40,018 research outputs found
Non-negative matrix factorization for self-calibration of photometric redshift scatter in weak lensing surveys
Photo-z error is one of the major sources of systematics degrading the
accuracy of weak lensing cosmological inferences. Zhang et al. (2010) proposed
a self-calibration method combining galaxy-galaxy correlations and galaxy-shear
correlations between different photo-z bins. Fisher matrix analysis shows that
it can determine the rate of photo-z outliers at a level of 0.01-1% merely
using photometric data and do not rely on any prior knowledge. In this paper,
we develop a new algorithm to implement this method by solving a constrained
nonlinear optimization problem arising in the self-calibration process. Based
on the techniques of fixed-point iteration and non-negative matrix
factorization, the proposed algorithm can efficiently and robustly reconstruct
the scattering probabilities between the true-z and photo-z bins. The algorithm
has been tested extensively by applying it to mock data from simulated stage IV
weak lensing projects. We find that the algorithm provides a successful
recovery of the scatter rates at the level of 0.01-1%, and the true mean
redshifts of photo-z bins at the level of 0.001, which may satisfy the
requirements in future lensing surveys.Comment: 12 pages, 6 figures. Accepted for publication in ApJ. Updated to
match the published versio
Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions
In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology, an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions
Identification of structural dynamic discrete choice models
This paper presents new identification results for the class of structural dynamic discrete choice models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where utility function of one choice in the model is parametric but the distribution of unobserved heterogeneities is nonparametric. The proposed identification method does not rely on the availability of terminal period data and hence can be applied to infinite horizon structural dynamic models. For identification we assume availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the structural dynamic discrete choice model is semiparametrically identified using the control function approach. This is a substantial revision of "Semiparametric identification of structural dynamic optimal stopping time models", CWP06/07.
Semiparametric identification of structural dynamic optimal stopping time models
This paper presents new identification results for the class of structural dynamic optimal stopping time models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where the utility function of an absorbing choice in the model is parametric but the distribution of unobserved heterogeneity is nonparametric. Our identification strategy depends on availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the dynamic optimal stopping model is semiparametrically identified using control function approaches.Structural dynamic discrete choice models, semiparametric identification, optimal stopping
Spontaneous Formation of Stable Capillary Bridges for Firming Compact Colloidal Microstructures in Phase Separating Liquids: A Computational Study
Computer modeling and simulations are performed to investigate capillary
bridges spontaneously formed between closely packed colloidal particles in
phase separating liquids. The simulations reveal a self-stabilization mechanism
that operates through diffusive equilibrium of two-phase liquid morphologies.
Such mechanism renders desired microstructural stability and uniformity to the
capillary bridges that are spontaneously formed during liquid solution phase
separation. This self-stabilization behavior is in contrast to conventional
coarsening processes during phase separation. The volume fraction limit of the
separated liquid phases as well as the adhesion strength and thermodynamic
stability of the capillary bridges are discussed. Capillary bridge formations
in various compact colloid assemblies are considered. The study sheds light on
a promising route to in-situ (in-liquid) firming of fragile colloidal crystals
and other compact colloidal microstructures via capillary bridges
- …