An Empirical Study on the Relationship between Entrepreneur’s Reputation and Financing Constraints

Information asymmetry is an important reason that causes external financing constraints. Because reputation has the function of signal transmission, a better reputation of an entrepreneur can reduce the degree of the firm information asymmetry and alleviate financing constraints of the firm. Based on the grouped sample of 94 listed companies of China from 2007 to 2009, this paper did empirical study on the relationship between entrepreneur’s reputation and financing constraints. The results show that entrepreneur’s reputation has a significant effect on firm financing activity. In other words, higher entrepreneur’s reputation leads to lower financing constraints. This study has a significant impact in helping managers and investors realize the importance of signaling effect of a good reputation in capital market. Meanwhile, it helps motivate entrepreneurs to establish good reputation, increasing the efficiency of capital market

Spatial-temporal Transformers for EEG Emotion Recognition

Electroencephalography (EEG) is a popular and effective tool for emotion recognition. However, the propagation mechanisms of EEG in the human brain and its intrinsic correlation with emotions are still obscure to researchers. This work proposes four variant transformer frameworks~(spatial attention, temporal attention, sequential spatial-temporal attention and simultaneous spatial-temporal attention) for EEG emotion recognition to explore the relationship between emotion and spatial-temporal EEG features. Specifically, spatial attention and temporal attention are to learn the topological structure information and time-varying EEG characteristics for emotion recognition respectively. Sequential spatial-temporal attention does the spatial attention within a one-second segment and temporal attention within one sample sequentially to explore the influence degree of emotional stimulation on EEG signals of diverse EEG electrodes in the same temporal segment. The simultaneous spatial-temporal attention, whose spatial and temporal attention are performed simultaneously, is used to model the relationship between different spatial features in different time segments. The experimental results demonstrate that simultaneous spatial-temporal attention leads to the best emotion recognition accuracy among the design choices, indicating modeling the correlation of spatial and temporal features of EEG signals is significant to emotion recognition

Adversarially Robust Neural Architectures

Deep Neural Network (DNN) are vulnerable to adversarial attack. Existing methods are devoted to developing various robust training strategies or regularizations to update the weights of the neural network. But beyond the weights, the overall structure and information flow in the network are explicitly determined by the neural architecture, which remains unexplored. This paper thus aims to improve the adversarial robustness of the network from the architecture perspective with NAS framework. We explore the relationship among adversarial robustness, Lipschitz constant, and architecture parameters and show that an appropriate constraint on architecture parameters could reduce the Lipschitz constant to further improve the robustness. For NAS framework, all the architecture parameters are equally treated when the discrete architecture is sampled from supernet. However, the importance of architecture parameters could vary from operation to operation or connection to connection, which is not explored and might reduce the confidence of robust architecture sampling. Thus, we propose to sample architecture parameters from trainable multivariate log-normal distributions, with which the Lipschitz constant of entire network can be approximated using a univariate log-normal distribution with mean and variance related to architecture parameters. Compared with adversarially trained neural architectures searched by various NAS algorithms as well as efficient human-designed models, our algorithm empirically achieves the best performance among all the models under various attacks on different datasets.Comment: 9 pages, 3 figures, 5 table

Methyl 3,4-O-isopropyl­idene-2-O-[(methyl­sulfan­yl)thio­carbon­yl]-β-l-arabinoside

In the title compound, C11H18O5S2, the six- and five-membered rings adopt a chair and an approximately planar conformation, respectively

Behavior Characteristics of Indexing Investment Entities

Based on the characteristics of investor behavior, this article analyzes the impact of institutional investors and investor sentiment on the liquidity, profitability and stability of the capital market, and analyzes the impact of investor overreaction on the market. Through multiple regression analysis, it is verified that the holding ratio of institutional investors has a significantly negative relationship with the turnover rate of funds, and it has a positive relationship with the annual rate of return of the fund and the annual volatility of the fund. Investor sentiment shows a positive correlation with the turnover rate of funds, the yield of the fund and the volatility of the fund. Through quantile regression, it is found that when the volatility of an index is at a high level, it is more susceptible to the negative impact of the previous trading volume. Keywords: behaviour characteristics, indexing investment, investor sentiment, capital market DOI: 10.7176/EJBM/13-22-01 Publication date: November 30th 202

17α-Ethynyl-3-methoxy­estra-1,3,5(10),9(11)-tetraen-17-ol

In the title compound, C21H24O2, rings B, C and D adopt half-chair, distorted half-chair and envelope conformations, respectively. In the crystal structure, there is an inter­molecular O—H⋯O hydrogen bond. The mol­ecules are arranged in a head-to-tail fashion, with the meth­oxy and hydr­oxy groups forming a two-dimensional hydrogen-bond network