We study non-topological, charged planar walls （Q-walls） in the context of a particle physics model with supersymmetry broken by low-energy gauge mediation. Analytical properties are derived within the fiat-potentia...We study non-topological, charged planar walls （Q-walls） in the context of a particle physics model with supersymmetry broken by low-energy gauge mediation. Analytical properties are derived within the fiat-potential approximation for the flat-direction raising potential, while a numerical study is performed using the fall two-loop supersymmetric potential. We analyze the energetics of finite-size Q-walls and compare them to Q-balls, non-topological solitons possessing spherical symmetry and arising in the same supersymmetric model. This allows us to draw a phase diagram in the charge-transverse length plane, which shows a region where Q-wall solutions are energetically favored over Q-balls. However, due to their finiteness, such finite-size Q-walls are dynamically unstable and decay into Q-balls in a time which is less than their typical scale-length.展开更多
The spread of events happens all the time in social networks. The prediction of event propagation has received extensive attention in data mining community. In prior studies, topologies in social networks are usually ...The spread of events happens all the time in social networks. The prediction of event propagation has received extensive attention in data mining community. In prior studies, topologies in social networks are usually exploited to predict the scope of event propagation. User’s action logs can be obtained in reality, but it is difficult to get topologies in social networks. In this paper, NTGP, a prediction model for non-topological event propagation, is proposed. Firstly a time decay sampling method was used to extract the walk paths from user’s action log, and then deep learning method was applied to learn the sampling paths and predict the future propagation range of the target event. Extensive experiments demonstrate effectiveness of NT-GP.展开更多
文摘We study non-topological, charged planar walls （Q-walls） in the context of a particle physics model with supersymmetry broken by low-energy gauge mediation. Analytical properties are derived within the fiat-potential approximation for the flat-direction raising potential, while a numerical study is performed using the fall two-loop supersymmetric potential. We analyze the energetics of finite-size Q-walls and compare them to Q-balls, non-topological solitons possessing spherical symmetry and arising in the same supersymmetric model. This allows us to draw a phase diagram in the charge-transverse length plane, which shows a region where Q-wall solutions are energetically favored over Q-balls. However, due to their finiteness, such finite-size Q-walls are dynamically unstable and decay into Q-balls in a time which is less than their typical scale-length.
基金the National Natural Science Foundation of China (No. 61602159)he Natural Science Foundation of Heilongjiang Province (No. F201430)+1 种基金the Innovation Talents Project of Science and Technology Bureau of Harbin (No. 2017RAQXJ094, No. 2017RAQXJ131)the fundamental research funds of universities in Heilongjiang Province, special fund of Heilongjiang University (No. HDJCCX-201608).
文摘The spread of events happens all the time in social networks. The prediction of event propagation has received extensive attention in data mining community. In prior studies, topologies in social networks are usually exploited to predict the scope of event propagation. User’s action logs can be obtained in reality, but it is difficult to get topologies in social networks. In this paper, NTGP, a prediction model for non-topological event propagation, is proposed. Firstly a time decay sampling method was used to extract the walk paths from user’s action log, and then deep learning method was applied to learn the sampling paths and predict the future propagation range of the target event. Extensive experiments demonstrate effectiveness of NT-GP.
