Adaptive multiscale detection of filamentary structures in a background of uniform random points

We are given a set of $n$ points that might be uniformly distributed in the unit square $[0,1]^2$. We wish to test whether the set, although mostly consisting of uniformly scattered points, also contains a small fraction of points sampled from some (a priori unknown) curve with $C^{\alpha}$-norm bounded by $\beta$. An asymptotic detection threshold exists in this problem; for a constant $T_-(\alpha,\beta)>0$, if the number of points sampled from the curve is smaller than $T_-(\alpha,\beta)n^{1/(1+\alpha)}$, reliable detection is not possible for large $n$. We describe a multiscale significant-runs algorithm that can reliably detect concentration of data near a smooth curve, without knowing the smoothness information $\alpha$ or $\beta$ in advance, provided that the number of points on the curve exceeds $T_*(\alpha,\beta)n^{1/(1+\alpha)}$. This algorithm therefore has an optimal detection threshold, up to a factor $T_*/T_-$. At the heart of our approach is an analysis of the data by counting membership in multiscale multianisotropic strips. The strips will have area $2/n$ and exhibit a variety of lengths, orientations and anisotropies. The strips are partitioned into anisotropy classes; each class is organized as a directed graph whose vertices all are strips of the same anisotropy and whose edges link such strips to their ``good continuations.'' The point-cloud data are reduced to counts that measure membership in strips. Each anisotropy graph is reduced to a subgraph that consist of strips with significant counts. The algorithm rejects $\mathbf{H}_0$ whenever some such subgraph contains a path that connects many consecutive significant counts.Comment: Published at http://dx.doi.org/10.1214/009053605000000787 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optical stark effect in the 2-photon spectrum of NO

A large optical Stark effect has been observed in the two-photon spectrum X(2)Pi yields A(2)Sigma(+)_ in NO. It is explained as a near-resonant process in which the upper state of the two-photon transition is perturbed by interactions with higher-lying electronic states coupled by the laser field. A theoretical analysis is presented along with coupling parameters determined from ab initio wave functions. The synthetic spectrum reproduces the major experimental features

Genomic structure and transcriptional regulation of grass carp calmodulin gene

A fish calmodulin (CaM) gene was characterized for the first time in grass carp. The CaM gene is about 12-Kb in size with identical intron/exon organization as that of mammalian CaM genes. When compared to mammalian counterparts, the 5′-promoter region of grass carp CaM gene contains a TATA box and has a much lower GC content and CpG dinucleotide frequency. Interestingly, the 5′-promoter of carp CaM gene is AT-rich with multiple IRS elements and putative binding sites for Pit-1, Sp1/Sp3 and AP1. Using luciferase reporter assay, a potent silencer region was identified in the distal region of grass carp CaM promoter. Besides, the CaM promoter activity could be upregulated by IGF but suppressed by PACAP, forskolin and over-expression of Sp1 and Sp3. These findings, taken together, indicate that grass carp CaM gene does not exhibit the typical features of housekeeping genes and its expression is under the control of hormone factors, presumably by coupling with the appropriate signaling pathways/transcription factors.postprin

(2+1) resonant enhanced multiphoton ionization of H_2 via the E, F^(1)Σ^+_g state

In this paper, we report the results of ab initio calculations of photoelectron angular distributions and vibrational branching ratios for the (2+1) REMPI of H_2 via the E, F^(1)Σ^+_g state, and compare these with the experimental data of Anderson et al. [Chem. Phys. Lett. 105, 22 (1984)]. These results show that the observed non‐Franck–Condon behavior is predominantly due to the R dependence of the transition matrix elements, and to a lesser degree to the energy dependence. This work presents the first molecular REMPI study employing a correlated wave function to describe the Rydberg–valence mixing in the resonant intermediate state

Automatically Generating Natural Language Descriptions of Images by a Deep Hierarchical Framework.

Automatically generating an accurate and meaningful description of an image is very challenging. However, the recent scheme of generating an image caption by maximizing the likelihood of target sentences lacks the capacity of recognizing the human-object interaction (HOI) and semantic relationship between HOIs and scenes, which are the essential parts of an image caption. This article proposes a novel two-phase framework to generate an image caption by addressing the above challenges: 1) a hybrid deep learning and 2) an image description generation. In the hybrid deep-learning phase, a novel factored three-way interaction machine was proposed to learn the relational features of the human-object pairs hierarchically. In this way, the image recognition problem is transformed into a latent structured labeling task. In the image description generation phase, a lexicalized probabilistic context-free tree growing scheme is innovatively integrated with a description generator to transform the descriptions generation task into a syntactic-tree generation process. Extensively comparing state-of-the-art image captioning methods on benchmark datasets, we demonstrated that our proposed framework outperformed the existing captioning methods in different ways, such as significantly improving the performance of the HOI and relationships between HOIs and scenes (RHIS) predictions, and quality of generated image captions in a semantically and structurally coherent manner.\enlargethispage-8pt