Exploring the Higgs Sector of a Most Natural NMSSM and its Prediction on Higgs Pair Production at the LHC

As a most natural realization of the Next-to Minimal Supersymmetry Standard Model (NMSSM), {\lambda}-SUSY is parameterized by a large {\lambda} around one and a low tan$\beta$ below 10. In this work, we first scan the parameter space of {\lambda}-SUSY by considering various experimental constraints, including the limitation from the Higgs data updated by the ATLAS and CMS collaborations in the summer of 2014, then we study the properties of the Higgs bosons. We get two characteristic features of {\lambda}-SUSY in experimentally allowed parameter space. One is the triple self coupling of the SM-like Higgs boson may get enhanced by a factor over 10 in comparison with its SM prediction. The other is the pair production of the SM-like Higgs boson at the LHC may be two orders larger than its SM prediction. All these features seems to be unachievable in the Minimal Supersymmetric Standard Model and in the NMSSM with a low {\lambda}. Moreover, we also find that naturalness plays an important role in selecting the parameter space of {\lambda}-SUSY, and that the Higgs $\chi^2$ obtained with the latest data is usually significantly smaller than before due to the more consistency of the two collaboration measurements

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

Interpreting the galactic center gamma-ray excess in the NMSSM

In the Next-to-Minimal Supersymmetric Standard Model (NMSSM), all singlet-dominated particles including one neutralino, one CP-odd Higgs boson and one CP-even Higgs boson can be simultaneously lighter than about 100 GeV. Consequently, dark matter (DM) in the NMSSM can annihilate into multiple final states to explain the galactic center gamma-ray excess (GCE). In this work we take into account the foreground and background uncertainties for the GCE and investigate these explanations. We carry out a sophisticated scan over the NMSSM parameter space by considering various experimental constraints such as the Higgs data, $B$-physics observables, DM relic desnity, LUX experiment and the dSphs constraints. Then for each surviving parameter point we perform a fit to the GCE spectrum by using the correlation matrix that incorporates both the statistical and systematic uncertainties of the measured excess. After examining the properties of the obtained GCE solutions, we conclude that the GCE can be well explained by the pure annihilations $\tilde{\chi}_1^0 \tilde{\chi}_1^0 \to b \bar{b}$ and $\tilde{\chi}_1^0 \tilde{\chi}_1^0 \to A_1 H_i$ with $A_1$ being the lighter singlet-dominated CP-odd Higgs boson and $H_i$ denoting the singlet-dominated CP-even Higgs boson or SM-like Higgs boson, and it can also be explained by the mixed annihilation $\tilde{\chi}_1^0 \tilde{\chi}_1^0 \to W^+ W^-, A_1 H_1$. Among these annihilation channels, $\tilde{\chi}_1^0 \tilde{\chi}_1^0 \to A_1 H_i$ can provide the best interpretation with the corresponding $p$-value reaching 0.55. We also discuss to what extent the future DM direct detection experiments can explore the GCE solutions and conclude that the XENON-1T experiment is very promising in testing nearly all the solutions.Comment: 31 pages, 7 figure

OVSNet : Towards One-Pass Real-Time Video Object Segmentation

Video object segmentation aims at accurately segmenting the target object regions across consecutive frames. It is technically challenging for coping with complicated factors (e.g., shape deformations, occlusion and out of the lens). Recent approaches have largely solved them by using backforth re-identification and bi-directional mask propagation. However, their methods are extremely slow and only support offline inference, which in principle cannot be applied in real time. Motivated by this observation, we propose a efficient detection-based paradigm for video object segmentation. We propose an unified One-Pass Video Segmentation framework (OVS-Net) for modeling spatial-temporal representation in a unified pipeline, which seamlessly integrates object detection, object segmentation, and object re-identification. The proposed framework lends itself to one-pass inference that effectively and efficiently performs video object segmentation. Moreover, we propose a maskguided attention module for modeling the multi-scale object boundary and multi-level feature fusion. Experiments on the challenging DAVIS 2017 demonstrate the effectiveness of the proposed framework with comparable performance to the state-of-the-art, and the great efficiency about 11.5 FPS towards pioneering real-time work to our knowledge, more than 5 times faster than other state-of-the-art methods.Comment: 10 pages, 6 figure

More than Vanilla Fusion: a Simple, Decoupling-free, Attention Module for Multimodal Fusion Based on Signal Theory

The vanilla fusion methods still dominate a large percentage of mainstream audio-visual tasks. However, the effectiveness of vanilla fusion from a theoretical perspective is still worth discussing. Thus, this paper reconsiders the signal fused in the multimodal case from a bionics perspective and proposes a simple, plug-and-play, attention module for vanilla fusion based on fundamental signal theory and uncertainty theory. In addition, previous work on multimodal dynamic gradient modulation still relies on decoupling the modalities. So, a decoupling-free gradient modulation scheme has been designed in conjunction with the aforementioned attention module, which has various advantages over the decoupled one. Experiment results show that just a few lines of code can achieve up to 2.0% performance improvements to several multimodal classification methods. Finally, quantitative evaluation of other fusion tasks reveals the potential for additional application scenarios

A Spatio-Temporal Graph Convolutional Network for Gesture Recognition from High-Density Electromyography

Accurate hand gesture prediction is crucial for effective upper-limb prosthetic limbs control. As the high flexibility and multiple degrees of freedom exhibited by human hands, there has been a growing interest in integrating deep networks with high-density surface electromyography (HD-sEMG) grids to enhance gesture recognition capabilities. However, many existing methods fall short in fully exploit the specific spatial topology and temporal dependencies present in HD-sEMG data. Additionally, these studies are often limited number of gestures and lack generality. Hence, this study introduces a novel gesture recognition method, named STGCN-GR, which leverages spatio-temporal graph convolution networks for HD-sEMG-based human-machine interfaces. Firstly, we construct muscle networks based on functional connectivity between channels, creating a graph representation of HD-sEMG recordings. Subsequently, a temporal convolution module is applied to capture the temporal dependences in the HD-sEMG series and a spatial graph convolution module is employed to effectively learn the intrinsic spatial topology information among distinct HD-sEMG channels. We evaluate our proposed model on a public HD-sEMG dataset comprising a substantial number of gestures (i.e., 65). Our results demonstrate the remarkable capability of the STGCN-GR method, achieving an impressive accuracy of 91.07% in predicting gestures, which surpasses state-of-the-art deep learning methods applied to the same dataset

Impact of Dual Gauge Railway Tracks on Traffic Load Induced Permanent Deformation of Low Embankments

AbstractThere is a growing interest in recent years of many African countries to revamp their neglected railways in order to promote regional trade and transportation integration. Investors are faced with problems of railway track gauge conversions to promote railway inter operability. The objective of the work documented here was to numerically evaluate the impact of track gauge conversions on traffic load induced permanent deformation (PD) of low embankment on soft sub-grade. A method to predict the traffic load induced settlement of low embankment on soft sub-grade is proposed. Using the user-defined material subroutines (UMAT) in ABAQUS, a 2-D finite element (FE) model was formulated. These models are converted into a numerical formulation for implementation in FE analysis and the traffic load induced dynamic stress in the sub grade are calculated by using the multi-layer elastic theory. Then the plastic vertical strain in the sub-grade is calculated by an empirical equation, whose constants are related to the physical and mechanical properties of the sub-grade soil. The method was applied to analyze a 700m long section of a low embankment on the soft black cotton soil of Nakuru plains in Kenya. Corresponding results showed that the application of traffic loads on alternate rail tracks due to gauge conversions have a significant effect on the permanent deformation of the sub grade soil. The depth significantly influenced by traffic loading was found to be close to 6 m below the base of the embankment. The analysis also shows that increasing the thickness and stiffness of the sub grade is a very effective way of reducing the traffic load induced permanent deformation of soft sub grade soil. The proposed method can be used for settlement analysis on low embankments as well as a useful tool for making decisions on railway track gauge conversions

Freeze-in bino dark matter in high scale supersymmetry

We explore a scenario of high scale supersymmetry where all supersymmetric particles except gauginos stay at a high energy scale $M_{\rm SUSY}$ which is much larger than the reheating temperature $T_\text{RH}$. The dark matter is dominated by bino component with mass around the electroweak scale and the observed relic abundance is mainly generated by the freeze-in process during the early universe. Considering the various constraints, we identify two available scenarios in which the supersymmetric sector at an energy scale below $T_\text{RH}$ consists of: a) bino; b) bino and wino. Typically, for a bino mass around 0.1-1 TeV and a wino mass around 2 TeV, we find that $M_{\rm SUSY}$ should be around $10^{12-14}$ GeV with $T_\text{RH}$ around $10^{4-6}$ GeV.Comment: 23 pages, 4 figures, revised version accepted by Physical Review D for publicatio

Design Method and Cost-Benefit Analysis of Hybrid Fiber Used in Asphalt Concrete

Fiber, as an additive, can improve the performance of asphalt concrete and be widely studied, but only a few works have been done for hybrid fiber. This paper presents a new and convenient method to design hybrid fiber and verifies hybrid fiber’s superiority in asphalt pavement engineering. Firstly, this paper expounds the design method used as its applied example with the hybrid fiber composed of lignin, polyester, and polypropylene fibers. In this method, a direct shear device (DSD) is used to measure the shear damage energy density (SDED) of hybrid fiber modified asphalts, and range and variance statistical analysis are applied to determine the composition proportion of hybrid fiber. Then, the engineering property of hybrid fiber reinforced asphalt concrete (AC-13) is investigated. Finally, a cost-benefit model is developed to analyze the advantage of hybrid fiber compared to single fibers. The results show that the design method employed in this paper can offer a beneficial reference. A combination of 1.8% of lignin fiber and 2.4% of polyester fiber plus 3.0% polypropylene fiber presented the best reinforcement of the hybrid fiber. The cost-benefit model verifies that the hybrid fiber can bring about comprehensive pavement performance and good economy