University of Electro-Communications

About: University of Electro-Communications is a education organization based out in Tokyo, Japan. It is known for research contribution in the topics: Laser & Robot. The organization has 8041 authors who have published 16950 publications receiving 235832 citations. The organization is also known as: UEC & Denki-Tsūshin Daigaku.

Retrieving photorecombination cross sections of atoms from high-order harmonic spectra

Abstract: We observe high-order harmonic spectra generated from a thin atomic medium, Ar, Kr, and Xe, by intense $800\text{\ensuremath{-}}\mathrm{nm}$ and $1300\text{\ensuremath{-}}\mathrm{nm}$ femtosecond pulses. A clear signature of a single-atom response is observed in the harmonic spectra. Especially in the case of Ar, a Cooper minimum, reflecting the electronic structure of the atom, is observed in the harmonic spectra. We successfully extract the photorecombination cross sections of the atoms in the field-free condition with the help of an accurate recolliding electron wave packet. The present protocol paves the way for exploring ultrafast imaging of molecular dynamics with attosecond resolution.

49.2: Reduction of LCTV Backlight Power and Enhancement of Gray Scale Capability by Using an Adaptive Dimming Technique

Abstract: An adaptive dimming technique is introduced for reducing the backlight power consumption in LCTVs. With the technique, the backlight luminance is adjusted according to the input TV signal. For example, when the original input signal is small, the backlight luminance is reduced and the signal is increased so that the perceived luminance is equal to that before the signal processing. From a simulation, backlight power reduction to 1/3 can be expected for APL 25% image. The gray scale capability at low luminance levels is also enhanced.

A Relationship between Difference Hierarchies and Relativized Polynomial Hierarchies.

Abstract: Chang and Kadin have shown that if the difference hierarchy over NP collapses to level $k$, then the polynomial hierarchy (PH) is equal to the $k$th level of the difference hierarchy over $\Sigma_{2}^{p}$. We simplify their proof and obtain a slightly stronger conclusion: If the difference hierarchy over NP collapses to level $k$, then PH = $\left(P_{(k-1)-tt}^{NP}\right)^{NP}$. We also extend the result to classes other than NP: For any class $C$ that has $\leq_{m}^{p}$-complete sets and is closed under $\leq_{conj}^{p}$and $\leq_{m}^{NP}$-reductions, if the difference hierarchy over $C$ collapses to level $k$, then $PH^{C} = $\left(P_{(k-1)-tt}^{NP}\right)^{C}$. Then we show that the exact counting class $C_{=}P$ is closed under $\leq_{disj}^{p}$and $\leq_{m}^{co-NP}$-reductions. Consequently, if the difference hierarchy over $C_{=}P$ collapses to level $k$ then $PH^{PP}$ is equal to $\left(P_{(k-1)-tt}^{NP}\right)^{PP}$. In contrast, the difference hierarchy over the closely related class PP is known to collapse. Finally, we consider two ways of relativizing the bounded query class $P_{k-tt}^{NP}$: the restricted relativization $P_{k-tt}^{NP^{C}}$, and the full relativization $\left(P_{k-tt}^{NP}\right)^{C}$. If $C$ is NP-hard, then we show that the two relativizations are different unless $PH^{C}$ collapses.

Self-controlling dominance area of solutions in evolutionary many-objective optimization

Abstract: Controlling dominance area of solutions (CDAS) relaxes the concepts of Pareto dominance with an user-defined parameter S. This method enhances the search performance of dominance-based MOEA in many-objective optimization problems (MaOPs). However, to bring out desirable search performance, we have to experimentally find out S that controls dominance area appropriately. Also, there is a tendency to deteriorate the diversity of solutions obtained by CDAS when we decrease S from 0.5. To solve these problems, in this work, we propose a modification of CDAS called self-controlling dominance area of solutions (S-CDAS). In S-CDAS, the algorithm self-controls dominance area for each solution without the need of an external parameter. S-CDAS considers convergence and diversity and realizes a fine grained ranking that is different from conventional CDAS. In this work, we use many-objective 0/1 knapsack problems with m = 4 ∼ 10 objectives to verify the search performance of the proposed method. Simulation results show that SCDAS achieves well-balanced search performance on both convergence and diversity compared to conventional NSGA-II, CDAS, IBEAe+ and MSOPS.