedding (MRPE) in this paper. MRPE
reconstructs each sample by utilizing its k nearest neighbors. The similarities
between each sample and its neighbors are primely mapped into lower-dimensional
space in order to preserve the underlying neighborhood structure of the
original manifold. MRPE fully exploits correlations between each sample and its
neighbors from multiple views by linear reconstruction. Furthermore, MRPE
constructs an optimization problem and derives an iterative procedure to obtain
the low-dimensional embedding. Various evaluations based on the applications of
document classification, face recognition and image retrieval demonstrate the
effectiveness of our proposed approach on multi-view dimension reduction.Comment: 17 pages, 6 figure
Hysteresis from nonlinear dynamics of Majorana modes in topological Josephson junctions
We reveal that topological Josephson junctions provide a natural platform for
the interplay between the Josephson effect and the Landau-Zener effect through
a two-level system formed by coupled Majorana modes. We build a quantum
resistively shunted junction (RSJ) model by modifying the standard textbook RSJ
model to take account of the two-level system from the Majorana modes at the
junction. We show that the dynamics of the two-level system is governed by a
nonlinear Schr\"odinger equation and solve the equations analytically via a
mapping to a classical dynamical problem. This nonlinear dynamics leads to
hysteresis in the I-V characteristics, which can give a quantitative
explanation to recent experiments. We also predict the coexistence of two
interference patterns with periods and in topological
superconducting quantum interference devices.Comment: 17 pages, 11figure
High-precision evaluation of Wigner's d-matrix by exact diagonalization
The precise calculations of the Wigner's d-matrix are important in various
research fields. Due to the presence of large numbers, direct calculations of
the matrix using the Wigner's formula suffer from loss of precision. We present
a simple method to avoid this problem by expanding the d-matrix into a complex
Fourier series and calculate the Fourier coefficients by exactly diagonalizing
the angular-momentum operator in the eigenbasis of . This method
allows us to compute the d-matrix and its various derivatives for spins up to a
few thousand. The precision of the d-matrix from our method is about
for spins up to .Comment: 4 pages, 3 figures; a Fortran90 code is included; resubmitted to
Phys. Rev.
All-optical transistor based on Rydberg atom-assisted opto-mechanical system
We study the optical response of double optomechanical cavity system assisted
by Rydberg atomic ensembles. And atomic ensembles are only coupled with one
side cavity by a single cavity mode. It has been realized that a long-range
manipulation for optical properties of hybrid system, by controlling the
Rydberg atomic ensembles decoupled with the optomechanical cavity. Switching on
the coupling between atoms and cavity mode, the original time reversal symmetry
of double cavity structure has been broken. Based on the controlled optical
non-reciprocity, we put forward the theoretical schemes of all-optical
controlled diode, rectifier and transistor
Opposite Changes in Gap Width of Opposite Spin States Induced by Rashba Effect in Anti-ferromagnetic Graphene on Ni(111)
Graphene is a promising candidate for applications in spintronics. In this
paper, Density Functional Theory method is used to calculate the band structure
and magnetic properties of graphene on Ni(111). Our results show that once
there is antiferromagnetic order in graphene, an external electric field at the
order of 10^9 V/m can induce a gap width difference of tens of meV for opposite
spin states near the Fermi surface.Comment: 5 pages, 5 figure
Qualitative detection of oil adulteration with machine learning approaches
The study focused on the machine learning analysis approaches to identify the
adulteration of 9 kinds of edible oil qualitatively and answered the following
three questions: Is the oil sample adulterant? How does it constitute? What is
the main ingredient of the adulteration oil? After extracting the
high-performance liquid chromatography (HPLC) data on triglyceride from 370 oil
samples, we applied the adaptive boosting with multi-class Hamming loss
(AdaBoost.MH) to distinguish the oil adulteration in contrast with the support
vector machine (SVM). Further, we regarded the adulterant oil and the pure oil
samples as ones with multiple labels and with only one label, respectively.
Then multi-label AdaBoost.MH and multi-label learning vector quantization
(ML-LVQ) model were built to determine the ingredients and their relative ratio
in the adulteration oil. The experimental results on six measures show that
ML-LVQ achieves better performance than multi-label AdaBoost.MH.Comment: 18 pages, 4 figures, 5 table
Finite-volume formalism in the transition: an application to the lattice QCD calculation of double beta decays
We present the formalism for connecting a second-order electroweak
transition amplitudes in the finite volume (with
two hadrons in the initial and final states) to the physical amplitudes in the
infinite volume. Our study mainly focus on the case where the low-lying
intermediate state consists of two scattering hadrons. As a side product we
also reproduce the finite-volume formula for
transition, originally obtained by Brice\~no and Hansen. With the available
finite-volume formalism, we further discuss how to treat with the finite-volume
problem in the double beta decays and .Comment: 18 page
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