Charmless two-body B decays: A global analysis with QCD factorization

In this paper, we perform a global analysis of $B \to PP$ and $PV$ decays with the QCD factorization approach. It is encouraging to observe that the predictions of QCD factorization are in good agreement with experiment. The best fit $\gamma$ is around $79^\circ$. The penguin-to-tree ratio $|P_{\pi \pi}/T_{\pi \pi}|$ of $\pi^+ \pi^-$ decays is preferred to be larger than 0.3. We also show the confidence levels for some interesting channels: $B^0 \to \pi^0 \pi^0$, $K^+ K^-$ and $B^+ \to \omega \pi^+$, $\omega K^+$. For $B \to \pi K^\ast$ decays, they are expected to have smaller branching ratios with more precise measurements.Comment: 20 pages, 4 figures, version to appear in Phys. Rev.

Deep Learning Based Vehicle Make-Model Classification

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.Comment: 10 pages, ICANN 2018: Artificial Neural Networks and Machine Learnin

The Value of the Trout Fishery at Rhodes, North Eastern Cape, South Africa, A Travel Cost Analysis Using Count Data Models

The National Environmental Management: Biodiversity Act, no.10 of 2004) makes provision for the presence of alien trout in South African waters by means of a zoning system, partly in recognition of the significant income generating potential of trout fishing in South Africa. This paper reports the first formal recreational valuation of a trout fishery in South Africa, the one in and around Rhodes village, North Eastern Cape. The valuation is carried out by applying the individual travel cost method using several count data models. The zero truncated negative binomial model yielded the most appealing results. It accounts for the non-negative integer nature of the trip data, for truncation and over-dispersion. The paper finds that in 2007 consumer surplus per day visit to the Rhodes trout fishery was R2 668, consumer surplus per trip visit was R13 072, and the total consumer surplus generated was R18 026 288.

The hydrostatic equilibrium and Tsallis equilibrium for self-gravitating systems

Self-gravitating systems are generally thought to behavior non-extensively due to the long-range nature of gravitational forces. We obtain a relation between the nonextensive parameter q of Tsallis statistics, the temperature gradient and the gravitational potential based on the equation of hydrostatic equilibrium of self-gravitating systems. It is suggested that the nonextensive parameter in Tsallis statistics has a clear physical meaning with regard to the non-isothermal nature of the systems with long-range interactions and Tsallis equilibrium distribution for the self-gravitating systems describes the property of hydrostatic equilibrium of the systems.Comment: 7 pages, 9 Reference

Comment on "Self-Purification in Semiconductor Nanocrystals"

In a recent Letter [PRL 96, 226802 (2006)], Dalpian and Chelikowsky claimed that formation energies of Mn impurities in CdSe nanocrystals increase as the size of the nanocrystal decreases, and argued that this size dependence leads to "self-purification" of small nanocrystals. They presented density-functional-theory (DFT) calculations showing a strong size dependence for Mn impurity formation energies, and proposed a general explanation. In this Comment we show that several different DFT codes, pseudopotentials, and exchange-correlation functionals give a markedly different result: We find no such size dependence. More generally, we argue that formation energies are not relevant to substitutional doping in most colloidally grown nanocrystals.Comment: 1 page, 1 figur

Semiclassical Green Function in Mixed Spaces

A explicit formula on semiclassical Green functions in mixed position and momentum spaces is given, which is based on Maslov's multi-dimensional semiclassical theory. The general formula includes both coordinate and momentum representations of Green functions as two special cases of the form.Comment: 8 pages, typeset by Scientific Wor

On the Approximability and Hardness of the Minimum Connected Dominating Set with Routing Cost Constraint

In the problem of minimum connected dominating set with routing cost constraint, we are given a graph $G=(V,E)$, and the goal is to find the smallest connected dominating set $D$ of $G$ such that, for any two non-adjacent vertices $u$ and $v$ in $G$, the number of internal nodes on the shortest path between $u$ and $v$ in the subgraph of $G$ induced by $D \cup \{u,v\}$ is at most $\alpha$ times that in $G$. For general graphs, the only known previous approximability result is an $O(\log n)$-approximation algorithm ($n=|V|$) for $\alpha = 1$ by Ding et al. For any constant $\alpha > 1$, we give an $O(n^{1-\frac{1}{\alpha}}(\log n)^{\frac{1}{\alpha}})$-approximation algorithm. When $\alpha \geq 5$, we give an $O(\sqrt{n}\log n)$-approximation algorithm. Finally, we prove that, when $\alpha =2$, unless $NP \subseteq DTIME(n^{poly\log n})$, for any constant $\epsilon > 0$, the problem admits no polynomial-time $2^{\log^{1-\epsilon}n}$-approximation algorithm, improving upon the $\Omega(\log n)$ bound by Du et al. (albeit under a stronger hardness assumption)