Role of h --> eta eta in Intermediate-Mass Higgs Boson Searches at the Large Hadron Collider

The dominance of $h\to \eta \eta$ decay mode for the intermediate mass Higgs boson is highly motivated to solve the little hierarchy problem and to ease the tension with the precision data. However, the discovery modes for m_h \alt 150 GeV, $h \to \gamma\gamma$ and $W/Z h \to (\ell\nu/\ell \bar \ell) (b\bar b)$, will be substantially affected. In this Letter, we show that $h \to \eta \eta \to 4b$ is complementary and we can use this decay mode to detect the intermediate Higgs boson at the LHC, via $Wh$ and $Zh$ production. Requiring at least one charged lepton and 4 $B$-tags in the final state, we can identify a clean Higgs boson signal for m_h \alt 150 GeV with a high significance and with a full Higgs mass reconstruction. We use the next-to-minimal supersymmetric standard model and the simplest little Higgs model for illustration.Comment: 4 pages, 1 figure, revtex. This version matches the published version in Phys. Rev. Let

Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition

Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition due to its ability of modeling the temporal information in various ranges of dynamic contexts. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions change over time. To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people. Specifically, for each frame, two sub-memory units store individual motion information, while a concurrent LSTM unit selectively integrates and stores inter-related motion information between interacting people from these two sub-memory units via a new co-memory cell. Experimental results on the BIT and UT datasets show the superiority of Co-LSTSM compared with the state-of-the-art methods

Logarithmic correction in the deformed AdS5{\rm AdS}_5 model to produce the heavy quark potential and QCD beta function

We stude the \textit{holographic} QCD model which contains a quadratic term $-\sigma z^2$ and a logarithmic term $-c_0\log[(z_{IR}-z)/z_{IR}]$ with an explicit infrared cut-off $z_{IR}$ in the deformed ${\rm AdS}_5$ warp factor. We investigate the heavy quark potential for three cases, i.e, with only quadratic correction, with both quadratic and logarithmic corrections and with only logarithmic correction. We solve the dilaton field and dilation potential from the Einstein equation, and investigate the corresponding beta function in the G{\"u}rsoy -Kiritsis-Nitti (GKN) framework. Our studies show that in the case with only quadratic correction, a negative $\sigma$ or the Andreev-Zakharov model is favored to fit the heavy quark potential and to produce the QCD beta-function at 2-loop level, however, the dilaton potential is unbounded in infrared regime. One interesting observing for the case of positive $\sigma$, or the soft-wall ${\rm AdS}_5$ model is that the corresponding beta-function exists an infrared fixed point. In the case with only logarithmic correction, the heavy quark Cornell potential can be fitted very well, the corresponding beta-function agrees with the QCD beta-function at 2-loop level reasonably well, and the dilaton potential is bounded from below in infrared. At the end, we propose a more compact model which has only logarithmic correction in the deformed warp factor and has less free parameters.Comment: 24 pages, 16 figure

Dilation-Erosion for Single-Frame Supervised Temporal Action Localization

To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal location for an action but implicitly overstates the supervision from the annotated-frame during training, leading to the confusion between actions and backgrounds, i.e., action incompleteness and background false positives. To tackle the two challenges, in this work, we present the Snippet Classification model and the Dilation-Erosion module. In the Dilation-Erosion module, we expand the potential action segments with a loose criterion to alleviate the problem of action incompleteness and then remove the background from the potential action segments to alleviate the problem of action incompleteness. Relying on the single-frame annotation and the output of the snippet classification, the Dilation-Erosion module mines pseudo snippet-level ground-truth, hard backgrounds and evident backgrounds, which in turn further trains the Snippet Classification model. It forms a cyclic dependency. Furthermore, we propose a new embedding loss to aggregate the features of action instances with the same label and separate the features of actions from backgrounds. Experiments on THUMOS14 and ActivityNet 1.2 validate the effectiveness of the proposed method. Code has been made publicly available (https://github.com/LingJun123/single-frame-TAL).Comment: 28 pages, 8 figure