Masses of Scalar and Axial-Vector B Mesons Revisited

The SU(3) quark model encounters a great challenge in describing even-parity mesons. Specifically, the $q\bar q$ quark model has difficulties in understanding the light scalar mesons below 1 GeV, scalar and axial-vector charmed mesons and $1^+$ charmonium-like state $X(3872)$. A common wisdom for the resolution of these difficulties lies on the coupled channel effects which will distort the quark model calculations. In this work, we focus on the near mass degeneracy of scalar charmed mesons, $D_{s0}^*$ and $D_0^{*0}$, and its implications. Within the framework of heavy meson chiral perturbation theory, we show that near degeneracy can be qualitatively understood as a consequence of self-energy effects due to strong coupled channels. Quantitatively, the closeness of $D_{s0}^*$ and $D_0^{*0}$ masses can be implemented by adjusting two relevant strong couplings and the renormalization scale appearing in the loop diagram. Then this in turn implies the mass similarity of $B_{s0}^*$ and $B_0^{*0}$ mesons. The $P_0^* P'_1$ interaction with the Goldstone boson is crucial for understanding the phenomenon of near degeneracy. Based on heavy quark symmetry in conjunction with corrections from QCD and $1/m_Q$ effects, we obtain the masses of $B^*_{(s)0}$ and $B'_{(s)1}$ mesons, for example, $M_{B_{s0}^*}= (5715\pm1)\,{\rm MeV}+\delta\Delta_S$, $M_{B'_{s1}}=(5763\pm1)\,{\rm MeV}+\delta\Delta_S$ with $\delta\Delta_S$ being $1/m_Q$ corrections. We find that the predicted mass difference of 48 MeV between $B'_{s1}$ and $B_{s0}^*$ is larger than that of $20\sim 30$ MeV inferred from the relativistic quark models, whereas the difference of 15 MeV between the central values of $M_{B'_{s1}}$ and $M_{B'_1}$ is much smaller than the quark model expectation of $60-100$ MeV.Comment: 21 pages, 1 figure, to appear in Eur. Phys. J. (2017). arXiv admin note: text overlap with arXiv:1404.377

Protoporphyrinogen Oxidase Inhibitor: An Ideal Target for Herbicide Discovery

As the last common enzyme in the biosynthetic pathway leading to heme and chlorophyll, protoporphyrinogen oxidase (PPO; EC 1.3.3.4) is an ideal target for herbicide development. Currently, about 30 PPO inhibitors have been developed as agricultural herbicides. PPO inhibitors have displayed environmentally benign, but advantageous characteristics, including low toxicity, low effective concentration, broad herbicidal spectrum (active against both monocotyledon and dicotyledon weeds), quick onset of action, and long lasting effect. Over the last several years, great achievements have been made in revealing the structural biology of PPO. Five PPO crystal structures, four isolated in enzyme-inhibitor complexes and one in the native form, have been determined, including those from Nicotiana tabacum, Myxococcus Xanthus, Bacillus subtilis, and human. Although PPO inhibitors have been developed for over forty years, we continue to uncover exciting future prospects for novel PPO-inhibiting herbicides. In this review, we have summarized the structures of PPOs from plants, human, and bacteria; the interactions between PPOs and inhibitors; the quantitative structure–activity relationships of PPO inhibitors; and the molecular design of new PPO inhibitors

View suggestion for interactive segmentation of indoor scenes

Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is very time-consuming. In this paper, we present a novel interactive system for segmenting point cloud scenes. Our system automatically suggests a series of camera views, in which users can conveniently specify segmentation guidance. In this way, users may focus on specifying segmentation hints instead of manually searching for desirable views of unsegmented objects, thus significantly reducing user effort. To achieve this, we introduce a novel view preference model, which is based on a set of dedicated view attributes, with weights learned from a user study. We also introduce support relations for both graph-cut-based segmentation and finding similar objects. Our experiments show that our segmentation technique helps users quickly segment various types of scenes, outperforming alternative methods

Carbon-Neutralized Joint User Association and Base Station Switching for Green Cellular Networks

Mitigating climate change and its impacts is one of the sustainable development goals (SDGs) required by United Nations for an urgent action. Increasing carbon emissions due to human activities is the root cause to climate change. Telecommunication networks that provide service connectivity to mobile users contribute great amount of carbon emissions by consuming lots of non-renewable energy sources. Beyond the improvement on energy efficiency, to reduce the carbon footprint, telecom operators are increasing their adoption of renewable energy (e.g., wind power). The high variability of renewable energy in time and location; however, creates difficulties for operators when utilizing renewables for the reduction of carbon emissions. In this paper, we consider a heterogeneous network consisted of one macro base station (MBS) and multiple small base stations (SBSs) where each base station (BS) is powered by both of renewable and non-renewable energy. Different from the prior works that target on the total power consumption, we propose a novel scheme to minimize the carbon footprint of networks by dynamically switching the ON/OFF modes of SBSs and adjusting the association between users and BSs to access renewables as much as possible. Our numerical analysis shows that the proposed scheme significantly reduces up to 86% of the nonrenewable energy consumption compared to two representative baselines.Comment: To appear in IEEE International Conference on Communications (ICC) 202

A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism