This paper investigates the appearance manifold of facial expression: embedding image sequences of facial expression from the high dimensional appearance feature space to a low dimensional manifold. We explore Locality Preserving Projections (LPP) to learn expression manifolds from two kinds of feature space: raw image data and Local Binary Patterns (LBP). For manifolds of different subjects, w...

The paper presents a mathematical model and steady-state operational characteristics of a Unified Power Flow Controller (UPFC). The mathematical description is based on the transformation of a three-phase system into an orthogonal synchronously rotating coordinate system, in which, under steady-state conditions, all the system quantities are constant values. Thus, at given parameters, it is gen...

The prefrontal cortex (PFC) subserves cognitive control: the ability to coordinate thoughts or actions in relation with internal goals. Its functional architecture, however, remains poorly understood. Using brain imaging in humans, we showed that the lateral PFC is organized as a cascade of executive processes from premotor to anterior PFC regions that control behavior according to stimuli, the...

When athletes are placed into annual age groups to organize and coordinate sport participation, certain (dis)advantages occur as a result of the subtle age differences within these groups. These differences, termed "relative age effects", have been consistently related to youth and adult sport attainment. However, there has been a lack of consistency in the terminology used in this area of rese...

Minimalist grammars cannot provide adequate descriptions of constructions in which a single filler saturates two mutually independent gaps, as is commonly analyzed to be the case in parasitic gap constructions and other across-the-board extraction phenomena. In this paper, I show how a simple addition to the minimalist grammar formalism allows for a unified treatment of control and parasitic ga...

We present a unified framework for online learning of structured classifiers that handles a wide family of convex loss functions, properly including CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family. For the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, it can be regarded as a d...

We present a unified derivation of covariant time derivatives, which transform as tensors under a time-dependent coordinate change. Such derivatives are essential for formulating physical laws in a frame-independent manner. Three specific derivatives are described: convective, corotational, and directional. The covariance is made explicit by working in arbitrary time-dependent coordinates, inst...

We present a unified framework for online learning of structured classifiers that handles a wide family of convex loss functions, properly including CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family. For the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, it can be regarded as a d...

We derive the relativistic transformation laws for the annihilation operators of the scalar field, the massive spin-1 vector field, the electromagnetic field and the spinor field. The technique developed here involves straightforward mathematical techniques based on fundamental quantum field theory, and is applicable to the study of entanglement in arbitrary coordinate transformations. In parti...

The efficient execution of serially ordered actions is crucial for many everyday tasks. Rather than emerge from a singular learning process, a growing body of evidence in both cognitive science and neuroscience suggests that the acquisition of habitual motor sequences relies on a multitude of learning systems that fall under two general classes of computation: fast prediction of transition prob...