Nucleon Helicity and Transversity Parton Distributions from Lattice QCD

We present the first lattice-QCD calculation of the isovector polarized parton distribution functions (both helicity and transversity) using the large-momentum effective field theory (LaMET) approach for direct Bjorken-$x$ dependence. We first review the detailed steps of the procedure in the unpolarized case, then generalize to the helicity and transversity cases. We also derive a new mass-correction formulation for all three cases. We then compare the effects of each finite-momentum correction using lattice data calculated at $M_\pi\approx 310$ MeV. Finally, we discuss the implications of these results for the poorly known antiquark structure and predict the sea-flavor asymmetry in the transversely polarized nucleon.Comment: 21 pages, 6 figure

Replacing Backpropagation with Biological Plausible Top-down Credit Assignment in Deep Neural Networks Training

Top-down connections in the biological brain has been shown to be important in high cognitive functions. However, the function of this mechanism in machine learning has not been defined clearly. In this study, we propose to lay out a framework constituted by a bottom-up and a top-down network. Here, we use a Top-down Credit Assignment Network (TDCA-network) to replace the loss function and back propagation (BP) which serve as the feedback mechanism in traditional bottom-up network training paradigm. Our results show that the credit given by well-trained TDCA-network outperforms the gradient from backpropagation in classification task under different settings on multiple datasets. In addition, we successfully use a credit diffusing trick, which can keep training and testing performance remain unchanged, to reduce parameter complexity of the TDCA-network. More importantly, by comparing their trajectories in the parameter landscape, we find that TDCA-network directly achieved a global optimum, in contrast to that backpropagation only can gain a localized optimum. Thus, our results demonstrate that TDCA-network not only provide a biological plausible learning mechanism, but also has the potential to directly achieve global optimum, indicating that top-down credit assignment can substitute backpropagation, and provide a better learning framework for Deep Neural Networks

IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making

Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity. With strong capabilities in sequential decision-making, Reinforcement Learning (RL) technology has achieved remarkable success in quantitative trading. Nonetheless, most existing RL-based MM methods focus on optimizing single-price level strategies which fail at frequent order cancellations and loss of queue priority. Strategies involving multiple price levels align better with actual trading scenarios. However, given the complexity that multi-price level strategies involves a comprehensive trading action space, the challenge of effectively training profitable RL agents for MM persists. Inspired by the efficient workflow of professional human market makers, we propose Imitative Market Maker (IMM), a novel RL framework leveraging both knowledge from suboptimal signal-based experts and direct policy interactions to develop multi-price level MM strategies efficiently. The framework start with introducing effective state and action representations adept at encoding information about multi-price level orders. Furthermore, IMM integrates a representation learning unit capable of capturing both short- and long-term market trends to mitigate adverse selection risk. Subsequently, IMM formulates an expert strategy based on signals and trains the agent through the integration of RL and imitation learning techniques, leading to efficient learning. Extensive experimental results on four real-world market datasets demonstrate that IMM outperforms current RL-based market making strategies in terms of several financial criteria. The findings of the ablation study substantiate the effectiveness of the model components

Virus efficacy of recombined Autographa californica M nucleopolyhedrovirus (AcMNPV) on tea pest Ectropis obliqua

Ectropis obliqua is a major tea pest and chitin synthase (CHS) plays a key role in the pest growth and development. A 192 bp conserved domain from E. obliqua CHS gene was cloned and it was used to construct recombined Autographa californica M nucleopolyhedrovirus (AcMNPV) with double-stranded RNA interference (dsRNAi) method. The recombined AcMNPV virus could propagate in host cells sf9. Injection test showed that the virus efficacy of the recombined AcMNPV on E. obliqua larvae was significantly enhanced. It is considered that the CHS dsRNAi mediated by the nuclear polyhedrosis virus will be interesting for development of alternative bio-pesticide to control the tea pest E. obliqua.Keywords: Chitin synthase, baculovirus, double-stranded RNA interference, Ectropis obliquaAfrican Journal of Biotechnology Vol. 9(33), pp. 5412-5418, 16 August, 201

ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation

Image-grounded dialogue systems benefit greatly from integrating visual information, resulting in high-quality response generation. However, current models struggle to effectively utilize such information in zero-resource scenarios, mainly due to the disparity between image and text modalities. To overcome this challenge, we propose an innovative multimodal framework, called ZRIGF, which assimilates image-grounded information for dialogue generation in zero-resource situations. ZRIGF implements a two-stage learning strategy, comprising contrastive pre-training and generative pre-training. Contrastive pre-training includes a text-image matching module that maps images and texts into a unified encoded vector space, along with a text-assisted masked image modeling module that preserves pre-training visual features and fosters further multimodal feature alignment. Generative pre-training employs a multimodal fusion module and an information transfer module to produce insightful responses based on harmonized multimodal representations. Comprehensive experiments conducted on both text-based and image-grounded dialogue datasets demonstrate ZRIGF's efficacy in generating contextually pertinent and informative responses. Furthermore, we adopt a fully zero-resource scenario in the image-grounded dialogue dataset to demonstrate our framework's robust generalization capabilities in novel domains. The code is available at https://github.com/zhangbo-nlp/ZRIGF.Comment: ACM Multimedia 2023 Accpeted, Repo: https://github.com/zhangbo-nlp/ZRIG

Travelling wave solutions for Kolmogorov-type delayed lattice reaction–diffusion systems

[[abstract]]This work investigates the existence and non-existence of travelling wave solutions for Kolmogorov-type delayed lattice reaction–diffusion systems. Employing the cross iterative technique coupled with the explicit construction of upper and lower solutions in the theory of quasimonotone dynamical systems, we can find two threshold speeds c∗ and c∗ with c∗≥c∗>0. If the wave speed is greater than c∗, then we establish the existence of travelling wave solutions connecting two different equilibria. On the other hand, if the wave speed is smaller than c∗, we further prove the non-existence result of travelling wave solutions. Finally, several ecological examples including one-species, two-species and three-species models with various functional responses and time delays are presented to illustrate the analytical results.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]GB

A multichannel thiacalix[4]arene-based fluorescent chemosensor for Zn²⁺, F⁻ ions and imaging of living cells

The fluorescent sensor (3) based on the 1,3-alternate conformation of the thiacalix[4]arene bearing the coumarin fluorophore, appended via an imino group, has been synthesised. Sensing properties were evaluated in terms of a colorimetric and fluorescence sensor for Zn 2+ and F - . High selectivity and excellent sensitivity were exhibited, and off-on optical behaviour in different media was observed. All changes were visible to the naked eye, whilst the presence of the Zn 2+ and F - induces fluorescence enhancement and the formation of a 1:1 complex with 3. In addition, 3 exhibits low cytotoxicity and good cell permeability and can readily be employed for assessing the change of intracellular levels of Zn 2+ and F -