Is f1(1420)f_1(1420) the partner of f1(1285)f_1(1285) in the 3P1^3P_1 qqˉq\bar{q} nonet?

Based on a $2\times 2$ mass matrix, the mixing angle of the axial vector states $f_1(1420)$ and $f_1(1285)$ is determined to be $51.5^{\circ}$, and the theoretical results about the decay and production of the two states are presented. The theoretical results are in good agreement with the present experimental results, which suggests that $f_1(1420)$ can be assigned as the partner of $f_1(1285)$ in the $^3P_1$ $q\bar{q}$ nonet. We also suggest that the existence of $f_1(1510)$ needs further experimental confirmation.Comment: Latex, 6 pages, to be published in Chin. Phys. let

Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction

Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming data has the characteristic that the underlying distribution drifts over time and may recur. This can lead to catastrophic forgetting if the model simply adapts to new data distribution all the time. Also, it's inefficient to relearn distribution that has been occurred. Due to memory constraints and diversity of data distributions in large-scale industrial applications, conventional strategies for catastrophic forgetting such as replay, parameter isolation, and knowledge distillation are difficult to be deployed. In this work, we design a novel drift-aware incremental learning framework based on ensemble learning to address catastrophic forgetting in CTR prediction. With explicit error-based drift detection on streaming data, the framework further strengthens well-adapted ensembles and freezes ensembles that do not match the input distribution avoiding catastrophic interference. Both evaluations on offline experiments and A/B test shows that our method outperforms all baselines considered.Comment: This work has been accepted by SIGIR2

Category-Specific CNN for Visual-aware CTR Prediction at JD.com

As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images.This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms

Low- and high-thermogenic brown adipocyte subpopulations coexist in murine adipose tissue

Brown adipose tissue (BAT), as the main site of adaptive thermogenesis, exerts beneficial metabolic effects on obesity and insulin resistance. BAT has been previously assumed to contain a homogeneous population of brown adipocytes. Utilizing multiple mouse models capable of genetically labeling different cellular populations, as well as single-cell RNA sequencing and 3D tissue profiling, we discovered a new brown adipocyte subpopulation with low thermogenic activity coexisting with the classical high-thermogenic brown adipocytes within the BAT. Compared with the high-thermogenic brown adipocytes, these low-thermogenic brown adipocytes had substantially lower Ucp1 and Adipoq expression, larger lipid droplets, and lower mitochondrial content. Functional analyses showed that, unlike the high-thermogenic brown adipocytes, the low-thermogenic brown adipocytes have markedly lower basal mitochondrial respiration, and they are specialized in fatty acid uptake. Upon changes in environmental temperature, the 2 brown adipocyte subpopulations underwent dynamic interconversions. Cold exposure converted low-thermogenic brown adipocytes into high-thermogenic cells. A thermoneutral environment had the opposite effect. The recruitment of high-thermogenic brown adipocytes by cold stimulation is not affected by high fat diet feeding, but it does substantially decline with age. Our results revealed a high degree of functional heterogeneity of brown adipocytes

Molecular Characterization of a Fus3/Kss1 Type MAPK from Puccinia striiformis f. sp. tritici, PsMAPK1

Puccinia striiformis f. sp. tritici (Pst) is an obligate biotrophic fungus that causes the destructive wheat stripe rust disease worldwide. Due to the lack of reliable transformation and gene disruption method, knowledge about the function of Pst genes involved in pathogenesis is limited. Mitogen-activated protein kinase (MAPK) genes have been shown in a number of plant pathogenic fungi to play critical roles in regulating various infection processes. In the present study, we identified and characterized the first MAPK gene PsMAPK1 in Pst. Phylogenetic analysis indicated that PsMAPK1 is a YERK1 MAP kinase belonging to the Fus3/Kss1 class. Single nucleotide polymerphisms (SNPs) and insertion/deletion were detected in the coding region of PsMAPK1 among six Pst isolates. Real-time RT-PCR analyses revealed that PsMAPK1 expression was induced at early infection stages and peaked during haustorium formation. When expressed in Fusarium graminearum, PsMAPK1 partially rescued the map1 mutant in vegetative growth and pathogenicity. It also partially complemented the defects of the Magnaporthe oryzae pmk1 mutant in appressorium formation and plant infection. These results suggest that F. graminearum and M. oryzae can be used as surrogate systems for functional analysis of well-conserved Pst genes and PsMAPK1 may play a role in the regulation of plant penetration and infectious growth in Pst