Recently, scientists have made great progresses in experiments in searching for the excited states of $Xi_{b}$ and $Lambda_{b}$ baryons such as the $Lambda_{b}(6072)$, $Lambda_{b}(6146)$, $Lambda_{b}(6152)$, $Xi_{b}(6227)$, $Xi_{b}(6100)$, $Xi_{b}(63
27)$ and $Xi_{b}(6333)$. Stimulated by these progresses, we give a systematical analysis about the $1D$ and $2D$ states of $Xi_{b}$ and $Lambda_{b}$ baryons with the method of QCD sum rules. By constructing three types of interpolating currents, we calculate the masses and pole residues of these heavy baryons with different excitation modes $(L_{rho},L_{lambda})=(0,2)$, $(2,0)$ and $(1,1)$. As a result, we decode the inner structures of $Lambda_{b}(6146)$, $Lambda_{b}(6152)$, $Xi_{b}(6327)$ and $Xi_{b}(6333)$, and favor assigning these states as the $1D$ baryons with the quantum numbers $(L_{rho},L_{lambda})=(0,2)$ and $frac{3}{2}^{+}$, $frac{5}{2}^{+}$, $frac{3}{2}^{+}$ and $frac{5}{2}^{+}$, respectively. In addition, the predictions about the masses and pole residues of the other $1D$ and $2D$ states of $Xi_{b}$ and $Lambda_{b}$ baryons in this paper are helpful in studying the D-wave bottom baryons in experiments in the future.
In this paper, we develop a new class of high-order energy-preserving schemes for the Korteweg-de Vries equation based on the quadratic auxiliary variable technique, which can conserve the original energy of the system. By introducing a quadratic aux
iliary variable, the original system is reformulated into an equivalent form with a modified quadratic energy, where the way of the introduced variable naturally produces a quadratic invariant of the new system. A class of Runge-Kutta methods satisfying the symplectic condition is applied to discretize the reformulated model in time, arriving at arbitrarily high-order schemes, which naturally conserve the modified quadratic energy and the produced quadratic invariant. Under the consistent initial condition, the proposed methods are rigorously proved to maintain the original energy conservation law of the Korteweg-de Vries equation. In order to match the high order precision of time, the Fourier pseudo-spectral method is employed for spatial discretization, resulting in fully discrete energy-preserving schemes. To solve the proposed nonlinear schemes effectively, we present a very efficient practically-structure-preserving iterative technique, which not only greatly saves the calculation cost, but also achieves the purpose of practically preserving structure. Ample numerical results are addressed to confirm the expected order of accuracy, conservative property and efficiency of the proposed schemes. This new class of numerical strategies is rather general so that they can be readily generalized for any conservative systems with a polynomial energy.
In this article, we construct the six-quark currents with the $J^P=0^+$, $0^-$, $1^+$ and $1^-$ to study the $Lambda_c$$Lambda_c$ dibaryon and $Lambda_c$$bar{Lambda}_c$ baryonium states via QCD sum rules. We consider the vacuum condensates up to dime
nsion 16 and truncation of the order $mathcal{O}(alpha_s^k )$ with $kleq3$. The predicted masses are $5.11_{-0.12}^{+0.15}$GeV, $4.66_{-0.06}^{+0.10}$GeV, $4.99_{-0.09}^{+0.10}$GeV $4.68^{+0.08}_{-0.08}$GeV for the $J^P=0^+$, $0^-$, $1^+$ and $1^-$ states, respectively, which can be confronted to the experimental data in the future considering the high integrated luminosity at the center-of-mass energy about $4.8,rm{GeV}$ at the BESIII. We find the terms with $frac{3}{2}< k leq 3$ do play a tiny role, and we can ignore these terms safely in the QCD sum rules.
In this paper, we present SpecAugment++, a novel data augmentation method for deep neural networks based acoustic scene classification (ASC). Different from other popular data augmentation methods such as SpecAugment and mixup that only work on the i
nput space, SpecAugment++ is applied to both the input space and the hidden space of the deep neural networks to enhance the input and the intermediate feature representations. For an intermediate hidden state, the augmentation techniques consist of masking blocks of frequency channels and masking blocks of time frames, which improve generalization by enabling a model to attend not only to the most discriminative parts of the feature, but also the entire parts. Apart from using zeros for masking, we also examine two approaches for masking based on the use of other samples within the minibatch, which helps introduce noises to the networks to make them more discriminative for classification. The experimental results on the DCASE 2018 Task1 dataset and DCASE 2019 Task1 dataset show that our proposed method can obtain 3.6% and 4.7% accuracy gains over a strong baseline without augmentation (i.e. CP-ResNet) respectively, and outperforms other previous data augmentation methods.
In this article, we firstly derive two QCD sum rules QCDSR I and QCDSR II which are respectively used to extract observable quantities of the ground states and the first radially excited states of D-wave vector $rho$ and $phi$ mesons. In our calculat
ions, we consider the contributions of vacuum condensates up to dimension-7 in the operator product expansion. The predicted masses for $1^{3}D_{1}$ $rho$ meson and $2^{3}D_{1}$ $phi$ meson are consistent well with the experimental data of $rho$($1700$) and $phi$($2170$). Besides, our analysis indicates that it is reliable to assign the recent reported $Y$($2040$) state as the $2^{3}D_{1}$ $rho$ meson. Finally, we obtain the decay constants of these states with QCDSR I and QCDSR II. These predictions are helpful not only to reveal the structure of the newly observed $Y$($2040$) state but also to establish $phi$ meson and $rho$ meson families.
Weakly labelled audio tagging aims to predict the classes of sound events within an audio clip, where the onset and offset times of the sound events are not provided. Previous works have used the multiple instance learning (MIL) framework, and exploi
ted the information of the whole audio clip by MIL pooling functions. However, the detailed information of sound events such as their durations may not be considered under this framework. To address this issue, we propose a novel two-stream framework for audio tagging by exploiting the global and local information of sound events. The global stream aims to analyze the whole audio clip in order to capture the local clips that need to be attended using a class-wise selection module. These clips are then fed to the local stream to exploit the detailed information for a better decision. Experimental results on the AudioSet show that our proposed method can significantly improve the performance of audio tagging under different baseline network architectures.
For underwater applications, the effects of light absorption and scattering result in image degradation. Moreover, the complex and changeable imaging environment makes it difficult to provide a universal enhancement solution to cope with the diversit
y of water types. In this letter, we present a novel underwater image enhancement (UIE) framework termed SCNet to address the above issues. SCNet is based on normalization schemes across both spatial and channel dimensions with the key idea of learning water type desensitized features. Considering the diversity of degradation is mainly rooted in the strong correlation among pixels, we apply whitening to de-correlates activations across spatial dimensions for each instance in a mini-batch. We also eliminate channel-wise correlation by standardizing and re-injecting the first two moments of the activations across channels. The normalization schemes of spatial and channel dimensions are performed at each scale of the U-Net to obtain multi-scale representations. With such latent encodings, the decoder can easily reconstruct the clean signal, and unaffected by the distortion types caused by the water. Experimental results on two real-world UIE datasets show that the proposed approach can successfully enhance images with diverse water types, and achieves competitive performance in visual quality improvement.
The field of physics-based animation is gaining importance due to the increasing demand for realism in video games and films, and has recently seen wide adoption of data-driven techniques, such as deep reinforcement learning (RL), which learn control
from (human) demonstrations. While RL has shown impressive results at reproducing individual motions and interactive locomotion, existing methods are limited in their ability to generalize to new motions and their ability to compose a complex motion sequence interactively. In this paper, we propose a physics-based universal neural controller (UniCon) that learns to master thousands of motions with different styles by learning on large-scale motion datasets. UniCon is a two-level framework that consists of a high-level motion scheduler and an RL-powered low-level motion executor, which is our key innovation. By systematically analyzing existing multi-motion RL frameworks, we introduce a novel objective function and training techniques which make a significant leap in performance. Once trained, our motion executor can be combined with different high-level schedulers without the need for retraining, enabling a variety of real-time interactive applications. We show that UniCon can support keyboard-driven control, compose motion sequences drawn from a large pool of locomotion and acrobatics skills and teleport a person captured on video to a physics-based virtual avatar. Numerical and qualitative results demonstrate a significant improvement in efficiency, robustness and generalizability of UniCon over prior state-of-the-art, showcasing transferability to unseen motions, unseen humanoid models and unseen perturbation.
Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL. However, research in model-based RL has not been very standardized. It is fairly common for authors to expe
riment with self-designed environments, and there are several separate lines of research, which are sometimes closed-sourced or not reproducible. Accordingly, it is an open question how these various existing MBRL algorithms perform relative to each other. To facilitate research in MBRL, in this paper we gather a wide collection of MBRL algorithms and propose over 18 benchmarking environments specially designed for MBRL. We benchmark these algorithms with unified problem settings, including noisy environments. Beyond cataloguing performance, we explore and unify the underlying algorithmic differences across MBRL algorithms. We characterize three key research challenges for future MBRL research: the dynamics bottleneck, the planning horizon dilemma, and the early-termination dilemma. Finally, to maximally facilitate future research on MBRL, we open-source our benchmark in http://www.cs.toronto.edu/~tingwuwang/mbrl.html.
The tunneling junction between one-dimensional topological superconductor and integer (fractional) topological insulator (TI), realized via point contact, is investigated theoretically with bosonization technology and renormalization group methods. F
or the integer TI case, in a finite range of edge interaction parameter, there is a non-trivial stable fixed point which corresponds to the physical picture that the edge of TI breaks up into two sections at the junction, with one side coupling strongly to the Majorana fermion and exhibiting perfect Andreev reflection, while the other side decouples, exhibiting perfect normal reflection at low energies. This fixed point can be used as a signature of the Majorana fermion and tested by nowadays experiment techniques. For the fractional TI case, the universal low-energy transport properties are described by perfect normal reflection, perfect Andreev reflection, or perfect insulating fixed points dependent on the filling fraction and edge interaction parameter of fractional TI.
