Hidden itinerant-spin phase in heavily-overdoped La2-xSrxCuO4 revealed by dilute Fe doping: A combined neutron scattering and angle-resolved photoemission study

We demonstrated experimentally a direct way to probe a hidden propensity to the formation of spin density wave (SDW) in a non-magnetic metal with strong Fermi surface nesting. Substituting Fe for a tiny amount of Cu (1%) induced an incommensurate magnetic order below 20 K in heavily-overdoped La2-xSrxCuO4 (LSCO). Elastic neutron scattering suggested that this order cannot be ascribed to the localized spins on Cu or doped Fe. Angle-resolved photoemission spectroscopy (ARPES), combined with numerical calculations, revealed a strong Fermi surface nesting inherent in the pristine LSCO that likely drives this order. The heavily-overdoped Fe-doped LSCO thus represents the first plausible example of the long-sought "itinerant-spin extreme" of cuprates, where the spins of itinerant doped holes define the magnetic ordering ground state. This finding complements the current picture of cuprate spin physics that highlights the predominant role of localized spins at lower dopings. The demonstrated set of methods could potentially apply to studying hidden density-wave instabilities of other "nested" materials on the verge of density wave ordering.Comment: Abstract and discussion revised; to appear in Phys. Rev. Let

Exploiting Web Images for Dataset Construction: A Domain Robust Approach

© 2017 IEEE. Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time-consuming and labor intensive. To reduce the cost of manual labeling, there has been increased research interest in automatically constructing image datasets by exploiting web images. Datasets constructed by existing methods tend to have a weak domain adaptation ability, which is known as the "dataset bias problem." To address this issue, we present a novel image dataset construction framework that can be generalized well to unseen target domains. Specifically, the given queries are first expanded by searching the Google Books Ngrams Corpus to obtain a rich semantic description, from which the visually nonsalient and less relevant expansions are filtered out. By treating each selected expansion as a "bag" and the retrieved images as "instances," image selection can be formulated as a multi-instance learning problem with constrained positive bags. We propose to solve the employed problems by the cutting-plane and concave-convex procedure algorithm. By using this approach, images from different distributions can be kept while noisy images are filtered out. To verify the effectiveness of our proposed approach, we build an image dataset with 20 categories. Extensive experiments on image classification, cross-dataset generalization, diversity comparison, and object detection demonstrate the domain robustness of our dataset

Automatic image dataset construction with multiple textual metadata

© 2016 IEEE. The goal of this work is to automatically collect a large number of highly relevant images from the Internet for given queries. A novel image dataset construction framework is proposed by employing multiple textual metadata. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora to obtain a richer semantic description, from which the visually non-salient and less relevant expansions are then filtered. After retrieving images from the Internet with filtered expansions, we further filter noisy images by clustering and progressively Convolutional Neural Networks (CNN). To verify the effectiveness of our proposed method, we construct a dataset with 10 categories, which is not only much larger than but also have comparable cross-dataset generalization ability with manually labeled dataset STL-10 and CIFAR-10

A new web-supervised method for image dataset constructions

© 2017 The goal of this work is to automatically collect a large number of highly relevant natural images from Internet for given queries. A novel automatic image dataset construction framework is proposed by employing multiple query expansions. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora to obtain a richer semantic descriptions, from which the visually non-salient and less relevant expansions are then filtered. After retrieving images from the Internet with filtered expansions, we further filter noisy images by clustering and progressively Convolutional Neural Networks (CNN) based methods. To evaluate the performance of our proposed method for image dataset construction, we build an image dataset with 10 categories. We then run object detections on our image dataset with three other image datasets which were constructed by weak supervised, web supervised and full supervised learning, the experimental results indicated the effectiveness of our method is superior to weak supervised and web supervised state-of-the-art methods. In addition, we do a cross-dataset classification to evaluate the performance of our dataset with two publically available manual labelled dataset STL-10 and CIFAR-10

Traffic agents for improving QoS in mixed infrastructure and ad hoc modes wireless LAN

As an important complement to infrastructured wireless networks, mobile ad hoc networks (MANET) are more flexible in providing wireless access services, but more difficult in meeting different quality of service (QoS) requirements for mobile customers. Both infrastructure and ad hoc network structures are supported in wireless local area networks (WLAN), which can offer high data-rate wireless multimedia services to the mobile stations (MSs) in a limited geographical area. For those out-of-coverage MSs, how to effectively connect them to the access point (AP) and provide QoS support is a challenging issue. By mixing the infrastructure and the ad hoc modes in WLAN, we propose in this paper a new coverage improvement scheme that can identify suitable idle MSs in good service zones as traffic agents (TAs) to relay traffic from those out-of-coverage MSs to the AP. The service coverage area of WLAN is then expanded. The QoS requirements (e.g., bandwidth) of those MSs are considered in the selection process of corresponding TAs. Mathematical analysis, verified by computer simulations, shows that the proposed TA scheme can effectively reduce blocking probability when traffic load is light

Fully gapped topological surface states in Bi2_2Se3_3 films induced by a d-wave high-temperature superconductor

Topological insulators are a new class of materials, that exhibit robust gapless surface states protected by time-reversal symmetry. The interplay between such symmetry-protected topological surface states and symmetry-broken states (e.g. superconductivity) provides a platform for exploring novel quantum phenomena and new functionalities, such as 1D chiral or helical gapless Majorana fermions, and Majorana zero modes which may find application in fault-tolerant quantum computation. Inducing superconductivity on topological surface states is a prerequisite for their experimental realization. Here by growing high quality topological insulator Bi$_2$Se$_3$ films on a d-wave superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ using molecular beam epitaxy, we are able to induce high temperature superconductivity on the surface states of Bi$_2$Se$_3$ films with a large pairing gap up to 15 meV. Interestingly, distinct from the d-wave pairing of Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, the proximity-induced gap on the surface states is nearly isotropic and consistent with predominant s-wave pairing as revealed by angle-resolved photoemission spectroscopy. Our work could provide a critical step toward the realization of the long sought-after Majorana zero modes.Comment: Nature Physics, DOI:10.1038/nphys274

From a single-band metal to a high-temperature superconductor via two thermal phase transitions (Supporting Material)

In this supporting material for the main paper (the preceding submission), we show, in addition to the related information for the experiments, additional discussion that cannot fit in the main paper (due to the space constraint). It includes further discussion about our experimental observations, wider implications of our main findings with various reported candidates for the pseudogap order, and a simple mean-field argument that favors interpretations based on a finite-Q order (density wave) for the pseudogap seen by ARPES (whether "the pseudogap order" is a single order or contains multiple ingredients, is an independent, open issue). We also include a detailed simulation section, in which we model different candidates (various density wave/nematic order) for the pseudogap order in simple forms using a mean-field approach, and discuss their partial success as well as limitations in describing the experimental observations. These simulations are based on a tight-binding model with parameters fitted globally (and reasonably well) to the experimental band dispersions (by tracking the maximum of the energy distribution curve), which could be useful for further theoretical explorations on this issue

Pairing symmetry and properties of iron-based high temperature superconductors

Pairing symmetry is important to indentify the pairing mechanism. The analysis becomes particularly timely and important for the newly discovered iron-based multi-orbital superconductors. From group theory point of view we classified all pairing matrices (in the orbital space) that carry irreducible representations of the system. The quasiparticle gap falls into three categories: full, nodal and gapless. The nodal-gap states show conventional Volovik effect even for on-site pairing. The gapless states are odd in orbital space, have a negative superfluid density and are therefore unstable. In connection to experiments we proposed possible pairing states and implications for the pairing mechanism.Comment: 4 pages, 1 table, 2 figures, polished versio

Prevalence of overweight and obesity among Chinese Yi nationality: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Overweight and obesity are considered a serious health problem. There are little data on the prevalence of overweight and obesity among the Yi ethnic group in China. This study aimed to investigate the epidemiologic features of overweight/obesity among Chinese Yi nationality.</p> <p>Methods</p> <p>A cross-sectional study, including 1255 subjects aged 20-75 years, was carried out in Liangshan Yi Autonomous Prefecture of Sichuan province from 2007 to 2008. Overweight/overall obesity was defined by World Health Organization (WHO) or the Working Group on Obesity in China.</p> <p>Results</p> <p>Overall, the prevalence of overweight and obesity was 19.0% and 2.9%, respectively, based on the WHO definition, while it was 21.0% and 7.4%, respectively, according to the Working Group on Obesity in China, which is similar to data reported in the 2002 Chinese National Nutrition and Health Survey. Urban residents had a significantly higher prevalence of obesity (WHO criteria: 4.3% vs 1.7% <it>p </it>= 0.008; China criteria: 11.4% vs 3.7%, <it>p </it>< 0.001) and overweight (WHO criteria: 28.9% vs 8.9% <it>p </it>< 0.001; China criteria: 31.2% vs 10.4%, <it>p </it>< 0.001) than that in rural residents. Older age, a family history of obesity, higher income, drinking and urban residence were significantly associated with an increased risk of overweight/obesity.</p> <p>Conclusions</p> <p>The prevalence of overweight/obesity in the Yi nationality is similar to that in Chinese adults 5 years ago. However, urban residents have a much higher prevalence of overweight/obesity than their rural counterparts. Lifestyle and diet patterns associated with socioeconomic status may explain the difference between urban and rural residents. The prevention of overweight/obesity among urban inhabitants deserves more attention in national health education programs.</p