Local Spin Susceptibility of the S=1/2 Kagome Lattice in ZnCu3(OD)6Cl2

We report single-crystal 2-D NMR investigation of the nearly ideal spin S=1/2 kagome lattice ZnCu3(OD)6Cl2. We successfully identify 2-D NMR signals originating from the nearest-neighbors of Cu2+ defects occupying Zn sites. From the 2-D Knight shift measurements, we demonstrate that weakly interacting Cu2+ spins at these defects cause the large Curie-Weiss enhancement toward T=0 commonly observed in the bulk susceptibility data. We estimate the intrinsic spin susceptibility of the kagome planes by subtracting defect contributions, and explore several scenarios.Comment: 4 figures; published in PR-B Rapid Communication

Transductive Multi-View Zero-Shot Learning

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Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

Single-Site Vanadyl Species Isolated within Molybdenum Oxide Monolayers in Propane Oxidation

The cooperation of metal oxide subunits in complex mixed metal oxide catalysts for selective oxidation of alkanes still needs deeper understanding to allow for a rational tuning of catalyst performance. Herein we analyze the interaction between vanadium and molybdenum oxide species in a monolayer supported on mesoporous silica SBA-15. Catalysts with variable Mo/V ratio between 10 and 1 were studied in the oxidation of propane and characterized by FTIR, Raman, and EPR spectroscopies, temperature-programmed reduction, UV/vis spectroscopy in combination with DFT calculations, and time-resolved experiments to analyze the redox properties of the catalysts. Molybdenum oxide (sub)monolayers on silica contain mainly dioxo (Si–O−)2Mo(═O)2 species. Dilution of silica-supported vanadium oxide species by (Si–O−)2Mo(═O)2 prevents the formation of V–O–V bonds, which are abundant in the pure vanadium oxide catalyst that predominantly contains two-dimensional vanadium oxide oligomers. Existing single vanadyl (Si–O−)3V(═O) sites and neighboring (Si–O−)2Mo(═O)2 sites do not strongly interact. The rates of reduction in propane and of oxidation in oxygen are lower for single metal oxide sites compared to those for oligomers. The rate of propane oxidation correlates with the overall redox rates of the catalysts but not linearly with the chemical composition. Retarded redox behavior facilitates selectivity toward acrolein on single-site catalysts. The abundance of M–O–M bonds is more important in terms of activity and selectivity compared to the nature of the central atom (molybdenum versus vanadium)

Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation

Abstract. Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. Such a semantic representation is shared between an annotated auxiliary dataset and a target dataset with no annotation. A projection from a low-level feature space to the seman-tic space is learned from the auxiliary dataset and is applied without adaptation to the target dataset. In this paper we identify an inher-ent limitation with this approach. That is, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift prob-lem and propose a novel framework, transductive multi-view embedding, to solve it. It is ‘transductive ’ in that unlabelled target data points are explored for projection adaptation, and ‘multi-view ’ in that both low-level feature (view) and multiple semantic representations (views) are embedded to rectify the projection shift. We demonstrate through ex-tensive experiments that our framework (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complemen-tarity of multiple semantic representations, (3) achieves state-of-the-art recognition results on image and video benchmark datasets, and (4) en-ables novel cross-view annotation tasks.

Comparison between the Torquato-Rintoul theory of the interface effect in composite media and elementary results

We show that the interface effect on the properties of composite media recently proposed by Torquato and Rintoul (TR) [Phys. Rev. Lett. 75, 4067 (1995)] is in fact elementary, and follows directly from taking the limit in the dipolar polarizability of a coated sphere: the TR ``critical values'' are simply those that make the dipolar polarizability vanish. Furthermore, the new bounds developed by TR either coincide with the Clausius-Mossotti (CM) relation or provide poor estimates. Finally, we show that the new bounds of TR do not agree particularly well with the original experimental data that they quote.Comment: 13 pages, Revtex, 8 Postscript figure

Spin-Wave and Electromagnon Dispersions in Multiferroic MnWO4 as Observed by Neutron Spectroscopy: Isotropic Heisenberg Exchange versus Anisotropic Dzyaloshinskii-Moriya Interaction

High resolution inelastic neutron scattering reveals that the elementary magnetic excitations in multiferroic MnWO4 consist of low energy dispersive electromagnons in addition to the well-known spin-wave excitations. The latter can well be modeled by a Heisenberg Hamiltonian with magnetic exchange coupling extending to the 12th nearest neighbor. They exhibit a spin-wave gap of 0.61(1) meV. Two electromagnon branches appear at lower energies of 0.07(1) meV and 0.45(1) meV at the zone center. They reflect the dynamic magnetoelectric coupling and persist in both, the collinear magnetic and paraelectric AF1 phase, and the spin spiral ferroelectric AF2 phase. These excitations are associated with the Dzyaloshinskii-Moriya exchange interaction, which is significant due to the rather large spin-orbit coupling.Comment: 8 pages, 6 figures, accepted for publication in Physical Review

Topological meaning of Z2_2 numbers in time reversal invariant systems

We show that the Z$_2$ invariant, which classifies the topological properties of time reversal invariant insulators, has deep relationship with the global anomaly. Although the second Chern number is the basic topological invariant characterizing time reversal systems, we show that the relative phase between the Kramers doublet reduces the topological quantum number Z to Z$_2$.Comment: 4 pages, typos correcte

Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement

A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim–Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions

Optical fiber sensors for coal mine shaft integrity and equipment condition monitoring

Shaft is an important structure of mine. Deep mining increases mine pressure, induces shaft deformation and affects mine normal lifting. How to improve the inspection efficiency, reduce the maintenance cost and ensure the normal operation of the shaft is an important problem facing the mine. The paper introduces the optical fiber sensing technology to monitor the equipment status of the main shaft, puts forward the implementation scheme of the optical fiber monitoring of shaft deformation, and sets up a shaft equipment condition monitoring system based on the optical fiber sensing technology. It can realize equipment displacement monitoring, strain monitoring and vibration signal monitoring in the process of shaft operation. Comprehensive on-line monitoring of shaft running state can be realized, which opens up a new method for shaft deformation monitoring technology. Fiber optic sensing monitoring technology is of great significance to the safe operation of shaft