The quantitative adiabatic condition (QAC), or quantitative condition, is a convenient (a priori) tool for estimating the adiabaticity of quantum evolutions. However, the range of the applicability of QAC is not well understood. It has been shown that QAC can become insufficient for guaranteeing the validity of the adiabatic approximation, but under what conditions the QAC would become necessary has become controversial. Furthermore, it is believed that the inability for the QAC to reveal quantum adiabaticity is due to induced resonant transitions. However, it is not clear how to quantify these transitions in general. Here we present a progress to this problem by finding an exact relation that can reveal how transition amplitudes are related to QAC directly. As a posteriori condition for quantum adiabaticity, our result is universally applicable to any (nondegenerate) quantum system and gives a clear picture on how QAC could become insufficient or unnecessary for the adiabatic approximation, which is a problem that has gained considerable interest in the literature in recent years

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We study the distribution of harmonic measure on connected Julia sets of unicritical polynomials. Harmonic measure on a full compact set in ℂ is always concentrated on a set which is porous for a positive density of scales. We prove that there is a topologically generic set $\mathcal{A}$ in the boundary of the Mandelbrot set such that for every $c\in \mathcal{A}$ ,$β$>0, and$λ$∈(0,1), the corresponding Julia set is a full compact set with harmonic measure concentrated on a set which is not$β$-porous in scale$λ$^{$n$}for$n$from a set with positive density amongst natural numbers.

In virtual reality/augmented reality (VR/AR) applications, especially medical image visualization, it is highly desirable to magnify region of interests. In this work, we propose a novel method for virtual magnifying glass for visualizing volumetric data based on Optimal Mass Transportation. An optimal mass transportation map deforms a volume to itself, transforms the source measure (volumetric element) to the target measure with the minimal transportation cost. Solving the optimal mass transportation problem is equivalent to a convex optimization, and can be converted to computing power Voronoi diagrams in classical computational geometry. The proposed method allows the user to accurately control the target measure, and select multiple regions of interests with irregular shapes. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications.