Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at: https://github.com/mapleandfire/300VW-Mas

The booms and busts of beta arbitrage

Low-beta stocks deliver high average returns and low risk relative to high-beta stocks, an opportunity for professional investors to “arbitrage” away. We argue that beta-arbitrage activity generates booms and busts in the strategy’s abnormal trading profits. In times of low arbitrage activity, the beta-arbitrage strategy exhibits delayed correction, taking up to three years for abnormal returns to be realized. In contrast, when arbitrage activity is high, prices overshoot and then revert in the long run. We document a novel positive-feedback channel operating through firm leverage that facilitates these boom-and-bust cycles

Why don't most mutual funds short sell?

An intriguing observation in the US mutual fund industry is that most equity funds do not short sell, even though virtually all regulatory restrictions on short selling had been lifted by 1997. We shed light on this puzzle by conducting the first systematic analysis of long-short equity funds' portfolio compositions, fund performance, and capital flows. Our results reveal that: 1) long-short mutual funds hold substantially more cash than their long-only peers and have a market beta significantly below one; 2) long-short funds generate a large positive alpha of 5% a year in risky holdings but slightly underperform their long-only peers in total returns; and 3) long-short funds face much higher flow-performance sensitivities and are more prone to use cash to absorb capital flows. We discuss several possible explanations for these findings

Offsetting disagreement and security prices

We propose that investor beliefs frequently “cross” in the sense that an investor may like company A but dislike company B, whereas another investor may like company B but dislike company A. Such belief-crossing makes it almost impossible to construct a portfolio that is composed solely of every investor’s most favored companies. This causes the level of excitement for portfolios to be generally lower than the levels of excitement that individual companies generate among their most fervent supporters. Coupled with short-sale constraints, wherein prices are set by the most optimistic investors, this causes portfolios to trade at discounts. Utilizing several settings whereby the value of a portfolio and the values of the underlying components can be evaluated separately (e.g., closed-end funds), we present evidence supporting our proposition that, in financial markets, the “whole” is often less than the “sum of its parts.

Solvent Isotope Effect on Transfer Hydrogenation of H2O with Glycerine under Alkaline Hydrothermal Conditions

Solvent isotope effect was investigated with 1H-, 2H-NMR, LC-MS and Gas-MS analyses on transfer hydrogenation of H2O with glycerine under alkaline hydrothermal conditions. The results from solvent isotope studies showed that (1) the H on the β-C of lactate was almost exchanged by D2O, which suggests that the hydroxyl (-OH) group on the 2-C of glycerine was first transformed into a carbonyl (C=O) group and then was converted back into a -OH group to form lactate; (2) The presence of large amounts of D was found in the produced hydrogen gas, which shows that the water molecules acted as a reactant; and (3) D% in the produced hydrogen gas was far more than 50%, which straightforwardly shows that acetol was formed in the first place as the most probable intermediate by undergoing a dehydration reaction rather than a dehydrogenation reaction

Solvent Isotope Effect on Hydrogen-Transfer Reduction of CO2 into Formate with Glycerine by Alkaline Hydrothermal Reaction

To examine the solvent isotope effect on hydrogen-transfer reduction of CO2 into formate with glycerine by alkaline hydrothermal reaction, intermediates were identified by 13C-NMR, 1H-NMR, 2H-NMR, LC-MS analyses. The results showed that (1) CO2 was indeed converted into abiogenic formate; (2) a ketone carbonyl group as intermediate product was formed on hydrogen-transfer reduction of CO2 into formate with glycerine by alkaline hydrothermal reaction; (3) acetol was the most probable intermediate in the first reaction by undergoing a dehydration rather than a dehydrogenation

Early Second-Trimester Serum MiRNA Profiling Predicts Gestational Diabetes Mellitus

BACKGROUND: Gestational diabetes mellitus (GDM) is one type of diabetes that presents during pregnancy and significantly increases the risk of a number of adverse consequences for the fetus and mother. The microRNAs (miRNA) have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. However, no reported study investigates the associations between serum miRNA and GDM. METHODOLOGY/PRINCIPAL FINDINGS: We systematically used the TaqMan Low Density Array followed by individual quantitative reverse transcription polymerase chain reaction assays to screen miRNAs in serum collected at 16-19 gestational weeks. The expression levels of three miRNAs (miR-132, miR-29a and miR-222) were significantly decreased in GDM women with respect to the controls in similar gestational weeks in our discovery evaluation and internal validation, and two miRNAs (miR-29a and miR-222) were also consistently validated in two-centric external validation sample sets. In addition, the knockdown of miR-29a could increase Insulin-induced gene 1 (Insig1) expression level and subsequently the level of Phosphoenolpyruvate Carboxy Kinase2 (PCK2) in HepG2 cell lines. CONCLUSIONS/SIGNIFICANCE: Serum miRNAs are differentially expressed between GDM women and controls and could be candidate biomarkers for predicting GDM. The utility of miR-29a, miR-222 and miR-132 as serum-based non-invasive biomarkers warrants further evaluation and optimization

Putative DHHC-Cysteine-Rich Domain S-Acyltransferase in Plants

Protein S-acyltransferases (PATs) containing Asp-His-His-Cys within a Cys-rich domain (DHHC-CRD) are polytopic transmembrane proteins that are found in eukaryotic cells and mediate the S-acylation of target proteins. S-acylation is an important secondary and reversible modification that regulates the membrane association, trafficking and function of target proteins. However, little is known about the characteristics of PATs in plants. Here, we identified 804 PATs from 31 species with complete genomes. The analysis of the phylogenetic relationships suggested that all of the PATs fell into 8 groups. In addition, we analysed the phylogeny, genomic organization, chromosome localisation and expression pattern of PATs in Arabidopsis, Oryza sative, Zea mays and Glycine max. The microarray data revealed that PATs genes were expressed in different tissues and during different life stages. The preferential expression of the ZmPATs in specific tissues and the response of Zea mays to treatments with phytohormones and abiotic stress demonstrated that the PATs play roles in plant growth and development as well as in stress responses. Our data provide a useful reference for the identification and functional analysis of the members of this protein family