Search

Combination of recent measurements for $h \to Z \gamma$ from ALTLAS and CMS shows an excess that the ratio of the observed and standard model (SM) predicted branching ratios $\mu = (\sigma\cdot{\cal B})_{\mathrm{obs}}/(\sigma\cdot{\cal B})_{\mathrm{SM}}$ is $2.2\pm 0.7$. If confirmed, it is a signal of new physics (NP) beyond SM. We study NP explanation for this excess. In general, for a given model it also affects the process $h \to \gamma \gamma$. Since measured branching ratio for this process agrees with SM prediction well, the model is constrained severely. We find that a minimally fermion singlets and doublet extended NP model can explain simultaneously the current data for $h \to Z \gamma$ and $h\to \gamma\gamma$. There are two solutions. One is the SM amplitude $c_Z^{\text{SM}}$ is enhanced by $\delta c_Z$ for $h \to Z \gamma$ to the observed value, but the $h \to \gamma \gamma$ amplitude $c_\gamma^{\text{SM}} +\delta c_\gamma$ is decreased to $-c_\gamma^{\text{SM}}$ to give the observed branching ratio. This seems to be a contrived solution that although cannot be ruled out simply using branching ratio measurements. We, however, find another solution which naturally enhances the $h \to Z \gamma$ to the measured value, but keeps the $h \to \gamma\gamma$ close to its SM prediction. We also comment on some phenomenology of these new fermions., Comment: 8 pages, 7 figures

Due to finite masses and mixing, for neutrinos propagation in space-time, there is a chiral oscillation between left- and right- chiral neutrinos, besides the usual oscillation between different generations. The probability of chiral oscillation is suppressed by a factor of $m^2/E^2$ making the effect small for relativistic neutrinos. However, for non-relativistic neutrinos, this effect can be significant. In matter, the equation of motion is modified. When neutrinos produced in weak interaction pass through the matter, the eigen-energies are split into two different ones depending on the helicity of the neutrino. This results in different oscillation behavior for neutrinos with different helicity, in particular there is a new resonant effect related to the helicity state of neutrino different than the usual MSW effect. For Majorana neutrinos, chiral oscillation also depends on Majorana phases., Comment: 7 pages, 3 figures, published version

In a periodic system with parity-time (PT) symmetry, spontaneous breaking of PT symmetry occurs when non-Hermiticity exceeds a critical value, thereby dividing the Bloch bands into PT-exact phase and PT-broken phase in two momentum regimes.From another perspective, PT-broken momentum regime can be deemed as a momentum gap if we consider Bloch k as eigenvalue of the problem instead.The topological aspects of such a type of momentum gap (PT-broken regime) remain unexplored. Here, we study the topological properties of momentum bandgap in one-dimensional (1D) PT-symmetric photonic crystals (PCs).k*p analysis shows that reversing the system's non-Hermiticity would lead to a topological phase transition, which is manifested as a local geometric phase along the momentum band.As a consequence of such geometric phase, a temporal boundary state (TBS) exists in the middle of momentum gap. Its robustness against perturbations and disorders is numerically verified by temporal simulations.

In Type-II seesaw model, an electroweak triplet scalar field $\Delta$ with a non-zero vacuum expectation value (vev) $v_\Delta$ is introduced to facilitate the generation of small neutrino masses. A non-zero $v_\Delta$ also affects the W mass through the electroweak $\rho$ parameter, making it to be less than 1 as predicted by standard model (SM). The component fields in $\Delta$ come along introduce additional contributions to reduce the SM rare neutrino trident scattering cross section. These fields also induce new processes not existed in SM, such as $l_i \to \overline{ l_j} l_k l_l$ and $l_i \to l_j \gamma$. There are severe constraints on these processes which limit the effects on neutrino trident scattering and the $\rho$ parameter and therefore the W mass. The newly measured W mass by CDF makes the central value of $\rho$ parameter to be larger than 1, even larger than previously expected. Combining neutrinoless double beta decay, direct neutrino mass and oscillation data, we find a lower limit for $v_\Delta$ as a function of the triplet scalar mass $m_\Delta$, $v_\Delta > (6.3 \sim 8.4) \mathrm{eV} (100 \mathrm{GeV}/m_\Delta)$. To have significant effect on $\rho$ in this model, $v_\Delta$ needs to be in the range of a GeV or so. However this implies a very small $m_\Delta$ which is ruled out by data. We conclude that the effect of triplet vev $v_\Delta$ on the W mass can be neglected. We also find that at 3$\sigma$ level, the deviation of the ratio for Type-II Seesaw to SM neutrino trident scattering cross section predictions is reduced to be below 1, but is restricted to be larger than 0.98., Comment: 9 pages, 5 figures, match the published version in PLB

We study the magnetic moments and transition magnetic moments of $P_c$ and $P_{cs}$ states in the molecular picture. We first revisit the magnetic moments of $P_c$ states as the $S$ wave molecular states without coupled channel effects. The coupled channel effects and the $D$ wave contributions are then investigated carefully. The coupled channel effects contribute to the change of $0.1\sim 0.4$ nuclear magneton $\mu_N$ for most cases while the $D$ wave only induces the variation of less than $0.03 ~\mu_N$. In addition, we obtain the transition magnetic moments between different $P_c$ states and the related electromagnetic decay widths of $P_c'\to P_c\gamma$. The magnetic moments of $P_{cs}$ are much different for the assumption of spin being 1/2 and 3/2. The study of electromagnetic properties will help us disclose further the structure of these unconventional states., Comment: 8 pages, 5 tables; version appeared in Phys. Rev. D

Due to its low storage cost and fast query speed, hashing has been widely used in large-scale image retrieval tasks. Hash bucket search returns data points within a given Hamming radius to each query, which can enable search at a constant or sub-linear time cost. However, existing hashing methods cannot achieve satisfactory retrieval performance for hash bucket search in complex scenarios, since they learn only one hash code for each image. More specifically, by using one hash code to represent one image, existing methods might fail to put similar image pairs to the buckets with a small Hamming distance to the query when the semantic information of images is complex. As a result, a large number of hash buckets need to be visited for retrieving similar images, based on the learned codes. This will deteriorate the efficiency of hash bucket search. In this paper, we propose a novel hashing framework, called multiple code hashing (MCH), to improve the performance of hash bucket search. The main idea of MCH is to learn multiple hash codes for each image, with each code representing a different region of the image. Furthermore, we propose a deep reinforcement learning algorithm to learn the parameters in MCH. To the best of our knowledge, this is the first work that proposes to learn multiple hash codes for each image in image retrieval. Experiments demonstrate that MCH can achieve a significant improvement in hash bucket search, compared with existing methods that learn only one hash code for each image., Comment: 12 pages, 9 figures, 3 tables

Traditional person re-identification (ReID) methods typically represent person images as real-valued features, which makes ReID inefficient when the gallery set is extremely large. Recently, some hashing methods have been proposed to make ReID more efficient. However, these hashing methods will deteriorate the accuracy in general, and the efficiency of them is still not high enough. In this paper, we propose a novel hashing method, called deep multi-index hashing (DMIH), to improve both efficiency and accuracy for ReID. DMIH seamlessly integrates multi-index hashing and multi-branch based networks into the same framework. Furthermore, a novel block-wise multi-index hashing table construction approach and a search-aware multi-index (SAMI) loss are proposed in DMIH to improve the search efficiency. Experiments on three widely used datasets show that DMIH can outperform other state-of-the-art baselines, including both hashing methods and real-valued methods, in terms of both efficiency and accuracy., Comment: 10 pages, 6 figures, 2 tables

Due to its low storage cost and fast query speed, hashing has been widely used for large-scale approximate nearest neighbor (ANN) search. Bucket search, also called hash lookup, can achieve fast query speed with a sub-linear time cost based on the inverted index table constructed from hash codes. Many metrics have been adopted to evaluate hashing algorithms. However, all existing metrics are improper to evaluate the hash codes for bucket search. On one hand, all existing metrics ignore the retrieval time cost which is an important factor reflecting the performance of search. On the other hand, some of them, such as mean average precision (MAP), suffer from the uncertainty problem as the ranked list is based on integer-valued Hamming distance, and are insensitive to Hamming radius as these metrics only depend on relative Hamming distance. Other metrics, such as precision at Hamming radius R, fail to evaluate global performance as these metrics only depend on one specific Hamming radius. In this paper, we first point out the problems of existing metrics which have been ignored by the hashing community, and then propose a novel evaluation metric called radius aware mean average precision (RAMAP) to evaluate hash codes for bucket search. Furthermore, two coding strategies are also proposed to qualitatively show the problems of existing metrics. Experiments demonstrate that our proposed RAMAP can provide more proper evaluation than existing metrics.

Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse learning is to use $L_1$ regularization. In this paper, we propose a novel method, called proximal \mbox{SCOPE}~(\mbox{pSCOPE}), for distributed sparse learning with $L_1$ regularization. pSCOPE is based on a \underline{c}ooperative \underline{a}utonomous \underline{l}ocal \underline{l}earning~(\mbox{CALL}) framework. In the \mbox{CALL} framework of \mbox{pSCOPE}, we find that the data partition affects the convergence of the learning procedure, and subsequently we define a metric to measure the goodness of a data partition. Based on the defined metric, we theoretically prove that pSCOPE is convergent with a linear convergence rate if the data partition is good enough. We also prove that better data partition implies faster convergence rate. Furthermore, pSCOPE is also communication efficient. Experimental results on real data sets show that pSCOPE can outperform other state-of-the-art distributed methods for sparse learning.