A decision-theoretic approach for segmental classification

This paper is concerned with statistical methods for the segmental classification of linear sequence data where the task is to segment and classify the data according to an underlying hidden discrete state sequence. Such analysis is commonplace in the empirical sciences including genomics, finance and speech processing. In particular, we are interested in answering the following question: given data $y$ and a statistical model $\pi(x,y)$ of the hidden states $x$, what should we report as the prediction $\hat{x}$ under the posterior distribution $\pi (x|y)$? That is, how should you make a prediction of the underlying states? We demonstrate that traditional approaches such as reporting the most probable state sequence or most probable set of marginal predictions can give undesirable classification artefacts and offer limited control over the properties of the prediction. We propose a decision theoretic approach using a novel class of Markov loss functions and report $\hat{x}$ via the principle of minimum expected loss (maximum expected utility). We demonstrate that the sequence of minimum expected loss under the Markov loss function can be enumerated exactly using dynamic programming methods and that it offers flexibility and performance improvements over existing techniques. The result is generic and applicable to any probabilistic model on a sequence, such as Hidden Markov models, change point or product partition models.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS657 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The deformed Hermitian-Yang-Mills equation in geometry and physics

We provide an introduction to the mathematics and physics of the deformed Hermitian-Yang-Mills equation, a fully nonlinear geometric PDE on Kahler manifolds which plays an important role in mirror symmetry. We discuss the physical origin of the equation, and some recent progress towards its solution. In dimension 3 we prove a new Chern number inequality and discuss the relationship with algebraic stability conditions.Comment: 20 page

穴位的起源

Acupoints originated from the ancient belief that diseases were caused by ghosts and evil spirits haunting the body. Acupoints were believed to be where the ghost and evil spirits hid, and thus, the rationale for healing was to expel the ghost and evil spirits directly from the diseased body part. Huang Di Nei Jing (Yellow Emperor's Inner Canon), an ancient Chinese medical text, mentions "pain as the point" in describing how to locate and manipulate the acupoint. During the era in which Huang Di Nei Jing (Yellow Emperor's Inner Canon) was written, the wide applications of filiform needle acupuncture expedited the amalgamation between acupoint and meridian theories. As a result, the concept of acupoints were further strengthened and expanded in their structures and functions. In the meantime, acupoints had developed to become the key points for qi and blood circulating inside the human body rather than where evil spirits hid. The formation and finalization of acupoints actually reveal a historical progression from witchcraft to medicine. © 2012 Shanghai Research Institute of Acupuncture and Meridian and Springer-Verlag Berlin Heidelberg.postprin

Representations of hom-Lie algebras

In this paper, we study representations of hom-Lie algebras. In particular, the adjoint representation and the trivial representation of hom-Lie algebras are studied in detail. Derivations, deformations, central extensions and derivation extensions of hom-Lie algebras are also studied as an application.Comment: 16 pages, multiplicative and regular hom-Lie algebras are used, Algebra and Representation Theory, 15 (6) (2012), 1081-109

Superdiffusivity of asymmetric exclusion process in dimensions one and two

We prove that the diffusion coefficient for the asymmetric exclusion process diverges at least as fast as $t^{1/4}$ in dimension $d=1$ and $(\log t)^{1/2}$ in $d=2$. The method applies to nearest and non-nearest neighbor asymmetric exclusion processes