Analyticity for the (generalized) Navier-Stokes equations with rough initial data

We study the Cauchy problem for the (generalized) incompressible Navier-Stokes equations \begin{align} u_t+(-\Delta)^{\alpha}u+u\cdot \nabla u +\nabla p=0, \ \ {\rm div} u=0, \ \ u(0,x)= u_0. \nonumber \end{align} We show the analyticity of the local solutions of the Navier-Stokes equation ($\alpha=1$) with any initial data in critical Besov spaces $\dot{B}^{n/p-1}_{p,q}(\mathbb{R}^n)$ with $1< p<\infty, \ 1\le q\le \infty$ and the solution is global if $u_0$ is sufficiently small in $\dot{B}^{n/p-1}_{p,q}(\mathbb{R}^n)$. In the case $p=\infty$, the analyticity for the local solutions of the Navier-Stokes equation ($\alpha=1$) with any initial data in modulation space $M^{-1}_{\infty,1}(\mathbb{R}^n)$ is obtained. We prove the global well-posedness for a fractional Navier-stokes equation ($\alpha=1/2$) with small data in critical Besov spaces $\dot{B}^{n/p}_{p,1}(\mathbb{R}^n) \ (1\leq p\leq\infty)$ and show the analyticity of solutions with small initial data either in $\dot{B}^{n/p}_{p,1}(\mathbb{R}^n) \ (1\leq p<\infty)$ or in $\dot{B}^0_{\infty,1} (\mathbb{R}^n)\cap {M}^0_{\infty,1}(\mathbb{R}^n)$. Similar results also hold for all $\alpha\in (1/2,1)$.Comment: 31 page

Long Trend Dynamics in Social Media

A main characteristic of social media is that its diverse content, copiously generated by both standard outlets and general users, constantly competes for the scarce attention of large audiences. Out of this flood of information some topics manage to get enough attention to become the most popular ones and thus to be prominently displayed as trends. Equally important, some of these trends persist long enough so as to shape part of the social agenda. How this happens is the focus of this paper. By introducing a stochastic dynamical model that takes into account the user's repeated involvement with given topics, we can predict the distribution of trend durations as well as the thresholds in popularity that lead to their emergence within social media. Detailed measurements of datasets from Twitter confirm the validity of the model and its predictions

Purification of an elicitor from Magnaporthe oryzae inducing defense resistance in rice

Inducible defenses that contribute to overall resistance in plant can be triggered by elicitors. A novel elicitor, derived from the mycelia of the blast fungus Magnaporthe oryzae, was purified to homogeneity by HiPrep 16/20 DEAE-Sepharose FF, Concanavalin A-Sepharose 4B and HiPrep 16/60 Sephacryl S-100 column chromatography. The purified elicitor appeared as single band corresponding to a molecular weight of 48.53 kDa on sodium dodecyl sulfate-polyacrylamide gel electrophresis (SDS-PAGE) and a pI of 6.01 on isoeletric focusing (IEF) gel. Treatment with the purified elicitor increased the activities of phenylalanine ammonium-lyase (PAL) and peroxidase (POD) in rice susceptible cultivar CO39. Timecourse analysis showed peak accumulation of PAL appeared at 24 h after treatment, and it was higher in challenge-inoculated plants than non-challenge plants. POD accumulation showed similar kinetics with PAL, but the largest peak appeared at 36 h after treatment. Compared to the untreated control plants, pretreatment of rice leaves with the purified elicitor provided an enhanced level of protection against M. oryzae. N-terminal blocked elicitor was identified as hypothetical protein MG 05155.4 with 26.28% mass fingerprint coverage by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The results suggest that the purified elicitor is involved in inducing resistance against blast fungus.Keywords: Magnaporthe oryzae, elicitor, purification, induced resistanc

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition

An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss

Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an end-to-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses.Comment: AAAI-1

Trends in Social Media : Persistence and Decay

Social media generates a prodigious wealth of real-time content at an incessant rate. From all the content that people create and share, only a few topics manage to attract enough attention to rise to the top and become temporal trends which are displayed to users. The question of what factors cause the formation and persistence of trends is an important one that has not been answered yet. In this paper, we conduct an intensive study of trending topics on Twitter and provide a theoretical basis for the formation, persistence and decay of trends. We also demonstrate empirically how factors such as user activity and number of followers do not contribute strongly to trend creation and its propagation. In fact, we find that the resonance of the content with the users of the social network plays a major role in causing trends

Analytical study of the holographic superconductor from higher derivative theory

In this paper, we analytically study the holographic superconductor models with the high derivative (HD) coupling terms. Using the Sturm-Liouville (SL) eigenvalue method, we perturbatively calculate the critical temperature. The analytical results are in good agreement with the numerical results. It confirms that the perturbative method in terms of the HD coupling parameters is available. Along the same line, we analytically calculate the value of the condensation near the critical temperature. We find that the phase transition is second order with mean field behavior, which is independent of the HD coupling parameters. Then in the low temperature limit, we also calculate the conductivity, which is qualitatively consistent with the numerical one. We find that the superconducting energy gap is proportional to the value of the condensation. But we note that since the condensation changes with the HD coupling parameters, as the function of the HD coupling parameters, the superconducting energy gap follows the same change trend as that of the condensation.Comment: 10 pages, 5 figure

Textual and Quantitative Research on China’s Action Plan for Promoting the Development of Big Data From the Perspective of Policy Tools

The research on the development of big data from the perspective of policy tools, to help policy makers look for policy tools that can provide guidance and support for the development of big data. The research is of significant theoretical and practical value for promoting development of big data and realizing the strategy of data power country. Using content analysis method and quantitative analysis methods, this paper evaluates and discusses China’s action plan for the development of big data from the perspective of policy tools. Government uses more supply-side and demand-side policy tools to stimulate and support the development of big data. Nevertheless, the stage of technology research and development stage has not been given enough attention. To improve and update China’s action plan for promoting the development of big data, policy tools system needs to be integrated or coordinated with data powerful country’s value-chain