Study on the mechanism of open-flavor strong decays

The open-flavor strong decays are studied based on the interaction of potential quark model. The decay process is related to the s-channel contribution of the same scalar confinment and one-gluon-exchange(OGE) interaction in the quark model. After we adopt the prescription of massive gluons in time-like region from the lattice calculation, the approximation of four-fermion interaction is applied. The numerical calculation is performed to the meson decays in $u$, $d$, $s$ light flavor sector. The analysis of the $D/S$ ratios of $b_1\rightarrow \omega \pi$ and $a_1\rightarrow \rho \pi$ show that the scalar interaction should be dominant in the open-flavor decays

Testing Lorentz Invariance with Ultra High Energy Cosmic Ray Spectrum

The GZK cutoff predicted at the Ultra High Energy Cosmic Ray (UHECR) spectrum as been observed by the HiRes and Auger experiments. The results put severe constraints on the effect of Lorentz Invariance Violation(LIV) which has been introduced to explain the absence of GZK cutoff indicated in the AGASA data. Assuming homogeneous source distribution with a single power law spectrum, we calculate the spectrum of UHECRs observed on Earth by taking the processes of photopion production, $e^+e^-$ pair production and adiabatic energy loss into account. The effect of LIV is also taken into account in the calculation. By fitting the HiRes monocular spectra and the Auger combined spectra, we show that the LIV parameter is constrained to $\xi=-0.8^{+3.2}_{-0.5}\times10^{-23}$ and $0.0^{+1.0}_{-0.4}\times10^{-23}$ respectively, which is well consistent with strict Lorentz Invariance up to the highest energy.Comment: Accepted for publication in Physical Review D 12 pages, 4 figure

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

<p>Abstract</p> <p>Background</p> <p>Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.</p> <p>Methods</p> <p>Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.</p> <p>Results</p> <p>The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.</p> <p>Conclusions</p> <p>The results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.</p

Dynamical study of the possible molecular state X(3872) with the s-channel one gluon exchange interaction

The recently observed X(3872) resonance, which is difficult to be assigned a conventional $c\bar{c}$ charmonium state in the quark model, may be interpreted as a molecular state. Such a molecular state is a hidden flavor four quark state because of its charmonium-like quantum numbers. The s-channel one gluon exchange is an interaction which only acts in the hidden flavor multi-quark system. In this paper, we will study the X(3872) and other similiar hidden flavor molecular states in a quark model by taking into account of the s-channel one gluon exchange interaction

B→KK∗B \to K K^{*} Decays in the Perturbative QCD Approach

We calculate the branching ratios and CP-violating asymmetries for B^0 \to K^{0} \ov K^{*0}, \ov K^{0} K^{*0}, $K^+ K^{*-}$, $K^- K^{*+}$, and B^+\to K^+ \ov K^{*0} and \ov K^0 K^{*+} decays by employing the low energy effective Hamiltonian and the perturbative QCD (pQCD) factorization approach. The theoretical predictions for the branching ratios are Br(B^0/\ov B^0 \to K^{\pm} K^{*\mp}) \approx 7.4 \times 10^{-8}, Br(B^0/\ov B^0 \to K^{0} \ov K^{*0}(\ov K^{0} K^{*0})) \approx 19.6 \times 10^{-7}, Br(B^+\to K^+ \ov K^{*0}) \approx 3 \times 10^{-7} and Br(B^+\to K^{*+} \ov K^0) \approx 18.3 \times 10^{-7}, which are consistent with currently available experimental upper limits. We also predict large CP-violating asymmetries in these decays: A_{CP}^{dir}(K^\pm \ov K^{*0})\approx -20 %, A_{CP}^{dir}(K^{*\pm} \ov K^0)\approx -49%, which can be tested by the forthcoming B meson experiments.Comment: 25 pages, 7 figures, RevTex, some corrections on the numerical results and contents, typos removed, new references adde

Exact calculations of vertex sˉγb\bar{s}\gamma b and sˉZb\bar{s} Z b in the unitary gauge

In this paper, we present the exact calculations for the vertex $\bar{s}\gamma b$ and $\bar{s} Z b$ in the unitary gauge. We found that (a) the divergent- and $\mu$-dependent terms are left in the effective vertex function $\Gamma^\gamma_\mu(p,k)$ for $b \to s \gamma$ transition even after we sum up the contributions from four related Feynman diagrams; (b) for an on-shell photon, such terms do not contribute et al; (c) for off-shell photon, these terms will be canceled when the contributions from both vertex $\bar{s}\gamma b$ and $\bar{s} Z b$ are taken into account simultaneously, and therefore the finite and gauge independent function $Z_0(x_t)=C_0(x_t)+ D_0(x_t)/4$, which governs the semi-leptonic decay $b \to s l^- l^+$, is derived in the unitary gauge.Comment: 13 pages, 2 figures, Revte

Association Graph Learning for Multi-Task Classification with Category Shifts

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, individual tasks do not contain complete training data for the categories in the test set. To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks. To this end, we propose learning an association graph to transfer knowledge among tasks for missing classes. We construct the association graph with nodes representing tasks, classes and instances, and encode the relationships among the nodes in the edges to guide their mutual knowledge transfer. By message passing on the association graph, our model enhances the categorical information of each instance, making it more discriminative. To avoid spurious correlations between task and class nodes in the graph, we introduce an assignment entropy maximization that encourages each class node to balance its edge weights. This enables all tasks to fully utilize the categorical information from related tasks. An extensive evaluation on three general benchmarks and a medical dataset for skin lesion classification reveals that our method consistently performs better than representative baselines

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a probabilistic framework based on variational Bayesian inference, by incorporating uncertainty into neural network weights. We couple domain invariance in a probabilistic formula with the variational Bayesian inference. This enables us to explore domain-invariant learning in a principled way. Specifically, we derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network. We empirically demonstrate the effectiveness of our proposal on four widely used cross-domain visual recognition benchmarks. Ablation studies validate the synergistic benefits of our Bayesian treatment when jointly learning domain-invariant representations and classifiers for domain generalization. Further, our method consistently delivers state-of-the-art mean accuracy on all benchmarks.Comment: accepted to ICML 202

Learning Variational Neighbor Labels for Test-Time Domain Generalization

This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed at unseen target domains. We follow the strict separation of source training and target testing but exploit the value of the unlabeled target data itself during inference. We make three contributions. First, we propose probabilistic pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. We formulate the generalization at test time as a variational inference problem by modeling pseudo labels as distributions to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels. Second, we learn variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels. Third, to learn the ability to incorporate more representative target information and generate more precise and robust variational neighbor labels, we introduce a meta-generalization stage during training to simulate the generalization procedure. Experiments on six widely-used datasets demonstrate the benefits, abilities, and effectiveness of our proposal.Comment: Under revie

In situ growth of SnO2 on graphene nanosheets as advanced anode materials for rechargeable lithium batteries

Graphene with a single layer of carbon atoms densely packed in a honeycomb crystal lattice is one of attractive materials for the intercalation of lithium ion, but it has low volumetric capacity owing to low tap density. We report a method for in situ growth of SnO2 on graphene nanosheets (SGN) as anode materials for rechargeable lithium batteries. The results indicated that the SnO2 nanoparticles with size in the range of 5-10 nm and a polycrystalline structure are homogeneously supported on graphene nanosheets. The charge and discharge capacities of SGN attained to 1559.7 and 779.7 mAh/g in the first cycle at a current density of 300 mA/g. The specific discharge capacities remained at 620 mAh⋅g-1 in the 200th cycle. The SGN exhibits a superior Listorage performance with good cycle life and high capacity