1,104 research outputs found
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 tetraquark system
Inspired by the recent observation of , and
by the LHCb Collaboration and some exotic resonances such as
, , etc. by several experiment collaborations, the
tetraquark systems with , and 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 with can be supposed
to explain the . Besides, by using the stabilization method,
several resonant states are obtained. There are four states
around the resonance mass 4035 MeV, 4385 MeV, 4524 MeV, and 4632 MeV,
respectively; one state around the resonance mass 4327 MeV; and
two states around the resonance mass 4419 MeV and 4526 MeV,
respectively. All of them are compact tetraquarks. Among these states,
, and can be explained as the compact tetraquark
state with , and the is possible to be a candidate of
the compact tetraquark state with . More experimental tests are
expected to check the existence of all these possible resonance states.Comment: 10 pages, 3 figure
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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
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