Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data

Recent advances in deep learning have demonstrated excellent results for Facial Attribute Recognition (FAR), typically trained with large-scale labeled data. However, in many real-world FAR applications, only limited labeled data are available, leading to remarkable deterioration in performance for most existing deep learning-based FAR methods. To address this problem, here we propose a method termed Spatial-Semantic Patch Learning (SSPL). The training of SSPL involves two stages. First, three auxiliary tasks, consisting of a Patch Rotation Task (PRT), a Patch Segmentation Task (PST), and a Patch Classification Task (PCT), are jointly developed to learn the spatial-semantic relationship from large-scale unlabeled facial data. We thus obtain a powerful pre-trained model. In particular, PRT exploits the spatial information of facial images in a selfsupervised learning manner. PST and PCT respectively capture the pixel-level and image-level semantic information of facial images based on a facial parsing model. Second, the spatial-semantic knowledge learned from auxiliary tasks is transferred to the FAR task. By doing so, it enables that only a limited number of labeled data are required to fine-tune the pre-trained model. We achieve superior performance compared with state-of-the-art methods, as substantiated by extensive experiments and studies

An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation

In this paper, an adaptive fusion algorithm is proposed to robustly estimate the state of charge of lithium-ion batteries. An improved recursive least square algorithm with a forgetting factor is employed to identify parameters of the built equivalent circuit model, and the least square support vector machine algorithm is synchronously leveraged to estimate the battery state of health. On this basis, an adaptive H-infinity filter algorithm is applied to predict the battery state of charge and to cope with uncertainty of model errors and prior noise evaluation. The proposed algorithm is comprehensively validated within a full operational temperature range of battery and with different aging status. Experimental results reveal that the maximum absolute error of the fusion estimation algorithm is less than 1.2%, manifesting its effectiveness and stability when subject to internal capacity degradation of battery and operating temperature variation

Epidemiological and virological characteristics of pandemic influenza A (H1N1) 2009 in school outbreaks in China

Background: During the 2009 pandemic influenza H1N1 (2009) virus (pH1N1) outbreak, school students were at an increased risk of infection by the pH1N1 virus. However, the estimation of the attack rate showed significant variability. Methods: Two school outbreaks were investigated in this study. A questionnaire was designed to collect information by interview. Throat samples were collected from all the subjects in this study 6 times and sero samples 3 times to confirm the infection and to determine viral shedding. Data analysis was performed using the software STATA 9.0. Findings: The attack rate of the pH1N1 outbreak was 58.3% for the primary school, and 52.9% for the middle school. The asymptomatic infection rates of the two schools were 35.8% and 37.6% respectively. Peak virus shedding occurred on the day of ARI symptoms onset, followed by a steady decrease over subsequent days (p = 0.026). No difference was found either in viral shedding or HI titer between the symptomatic and the asymptomatic infectious groups. Conclusions: School children were found to be at a high risk of infection by the novel virus. This may be because of a heightened risk of transmission owing to increased mixing at boarding school, or a lack of immunity owing to socioeconomic status. We conclude that asymptomatically infectious cases may play an important role in transmission of the pH1N1 virus

FFT-LB modeling of thermal liquid-vapor systems

We further develop a thermal LB model for multiphase flows. In the improved model, we propose to use the FFT scheme to calculate both the convection term and external force term. The usage of FFT scheme is detailed and analyzed. By using the FFT algorithm spatiotemporal discretization errors are decreased dramatically and the conservation of total energy is much better preserved. A direct consequence of the improvement is that the unphysical spurious velocities at the interfacial regions can be damped to neglectable scale. Together with the better conservation of total energy, the more accurate flow velocities lead to the more accurate temperature field which determines the dynamical and final states of the system. With the new model, the phase diagram of the liquid-vapor system obtained from simulation is more consistent with that from theoretical calculation. Very sharp interfaces can be achieved. The accuracy of simulation results are also verified by the Laplace law. The FFT scheme can be easily applied to other models for multiphase flows.Comment: 34 pages, 21 figure

The 13N(d,n)14O Reaction and the Astrophysical 13N(p,g)14O Reaction Rate

$^{13}$N($p,\gamma$)$^{14}$O is one of the key reactions in the hot CNO cycle which occurs at stellar temperatures around $T_9$ $\geq$ 0.1. Up to now, some uncertainties still exist for the direct capture component in this reaction, thus an independent measurement is of importance. In present work, the angular distribution of the $^{13}$N($d,n$)$^{14}$O reaction at $E_{\rm{c.m.}}$ = 8.9 MeV has been measured in inverse kinematics, for the first time. Based on the distorted wave Born approximation (DWBA) analysis, the nuclear asymptotic normalization coefficient (ANC), $C^{^{14}O}_{1,1/2}$, for the ground state of $^{14}$O $\to$ $^{13}$N + $p$ is derived to be $5.42 \pm 0.48$ fm$^{-1/2}$. The $^{13}$N($p,\gamma$)$^{14}$O reaction is analyzed with the R-matrix approach, its astrophysical S-factors and reaction rates at energies of astrophysical relevance are then determined with the ANC. The implications of the present reaction rates on the evolution of novae are then discussed with the reaction network calculations.Comment: 17 pages and 8 figure

Determination of astrophysical 12N(p,g)13O reaction rate from the 2H(12N, 13O)n reaction and its astrophysical implications

The evolution of massive stars with very low-metallicities depends critically on the amount of CNO nuclides which they produce. The $^{12}$N($p$,\,$\gamma$)$^{13}$O reaction is an important branching point in the rap-processes, which are believed to be alternative paths to the slow 3$\alpha$ process for producing CNO seed nuclei and thus could change the fate of massive stars. In the present work, the angular distribution of the $^2$H($^{12}$N,\,$^{13}$O)$n$ proton transfer reaction at $E_{\mathrm{c.m.}}$ = 8.4 MeV has been measured for the first time. Based on the Johnson-Soper approach, the square of the asymptotic normalization coefficient (ANC) for the virtual decay of $^{13}$O$_\mathrm{g.s.}$ $\rightarrow$ $^{12}$N + $p$ was extracted to be 3.92 $\pm$ 1.47 fm$^{-1}$ from the measured angular distribution and utilized to compute the direct component in the $^{12}$N($p$,\,$\gamma$)$^{13}$O reaction. The direct astrophysical S-factor at zero energy was then found to be 0.39 $\pm$ 0.15 keV b. By considering the direct capture into the ground state of $^{13}$O, the resonant capture via the first excited state of $^{13}$O and their interference, we determined the total astrophysical S-factors and rates of the $^{12}$N($p$,\,$\gamma$)$^{13}$O reaction. The new rate is two orders of magnitude slower than that from the REACLIB compilation. Our reaction network calculations with the present rate imply that $^{12}$N($p,\,\gamma$)$^{13}$O will only compete successfully with the $\beta^+$ decay of $^{12}$N at higher ($\sim$two orders of magnitude) densities than initially predicted.Comment: 8 figures, 2 tables, Submitted to Physical Review