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In recent years, tagging system has become a building block o summarize the content of items for further functions like retrieval or personalized recommendation in various web applications. One nontrivial requirement is to precisely deliver a list of suitable items when users interact with the systems via inputing a specific tag (i.e. a query term). Different from traditional recommender systems, we need deal with a collaborative retrieval (CR) problem, where both characteristics of retrieval and recommendation should be considered to model a ternary relationship involved with query × user × item . Recently, several works are proposed to study CR task from users’ perspective. However, they miss a significant challenge raising from the sparse content of items. In this work, we argue that items will suffer from the sparsity problem more severely than users, since items are usually observed with fewer features to support a feature-based or content-based algorithm. To tackle this problem, we aim to sufficiently explore the sophisticated relationship of each query × user × item triple from items’ perspective. By integrating item-based collaborative information for this joint task, we present an alternative factorized model that could better evaluate the ranks of those items with sparse information for the given query–user pair. In addition, we suggest to employ a recently proposed Bayesian Personalized Ranking (BPR) algorithm to optimize latent collaborative retrieval problem from pairwise learning perspective. The experimental results on two real-world datasets, (i.e. Last.fm, Yelp ), verified the efficiency and effectiveness of our proposed approach at top-k ranking metric. [ABSTRACT FROM AUTHOR]

Gradient features are known to be effective for full-reference (FR) image quality assessment (IQA). However, only a few metrics utilize gradient features for no-reference (NR) IQA. To investigate the potential benefits of gradient magnitude and phase for NR-IQA, we propose a novel and effective local-statistical-feature extraction metric, namely Local Gradient Patterns (LGP), for general-purpose NR-IQA. Using a Gaussian partial derivative filter, the image is first decomposed into two complementary components: gradient magnitude and phase. The local statistical features (e.g., the conditional probability distributions) are then extracted from the complementary components, using the derived local gradient magnitude and phase pattern operators. Finally, to facilitate NR-IQA, local statistical features that convey important structural information are mapped to the subjective mean opinion score of the image, using a support vector regression (SVR) procedure. We evaluated our proposed LGP metric using images from two publicly available test databases; the results confirm that the proposed LGP metric provides predictive performance that is superior to most state-of-the-art NR-IQA metrics and has an acceptable level of computational complexity. [ABSTRACT FROM AUTHOR]

The charmoniumlike state Y(4260) is described as predominantly a D1̅D molecule in a coupled-channel quark model [Phys. Rev. D 96, 114022 (2017)]. The heavy quark spin symmetry (HQSS) thus implies the possible emergence of its heavy quark spin partners with molecular configurations as D1̅D* and D*²̅D* below these charmed mesons' thresholds. We analyze the probabilities of various intermediate charmed meson loops for JPC=1-- exotic state Y(4360) and find that the channel D1̅D* couples more strongly around its mass regime, and the coupling behavior remains the same even if the mass of Y(4360) is pushed closer to D1̅D* threshold. This enlightens that the most favorable molecular scenario for the Y(4360) could be D1̅D*, and hence it can be interpreted as HQSS partner of the Y(4260). We also find the strong coupling behavior of D*²̅D* channel with the ψ(4415), which makes it a good candidate for a dominant D*²̅D* molecule. We discuss the important decay patterns of these resonances to disentangle their long- and short-distance structures. [ABSTRACT FROM AUTHOR]

Based on the linear matrix inequality method, in this note we introduce the robust stability of uncertain linear stochastic differential delay systems with delay dependence. The parameter uncertainty is normbounded and the delays are time varying. We then extend the proposed theory to discuss the robust stabilization of uncertain stochastic differential delay systems. [ABSTRACT FROM AUTHOR]

Explores character traits through international heroes depicted in children's literature. Lessons learned by children in reading about international heroes of their age; Character traits which are universally accepted including personal courage, caring for others, perseverance and optimism. INSET: Heroic Inspiration.

Time-delay interferometry is put forward to improve the signal-to-noise ratio of space-borne gravitational wave detectors by canceling the large laser phase noise with different combinations of measured data. Based on the Michelson data combination, the sensitivity function of the detector can be obtained by averaging the all-sky wave source positions. At present, there are two main methods to encode gravitational wave signal into detector. One is to adapt gravitational wave polarization angle depending on the arm orientation in the gravitational wave frame, and the other is to divide the gravitational wave signal into plus and cross polarizations in the detector frame. Although there are some attempts using the first method to provide the analytical expression of sensitivity function, only a semianalytical one could be obtained. Here, starting with the second method, we demonstrate the equivalence of both methods. First time to obtain the full analytical expression of sensitivity function, which provides a fast and accurate mean to evaluate and compare the performance of different space-borne detectors, such as LISA and TianQin. [ABSTRACT FROM AUTHOR]

Invoked by the recent CMS observation regarding candidates of the χb(3P) multiplet, we analyze the hyperfine and mass splittings among the 3P multiplet in our unquenched quark model studies. The mass difference of χb2 and χb1 in the 3P multiplet measured by the CMS Collaboration (10.6±0.64±0.17  MeV) is very close to our theoretical prediction (12 MeV). Our corresponding mass splitting of χb1 and χb0 enables us to predict more precisely the mass of χb0(3P) to be (10490±3) MeV. Moreover, we predict ratios of the radiative decays of χbJ(nP) candidates, both in the unquenched quark model and quark potential model. Our predicted relative branching fraction of χb0(3P)→Υ(3S)γ is one order of magnitude smaller than χb2(3P); this naturally explains the nonobservation of χb0(3P) in the recent CMS search. We hope these results provide useful references for forthcoming experimental searches. [ABSTRACT FROM AUTHOR]

We use the frequency comparisons of two kinds of clocks to reanalyze the test of special relativity in the Robertson-Mansouri-Sexl kinematical framework, which involves the Michelson-Morley experiment and the optical atomic clock-comparison experiment. The light clock involved in the Michelson-Morley experiment is composed of two mirrors and a photon propagating back and forth between them, which can be regarded as a kind of structure different from the point-particle clock. Similarly, the inner structure of the atom in optical atomic clock-frequency comparisons should also be considered, which has not been mentioned before. Because of the structure effect, our result shows that the measurable parameter in the optical atomic clock-comparison experiment should be α-(β+2δ)/3 instead of the widely recognized α, where β and δ are defined in the text. [ABSTRACT FROM AUTHOR]

We calculate the probabilities of various charmed meson molecules for X(4260) under the framework of the ³P0 model. The results indicate that, even though heavy quark spin symmetry forbids S wave coupling of D1D to the ³S1 charmonia [ψ(nS)], the D wave coupling is allowed and not negligible. Under this symmetry, the D1D can couple to ³D1 charmonia [ψ(nD)] via both S and D waves, and the overall coupling is around three times larger than that of ψ(nS). The X(4260) cannot be a pure molecule but a mixture of a charmonium and various charmed meson components. Since the D1D couples strongly to ψ(nD), our results suggest that, in the D1D molecular picture, the charmonium core of X(4260) is ψ(nD) instead of ψ(nS). As a result, the experimental fact that the R ratio has a dip around 4.26 GeV can be understood in the D1D molecular picture of the X(4260). [ABSTRACT FROM AUTHOR]

We propose a new model to create a light meson in the heavy quarkonium transition, which is inspired by the Nambu-Jona-Lasinio (NJL) model. Hadronic transitions of JPC = 1-- higher charmonia with the emission of an η meson are studied in the framework of the proposed model. The model shows its potential to reproduce the observed decay widths and make predictions for the unobserved channels. We present our predictions for the decay width of Ψ → J / ψ η and Ψ → h c (1 P) η, where Ψ are higher S and D wave vector charmonia, which provide useful references to search for higher charmonia and determine their properties in forthcoming experiments. The predicted branching fraction B (ψ (4415) → h c (1 P) η) = 4.62 × 10 - 4 is one order of magnitude smaller than the J / ψ η channel. Estimates of partial decay width Γ (Y → J / ψ η) are given for Y (4360), Y (4390), and Y (4660) by assuming them as c ¯ c bound states with quantum numbers 3³D1, 3³D1, and 5³S1, respectively. Our results are in favor of these assignments for Y (4360) and Y (4660). The corresponding experimental data for these Y states has large statistical errors which do not provide any constraint on the mixing angle if we introduce S - D mixing. To identify Y (4390), precise measurements on its hadronic branching fraction are required which are eagerly awaited from BESIII. [ABSTRACT FROM AUTHOR]

We study the excited B mesons' contributions to the coupled-channel effects under the framework of the 3P0 model for the bottomonium. Contrary to what has been widely accepted, the contributions of P wave B mesons are generally the largest, and to some extent, this result is independent of the potential parameters. We also push the calculation beyond B(1P) and carefully analyze the contributions of B(2S). A form factor is a key ingredient to suppress the contributions of B(2S) for low lying bottomonia. However, this suppression mechanism is not efficient for highly excited bottomonia, such as Υ(5S) and Υ(6S). We give explanations why this difficulty happens to the 3P0 model and suggest analyzing the flux-tube breaking model for the full calculation of coupled-channel effects. [ABSTRACT FROM AUTHOR]

With Gaussian expansion method (GEM), realistic wave functions are used to calculate coupled-channel effects for the bottomonium under the framework of P30 model. The simplicity and accuracy of GEM are explained. We calculate the mass shifts, probabilities of the B meson continuum, S-D mixing angles, strong and dielectric decay widths. Our calculation shows that both S-D mixing and the B meson continuum can contribute to the suppression of the vector meson's dielectric decay width. We suggest more precise measurements on the radiative decays of ?(10580) and ?(11020) to distinguish these two effects. The above quantities are also calculated with simple harmonic oscillator (SHO) wave function approximation for comparison. The deviation between GEM and SHO indicates that it is essential to treat the wave functions accurately for near threshold states. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a basic and long-standing research area. Depth images can enrich RGB (red-green-blue) images with their rich geometric information, so as to achieve accurate semantic segmentation. However, redundant information exists in RGB and depth images, and its handling has become an important problem. Filter group convolutions are widely used because they can eliminate redundant information and reduce computational complexity and parameter cost. Similarly, we propose a feature grouping mechanism network (FGMNet) using an attention mechanism and contextual information extraction for indoor scene semantic segmentation. First, modules of pyramid feature grouping attention and feature augmentation highlight the most useful information obtained by combining RGB and depth features. The enhanced features are then fed into a feature grouping contextual module. Results from extensive experiments on well-known indoor scene semantic segmentation datasets, NYUDv2 and SUN RGB-D, indicate that our FGMNet outperforms the most advanced existing methods in RGB-D semantic segmentation. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to present a 2.5-dimensional (2.5-D) frequency-domain nonlinear computer model for the beam-wave interaction of coupled-cavity traveling wave tubes (CC-TWTs). Design/methodology/approach – MKK (proposed by Malykhin, Konnov, and Komarov) equivalent circuit model is used to describe the coupled-cavity slow-wave structure. And the losses are taken into account in the MKK equivalent circuit. Instead of one-dimensional (1-D) disk model, the electron beam is divided into a set of discrete rays and the electron dynamics are treated using the three-dimensional (3-D) Lorentz force equations. Findings – The simulated result obtained show that the computer model can give a good predict for CC-TWTs in V-band. Practical implications – The computer model is capable of treating nonlinear problems. Compared with particle-in-cell simulation, the 2.5-D frequency-domain computer model spends less time. Besides, the 3-D electron trajectory can be used to design high-efficiency collectors for CC-TWTs. Originality/value – The computer model is able to simulate nonlinear problems of coupled-cavity TWT faster. [ABSTRACT FROM AUTHOR]

Abstract: The eigenvalue problems are considered for the fractional ordinary differential equations with different classes of boundary conditions including the Dirichlet, Neumann, Robin boundary conditions and the periodic boundary condition. The eigenvalues and eigenfunctions are characterized in terms of the Mittag–Leffler functions. The eigenvalues of several specified boundary value problems are calculated by using MATLAB subroutine for the Mittag–Leffler functions. When the order is taken as the value 2, our results degenerate to the classical ones of the second-ordered differential equations. When the order α satisfies 1< α <2 the eigenvalues can be finitely many. [Copyright &y& Elsevier]

In several existing red–green–blue and depth (RGB-D) semantic segmentation algorithms, schemes are used to supplement contextual information through multilayer feature interactions. However, these approaches ignore the complementation of the contextual information and the introduction of noise interfering with the segmentation process. To minimize noise interference during this process, we introduce a two-layer hop cascaded asymptotic network (THCANet) for robot-driving road-scene semantic segmentation in RGB-D images. To exploit the depth map and supervision to strengthen semantic segmentation, we propose an attention cross-fusion module for the interactive combination of RGB-D features through multimodality weighting. Notably, the information of the two modalities reduces noise during fusion. After fusing features from the RGB-D modalities, we also use a novel multiscale context module to fuse features at multiple scales and employ a jump cascade architecture between the modules to recover lost context information and suppress irrelevant noise. Moreover, multiple supervision is performed at different segmentation stages to improve accuracy. The proposed THCANet system demonstrates the best performance on a robot-driving road dataset compared with similar methods, and its generalization ability is demonstrated using the NYU-Depth V2 dataset. [ABSTRACT FROM AUTHOR]

Recent progress in salient object detection (SOD) has been fueled substantially by the development of convolutional neural networks. However, several SOD methods do not fully exploit information from different modalities, consequently performing only marginally better than methods using a single modality. Therefore, we propose a multitype fusion and enhancement network (MFENet), following three steps "Encoder- Pre-decoder- Decoder" for RGB-thermal (RGB-T) SOD by completely exploiting the advantages of the RGB and thermal modalities through feature integration and enhancement. To better fuse two modalities' features, we have designed the cross-modality fusion module (CMFM) in the encoder part. As shallow features describe details and deep features provide semantic information, a multiscale interactive refinement module is designed in the pre-decoder part to complement multilevel features. Additionally, to further sharpen salient objects, we have proposed a high-level, low-level module that takes inputs from adjacent layers for gradual translation into a saliency map in the decoder part. This module provides semantic information for shallower features and the boundaries of salient objects can be gradually sharpened with subtle details. Extensive experiments show the effectiveness and robustness of the proposed MFENet and its substantial improvement over state-of-the-art RGB-T SOD methods. The codes and results will be available at: https://github.com/wujunyi1412/MFENet_DSP. [ABSTRACT FROM AUTHOR]

Recent developments in convolutional neural networks (CNNs) have significantly improved the results of salient object detection (SOD), particularly RGB-D SOD. This article proposes BPA-Net (Boundary-aware Pyramid Attention Network), a network that addresses two key issues in RGB-D SOD based on CNNs: 1) accurately locking the position of an object when it is unclear whether it is a multi-object or a single object, and 2) depicting fine edges and fill pixels while maintaining robustness with complex scenes and similarly-colored backgrounds. Accordingly, we model three network branches to solve these problems separately. To address the first problem, we devise the Multi-scale Attention Branch, a pyramid attention network that collects the positions of objects, thereby eliminating interference from non-objects. The second is addressed via a Boundary Refine Branch that uses a depth image to capture the edges of objects. This step refines the boundaries of objects and emphasizes the importance of salient edge information. Such branches are learned for obtaining precise salient boundaries and for position estimation and are subsequently combined with a coarse salient map generated by the Coarse Salient Detection Branch, an encode-decode SOD network, to improve salient object segmentation. Extensive experiments show that our BPA-Net outperforms state-of-the-art approaches on two popular benchmarks. [ABSTRACT FROM AUTHOR]

Recently, deep-learning-based blind picture quality measurement (BPQM) metrics have gained significant attention. However, training a robust deep BPQM metric remains a difficult and challenging task because of the limited number of subject-rated training samples. State-of-the-art full-reference (FR) picture quality measurement (PQM) metrics are in good agreement with human subjective quality scores. Therefore, they can be employed to approximate human subjective quality scores to train BPQM metrics. Inspired by this, we propose a deep encoder–decoder architecture (DEDA) for opinion-unaware (OU) BPQM that does not require human-labeled distorted samples for training. In the training procedure, to avoid overfitting and to ensure the independency of the training and testing samples, we first construct 6,000 distorted pictures and use their objective quality/similarity maps obtained using a high-performance FR-PQM metric for distorted pictures as training labels. Subsequently, an end-to-end map between the distorted pictures and their objective quality/similarity maps (labels) is learned, represented as the DEDA that takes the distorted picture as the input and outputs its predicted quality/similarity map. In the DEDA, the pyramid supervision training strategy is used, which applies supervised learning over three scale layers to efficiently optimize the parameters. In the testing procedure, the quality/similarity maps of the testing samples that can help localize distortions can be predicted with the trained DEDA architecture. The predicted quality/similarity maps are then gradually pooled together to obtain the overall objective quality scores. Comparative experiments on three publicly available standard PQM datasets demonstrate that our proposed DEDA metric is in good agreement with subjective assessment compared to previous state-of-the-art OU-BPQM metrics. [ABSTRACT FROM AUTHOR]

Existing stereoscopic 3D (S3D) salient object detection (SOD) networks typically employ a two-branch architecture, in which the RGB and depth channels are learned independently. Conventional methods based on conventional neural networks generally fuse the two branches by combining their deep representations at a later stage with only one path, which can be inefficient and insufficient for retaining a large amount of cross-modal data. In this study, we combine the RGB branch and depth branch to generate a third branch. The first branch is the embedded attention branch containing the attention mechanism, and we introduce the embedded attention module in this branch to give the allocation of available processing resources to the most informative components of an input signal. The second branch is the boundary refinement branch combined with the low-level information of RGB and depth images. Additionally, we propose a new module, called the detail correlation module, to ensure clear object boundaries and salient object refinement. The third branch is the global deep-view branch containing the global view module, which fuses high-level information and expands the sensor field. We also use three different loss functions to match our special SOD network. Extensive experiments demonstrate the effectiveness and robustness of the proposed architecture and show that it represents a significant improvement over other state-of-the-art SOD approaches. [ABSTRACT FROM AUTHOR]