Bregman divergence as general framework to estimate unnormalized statistical models

We show that the Bregman divergence provides a rich framework to estimate unnormalized statistical models for continuous or discrete random variables, that is, models which do not integrate or sum to one, respectively. We prove that recent estimation methods such as noise-contrastive estimation, ratio matching, and score matching belong to the proposed framework, and explain their interconnection based on supervised learning. Further, we discuss the role of boosting in unsupervised learning

KINEMATIC ANALYSIS OF POP DANCE CHOREOGRAPHIES THROUGH MODULAR MOTOR SYNERGY

Pop dance, or popping, is a subgenre of hip hop dance popularized during the last decades, while its biomechanics is not well understood yet. This study aimed to understand pop dance motions through modular motor synergy analysis. Pop dance performance by three experienced dancers, measured with a markerless human pose estimation method, was analyzed with principal component analysis (PCA) to extract motor synergies. The synergies obtained indicated the movement complexity and specific motor coordination patterns of ten typical pop dance choreographies, with emphasis on elbow, shoulder, hip and knee contributions. The results will enhance our understanding of complex dance movements, making a step toward future applications to medicine or art

A Molecular Link between the Circadian Clock, DNA Damage Responses, and Oncogene Activation

Circadian clocks enhance the efficiency and survival of living things by organizing their behavior and body functions. There has been a long history of research seeking a link between circadian clock and tumorigenesis. Studies of animal models and human tumor samples have revealed that the dysregulation of circadian clocks is an important endogenous factor causing mammalian cancer development. The core circadian clock regulators have been implicated in the control of both the cell cycle and DNA damage responses (DDR). Conversely, several intracellular signaling cascades that play important roles in regulation of the cell cycle and the DDR also contribute to circadian clock regulation. This review describes selected regulatory aspects of circadian clocks, providing evidence of a molecular link of the circadian clocks with cellular DDR

Estimation of the Lin-Yang bound of the least static energy of the Faddeev model

Lin and Yang's upper bound E_Q <= cQ^(3/4) of the least static energy E_Q of the Faddeev model in a sector with a fixed Hopf index Q is investigated. By constructing an explicit trial configuration for the Faddeev field n, a possible value of the coefficient c is obtained numerically, which is much smaller than the value obtained quite recently by analytic discussions.Comment: 11 pages, 2 figure

Light-Dependent Regulation of Circadian Clocks in Vertebrates

Circadian clocks are intrinsic time-tracking systems that endow organisms with a survival advantage. The core of the circadian clock mechanism is a cell-autonomous and self-sustained oscillator called a cellular clock, which operates via a transcription-/translation-based negative feedback loop. Under natural conditions, circadian clocks are entrained to a 24-hour day by environmental time cues, most commonly light. In mammals, circadian clocks are regulated by cellular clocks located in the central nervous system, such as the suprachiasmatic nucleus (SCN), and in other peripheral tissues. Importantly, mammals have no photoreceptors in the peripheral tissues; therefore the effect of light on peripheral clocks is indirect. By striking contrast, zebrafish peripheral cellular clocks are directly light responsive. This characteristic of the zebrafish cellular clock has contributed to the identification of molecules and signaling pathways that are involved in the light-dependent regulation of the cellular clock. Here, selected light-dependent regulatory mechanisms of circadian clocks in mammals and zebrafish are described

Decomposition of meron configuration of SU(2) gauge field

For the meron configuration of the SU(2) gauge field in the four dimensional Minkowskii spacetime, the decomposition into an isovector field \bn, isoscalar fields $\rho$ and $\sigma$, and a U(1) gauge field $C_{\mu}$ is attained by solving the consistency condition for \bn. The resulting \bn turns out to possess two singular points, behave like a monopole-antimonopole pair and reduce to the conventional hedgehog in a special case. The $C_{\mu}$ field also possesses singular points, while $\rho$ and $\sigma$ are regular everywhere.Comment: 18 pages, 5 figures, Sec.4 rewritten. 5 refs. adde

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated "eigenconnectivity" patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra-and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods.Peer reviewe