The Influence Of Social Presence On Virtual Community Participation: The Relational View Based On Community-Trust Theory

Virtual communities constitute an online environment that offers not only a new form of communication through which community members share information and interact with each other, but also an arena in which members develop social relationships. Prior research on the conceptualization of social presence, the degree to which a person is perceived as real in a mediated communication, results in two lines of perspectives. The media richness view conceives social presence as a media attribute while the relational view considers social presence as a quality of relational systems, emphasizing the relational aspects of communication. Drawing upon the relational view of social presence, this research incorporates the commitment-trust theory to investigate the influence of social presence on virtual community members’ continual participation. Moreover, this research considers sense of virtual community (SOVC) as the mediator between social presence and virtual community participation. The contributions of this research are three-fold. First, this research contributes to social presence literature by focusing on the social relational aspects of communication that are dependent on the participants rather than on the medium. Second, this research examines the role and importance of social presence in SOVC and virtual community participation. Lastly, it helps clarify how social presence contributes to continual participation in virtual communities

Derivation of Electroweak Chiral Lagrangian from One Family Technicolor Model

Based on previous studies deriving the chiral Lagrangian for pseudo scalar mesons from the first principle of QCD in the path integral formalism, we derive the electroweak chiral Lagrangian and dynamically compute all its coefficients from the one family technicolor model. The numerical results of the $p^4$ order coefficients obtained in this paper are proportional to the technicolor number $N_{\rm TC}$ and the technifermion number $N_{\rm TF}$, which agrees with the arguments in previous works, and which confirms the reliability of this dynamical computation.Comment: 6 page

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI

Event-plane decorrelation over pseudo-rapidity and its effect on azimuthal anisotropy measurement in relativistic heavy-ion collisions

Within A Multi-Phase Transport model, we investigate decorrelation of event planes over pseudorapidity and its effect on azimuthal anisotropy measurements in relativistic heavy-ion collisions. The decorrelation increases with increasing {\eta} gap between particles used to reconstruct the event planes. The third harmonic event planes are found even anticorrelated between forward and backward rapidities, the source of which may root in the opposite orientation of the collision geometry triangularities. The decorrelation may call into question the anisotropic flow measurements with pseudorapidity gap designed to reduce nonflow contributions, hence the hydrodynamic properties of the quark-gluon plasma extracted from those measurements.Comment: 5 pages,4figure