Pair production of 125 GeV Higgs boson in the SM extension with color-octet scalars at the LHC

Although the Higgs boson mass and single production rate have been determined more or less precisely, its other properties may deviate significantly from its predictions in the standard model (SM) due to the uncertainty of Higgs data. In this work we study the Higgs pair production at the LHC in the Manohar-Wise model, which extends the SM by one family of color-octet and isospin-doublet scalars. We first scanned over the parameter space of the Manohar-Wise model considering exprimental constraints and performed fits in the model to the latest Higgs data by using the ATLAS and CMS data separately. Then we calculated the Higgs pair production rate and investigated the potential of its discovery at the LHC14. We conclude that: (i) Under current constrains including Higgs data after Run I of the LHC, the cross section of Higgs pair production in the Manohar-Wise model can be enhanced up to even $10^3$ times prediction in the SM. (ii) Moreover, the sizable enhancement comes from the contributions of the CP-odd color-octet scalar $S^A_I$. For lighter scalar $S^A_I$ and larger values of $|\lambda_I|$, the cross section of Higgs pair production can be much larger. (iii) After running again of LHC at 14 TeV, most of the parameter spaces in the Manohar-Wise model can be test. For an integrated luminosity of 100 fb$^{-1}$ at the LHC14, when the normalized ratio $R=10$, the process of Higgs pair production can be detected.Comment: 13 pages, 4 figure

Higgs Phenomenology in the Minimal Dilaton Model after Run I of the LHC

The Minimal Dilaton Model (MDM) extends the Standard Model (SM) by a singlet scalar, which can be viewed as a linear realization of general dilaton field. This new scalar field mixes with the SM Higgs field to form two mass eigenstates with one of them corresponding to the 125 GeV SM-like Higgs boson reported by the LHC experiments. In this work, under various theoretical and experimental constrains, we perform fits to the latest Higgs data and then investigate the phenomenology of Higgs boson in both the heavy dilaton scenario and the light dilaton scenario of the MDM. We find that: (i) If one considers the ATLAS and CMS data separately, the MDM can explain each of them well, but refer to different parameter space due to the apparent difference in the two sets of data. If one considers the combined data of the LHC and Tevatron, however, the explanation given by the MDM is not much better than the SM, and the dilaton component in the 125-GeV Higgs is less than about 20% at 2 sigma level. (ii) The current Higgs data have stronger constrains on the light dilaton scenario than on the heavy dilaton scenario. (iii) The heavy dilaton scenario can produce a Higgs triple self coupling much larger than the SM value, and thus a significantly enhanced Higgs pair cross section at hadron colliders. With a luminosity of 100 fb^{-1} (10 fb^{-1}) at the 14-TeV LHC, a heavy dilaton of 400 GeV (500 GeV) can be examined. (iv) In the light dilaton scenario, the Higgs exotic branching ratio can reach 43% (60%) at 2 sigma (3 sigma) level when considering only the CMS data, which may be detected at the 14-TeV LHC with a luminosity of 300 fb^{-1} and the Higgs Factory.Comment: 27 pages, 13 figures, discussions added, to appear in JHE

DySuse: Susceptibility Estimation in Dynamic Social Networks

Influence estimation aims to predict the total influence spread in social networks and has received surged attention in recent years. Most current studies focus on estimating the total number of influenced users in a social network, and neglect susceptibility estimation that aims to predict the probability of each user being influenced from the individual perspective. As a more fine-grained estimation task, susceptibility estimation is full of attractiveness and practical value. Based on the significance of susceptibility estimation and dynamic properties of social networks, we propose a task, called susceptibility estimation in dynamic social networks, which is even more realistic and valuable in real-world applications. Susceptibility estimation in dynamic networks has yet to be explored so far and is computationally intractable to naively adopt Monte Carlo simulation to obtain the results. To this end, we propose a novel end-to-end framework DySuse based on dynamic graph embedding technology. Specifically, we leverage a structural feature module to independently capture the structural information of influence diffusion on each single graph snapshot. Besides, {we propose the progressive mechanism according to the property of influence diffusion,} to couple the structural and temporal information during diffusion tightly. Moreover, a self-attention block {is designed to} further capture temporal dependency by flexibly weighting historical timestamps. Experimental results show that our framework is superior to the existing dynamic graph embedding models and has satisfactory prediction performance in multiple influence diffusion models.Comment: This paper has been published in Expert Systems With Application

Allocation of Electric Taxi Charging: Assessing the Layout of Charging Stations Based on Charging Frequency

Recent decades have witnessed the growth of the electric vehicles (EVs) industry due to technological developments. To overcome emerging environmental issues, some metropolises, i.e., Beijing, have employed electric taxi systems, which require tremendous investments in charging stations. However, the supporting charging facilities for EVs are not complete, and in terms of layout, there is also a situation where some charging stations have long charging queues, but some are unvisited. To overcome these difficulties, this paper aims to establish a set of charging stations layout assessment models for the electric taxi based on charging frequency and put forward targeted policy suggestions to make the charging frequency of each station more balanced, to avoid resource waste and undersupply. In this paper, a mathematical model based on integer programming is established in conjunction with the workflow of the electric taxi; in the case study, simulations are performed using the Anylogic platform and the results are statistically analyzed; moreover, we use real-time data to assess the layout of charging stations near and within the Fourth Ring Road in Beijing. The modeling and simulation results show that there is an imbalance in the current charging stations layout in Beijing. More specifically, there is a problem with charging frequency of some stations, which is being too low and some too high. Also, the charging frequency of stations will vary with passenger distribution factors. We classify the studied charging stations into four categories according to their actual usage characteristics and provide specific analysis and optimization suggestions for the different categories. Based on the assessment system in this paper, we also carried out some policy suggestions for further layout optimization. The optimized layout has a more balanced charging frequency, and the variance of charging frequency is reduced largely

Fairness-aware Competitive Bidding Influence Maximization in Social Networks

Competitive Influence Maximization (CIM) has been studied for years due to its wide application in many domains. Most current studies primarily focus on the micro-level optimization by designing policies for one competitor to defeat its opponents. Furthermore, current studies ignore the fact that many influential nodes have their own starting prices, which may lead to inefficient budget allocation. In this paper, we propose a novel Competitive Bidding Influence Maximization (CBIM) problem, where the competitors allocate budgets to bid for the seeds attributed to the platform during multiple bidding rounds. To solve the CBIM problem, we propose a Fairness-aware Multi-agent Competitive Bidding Influence Maximization (FMCBIM) framework. In this framework, we present a Multi-agent Bidding Particle Environment (MBE) to model the competitors' interactions, and design a starting price adjustment mechanism to model the dynamic bidding environment. Moreover, we put forward a novel Multi-agent Competitive Bidding Influence Maximization (MCBIM) algorithm to optimize competitors' bidding policies. Extensive experiments on five datasets show that our work has good efficiency and effectiveness.Comment: IEEE Transactions on Computational Social Systems (TCSS), 2023, early acces

Physical Constraint Finite Element Model for Medical Image Registration

Due to being derived from linear assumption, most elastic body based non-rigid image registration algorithms are facing challenges for soft tissues with complex nonlinear behavior and with large deformations. To take into account the geometric nonlinearity of soft tissues, we propose a registration algorithm on the basis of Newtonian differential equation. The material behavior of soft tissues is modeled as St. Venant-Kirchhoff elasticity, and the nonlinearity of the continuum represents the quadratic term of the deformation gradient under the Green- St.Venant strain. In our algorithm, the elastic force is formulated as the derivative of the deformation energy with respect to the nodal displacement vectors of the finite element; the external force is determined by the registration similarity gradient flow which drives the floating image deforming to the equilibrium condition. We compared our approach to three other models: 1) the conventional linear elastic finite element model (FEM); 2) the dynamic elastic FEM; 3) the robust block matching (RBM) method. The registration accuracy was measured using three similarities: MSD (Mean Square Difference), NC (Normalized Correlation) and NMI (Normalized Mutual Information), and was also measured using the mean and max distance between the ground seeds and corresponding ones after registration. We validated our method on 60 image pairs including 30 medical image pairs with artificial deformation and 30 clinical image pairs for both the chest chemotherapy treatment in different periods and brain MRI normalization. Our method achieved a distance error of 0.320±0.138 mm in x direction and 0.326±0.111 mm in y direction, MSD of 41.96±13.74, NC of 0.9958±0.0019, NMI of 1.2962±0.0114 for images with large artificial deformations; and average NC of 0.9622±0.008 and NMI of 1.2764±0.0089 for the real clinical cases. Student's t-test demonstrated that our model statistically outperformed the other methods in comparison (p-values <0.05)

Multicell power allocation method based on game theory for inter-cell interference coordination

As a new technology, coordinated multipoint (CoMP) transmission is included in LTE-Advanced study item. Moreover, the network architecture in LTE-Advanced system is modified to take into account coordinated transmission. Under this background, a novel power allocation game model is established to mitigate inter-cell interference with cellular coordination. In the light of cellular cooperation relationship and centralized control in eNodeB, the power allocation in each served antenna unit aims to make signal to interference plus noise ratio (SINR) balanced among inter-cells. Through the proposed power allocation game algorithm, the users' SINR can reach the Nash equilibrium, making it feasible to reduce the co-frequency interference by decreasing the transmitted power. Numerical results show that the proposed power allocation algorithm improves the throughput both in cell-center and cell-edge. Moreover, the blocking rate in cell-edge is reduced too

Performance of power control in inter-cell interference coordination for frequency reuse

To mitigate inter-cell interference in 3G evolution systems, a novel inter-cell interference coordination scheme called soft fractional frequency reuse is proposed in this article, which enables to improve the data rate in cell-edge. On this basis, an inter-cell power control is presented for the inter-cell interference coordination, and the inter-cell balanced signal to interference plus noise ratio (SINR) among users is established for power allocation, which enables mitigation of inter-cell interference. Especially, the power control is based on a novel exponential kernel equation at higher convergence speed than the traditional arithmetic kernel equations. Numerical results show that the proposed scheme improves the throughput and reduces the blocking rate compared to the existing power control algorithms