One-Dimensional Dispersive Magnon Excitation in the Frustrated Spin-2 Chain System Ca3Co2O6

Using inelastic neutron scattering, we have observed a quasi-one-dimensional dispersive magnetic excitation in the frustrated triangular-lattice spin-2 chain oxide Ca3Co2O6. At the lowest temperature (T = 1.5 K), this magnon is characterized by a large zone-center spin gap of ~27 meV, which we attribute to the large single-ion anisotropy, and disperses along the chain direction with a bandwidth of ~3.5 meV. In the directions orthogonal to the chains, no measurable dispersion was found. With increasing temperature, the magnon dispersion shifts towards lower energies, yet persists up to at least 150 K, indicating that the ferromagnetic intrachain correlations survive up to 6 times higher temperatures than the long-range interchain antiferromagnetic order. The magnon dispersion can be well described within the predictions of linear spin-wave theory for a system of weakly coupled ferromagnetic chains with large single-ion anisotropy, enabling the direct quantitative determination of the magnetic exchange and anisotropy parameters.Comment: 7 pages, 6 figures including one animatio

Influence of oxygen vacancy on the electronic structure of HfO2_2 film

We investigated the unoccupied part of the electronic structure of the oxygen-deficient hafnium oxide (HfO$_{\sim1.8}$) using soft x-ray absorption spectroscopy at O $K$ and Hf $N_3$ edges. Band-tail states beneath the unoccupied Hf 5$d$ band are observed in the O $K$-edge spectra; combined with ultraviolet photoemission spectrum, this indicates the non-negligible occupation of Hf 5$d$ state. However, Hf $N_3$-edge magnetic circular dichroism spectrum reveals the absence of a long-range ferromagnetic spin order in the oxide. Thus the small amount of $d$ electron gained by the vacancy formation does not show inter-site correlation, contrary to a recent report [M. Venkatesan {\it et al.}, Nature {\bf 430}, 630 (2004)].Comment: 5 pages, 4 figures, submitted to Phys. Rev.

Detecting Specific Health-Related Events Using an Integrated Sensor System for Vital Sign Monitoring

In this paper, a new method for the detection of apnea/hypopnea periods in physiological data is presented. The method is based on the intelligent combination of an integrated sensor system for long-time cardiorespiratory signal monitoring and dedicated signal-processing packages. Integrated sensors are a PVDF film and conductive fabric sheets. The signal processing package includes dedicated respiratory cycle (RC) and QRS complex detection algorithms and a new method using the respiratory cycle variability (RCV) for detecting apnea/hypopnea periods in physiological data. Results show that our method is suitable for online analysis of long time series data

Unconventional Charge Density Wave Order in the Pnictide Superconductor Ba(Ni1−x_{1-x}Cox_x)2_2As2_2

Ba(Ni$_{1-x}$Co$_x$)$_2$As$_2$ is a structural homologue of the pnictide high temperature superconductor, Ba(Fe$_{1-x}$Co$_x$)$_2$As$_2$, in which the Fe atoms are replaced by Ni. Superconductivity is highly suppressed in this system, reaching a maximum $T_c$ = 2.3 K, compared to 24 K in its iron-based cousin, and the origin of this $T_c$ suppression is not known. Using x-ray scattering, we show that Ba(Ni$_{1-x}$Co$_x$)$_2$As$_2$ exhibits a unidirectional charge density wave (CDW) at its triclinic phase transition. The CDW is incommensurate, exhibits a sizable lattice distortion, and is accompanied by the appearance of $\alpha$ Fermi surface pockets in photoemission [B. Zhou et al., Phys. Rev. B 83, 035110 (2011)], suggesting it forms by an unconventional mechanism. Co doping suppresses the CDW, paralleling the behavior of antiferromagnetism in iron-based superconductors. Our study demonstrates that pnictide superconductors can exhibit competing CDW order, which may be the origin of $T_c$ suppression in this system

Nematicity dynamics in the charge-density-wave phase of a cuprate superconductor

Understanding the interplay between charge, nematic, and structural ordering tendencies in cuprate superconductors is critical to unraveling their complex phase diagram. Using pump-probe time-resolved resonant x-ray scattering on the (0 0 1) Bragg peak at the Cu L3 and oxygen K resonances, we investigate non-equilibrium dynamics of Qa = Qb = 0 nematic order and its association with both charge density wave (CDW) order and lattice dynamics in La1.65Eu0.2Sr0.15CuO4. In contrast to the slow lattice dynamics probed at the apical oxygen K resonance, fast nematicity dynamics are observed at the Cu L3 and planar oxygen K resonances. The temperature dependence of the nematicity dynamics is correlated with the onset of CDW order. These findings unambiguously indicate that the CDW phase, typically evidenced by translational symmetry breaking, includes a significant electronic nematic component.Comment: 16 pages, 4 figure

Nonparametric inference for interval data using kernel methods

Symbolic data have become increasingly popular in the era of big data. In this paper, we consider density estimation and regression for interval-valued data, a special type of symbolic data, common in astronomy and official statistics. We propose kernel estimators with adaptive bandwidths to account for variability of each interval. Specifically, we derive cross-validation bandwidth selectors for density estimation and extend the Nadaraya–Watson estimator for regression with interval data. We assess the performance of the proposed methods in comparison with existing kernel methods by extensive simulation studies and real data analysis.</p

Fabrication of Highly Packed Plasmonic Nanolens Array Using Polymer Nanoimprinted Nanodots for an Enhanced Fluorescence Substrate

A simple and cost-effective fabrication method for plasmonic nanolens arrays (PNA) with a narrow gap has been proposed for fabricating enhanced fluorescence substrates, in which the fluorophores interacting with the enhanced electromagnetic field generated by localized surface plasmons provide a higher fluorescence signal. The PNA was fabricated by the sequential depositions of the SiO2 and Ag layers on a UV-nanoimprinted nanodot array with a pitch of 500 nm, a diameter of 250 nm, and a height of 100 nm. During the deposition processes, the shape of the nanodots changed to that of nanolenses, and the gap between the nanolenses was decreased via sidewall deposition. To examine the feasibility of the fabricated PNA for enhanced fluorescence application, a streptavidin-Cy5 (SA-Cy5) conjugate dissolved in a saline buffer solution was spotted on the PNA, and the fluorescence signals of the SA-Cy5 were measured and compared with those on a bare glass substrate. The enhancement factor was affected by the gap between the nanolenses, and the maximum enhancement factor of ~128 was obtained from the PNA with a SiO2 layer thickness of 150 nm and an Ag layer thickness of 100 nm. Finally, an electromagnetic field analysis was used to examine the fluorescence signal enhancement, and was conducted using rigorous coupled wave analysis

Accurate Graph-Based PU Learning without Class Prior

© 2021 IEEE.How can we classify graph-structured data only with positive labels? Graph-based positive-unlabeled (PU) learning is to train a binary classifier given only the positive labels when the relationship between examples is given as a graph. The problem is of great importance for various tasks such as detecting malicious accounts in a social network, which are difficult to be modeled by supervised learning when the true negative labels are absent. Previous works for graph-based PU learning assume that the prior distribution of positive nodes is known in advance, which is not true in many real-world cases. In this work, we propose GRAB (Graph-based Risk minimization with iterAtive Belief propagation), a novel end-to-end approach for graph-based PU learning that requires no class prior. GRAB models a given graph as a Markov network and runs the marginalization and update steps iteratively. The marginalization step estimates the marginals of latent variables, while the update step trains a classifier network utilizing the computed priors in the objective function. Extensive experiments on five datasets show that GRAB achieves state-of-the-art accuracy, even compared with previous methods that are given the true prior.N