Confronting pentaquark photoproduction with new LHCb observations

The newly measurement of production fractions of $P_c$ states by LHCb collaboration have put restriction on their branching ratios of $J/\psi p$ decay, thus constraining their photoproduction in $\gamma p\to J/\psi p$ reaction. We show the tension between LHCb results and the current experiments in search of $P_c$ photoproduction. We also find that the present information of branching ratios of $P_c\to J/\psi p$ has already confronted sharply with the models which study the nature of $P_c$

Implementation of quantum algorithms with resonant interactions

We propose a scheme for implementing quantum algorithms with resonant interactions. Our scheme only requires resonant interactions between two atoms and a cavity mode, which is simple and feasible. Moreover, the implementation would be an important step towards the fabrication of quantum computers in cavity QED system.Comment: 4 pages, 3 figure

Ricci flow on compact K\"ahler manifolds of positive bisectional curvature

We announce a new proof of the uniform estimate on the curvature of solutions to the Ricci flow on a compact K\"ahler manifold $M^n$ with positive bisectional curvature. In contrast to the recent work of X. Chen and G. Tian, our proof of the uniform estimate does not rely on the exsitence of K\"ahler-Einstein metrics on $M^n$, but instead on the first author's Harnack inequality for the K\"ahler-Ricc flow, and a very recent local injectivity radius estimate of Perelman for the Ricci flow.Comment: 4 page

Controlling Chaos in a Neural Network Based on the Phase Space Constraint

The chaotic neural network constructed with chaotic neurons exhibits very rich dynamic behaviors and has a nonperiodic associative memory. In the chaotic neural network, however, it is dicult to distinguish the stored patters from others, because the states of output of the network are in chaos. In order to apply the nonperiodic associative memory into information search and pattern identication, etc, it is necessary to control chaos in this chaotic neural network. In this paper, the phase space constraint method focused on the chaotic neural network is proposed. By analyzing the orbital of the network in phase space, we chose a part of states to be disturbed. In this way, the evolutional spaces of the strange attractors are constrained. The computer simulation proves that the chaos in the chaotic neural network can be controlled with above method and the network can converge in one of its stored patterns or their reverses which has the smallest Hamming distance with the initial state of the network. The work claries the application prospect of the associative dynamics of the chaotic neural network

Robust Dynamic Selection of Tested Modules in Software Testing for Maximizing Delivered Reliability

Software testing is aimed to improve the delivered reliability of the users. Delivered reliability is the reliability of using the software after it is delivered to the users. Usually the software consists of many modules. Thus, the delivered reliability is dependent on the operational profile which specifies how the users will use these modules as well as the defect number remaining in each module. Therefore, a good testing policy should take the operational profile into account and dynamically select tested modules according to the current state of the software during the testing process. This paper discusses how to dynamically select tested modules in order to maximize delivered reliability by formulating the selection problem as a dynamic programming problem. As the testing process is performed only once, risk must be considered during the testing process, which is described by the tester's utility function in this paper. Besides, since usually the tester has no accurate estimate of the operational profile, by employing robust optimization technique, we analysis the selection problem in the worst case, given the uncertainty set of operational profile. By numerical examples, we show the necessity of maximizing delivered reliability directly and using robust optimization technique when the tester has no clear idea of the operational profile. Moreover, it is shown that the risk averse behavior of the tester has a major influence on the delivered reliability.Comment: 19 pages, 4 figure