This study explores the dark side of leadership, treats creative self-efficacy as a mediator, and frames supervisor bullying and employee creativity in the context of social cognition and social ...comparison. We theorize that with a high social comparison orientation, the combination of high supervisory abuse toward themselves (own abusive supervision) and low supervisory abuse toward other team members (peer abusive supervision) leads to a double whammy effect: When employees are "singled out" for abuse, these victims suffer from not only low creative self-efficacy due to supervisory abuse but also low supervisory creativity ratings. Results based on our two-wave data collected from multiple sources—253 employees and their 77 immediate supervisors—support our theory. The significant three-way interaction effect reveals that when social comparison orientation is high and peer abusive supervision is low (Time 1), own abusive supervision (Time 1) creates the strongest negative impact on creative self-efficacy (Time 2), which is significantly related to supervisory low creativity rating (Time 2). Our discoveries of egregious bullying offer provocative theoretical, empirical, and practical implications to the fields of leadership, abusive supervision, creativity, and business ethics.
Low-light image enhancement algorithms can improve the visual quality of low-light images and support the extraction of valuable information for some computer vision techniques. However, existing ...techniques inevitably introduce color and lightness distortions when enhancing the images. To lower the distortions, we propose a novel enhancement framework using the response characteristics of cameras. First, we discuss how to determine a reasonable camera response model and its parameters. Then, we use the illumination estimation techniques to estimate the exposure ratio for each pixel. Finally, the selected camera response model is used to adjust each pixel to the desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with fewer color and lightness distortions compared with the several state-of-the-art methods.
In this article, drawing from a relational perspective, we explore the relationship between moral leadership and employee creativity, treat employee identification with leader and leader–member ...exchange (LMX) as two mediators, and develop a new theoretical model of employee creativity. Our data collected from 160 supervisor–subordinate dyads in the People's Republic of China demonstrate that moral leadership is positively related to both employee identification with leader and LMX. Further, employee identification with leader partially mediates the relationship between moral leadership and LMX. In particular, employee identification with leader greatly enhances LMX which leads to high creativity. Overall, the relationship between moral leadership and employee creativity is mediated by not only employee identification with leader but also LMX. Our findings offer a new theoretical framework for future theory development and testing on creativity as well as practical implications for researchers and managers in business ethics.
Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning ...task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.
Image inpainting techniques have shown significant improvements by using deep neural networks recently. However, most of them may either fail to reconstruct reasonable structures or restore ...fine-grained textures. In order to solve this problem, in this paper, we propose a two-stage model which splits the inpainting task into two parts: structure reconstruction and texture generation. In the first stage, edge-preserved smooth images are employed to train a structure reconstructor which completes the missing structures of the inputs. In the second stage, based on the reconstructed structures, a texture generator using appearance flow is designed to yield image details. Experiments on multiple publicly available datasets show the superior performance of the proposed network.
Label noise is a ubiquitous issue in GANs, which degrades the generalization ability of the discriminator and usually leads to instability when training GANs. This issue stems from both real data and ...generated data. Previous works either only consider one of these two sources, or are not robust enough to noisy labels. In this paper, we revisit spectral normalization in robust learning with noisy labels. Based on its pros and cons, we propose to combine spectral normalization and weight decay to regularize the discriminator, which enjoys a more robust training process. To extend to conditional GANs, we propose to balance the relative importance of marginal matching and conditional matching in the projection discriminator. The proposed Enhanced Spectral Normalization for Generative Adversarial Networks (ESNGAN) can be easily integrated into various existing GANs frameworks without excessive additional cost. The effectiveness of the proposed method is validated on the CIFAR10, LSUN Church, CelebA, and ImageNet datasets, including the unconditional image generation task and the class-conditional image generation task. We also show that the proposed method can further improve the performance of the high-resolution image generation task.
Neurotransmitters play essential roles in regulating neural circuit dynamics both in the central nervous system as well as at the peripheral, including the gastrointestinal tract
. Their real-time ...monitoring will offer critical information for understanding neural function and diagnosing disease
. However, bioelectronic tools to monitor the dynamics of neurotransmitters in vivo, especially in the enteric nervous systems, are underdeveloped. This is mainly owing to the limited availability of biosensing tools that are capable of examining soft, complex and actively moving organs. Here we introduce a tissue-mimicking, stretchable, neurochemical biological interface termed NeuroString, which is prepared by laser patterning of a metal-complexed polyimide into an interconnected graphene/nanoparticle network embedded in an elastomer. NeuroString sensors allow chronic in vivo real-time, multichannel and multiplexed monoamine sensing in the brain of behaving mouse, as well as measuring serotonin dynamics in the gut without undesired stimulations and perturbing peristaltic movements. The described elastic and conformable biosensing interface has broad potential for studying the impact of neurotransmitters on gut microbes, brain-gut communication and may ultimately be extended to biomolecular sensing in other soft organs across the body.
Pose-guided person image generation and animation aim to transform a source person image to target poses. These tasks require spatial manipulation of source data. However, Convolutional Neural ...Networks are limited by the lack of ability to spatially transform the inputs. In this article, we propose a differentiable global-flow local-attention framework to reassemble the inputs at the feature level. This framework first estimates global flow fields between sources and targets. Then, corresponding local source feature patches are sampled with content-aware local attention coefficients. We show that our framework can spatially transform the inputs in an efficient manner. Meanwhile, we further model the temporal consistency for the person image animation task to generate coherent videos. The experiment results of both image generation and animation tasks demonstrate the superiority of our model. Besides, additional results of novel view synthesis and face image animation show that our model is applicable to other tasks requiring spatial transformation. The source code of our project is available at https://github.com/RenYurui/Global-Flow-Local-Attention .
Generating portrait images by controlling the motions of existing faces is an important task of great consequence to social media industries. For easy use and intuitive control, semantically ...meaningful and fully disentangled parameters should be used as modifications. However, many existing techniques do not provide such fine-grained controls or use indirect editing methods i.e. mimic motions of other individuals. In this paper, a Portrait Image Neural Renderer (PIRenderer) is proposed to control the face motions with the parameters of three-dimensional morphable face models (3DMMs). The proposed model can generate photo-realistic portrait images with accurate movements according to intuitive modifications. Experiments on both direct and indirect editing tasks demonstrate the superiority of this model. Meanwhile, we further extend this model to tackle the audio-driven facial reenactment task by extracting sequential motions from audio inputs. We show that our model can generate coherent videos with convincing movements from only a single reference image and a driving audio stream. Our source code is available at https://github.com/RenYurui/PIRender.
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