Dimensionless ratios: characteristics of quantum liquids and their phase transitions

Dimensionless ratios of physical properties can characterize low-temperature phases in a wide variety of materials. As such, the Wilson ratio (WR), the Kadowaki-Woods ratio and the Wiedemann\--Franz law capture essential features of Fermi liquids in metals, heavy fermions, etc. Here we prove that the phases of many-body interacting multi-component quantum liquids in one dimension (1D) can be described by WRs based on the compressibility, susceptibility and specific heat associated with each component. These WRs arise due to additivity rules within subsystems reminiscent of the rules for multi-resistor networks in series and parallel --- a novel and useful characteristic of multi-component Tomonaga-Luttinger liquids (TLL) independent of microscopic details of the systems. Using experimentally realised multi-species cold atomic gases as examples, we prove that the Wilson ratios uniquely identify phases of TLL, while providing universal scaling relations at the boundaries between phases. Their values within a phase are solely determined by the stiffnesses and sound velocities of subsystems and identify the internal degrees of freedom of said phase such as its spin-degeneracy. This finding can be directly applied to a wide range of 1D many-body systems and reveals deep physical insights into recent experimental measurements of the universal thermodynamics in ultracold atoms and spins.Comment: 12 pages (main paper), (6 figures

On the dynamics of the South China Sea Warm Current

Author Posting. © American Geophysical Union, 2008. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 113 (2008): C08003, doi:10.1029/2007JC004427.The South China Sea Warm Current (SCSWC) flows northeastward over the shelf and continental slope in the northern South China Sea (SCS). This current persists in its northeastward direction in all seasons despite the fact that the annually averaged wind stress is decisively southwestward against it. Two major mechanisms have been proposed in previous studies, one attributing it directly to the wind stress forcing within the SCS and the other to the Kuroshio intrusion through the Luzon Strait. In this study we use a simple model to demonstrate that neither of them is the leading forcing mechanism. Instead, the SCSWC is a source- and sink-driven flow induced by the Taiwan Strait Current (TSC), a year-round northward flow through the Taiwan Strait. The two previously suggested mechanisms are important but secondary. The model simulations show that the local wind stress alone would force a current in the opposite direction to the SCSWC. Blocking the Kuroshio intrusion through the Luzon Strait, on the other hand, only weakens the SCSWC. The SCSWC vanishes when the Taiwan Strait is closed in the model.This study has been supported by the U.S. National Science Foundation (OCE-0351055), China’s International Science and Technology Cooperation Program (2006DFB21250), and China’s National Basic Research Priorities Program (2005CB422302)

Bis(2,2′-bipyridine)(5,5′-imino­ditetra­zolato)cadmium(II) 2,2′-bipyridine hemisolvate monohydrate

The title complex, [Cd(C2HN9)(C10H8N2)2]·0.5C10H8N2·H2O, was prepared under hydro­thermal reaction conditions. The asymmetric unit contains the cadmium complex, half a 2,2′-bipyridine solvent mol­ecule and a solvent water mol­ecule. The CdII ion is coordinated by four N atoms from two 2,2′-bipyridine ligands and two N atoms from an HBTA− anion ligand [where H2BTA is N,N-bis­(1H-tetra­zol-5-yl)amine], forming an octa­hedral geometry. The complex is linked into a three-dimensional network by O—H⋯N and N—H⋯N hydrogen bonds and by the stacking inter­actions of rings, with distances of 3.5–3.7 Å between the atoms of two parallel 2,2′-bipyridine rings

Training A Multi-stage Deep Classifier with Feedback Signals

Multi-Stage Classifier (MSC) - several classifiers working sequentially in an arranged order and classification decision is partially made at each step - is widely used in industrial applications for various resource limitation reasons. The classifiers of a multi-stage process are usually Neural Network (NN) models trained independently or in their inference order without considering the signals from the latter stages. Aimed at two-stage binary classification process, the most common type of MSC, we propose a novel training framework, named Feedback Training. The classifiers are trained in an order reverse to their actual working order, and the classifier at the later stage is used to guide the training of initial-stage classifier via a sample weighting method. We experimentally show the efficacy of our proposed approach, and its great superiority under the scenario of few-shot training