Abstract-Automatic self-localization is a critical need for the effective use of ad-hoc sensor networks in military or civilian applications. In general, self-localization involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. distance measurements between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of inter-sensor communication. We demonstrate that the information used for sensor localization is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then present and demonstrate the utility of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multi-modal uncertainty. Using simulations of small-to moderately-sized sensor networks, we show that NBP may be made robust to outlier measurement errors by a simple model augmentation, and that judicious message construction can result in better estimates. Furthermore, we provide an analysis of NBP's communications requirements, showing that typically only a few messages per sensor are required, and that even low bit-rate approximations of these messages can have little or no performance impact.

The role of the circadian clock in skin and the identity of genes participating in its chronobiology remain largely unknown, leading us to define the circadian transcriptome of mouse skin at two different stages of the hair cycle, telogen and anagen. The circadian transcriptomes of telogen and anagen skin are largely distinct, with the former dominated by genes involved in cell proliferation and metabolism. The expression of many metabolic genes is antiphasic to cell cycle-related genes, the former peaking during the day and the latter at night. Consistently, accumulation of reactive oxygen species, a byproduct of oxidative phosphorylation, and S-phase are antiphasic to each other in telogen skin. Furthermore, the circadian variation in S-phase is controlled by BMAL1 intrinsic to keratinocytes, because keratinocyte-specific deletion of Bmal1 obliterates time-of-day-dependent synchronicity of cell division in the epidermis leading to a constitutively elevated cell proliferation. In agreement with higher cellular susceptibility to UV-induced DNA damage during S-phase, we found that mice are most sensitive to UVB-induced DNA damage in the epidermis at night. Because in the human epidermis maximum numbers of keratinocytes go through S-phase in the late afternoon, we speculate that in humans the circadian clock imposes regulation of epidermal cell proliferation so that skin is at a particularly vulnerable stage during times of maximum UV exposure, thus contributing to the high incidence of human skin cancers.Arntl gene | circadian rhythm | UVB damage | cell cycle T he highly conserved circadian clock regulates organismal adaptation to the light:dark (LD) cycles caused by the rotation of the Earth (1). Located in the suprachiasmatic nucleus of the vertebrate hypothalamus, the central clock is an intrinsic pacemaker with a spontaneous firing rate and a nearly 24-h rhythmic gene expression. This central pacemaker is thought to synchronize peripheral clocks found in the vast majority of tissues and cells. The core circadian clock machinery is composed of the heterodimeric bHLH-PAS transcription factors CLOCK and BMAL1 that bind E-box elements to activate clock-controlled genes, as well as Period (Per1, 2, and 3) and Cryptochrome (Cry1 and 2). PERs and CRYs inhibit CLOCK/BMAL1 activity upon their translocation into the nucleus, thus constituting a negative arm in the circadian feedback loop. CLOCK/ BMAL1 also activate expression of nuclear receptors ROR and REV-ERBα, which in turn respectively activate and inhibit the transcription of Bmal1 and other target genes containing retinoic acid-related orphan receptor response elements.Although the circadian clock is active in most mammalian tissues, the battery of genes under circadian regulation is largely tissue specific (2), suggesting that the circadian clock modulates physiological processes unique to each organ. Here we have focused on the role of the clock within skin, an organ dominated on the one hand by the cycling hair follicles and on the other by the continuously r...

In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. However, due to the limitations of existing inference algorithms, it is often necessary to form coarse, discrete approximations to such models. In this paper, we develop a nonparametric belief propagation (NBP) algorithm, which uses stochastic methods to propagate kernel-based approximations to the true continuous messages. Each NBP message update is based on an efficient sampling procedure which can accomodate an extremely broad class of potential functions, allowing easy adaptation to new application areas. We validate our method using comparisons to continuous BP for Gaussian networks, and an application to the stereo vision problem.

In this paper we introduce a novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model. Our new method results in significant speedups on real world text corpora. Conventional Gibbs sampling schemes for LDA require O(K) operations per sample where K is the number of topics in the model. Our proposed method draws equivalent samples but requires on average significantly less then K operations per sample. On real-word corpora FastLDA can be as much as 8 times faster than the standard collapsed Gibbs sampler for LDA. No approximations are necessary, and we show that our fast sampling scheme produces exactly the same results as the standard (but slower) sampling scheme. Experiments on four real world data sets demonstrate speedups for a wide range of collection sizes. For the PubMed collection of over 8 million documents with a required computation time of 6 CPU months for LDA, our speedup of 5.7 can save 5 CPU months of computation.