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In this paper, a competitive method for 3-D face recognition (FR) using spherical harmonic features (SHF) is proposed. With this solution, 3-D face models are characterized by the energies contained in spherical harmonics with different frequencies, thereby enabling the capture of both gross shape and fine surface details of a 3-D facial surface. This is in(More)
Biotechnology advances have allowed investigation of heterogeneity of cellular responses to stimuli on the single-cell level. Functionally, this heterogeneity can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. However,(More)
Quantitative analysis of simple molecular networks is an important step forward understanding fundamental intracellular processes. As network motifs occurring recurrently in complex biological networks, gene auto-regulatory circuits have been extensively studied but gene expression dynamics remain to be fully understood, e.g., how promoter leakage affects(More)
Face registration is a necessary preprocessing step for 3D face recognition. An entirely automatic method for 3D face registration is proposed in this paper with high accuracy and good robustness to pose and facial expression variations. Our method consists of the following three stages. Firstly, the face shape is represented by Fitting Sphere(More)
Pose normalization is a necessary process of 3D face recognition. Automatic and robust normalization is still a challenge to existing techniques. We propose a novel method called Equipartition Fitting Sphere Representation (EPFSR) for shape representation. Convexes on face are localized firstly based on EPFSR. Then the tip and orientation of nose are(More)
Experimental evidence supports that signaling pathways can induce different dynamics of transcription factor (TF) activation, but how an input signal is encoded by such a dynamic, noisy TF and further decoded by downstream genes remains largely unclear. Here, using a system of stochastic transcription with signal regulation, we show that (1) keeping the(More)
Expression noise results in cell-to-cell variability in expression levels, and feedback regulation may complicate the tracing of sources of this noise. Using a representative model of gene expression with feedbacks, we analytically show that the expression noise (or the total noise) is decomposed into three parts: feedback-dependent promoter noise(More)