Di-neutron correlation and soft dipole excitation in medium mass neutron-rich nuclei near drip-line

The neutron pairing correlation and the soft dipole excitation in medium-mass nuclei near drip-line are investigated from a viewpoint of the di-neutron correlation. Numerical analyses by means of the coordinate-space HFB and the continuum QRPA methods are performed for even-even $^{18-24}$O, $^{50-58}$Ca and $^{80-86}$Ni. A clear signature of the di-neutron correlation is found in the HFB ground state; two neutrons are correlated at short relative distances \lesim 2 fm with large probability $\sim 50$. The soft dipole excitation is influenced strongly by the neutron pairing correlation, and it accompanies a large transition density for pair motion of neutrons. This behavior originates from a coherent superposition of two-quasiparticle configurations $[l\times (l+1)]_{L=1}$ consisting of continuum states with high orbital angular momenta $l$ reaching an order of $l\sim 10$. It raises a picture that the soft dipole excitation under the influence of neutron pairing is characterized by motion of di-neutron in the nuclear exterior against the remaining $A-2$ subsystem. Sensitivity to the density dependence of effective pair force is discussed.Comment: 35 pages, 22 figure

Quantum Nernst effect in a bismuth single crystal

We report a theoretical calculation explaining the quantum Nernst effect observed experimentally in a bismuth single crystal. Generalizing the edge-current picture in two dimensions, we show that the peaks of the Nernst coefficient survive in three dimensions due to a van Hove singularity. We also evaluate the phonon-drag effect on the Nernst coefficient numerically. Our result agrees with the experimental result for a bismuth single crystal.Comment: 4 pages, 4 figures, to be published in Proceedings of ISQM-Tokyo '0

A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202