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

A light Higgs scalar in the NMSSM confronted with the latest LHC Higgs data

In the Next-to-Minimal Supersymemtric Standard Model (NMSSM), one of the neutral Higgs scalars (CP-even or CP-odd) may be lighter than half of the SM-like Higgs boson. In this case, the SM-like Higgs boson h can decay into such a light scalar pair and consequently the diphoton and ZZ signal rates at the LHC will be suppressed. In this work, we examine the constraints of the latest LHC Higgs data on such a possibility. We perform a comprehensive scan over the parameter space of the NMSSM by considering various experimental constraints and find that the LHC Higgs data can readily constrain the parameter space and the properties of the light scalar, e.g., at 3 $\sigma$ level this light scalar should be highly singlet dominant and the branching ratio of the SM-like Higgs boson decay into the scalar pair should be less than about 30%. Also we investigate the detection of this scalar at various colliders. Through a detailed Monte Carlo simulation we find that under the constraints of the current Higgs data this light scalar can be accessible at the LHC-14 with an integrated luminosity over 300 fb$^{-1}$.Comment: Accepted by JHE

Responsible Active Learning via Human-in-the-loop Peer Study

Active learning has been proposed to reduce data annotation efforts by only manually labelling representative data samples for training. Meanwhile, recent active learning applications have benefited a lot from cloud computing services with not only sufficient computational resources but also crowdsourcing frameworks that include many humans in the active learning loop. However, previous active learning methods that always require passing large-scale unlabelled data to cloud may potentially raise significant data privacy issues. To mitigate such a risk, we propose a responsible active learning method, namely Peer Study Learning (PSL), to simultaneously preserve data privacy and improve model stability. Specifically, we first introduce a human-in-the-loop teacher-student architecture to isolate unlabelled data from the task learner (teacher) on the cloud-side by maintaining an active learner (student) on the client-side. During training, the task learner instructs the light-weight active learner which then provides feedback on the active sampling criterion. To further enhance the active learner via large-scale unlabelled data, we introduce multiple peer students into the active learner which is trained by a novel learning paradigm, including the In-Class Peer Study on labelled data and the Out-of-Class Peer Study on unlabelled data. Lastly, we devise a discrepancy-based active sampling criterion, Peer Study Feedback, that exploits the variability of peer students to select the most informative data to improve model stability. Extensive experiments demonstrate the superiority of the proposed PSL over a wide range of active learning methods in both standard and sensitive protection settings.Comment: 15 pages, 8 figure

Knowledge-Aware Federated Active Learning with Non-IID Data

Federated learning enables multiple decentralized clients to learn collaboratively without sharing the local training data. However, the expensive annotation cost to acquire data labels on local clients remains an obstacle in utilizing local data. In this paper, we propose a federated active learning paradigm to efficiently learn a global model with limited annotation budget while protecting data privacy in a decentralized learning way. The main challenge faced by federated active learning is the mismatch between the active sampling goal of the global model on the server and that of the asynchronous local clients. This becomes even more significant when data is distributed non-IID across local clients. To address the aforementioned challenge, we propose Knowledge-Aware Federated Active Learning (KAFAL), which consists of Knowledge-Specialized Active Sampling (KSAS) and Knowledge-Compensatory Federated Update (KCFU). KSAS is a novel active sampling method tailored for the federated active learning problem. It deals with the mismatch challenge by sampling actively based on the discrepancies between local and global models. KSAS intensifies specialized knowledge in local clients, ensuring the sampled data to be informative for both the local clients and the global model. KCFU, in the meantime, deals with the client heterogeneity caused by limited data and non-IID data distributions. It compensates for each client's ability in weak classes by the assistance of the global model. Extensive experiments and analyses are conducted to show the superiority of KSAS over the state-of-the-art active learning methods and the efficiency of KCFU under the federated active learning framework.Comment: 14 pages, 12 figure

A SM-like Higgs near 125 GeV in low energy SUSY: a comparative study for MSSM and NMSSM

Motivated by the recent LHC hints of a Higgs boson around 125 GeV, we assume a SM-like Higgs with the mass 123-127 GeV and study its implication in low energy SUSY by comparing the MSSM and NMSSM. We consider various experimental constraints at 2-sigma level (including the muon g-2 and the dark matter relic density) and perform a comprehensive scan over the parameter space of each model. Then in the parameter space which is allowed by current experimental constraints and also predicts a SM-like Higgs in 123-127 GeV, we examine the properties of the sensitive parameters (like the top squark mass and the trilinear coupling A_t) and calculate the rates of the di-photon signal and the VV^* (V=W,Z) signals at the LHC. Our typical findings are: (i) In the MSSM the top squark and A_t must be large and thus incur some fine-tuning, which can be much ameliorated in the NMSSM; (ii) In the MSSM a light stau is needed to enhance the di-photon rate of the SM-like Higgs to exceed its SM prediction, while in the NMSSM the di-photon rate can be readily enhanced in several ways; (iii) In the MSSM the signal rates of pp -> h -> VV^* at the LHC are never enhanced compared with their SM predictions, while in the NMSSM they may get enhanced significantly; (iv) A large part of the parameter space so far survived will be soon covered by the expected XENON100(2012) sensitivity (especially for the NMSSM).Comment: Version in JHEP (refs added

The regulations on cortical activation and functional connectivity of the dorsolateral prefrontal cortex-primary somatosensory cortex elicited by acupuncture with reinforcing-reducing manipulation

IntroductionTraditional acupuncture with reinforcing-reducing manipulation is essential for clinical effectiveness, whereas the underlying central mechanism of it remains unknown. This study with multiple-channels functional near-infrared spectroscopy (fNIRS) aims to explore cerebral-response modes during acupuncture with reinforcing-reducing manipulations.Materials and methodsFunctional near-infrared spectroscopy data were recorded from 35 healthy participants during the lifting-thrusting reinforcing manipulation, the lifting-thrusting reducing manipulation, and the even reinforcing-reducing manipulation with lifting-thrusting. The general linear model based (GLM) cortical activation analysis and the functional connectivity (FC) based on region of interest (ROI) analysis were combined to be conducted.ResultsIn comparison with the baseline, the results showed that three acupuncture with reinforcing-reducing manipulations similarly induced the hemodynamic responses in the bilateral dorsolateral prefrontal cortex (DLPFC) and increased FC between the DLPFC and primary somatosensory cortex (S1). Specifically, the even reinforcing-reducing manipulation deactivated the bilateral DLPFC, the frontopolar area (FP), the right primary motor cortex (M1), the bilateral S1, and the bilateral secondary somatosensory cortex (S2); The reducing manipulation deactivated the bilateral DLPFC; The reinforcing manipulation activated the bilateral DLPFC, the left S1, and the right S2. The between-group comparisons indicated that the reinforcing-reducing manipulation induced opposite hemodynamic responses in the bilateral DLPFC and the left S1 and exhibited different FC patterns in the left DLPFC-S1, within the right DLPFC, and between the left S1 and the left orbitofrontal cortex (OFC).ConclusionThese findings verified the feasibility of fNIRS for investigating cerebral functional activities of acupuncture manipulations, suggesting that the regulations on the DLPFC-S1 cortex may be the potential central mechanism for the realization of acupuncture with reinforcing-reducing manipulation’s effect.Clinical trial registrationClinicalTrials.gov, identifier, ChiCTR2100051893