Entrepreneurs' Networking Styles and Normative Underpinnings during Institutional transition

Existing network research has mainly adopted functional and/or structural approaches to study the instrumental goals behind entrepreneurs’ networking as well as the influence of personal position on access to resources and eventual performance. The variety of entrepreneurs’ networking styles and their normative underpinnings have not been adequately explored. Contextualized in China, this study asks: How do entrepreneurs’ understandings of social norms shape their networking styles? Through an inductive comparison of two entrepreneur generations in China, we identify three networking styles: guanxi-oriented networking, market-based networking, and mixed networking. We theorize that three types of norms shape these styles: market-inferred norms, dyadically formed norms, and identity-induced norms. This study provides new insights into the understanding of Chinese entrepreneurs’ distinctive networking styles and their normative underpinnings. Further, it suggests implications both for the wider study of entrepreneurs’ networking behaviors in transition economies, and for practitioners wishing to enhance their network building in China

Flexible Neural Image Compression via Code Editing

Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance. However, it usually requires a dedicated encoder-decoder pair for each point on R-D curve, which greatly hinders its practical deployment. While some recent works have enabled bitrate control via conditional coding, they impose strong prior during training and provide limited flexibility. In this paper we propose Code Editing, a highly flexible coding method for NIC based on semi-amortized inference and adaptive quantization. Our work is a new paradigm for variable bitrate NIC. Furthermore, experimental results show that our method surpasses existing variable-rate methods, and achieves ROI coding and multi-distortion trade-off with a single decoder.Comment: NeurIPS 202

Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos

Semi-supervised learning uses underlying relationships in data with a scarcity of ground-truth labels. In this paper, we introduce an uncertainty quantification (UQ) method for graph-based semi-supervised multi-class classification problems. We not only predict the class label for each data point, but also provide a confidence score for the prediction. We adopt a Bayesian approach and propose a graphical multi-class probit model together with an effective Gibbs sampling procedure. Furthermore, we propose a confidence measure for each data point that correlates with the classification performance. We use the empirical properties of the proposed confidence measure to guide the design of a human-in-the-loop system. The uncertainty quantification algorithm and the human-in-the-loop system are successfully applied to classification problems in image processing and ego-motion analysis of body-worn videos

Bit Allocation using Optimization

In this paper, we consider the problem of bit allocation in neural video compression (NVC). Due to the frame reference structure, current NVC methods using the same R-D (Rate-Distortion) trade-off parameter $\lambda$ for all frames are suboptimal, which brings the need for bit allocation. Unlike previous methods based on heuristic and empirical R-D models, we propose to solve this problem by gradient-based optimization. Specifically, we first propose a continuous bit implementation method based on Semi-Amortized Variational Inference (SAVI). Then, we propose a pixel-level implicit bit allocation method using iterative optimization by changing the SAVI target. Moreover, we derive the precise R-D model based on the differentiable trait of NVC. And we show the optimality of our method by proofing its equivalence to the bit allocation with precise R-D model. Experimental results show that our approach significantly improves NVC methods and outperforms existing bit allocation methods. Our approach is plug-and-play for all differentiable NVC methods, and it can be directly adopted on existing pre-trained models

Comparative Transcriptome of Isonuclear Alloplasmic Strain Revealed the Important Role of Mitochondrial Genome in Regulating <i>Flammulina filiformis</i>

The golden–needle mushroom Flammulina filiformis is one of the most precious cultivated edible fungi in the world. Despite recent progress in the study of F. filiformis, there is still a gap in the regulation of the mitochondrial genome during browning, which poses a serious threat to the golden–needle mushroom industry. Comparative transcriptome analysis of two isonuclear alloplasmic strains showed that changes in the mitochondrial genome lead to different gene expression and key biological pathways at different stages in the two isonuclear alloplasmic strains. Furthermore, transcriptome analysis revealed that the mitochondrial genome has a significant role in the regulation of a multitude of critical metabolic pathways relating to the browning of F. filiformis fruiting bodies. Functional enrichment analysis showed that the differentially expressed genes were mainly involved in many vital processes of mitochondria, mitochondrial membrane, and multiple amino acid metabolisms of F. filiformis. Taken together, the current study highlights the crucial role of the mitochondrial genome in the growth of F. filiformis and could be beneficial to genetic breeding of elite varieties of edible fungi

Long non‐coding RNA RACGAP1P promotes breast cancer invasion and metastasis via miR‐345‐5p/RACGAP1‐mediated mitochondrial fission

Long non‐coding RNAs (lncRNAs) are emerging as key molecules in various cancers, yet their potential roles in the pathogenesis of breast cancer are not fully understood. Herein, using microarray analysis, we revealed that the lncRNA RACGAP1P, the pseudogene of Rac GTPase activating protein 1 (RACGAP1), was up‐regulated in breast cancer tissues. Its high expression was confirmed in 25 pairs of breast cancer tissues and 8 breast cell lines by qRT‐PCR. Subsequently, we found that RACGAP1P expression was positively correlated with lymph node metastasis, distant metastasis, TNM stage, and shorter survival time in 102 breast cancer patients. Then, in vitro and in vivo experiments were designed to investigate the biological function and regulatory mechanism of RACGAP1P in breast cancer cell lines. Overexpression of RACGAP1P in MDA‐MB‐231 and MCF7 breast cell lines increased their invasive ability and enhanced their mitochondrial fission. Conversely, inhibition of mitochondrial fission by Mdivi‐1 could reduce the invasive ability of RACGAP1P‐overexpressing cell lines. Furthermore, the promotion of mitochondrial fission by RACGAP1P depended on its competitive binding with miR‐345‐5p against its parental gene RACGAP1, leading to the activation of dynamin‐related protein 1 (Drp1). In conclusion, lncRNA RACGAP1P promotes breast cancer invasion and metastasis via miR‐345‐5p/RACGAP1 pathway‐mediated mitochondrial fission