A Unified Gas-kinetic Scheme for Continuum and Rarefied Flows IV: full Boltzmann and Model Equations

Fluid dynamic equations are valid in their respective modeling scales. With a variation of the modeling scales, theoretically there should have a continuous spectrum of fluid dynamic equations. In order to study multiscale flow evolution efficiently, the dynamics in the computational fluid has to be changed with the scales. A direct modeling of flow physics with a changeable scale may become an appropriate approach. The unified gas-kinetic scheme (UGKS) is a direct modeling method in the mesh size scale, and its underlying flow physics depends on the resolution of the cell size relative to the particle mean free path. The cell size of UGKS is not limited by the particle mean free path. With the variation of the ratio between the numerical cell size and local particle mean free path, the UGKS recovers the flow dynamics from the particle transport and collision in the kinetic scale to the wave propagation in the hydrodynamic scale. The previous UGKS is mostly constructed from the evolution solution of kinetic model equations. This work is about the further development of the UGKS with the implementation of the full Boltzmann collision term in the region where it is needed. The central ingredient of the UGKS is the coupled treatment of particle transport and collision in the flux evaluation across a cell interface, where a continuous flow dynamics from kinetic to hydrodynamic scales is modeled. The newly developed UGKS has the asymptotic preserving (AP) property of recovering the NS solutions in the continuum flow regime, and the full Boltzmann solution in the rarefied regime. In the mostly unexplored transition regime, the UGKS itself provides a valuable tool for the flow study in this regime. The mathematical properties of the scheme, such as stability, accuracy, and the asymptotic preserving, will be analyzed in this paper as well

Identification and analysis of phosphorylation status of proteins in dormant terminal buds of poplar

<p>Abstract</p> <p>Background</p> <p>Although there has been considerable progress made towards understanding the molecular mechanisms of bud dormancy, the roles of protein phosphorylation in the process of dormancy regulation in woody plants remain unclear.</p> <p>Results</p> <p>We used mass spectrometry combined with TiO₂phosphopeptide-enrichment strategies to investigate the phosphoproteome of dormant terminal buds (DTBs) in poplar (<it>Populus simonii × P. nigra</it>). There were 161 unique phosphorylated sites in 161 phosphopeptides from 151 proteins; 141 proteins have orthologs in <it>Arabidopsis</it>, and 10 proteins are unique to poplar. Only 34 sites in proteins in poplar did not match well with the equivalent phosphorylation sites of their orthologs in <it>Arabidopsis</it>, indicating that regulatory mechanisms are well conserved between poplar and <it>Arabidopsis</it>. Further functional classifications showed that most of these phosphoproteins were involved in binding and catalytic activity. Extraction of the phosphorylation motif using Motif-X indicated that proline-directed kinases are a major kinase group involved in protein phosphorylation in dormant poplar tissues.</p> <p>Conclusions</p> <p>This study provides evidence about the significance of protein phosphorylation during dormancy, and will be useful for similar studies on other woody plants.</p

Supersymmetric Extension of the Minimal Dark Matter Model

The minimal dark matter model is given a supersymmetric extension. A super SU(2)L quintuplet is introduced with its fermionic neutral component still being the dark matter, the dark matter particle mass is about 19.7 GeV. Mass splitting among the quintplet due to supersymmetry particles is found to be negligibly small compared to the electroweak corrections. Other properties of this supersymmetry model are studied, it has the solutions to the PAMELA and Fermi-LAT anomaly, the predictions in higher energies need further experimental data to verify.Comment: 14 pages, 7 figures, accepted for publication in Chinese Physics C, typos correcte

Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that the ρ2 values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness

UNIDEAL: Curriculum Knowledge Distillation Federated Learning

Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients while preserving data privacy. However, cross-domain FL tasks, where clients possess data from different domains or distributions, remain a challenging problem due to the inherent heterogeneity. In this paper, we present UNIDEAL, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios and heterogeneous model architectures. The proposed method introduces Adjustable Teacher-Student Mutual Evaluation Curriculum Learning, which significantly enhances the effectiveness of knowledge distillation in FL settings. We conduct extensive experiments on various datasets, comparing UNIDEAL with state-of-the-art baselines. Our results demonstrate that UNIDEAL achieves superior performance in terms of both model accuracy and communication efficiency. Additionally, we provide a convergence analysis of the algorithm, showing a convergence rate of O(1/T) under non-convex conditions.Comment: Submitted to ICASSP 202

Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation

Sequential recommender systems aim to model users' evolving interests from their historical behaviors, and hence make customized time-relevant recommendations. Compared with traditional models, deep learning approaches such as CNN and RNN have achieved remarkable advancements in recommendation tasks. Recently, the BERT framework also emerges as a promising method, benefited from its self-attention mechanism in processing sequential data. However, one limitation of the original BERT framework is that it only considers one input source of the natural language tokens. It is still an open question to leverage various types of information under the BERT framework. Nonetheless, it is intuitively appealing to utilize other side information, such as item category or tag, for more comprehensive depictions and better recommendations. In our pilot experiments, we found naive approaches, which directly fuse types of side information into the item embeddings, usually bring very little or even negative effects. Therefore, in this paper, we propose the NOninVasive self-attention mechanism (NOVA) to leverage side information effectively under the BERT framework. NOVA makes use of side information to generate better attention distribution, rather than directly altering the item embedding, which may cause information overwhelming. We validate the NOVA-BERT model on both public and commercial datasets, and our method can stably outperform the state-of-the-art models with negligible computational overheads.Comment: Accepted at AAAI 202

Digital literacy and subjective happiness of low-income groups: Evidence from rural China

Improvements of the happiness of the rural population are an essential sign of the effectiveness of relative poverty governance. In the context of today’s digital economy, assessing the relationship between digital literacy and the subjective happiness of rural low-income groups is of great practicality. Based on data from China Family Panel Studies, the effect of digital literacy on the subjective well-being of rural low-income groups was empirically tested. A significant happiness effect of digital literacy on rural low-income groups was found. Digital literacy promotes the subjective happiness of rural low-income groups through income increase and consumption growth effects. The observed happiness effect is heterogeneous among different characteristic groups, and digital literacy significantly positively impacts the subjective happiness of rural low-income groups. Decomposition of subjective happiness into life satisfaction and job satisfaction shows that digital literacy significantly positively affects the job and life satisfaction of rural low-income groups. This paper demonstrates that digital literacy induces a practical happiness effect. To further strengthen the subjective welfare effect of digital literacy in the construction of digital villages, the government should focus on cultivating digital literacy among low-income groups from the demand side. The construction of digital infrastructure should be actively promoted from the supply side

High visibility on-chip quantum interference of single surface plasmons

Quantum photonic integrated circuits (QPICs) based on dielectric waveguides have been widely used in linear optical quantum computation. Recently, surface plasmons have been introduced to this application because they can confine and manipulate light beyond the diffraction limit. In this study, the on-chip quantum interference of two single surface plasmons was achieved using dielectric-loaded surface-plasmon-polariton waveguides. The high visibility (greater than 90%) proves the bosonic nature of single plasmons and emphasizes the feasibility of achieving basic quantum logic gates for linear optical quantum computation. The effect of intrinsic losses in plasmonic waveguides with regard to quantum information processing is also discussed. Although the influence of this effect was negligible in the current experiment, our studies reveal that such losses can dramatically reduce quantum interference visibility in certain cases; thus, quantum coherence must be carefully considered when designing QPIC devices.Comment: 6 pages, 4 figure