New existence and nonexistence results for strong external difference families

In this paper, we use character-theoretic techniques to give new nonexistence results for $(n,m,k,\lambda )$-strong external difference families (SEDFs). We also use cyclotomic classes to give two new classes of SEDFs with $m=2$.     

Submanifolds in Cauchy Riemann Geometry

In this paper I would like to make a report on the results about hypersurfaces in the Heisenberg group and invariant curves and surfaces in CR geometry. The results are contained in the papers [8, 9, 16] and [14]. Besides, I would also report on the results about the strong maximum principle for a class of mean curvature type operators in [10].     

Dynamical properties of a stochastic predator-prey model with functional response

A stochastic prey-predator model with functional response is investigated in this paper. A complete threshold analysis of coexistence and extinction is obtained. Moreover, we point out that the stochastic predator-prey model undergoes a stochastic Hopf bifurcation from the viewpoint of numerical simulations. Some numerical simulations are carried out to support our results.     

From the Vlasov–Poisson equation with strong local alignment to the pressureless Euler–Poisson system

This article provides a rigorous justification on a hydrodynamic limit from the Vlasov–Poisson system with strong local alignment to the pressureless Euler–Poisson system for repulsive dynamics.     

Global attractor for the Kirchhoff type equation with a strong dissipation

The paper studies the longtime behavior of the Kirchhoff type equation with a strong dissipation utt−M(‖∇u^‖2)Δu−Δut+h(ut)+g(u)=f(x). It proves that the related continuous semigroup S(t) possesses in the phase space with low regularity a global attractor which is connected. And an example is shown.     

Strong embeddings of minimum genus

A “folklore conjecture, probably due to Tutte” (as described in [P.D. Seymour, Sums of circuits, in: Graph Theory and Related Topics (Proc. Conf., Univ. Waterloo, 1977), Academic Press, 1979, pp. 341-355]) asserts that every bridgeless cubic graph can be embedded on a surface of its own genus in such a way that the face boundaries are cycles of the graph. Sporadic counterexamples to this conjecture have been known since the late 1970s. In this paper we consider closed 2-cell embeddings of graphs and show that certain (cubic) graphs (of any fixed genus) have closed 2-cell embedding only in surfaces whose genus is very large (proportional to the order of these graphs), thus providing a plethora of strong counterexamples to the above conjecture. The main result yielding such counterexamples may be of independent interest.     

SC-Hamiltonian graphs and digraphs: New necessary conditions and their impacts

A graph G on n vertices is said to be separable cost constant Hamiltonian (SC-Hamiltonian) if and only if G is Hamiltonian and for any cost matrix C=(c_(i,j)) associated with G where all tours have the same cost, there exist vectors a=(a₁,…,a_n) and b=(b₁,…,b_n) such that . In this paper we show that for symmetric digraphs strong Hamiltonicity is a necessary condition for SC-Hamiltonicity. As a surprising consequence, we prove that the symmetric digraph obtained from an undirected SC-Hamiltonian graph by edge duplication need not be SC-Hamiltonian. This settles a conjecture of Kabadi and Punnen. We then show that an undirected graph on an even number of nodes having an edge that appears in every Hamiltonian cycle cannot be SC-Hamiltonian. Using this we establish that multiple subdivision of an edge need not preserve SC-Hamiltonicity, disproving a previous claim. Further, we identify other necessary conditions for SC-Hamiltonicity and obtain new classes of SC-Hamiltonian graphs.     

On consistency of stationary points of stochastic optimization problems in a Banach space

Recently, Balaji and Xu studied the consistency of stationary points, in the sense of the Clarke generalized gradient, for the sample average approximations to a one-stage stochastic optimization problem in a separable Banach space with separable dual. We present an alternative approach, showing that the restrictive assumptions that the dual space is separable and the Clarke generalized gradient is a (norm) upper semicontinuous and compact-valued multifunction can be dropped. For that purpose, we use two results having independent interest: a strong law of large numbers and a multivalued Komlós theorem in the dual to a separable Banach space, and a result on the weak* closedness of the expectation of a random weak* compact convex set.     

On the effectiveness of a generalization of Miller’s primality theorem

Berrizbeitia and Olivieri showed in a recent paper that, for any integer r$r$, the notion of ω$\omega$-prime to base a$a$ leads to a primality test for numbers n≡1$n\equiv 1$ mod r$r$, that under the Extended Riemann Hypothesis (ERH) runs in polynomial time. They showed that the complexity of their test is at most the complexity of the Miller primality test (MPT), which is O((logn)^4+o(1))$O\left({(logn)}^{4+o\left(1\right)}\right)$. They conjectured that their test is more effective than the MPT if r$r$ is large.     

Epistemic irrelevance in credal nets: The case of imprecise Markov trees

We focus on credal nets, which are graphical models that generalise Bayesian nets to imprecise probability. We replace the notion of strong independence commonly used in credal nets with the weaker notion of epistemic irrelevance, which is arguably more suited for a behavioural theory of probability. Focusing on directed trees, we show how to combine the given local uncertainty models in the nodes of the graph into a global model, and we use this to construct and justify an exact message-passing algorithm that computes updated beliefs for a variable in the tree. The algorithm, which is linear in the number of nodes, is formulated entirely in terms of coherent lower previsions, and is shown to satisfy a number of rationality requirements. We supply examples of the algorithm’s operation, and report an application to on-line character recognition that illustrates the advantages of our approach for prediction. We comment on the perspectives, opened by the availability, for the first time, of a truly efficient algorithm based on epistemic irrelevance.