A Two-part Transformer Network for Controllable Motion Synthesis

Although part-based motion synthesis networks have been investigated to reduce the complexity of modeling heterogeneous human motions, their computational cost remains prohibitive in interactive applications. To this end, we propose a novel two-part transformer network that aims to achieve high-quality, controllable motion synthesis results in real-time. Our network separates the skeleton into the upper and lower body parts, reducing the expensive cross-part fusion operations, and models the motions of each part separately through two streams of auto-regressive modules formed by multi-head attention layers. However, such a design might not sufficiently capture the correlations between the parts. We thus intentionally let the two parts share the features of the root joint and design a consistency loss to penalize the difference in the estimated root features and motions by these two auto-regressive modules, significantly improving the quality of synthesized motions. After training on our motion dataset, our network can synthesize a wide range of heterogeneous motions, like cartwheels and twists. Experimental and user study results demonstrate that our network is superior to state-of-the-art human motion synthesis networks in the quality of generated motions.Comment: 16 pages, 26 figure

Evaluating the performance of Chinese commercial banks:A comparative analysis of different types of banks

This paper examines the cost and profit efficiency of four types of Chinese commercial banks over the period from 2002 to 2013. We find that the cost and profit efficiencies improved across all types of Chinese domestic banks in general and the banks are more profit-efficient than cost efficient. Foreign banks are the most cost efficient but the least profit efficient. The profit efficiency gap between foreign banks and domestic banks has widened after the World Trade Organization transition period (2007–2013). Ownership structure, market competition, bank size, and listing status are the main determinants of the efficiency of Chinese banks. We also find a causal relationship between efficiency and SROE by using the panel auto regression method. The evidence from the shadow return on equity (SROE) suggests that policy makers should be cautious of the adjustment costs imposed by the recapitalization process, which offsets the efficiency gains

Vaccaria hypaphorine alleviates lipopolysaccharide-induced inflammation via inactivation of NFκB and ERK pathways in Raw 264.7 cells

10.1186/s12906-017-1635-1BMC Complementary and Alternative Medicine17112

A causal convolutional neural network for multi-subject motion modeling and generation

Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.Comment: This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this article is published in Computational Visual Media, and is available online at https://doi.org/10.1007/s41095-022-0307-

Identification of novel bioactive proteins and their produced oligopeptides from Torreya grandis nuts using proteomic based prediction

Torreya grandis nut is a chief functional food in China consumed for centuries. Besides its rich protein composition, increasing studies are now focusing on T. grandis functional proteins that have not yet identified. In this study, liquid chromatography coupled with mass spectrometry detection of smaller and major proteins, revealed that the major peptide was 36935.00 Da. Proteome sequencing annotated 142 proteins in total. Bioactive proteins such as defensin 4 was annotated and its anti-microbial function was verified. Finally, functional oligopeptides were predicted by searching sequences of digested peptides in databases. Ten group of oligopeptides were suggested to exhibit antioxidant, Angiotensin-converting enzyme inhibition, anti-inflammatory. The predicted antioxidant activity was experimentally validated. It is interesting that a peptide GYCVSDNN digested from defensin 4 showed antioxidant activity. This study reports novel functional peptides from T. grandis nuts that have not been isolated and/or included as functional ingredients in nutraceuticals and in food industry

Application of deep eutectic solvents in protein extraction and purification

Deep eutectic solvents (DESs) are a mixture of hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) molecules that can consist, respectively, of natural plant metabolites such as sugars, carboxylic acids, amino acids, and ionic molecules, which are for the vast majority ammonium salts. Media such as DESs are modular tools of sustainability that can be pointed toward the extraction of bioactive molecules due to their excellent physicochemical properties, their relatively low price, and accessibility. The present review focuses on the application of DESs for protein extraction and purification. The in-depth effects and principles that apply to DES-mediated extraction using various renewable biomasses will be discussed as well. One of the most important observations being made is that DESs have a clear ability to maintain the biological and/or functional activity of the extracted proteins, as well as increase their stability compared to traditional solvents. They demonstrate true potential for a reproducible but more importantly, scalable protein extraction and purification compared to traditional methods while enabling waste valorization in some particular cases

Gender differentials in the payoff to schooling in rural China

This article examines the gender differential in the payoff to schooling in rural China. The analyses are based on a framework provided by the over education/required education/under education literature, and the decomposition developed by Chiswick and Miller (2008). It shows that the payoff to correctly matched education in rural China is much higher for females than for males. Associated with this, the wage penalty where workers are under qualified in their occupation is greater for females than for males. Over educated females, however, are advantaged compared with their male counterparts. These findings are interpreted using the explanations offered for the gender differential in the payoff to schooling in the growing literature on earnings determination in China

Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representations on Sequence Labelling Tasks

Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing applications. In this paper, we perform an extrinsic evaluation of five popular word embedding methods in the context of four sequence labelling tasks: POS-tagging, syntactic chunking, NER and MWE identification. A particular focus of the paper is analysing the effects of task-based updating of word representations. We show that when using word embeddings as features, as few as several hundred training instances are sufficient to achieve competitive results, and that word embeddings lead to improvements over OOV words and out of domain. Perhaps more surprisingly, our results indicate there is little difference between the different word embedding methods, and that simple Brown clusters are often competitive with word embeddings across all tasks we consider