Emotional Labor Strategies And Service Performance: The Mediating Role Of Employee Creativity

This paper theoretically and empirically investigates the effects of different emotional labor strategies on frontline employee creativity in the context of service industry, and it also studies the mediating role of frontline employee creativity in the relationships between frontline employee’s emotional labor strategies and the two aspects of customer service performance. Based on the data of 424 employee–supervisor dyads in China, the empirical results indicate that surface acting decreases employee creativity and extra role performance, while deep acting increases employee creativity, role-prescribed performance and extra role performance; employee creativity mediates both the negative influence of surface acting on extra role performance and the positive influences of deep acting on role-prescribed and extra role performances. The results have some theoretical and practical implications on service creativity and emotion management in service industry

Self-Supervision Can Be a Good Few-Shot Learner

Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic perspective, we propose an effective unsupervised FSL method, learning representations with self-supervision. Following the InfoMax principle, our method learns comprehensive representations by capturing the intrinsic structure of the data. Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training. Rather than supervised pre-training focusing on the discriminable features of the seen classes, our self-supervised model has less bias toward the seen classes, resulting in better generalization for unseen classes. We explain that supervised pre-training and self-supervised pre-training are actually maximizing different MI objectives. Extensive experiments are further conducted to analyze their FSL performance with various training settings. Surprisingly, the results show that self-supervised pre-training can outperform supervised pre-training under the appropriate conditions. Compared with state-of-the-art FSL methods, our approach achieves comparable performance on widely used FSL benchmarks without any labels of the base classes.Comment: ECCV 2022, code: https://github.com/bbbdylan/unisia

The explanation of some exotic states in the cscˉsˉcs\bar{c}\bar{s} tetraquark system

Inspired by the recent observation of $\chi_{c0}(3930)$, $X(4685)$ and $X(4630)$ by the LHCb Collaboration and some exotic resonances such as $X(4350)$, $X(4500)$, etc. by several experiment collaborations, the $cs\bar{c}\bar{s}$ tetraquark systems with $IJ^{P}=00^+$, $01^+$ and $02^+$ are systematically investigated in the framework of the quark delocalization color screening model(QDCSM). Two structures, the meson-meson and diquark-antidiquark structures, as well as the channel-coupling of all channels of these two configurations are considered in this work. The numerical results indicate that the molecular bound state $\bar{D}_{s}D_{s}$ with $IJ^{P}=00^+$ can be supposed to explain the $\chi_{c0}(3930)$. Besides, by using the stabilization method, several resonant states are obtained. There are four $IJ^{P}=00^{+}$ states around the resonance mass 4035 MeV, 4385 MeV, 4524 MeV, and 4632 MeV, respectively; one $IJ^{P}=01^{+}$ state around the resonance mass 4327 MeV; and two $IJ^{P}=02^{+}$ states around the resonance mass 4419 MeV and 4526 MeV, respectively. All of them are compact tetraquarks. Among these states, $X(4350)$, $X(4500)$ and $X(4700)$ can be explained as the compact tetraquark state with $IJ^{P}=00^{+}$, and the $X(4274)$ is possible to be a candidate of the compact tetraquark state with $IJ^{P}=01^{+}$. More experimental tests are expected to check the existence of all these possible resonance states.Comment: 10 pages, 3 figure

A photo-responsive F-box protein FOF2 regulates floral initiation by promoting FLC expression in Arabidopsis.

Floral initiation is regulated by various genetic pathways in response to light, temperature, hormones and developmental status; however, the molecular mechanisms underlying the interactions between different genetic pathways are not fully understood. Here, we show that the photoresponsive gene FOF2 (F-box of flowering 2) negatively regulates flowering. FOF2 encodes a putative F-box protein that interacts specifically with ASK14, and its overexpression results in later flowering under both long-day and short-day photoperiods. Conversely, transgenic plants expressing the F-box domain deletion mutant of FOF2 (FOF2ΔF), or double loss of function mutant of FOF2 and FOL1 (FOF2-LIKE 1) present early flowering phenotypes. The late flowering phenotype of the FOF2 overexpression lines is suppressed by the flc-3 loss-of-function mutation. Furthermore, FOF2 mRNA expression is regulated by autonomous pathway gene FCA, and the repressive effect of FOF2 in flowering can be overcome by vernalization. Interestingly, FOF2 expression is regulated by light. The protein level of FOF2 accumulates in response to light, whereas it is degraded under dark conditions via the 26S proteasome pathway. Our findings suggest a possible mechanistic link between light conditions and the autonomous floral promotion pathway in Arabidopsis

the upper bounds on differntial characteristics in block cipher SMS4

SMS4 is a 128-bit block cipher with a 128-bit user key and 32 rounds, which is used in the Chinese National Standard for Wireless LAN WAPI. In this paper, all possible differential patterns are divided into several sections by six designed rules. In order to evaluate the security against the differential cryptanalysis of SMS4, we calculate the lower bounds on the number of active S-Boxes for all kinds of sections, based on which the lower bounds on the number of active S-Boxes in all possible differential patterns can be derived. Finally, the upper bounds on differential characteristic probabilities of arbitrary round numbers are given, which can be used to estimate the strength of SMS4 against differential attack and linear attack

Involvement of Wnt/β-catenin pathway in the inhibition of invasion and epithelial-mesenchymal transition in ovarian cancer cells

Purpose: To investigate the effects of zerumbone on cell invasion, epithelial-mesenchymal transition (EMT) and the potential signaling pathway involved in ovarian cancer cells.Methods: Caov-3 cell proliferation was assessed using 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-diphenytetrazoliumromide (MTT) assay. Wound healing assay was used to determine Caov-3 cell migration while cell invasion was evaluated using Transwell assay. Protein expression was determinedby western blot.Results: Cell viability was reduced by 5, 10, 20, and 50 μM zerumbone (p < 0.05) in a concentrationdependent manner while cell migration and invasion were inhibited by 10 and 20 μM zerumbone (p < 0.05). Protein expression levels of E-cadherin and cytoplasm β-catenin were upregulated by zerumbone (p < 0.05) in a concentration-dependent manner. On the other hand, protein expression levels of Ncadherin, vimentin, ZEB1, nuclear β-catenin, and c-Myc were suppressed by zerumbone (p < 0.05) also in a concentration-dependent manner.Conclusion: The results demonstrate that zerumbone inhibits cell proliferation, migration and invasion, but represses the EMT process via inactivation of Wnt/β-catenin signaling pathway. Keywords: Zerumbone, Ovarian cancer, Wnt/β-catenin pathway, Epithelial-mesenchymal transitio

Adversarial Robustness through the Lens of Causality

The adversarial vulnerability of deep neural networks has attracted significant attention in machine learning. From a causal viewpoint, adversarial attacks can be considered as a specific type of distribution change on natural data. As causal reasoning has an instinct for modeling distribution change, we propose to incorporate causality into mitigating adversarial vulnerability. However, causal formulations of the intuition of adversarial attack and the development of robust DNNs are still lacking in the literature. To bridge this gap, we construct a causal graph to model the generation process of adversarial examples and define the adversarial distribution to formalize the intuition of adversarial attacks. From a causal perspective, we find that the label is spuriously correlated with the style (content-independent) information when an instance is given. The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution. Thus, DNNs that fit the spurious correlation are vulnerable to the adversarial distribution. Inspired by the observation, we propose the adversarial distribution alignment method to eliminate the difference between the natural distribution and the adversarial distribution. Extensive experiments demonstrate the efficacy of the proposed method. Our method can be seen as the first attempt to leverage causality for mitigating adversarial vulnerability