No news is not good news: evidence from the intraday return volatility-volume relationship in Shanghai Stock Exchange

We find that the asymmetric volatility phenomenon is reversed in the Shanghai Stock Exchange during bull markets. That is, volatility increases more with good news than with bad news. This evidence is inconsistent with the US markets (Wu 2001, and Bae, Kim and Nelson 2007). Further examination of this phenomenon reveals that the positive impact of good news on volatility is driven by return chasing behaviour of investors in large stocks during bull markets. We also find that volatility increases after stock price declines in bear markets especially for small stocks . This increase in volatility of small stocks after bad news in bear markets is partly driven by liquidity. After controlling for liquidity shifts, there are no significant patterns in the volatility of small stocks during bear markets. We posit that institutional and behavioural factors are the major driving forces of observed volatility patterns in Chinese stock market

Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport

Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distribution formed by only a few training examples. A recent solution to this problem is calibrating the distribution of these few sample classes by transferring statistics from the base classes with sufficient examples, where how to decide the transfer weights from base classes to novel classes is the key. However, principled approaches for learning the transfer weights have not been carefully studied. To this end, we propose a novel distribution calibration method by learning the adaptive weight matrix between novel samples and base classes, which is built upon a hierarchical Optimal Transport (H-OT) framework. By minimizing the high-level OT distance between novel samples and base classes, we can view the learned transport plan as the adaptive weight information for transferring the statistics of base classes. The learning of the cost function between a base class and novel class in the high-level OT leads to the introduction of the low-level OT, which considers the weights of all the data samples in the base class. Experimental results on standard benchmarks demonstrate that our proposed plug-and-play model outperforms competing approaches and owns desired cross-domain generalization ability, indicating the effectiveness of the learned adaptive weights

Knockdown of Brm and Baf170, Components of Chromatin Remodeling Complex, Facilitates Reprogramming of Somatic Cells

© Copyright 2015, Mary Ann Liebert, Inc. 2015. The SWI/SNF (SWItch/Sucrose NonFermentable or BAF, Brg/Brahma-associated factors) complexes are epigenetic modifiers of chromatin structure and undergo progressive changes in subunit composition during cellular differentiation. For example, in embryonic stem cells, esBAF contains Brg1 and Baf155, while their homologs, Brm and Baf170, are present in BAF of somatic cells. In this study, we sought to determine whether Brm and Baf170 play any roles in induced pluripotent stem cell (iPSC) reprogramming by using shRNA-mediated knockdown studies in the mouse model. We found that knocking down Brm during early, mid, and late stages (days 3, 6, and 9 after initial iPSC induction) and knocking down Baf170 during late-stage (day 9) reprogramming improve the numbers of iPSC colonies formed. We further showed that inhibition of these somatic BAF components also promotes complete reprogramming of partially reprogrammed somatic cells (pre-iPSCs). Finally, we found that the expression of Brm and Baf170 during reprogramming was regulated by Jak/Stat3 activity. Taken together, these data suggest that inhibiting somatic BAF improves complete reprogramming by facilitating the activation of the pluripotency circuitry

High-Sensitivity Vector Bend Sensor Based on a Fiber Directional Coupler Inscribed by a Femtosecond Laser

In this Letter, we demonstrate a high-sensitivity vector bend sensor based on a fiber directional coupler. The fiber directional coupler is composed of two parallel waveguides inscribed within a no-core fiber (NCF) by a femtosecond laser. Since the two written waveguides have closely matched refractive indices and geometries, the transmission spectrum of the fiber directional coupler possesses periodic resonant dips. Such a fiber directional coupler exhibits a good bending-dependent spectral shift response due to its asymmetric structure. Experimental results show that bending sensitivities of -97.11 nm/m-1 and 58.22 nm/m-1 are achieved for the 0° and 180° orientations in the curvature range of 0-0.62 m-1, respectively. In addition, the proposed fiber directional coupler is shown to be insensitive to external humidity changes, thus improving its suitability in high-accuracy bending measurements

Intrinsic Cerebro-Cerebellar Functional Connectivity Reveals the Function of Cerebellum VI in Reading-Related Skills

Funding This work was supported by grants from the National Natural Science Foundation of China (NSFC: 31971036, 31971039, and 31571158).Peer reviewedPublisher PD