Constraints on anomalous quartic gauge couplings via WγjjW\gamma jj production at the LHC

The vector boson scattering at the Large Hadron Collider (LHC) is sensitive to anomalous quartic gauge couplings (aQGCs). In this paper, we investigate the aQGC contribution to $W \gamma jj$ production at the LHC with $\sqrt{s}=13$ TeV in the context of an effective field theory (EFT). The unitarity bound is applied as a cut on the energy scale of this production process, which is found to have significant suppressive effects on the signals. To enhance the statistical significance, we analyse the kinematic and polarization features of the aQGC signals in detail. We find that the polarization effects induced by the aQGCs are unique and can discriminate the signals from the SM backgrounds well. With the proposed event selection strategy, we obtain the constraints on the coefficients of dimension-8 operators with current luminosity. The results indicate that the process $pp \to W \gamma jj$ is powerful for searching for the $O_{M_{2,3,4,5}}$ and $O_{T_{5,6,7}}$ operators.Comment: 29 pages, 11 figures, 7 tables, to be published in Chinese Physics

Exact soliton solutions of the generalized Gross-Pitaevskii equation based on expansion method

We give a more generalized treatment of the 1D generalized Gross-Pitaevskii equation (GGPE) with variable term coefficients. External harmonic trapping potential is fully considered and the nonlinearinteraction term is of arbitrary polytropic index of superfluid wave function. We also eliminate the interdependence between variable coefficients of the equation terms avoiding the restrictions that occur in some other works. The exact soliton solutions of the GGPE are obtained through the delicate combined utilization of modified lens-type transformation and F-expansion method with dominant features like soliton type properties highlighted

A Study of AI Population Dynamics with Million-agent Reinforcement Learning

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our intention is to put intelligent agents into a simulated natural context and verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning (DRL). In order to scale the population size up to millions agents, a large-scale DRL training platform with redesigned experience buffer is proposed. Our results show that the population dynamics of AI agents, driven only by each agent's individual self-interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International Conference on Autonomous Agents and Multiagent Systems

A Concentric-based Sleep Scheduling Scheme for Wireless Sensor Networks

[[abstract]]In Wireless Sensor Networks (WSNs), how to extend the lifetime is an important issue. Our research uses Sleeping Scheduling scheme which divides the network into many concentric layers and rotates sensors in different odd and even layers to sleep. By our scheme, we can balance the power consumption among all sensors and reduce power and transmission load of sensors near sink. Our research use Transmit Power Control (TPC) technique to control topology and divide concentric layers, and use the topology to transmit packets to sink. Finally, the performance of our scheme is better than other Sleeping Scheduling schemes in the simulations.[[sponsorship]]Tamkang University[[incitationindex]]EI[[conferencetype]]åé[[conferencetkucampus]]å°åæ ¡å[[conferencedate]]20150902~20150903[[booktype]]é»å­ç[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh