This paper studies a special class of population-size-dependent branching processes, in which the offspring distribution is supercritical when the population size does not exceed a given threshold 𝐾𝐾, and is subcritical or critical when the population size exceeds 𝐾𝐾. Up to now, the author has found no paper concerning continuous time threshold processes, and study of the discrete case is also limited to the extinction time 𝑇𝑇 and 𝔼𝔼𝑇𝑇. In this paper, both of the two cases were studied more thoroughly: for the discrete case, a limit theorem of the process has been proved; for the continuous case, the existence of Markovian threshold processes has been proved, and an equivalent condition for the process to extinct almost surely was also given. This paper also further generalized discrete time threshold processes by giving a random delay to the threshold’s influence of offspring distribution. The properties of these delayed threshold processes were studied, and a limit theorem was obtained.

Community detection is a fundamental problem in social network analysis. It is desirable to have fast community detection algorithm that applicable to large-scale networks we see today and also have reliable community detection results. This paper proposes Spectral Clustering On Ratios-of-eigenvectors (SCORE) as such an algorithm. The algorithm is fast in computation, is applicable to real large networks, and is competitive in real data performance when applied to some representative data sets in this area. We study the algorithm with the Degree-Corrected Block Model (DCBM). Compare to most works in this area that focus on the much narrower Block Models, our analysis is more difficult and requires new techniques. The novelty of our algorithm lies in that it uses entry-wise ratios between leading eigenvectors of the adjacency to reduce a seemingly hard problem to a problem that is very much tractable.

Gromov's compactness theorem for pseudo-holomorphic curves is a foundational result in symplectic geometry. It controls the compactness of the moduli space of pseudo-holomorphic curves with bounded area in a symplectic manifold. In this paper, we prove the analog of Gromov's compactness theorem in non-archimedean analytic geometry. We work in the framework of Berkovich spaces. First, we introduce a notion of Kähler structure in non-archimedean analytic geometry using metrizations of virtual line bundles. Second, we introduce formal stacks and non-archimedean analytic stacks. Then we construct the moduli stack of non-archimedean analytic stable maps using formal models, Artin's representability criterion and the geometry of stable curves. Finally, we reduce the non-archimedean problem to the known compactness results in algebraic geometry. The motivation of this paper is to provide the foundations for non-archimedean enumerative geometry.

The cosmological observations provide a strong evidence that there is a positive cosmological constant in our universe and thus the spacetime is asymptotical de Sitter space. The conjecture of gravity as the weakest force in the asymptotical dS space leads to a lower bound on the U(1) gauge coupling $g$, or equivalently, the positive cosmological constant gets an upper bound $\rho_V \leq g^2 M_p^4$ in order that the U(1) gauge theory can survive in four dimensions. This result has a simple explanation in string theory, i.e. the string scale $\sqrt{\alpha '}$ should not be greater than the size of the cosmic horizon. Our proposal in string theory can be generalized to U(N) gauge theory and gives a guideline to the microscopic explanation of the de Sitter entropy. The similar results are also obtained in the asymptotical anti-de Sitter space.

Rapid and reliable detection and identification of unknown chemical substances are critical to homeland security. It is challenging to identify chemical components from a wide range of explosives. There are two key steps involved. One is a non-destructive and informative spectroscopic technique for data acquisition. The other is an associated library of reference features along with a computational method for feature matching and meaningful detection within or beyond the library.