Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological in...Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological index is . In this paper, the Schultz, Modified Schultz polynomials and their topological indices of Jahangir graphs J<sub>2,m</sub> for all integer number m ≥ 3 are calculated.展开更多
Hornik, Stinchcombe & White have shown that the multilayer feed forward networks with enough hidden layers are universal approximators. Roux & Bengio have proved that adding hidden units yield a strictly impro...Hornik, Stinchcombe & White have shown that the multilayer feed forward networks with enough hidden layers are universal approximators. Roux & Bengio have proved that adding hidden units yield a strictly improved modeling power, and Restricted Boltzmann Machines (RBM) are universal approximators of discrete distributions. In this paper, we provide yet another proof. The advantage of this new proof is that it will lead to several new learning algorithms. We prove that the Deep Neural Networks implement an expansion and the expansion is complete. First, we briefly review the basic Boltzmann Machine and that the invariant distributions of the Boltzmann Machine generate Markov chains. We then review the θ-transformation and its completeness, i.e. any function can be expanded by θ-transformation. We further review ABM (Attrasoft Boltzmann Machine). The invariant distribution of the ABM is a θ-transformation;therefore, an ABM can simulate any distribution. We discuss how to convert an ABM into a Deep Neural Network. Finally, by establishing the equivalence between an ABM and the Deep Neural Network, we prove that the Deep Neural Network is complete.展开更多
Salt stress is a major abiotic stress which severely hinders crop production.However,the regulatory network controlling tomato resistance to salt remains unclear.Here,we found that the tomato WRKY transcription factor...Salt stress is a major abiotic stress which severely hinders crop production.However,the regulatory network controlling tomato resistance to salt remains unclear.Here,we found that the tomato WRKY transcription factor WRKY57 acted as a negative regulator in salt stress response by directly attenuating the transcription of salt-responsive genes(Sl RD29B and Sl DREB2)and an ion homeostasis gene(Sl SOS1).We further identified two VQ-motif containing proteins Sl VQ16 and Sl VQ21as Sl WRKY57-interacting proteins.Sl VQ16 positively,while Sl VQ21 negatively modulated tomato resistance to salt stress.Sl VQ16 and Sl VQ21 competitively interacted with Sl WRKY57 and antagonistically regulated the transcriptional repression activity of Sl WRKY57.Additionally,the Sl WRKY57-Sl VQ21/Sl VQ16 module was involved in the pathway of phytohormone jasmonates(JAs)by interacting with JA repressors JA-ZIM domain(JAZ)proteins.These results provide new insights into how the Sl WRKY57-Sl VQ21/Sl VQ16 module finely tunes tomato salt tolerance.展开更多
文摘Let G = (V;E) be a simple connected graph. The Wiener index is the sum of distances between all pairs of vertices of a connected graph. The Schultz topological index is equal to and the Modified Schultz topological index is . In this paper, the Schultz, Modified Schultz polynomials and their topological indices of Jahangir graphs J<sub>2,m</sub> for all integer number m ≥ 3 are calculated.
文摘Hornik, Stinchcombe & White have shown that the multilayer feed forward networks with enough hidden layers are universal approximators. Roux & Bengio have proved that adding hidden units yield a strictly improved modeling power, and Restricted Boltzmann Machines (RBM) are universal approximators of discrete distributions. In this paper, we provide yet another proof. The advantage of this new proof is that it will lead to several new learning algorithms. We prove that the Deep Neural Networks implement an expansion and the expansion is complete. First, we briefly review the basic Boltzmann Machine and that the invariant distributions of the Boltzmann Machine generate Markov chains. We then review the θ-transformation and its completeness, i.e. any function can be expanded by θ-transformation. We further review ABM (Attrasoft Boltzmann Machine). The invariant distribution of the ABM is a θ-transformation;therefore, an ABM can simulate any distribution. We discuss how to convert an ABM into a Deep Neural Network. Finally, by establishing the equivalence between an ABM and the Deep Neural Network, we prove that the Deep Neural Network is complete.
基金supported by the Project of Cultivation for young top-notch Talents of Beijing Municipal Institutions (Grant No.BPHR202203099)。
文摘Salt stress is a major abiotic stress which severely hinders crop production.However,the regulatory network controlling tomato resistance to salt remains unclear.Here,we found that the tomato WRKY transcription factor WRKY57 acted as a negative regulator in salt stress response by directly attenuating the transcription of salt-responsive genes(Sl RD29B and Sl DREB2)and an ion homeostasis gene(Sl SOS1).We further identified two VQ-motif containing proteins Sl VQ16 and Sl VQ21as Sl WRKY57-interacting proteins.Sl VQ16 positively,while Sl VQ21 negatively modulated tomato resistance to salt stress.Sl VQ16 and Sl VQ21 competitively interacted with Sl WRKY57 and antagonistically regulated the transcriptional repression activity of Sl WRKY57.Additionally,the Sl WRKY57-Sl VQ21/Sl VQ16 module was involved in the pathway of phytohormone jasmonates(JAs)by interacting with JA repressors JA-ZIM domain(JAZ)proteins.These results provide new insights into how the Sl WRKY57-Sl VQ21/Sl VQ16 module finely tunes tomato salt tolerance.