Cauchy’s Formula on Surfaces Embedded in the Quaternions

Cauchy’s theorem for analytic functions on complex numbers is extended to analytic functions on the quaternions. For this purpose, we carefully define the notions of differentiation and integration on two or three dimensional manifolds embedded in the quaternions.     

First eigenvalues of geometric operators under the Yamabe flow

Suppose \((M,g_0)\) is a compact Riemannian manifold without boundary of dimension \(n\ge 3\). Using the Yamabe flow, we obtain estimate for the first nonzero eigenvalue of the Laplacian of \(g_0\) with negative scalar curvature in terms of the Yamabe metric in its conformal class. On the other hand, we prove that the first eigenvalue of some geometric operators on a compact Riemannian manifold is nondecreasing along the unnormalized Yamabe flow under suitable curvature assumption. Similar results are obtained for manifolds with boundary and for CR manifold.     

Minimum Disparity Estimation in Linear Regression Models: Distribution and Efficiency

This paper deals with the minimum disparity estimation in linear regression models. The estimators are defined as statistical quantities which minimize the blended weight Hellinger distance between a weighted kernel density estimator of errors and a smoothed model density of errors. It is shown that the estimators of the regression parameters are asymptotic normally distributed and efficient at the model if the weights of the density estimators are appropriately chosen.     

A differential calculus on the quantum groupSU q (2) comparible with the gauge-transformation structure

We consider a three-dimensional bicovariant differential calculus on the quantum group SU_q(2) that is compatible with the structure of gauge transformations. In the approach based on the global covariance with respect to the stability subgroup U(1), we show that the gauge-covariant exterior differential algebra on SU_q(2) is uniquely determined. We construct explicit representations of q-deformed Lie algebras in terms of differential operators. Translated from Teoreticheskaya i Matematicheskaya Fizika, Vol. 116, No. 2, pp. 265–276, August. 1998.     

Four Questions on Birkhoff Polytope

We ask several questions on the structure of the polytope P_n of doubly stochastic n 2 n matrices, known as a Birkhoff polytope. We discuss the volume of P_n, the work of the simplex method, and the mixing of random walks on P_n.     

Balance of mass, momentum, and energy in splintering central collisions for 40Ar up to 115 MeV /Nucleon

For central collisions of (17-115)A MeV 40Ar+Cu, Ag, Au, an overall balance is determined for the average mass, energy, and longitudinal momentum. Light charged particles and fragments are separated into forward-focused and isotropic components in the frame of the heaviest fragment. Energy removal by the isotropic component reaches 1-2 GeV. For such high deposition energies, statistical multifragmentation models predict much more extensive nuclear disassembly than is observed.     

Effect of boundary layer losses on 2D detonation cellular structures

We evaluate the effect of boundary layer losses on two-dimensional H2/O2/Ar cellular detonations obtained in narrow channels. The experiments provide the details of the cellular structure and the detonation speed deficits from the ideal CJ speed. We model the effect of the boundary layer losses by incorporating the flow divergence in the third dimension due to the negative boundary layer displacement thickness, modeled using Mirels’ theory. The cellular structures obtained numerically with the resulting quasi-2D formulation of the reactive Euler equations with two-step chain-branching chemistry are found in excellent agreement with experiment, both in terms of cell dynamics and velocity deficits, provided the boundary layer constant of Mirels is modified by a factor of 2. A significant increase in the cell size is found with increasing velocity deficit. This is found to be very well captured by the induction zone increase in slower detonations due to the lower temperatures in the induction zone.     

Epistemic Communities under Active Inference

The spread of ideas is a fundamental concern of today’s news ecology. Understanding the dynamics of the spread of information and its co-option by interested parties is of critical importance. Research on this topic has shown that individuals tend to cluster in echo-chambers and are driven by confirmation bias. In this paper, we leverage the active inference framework to provide an in silico model of confirmation bias and its effect on echo-chamber formation. We build a model based on active inference, where agents tend to sample information in order to justify their own view of reality, which eventually leads to them to have a high degree of certainty about their own beliefs. We show that, once agents have reached a certain level of certainty about their beliefs, it becomes very difficult to get them to change their views. This system of self-confirming beliefs is upheld and reinforced by the evolving relationship between an agent’s beliefs and observations, which over time will continue to provide evidence for their ingrained ideas about the world. The epistemic communities that are consolidated by these shared beliefs, in turn, tend to produce perceptions of reality that reinforce those shared beliefs. We provide an active inference account of this community formation mechanism. We postulate that agents are driven by the epistemic value that they obtain from sampling or observing the behaviours of other agents. Inspired by digital social networks like Twitter, we build a generative model in which agents generate observable social claims or posts (e.g., ‘tweets’) while reading the socially observable claims of other agents that lend support to one of two mutually exclusive abstract topics. Agents can choose which other agent they pay attention to at each timestep, and crucially who they attend to and what they choose to read influences their beliefs about the world. Agents also assess their local network’s perspective, influencing which kinds of posts they expect to see other agents making. The model was built and simulated using the freely available Python package pymdp. The proposed active inference model can reproduce the formation of echo-chambers over social networks, and gives us insight into the cognitive processes that lead to this phenomenon.