Ternary Inorganic Electrides with Mixed Bonding

A high-throughput screening based on first-principles calculations was performed to search for new ternary inorganic electrides. From the available materials database, we identified three new thermodynamically stable materials (Li12Mg3Si4, NaBa2O, and Ca5Ga2N4) as potential electrides made by main group elements, in addition to the well known mayenite based electride (C12A7:e−). Different from those conventional inorganic electrides in which the excess electrons play only the role of anions, the three new materials, resembling the electrides found in simple metals under high pressure, possess mixed ionic and metallic bonding. The interplay between two competing mechanisms, together with the different crystal packing motifs, gives rise to a variety of geometries in anionic electrons and rich physical phenomena such as ferromagnetism, superconductivity, and metal-insulator transition. Our finding here bridges the gap between electrides found at ambient and high-pressure conditions

Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning

The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, we propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG) recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning. The proposed method, named Pairwise Alignment Bilinear Network (PABN), is an end-to-end deep neural network. Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric. In order to match base image features with query image features, we design feature alignment losses before the proposed pairwise bilinear pooling. Experiment results on four fine-grained classification datasets and one generic few-shot dataset demonstrate that the proposed model outperforms both the state-ofthe-art few-shot fine-grained and general few-shot methods.Comment: ICME 2019 Ora

Discovery of Points of Interest with Different Granularities for Tour Recommendation Using a City Adaptive Clustering Framework

Increasing demand for personalized tours for tourists travel in an urban area motivates more attention to points of interest (POI) and tour recommendation services. Recently, the granularity of POI has been discussed to provide more detailed information for tour planning, which supports both inside and outside routes that would improve tourists' travel experience. Such tour recommendation systems require a predefined POI database with different granularities, but existing POI discovery methods do not consider the granularity of POI well and treat all POIs as the same scale. On the other hand, the parameters also need to be tuned for different cities, which is not a trivial process. To this end, we propose a city adaptive clustering framework for discovering POIs with different granularities in this article. Our proposed method takes advantage of two clustering algorithms and is adaptive to different cities due to automatic identification of suitable parameters for different datasets. Experiments on two real-world social image datasets reveal the effectiveness of our proposed framework. Finally, the discovered POIs with two levels of granularity are successfully applied on inner and outside tour planning

Complex Landau levels and related transport properties in the strained zigzag graphene nanoribbons

The real magnetic fields (MFs) acting on the graphene can induce flat real Landau levels (LLs). As an analogy, strains in graphene can produce significant pseudo MFs, triggering the appearance of dispersive pseudo LLs. By analysing the low energy effective Hamiltonian, we introduce the concept of the effective orbital MFs to integrate the real MFs and pseudo MFs. Accordingly, we obtain the complex LLs which incorporate the real LLs and pseudo LLs, and calculate the related transport properties. With the aid of these ideas, we reveal the mechanism underlying the fragility of the pseudo LLs against disorders, and predict that the $K$ and $K'$ valleys have different robust performances against the Anderson disorders and dephasing effects. Furthermore, the tunability of the polarized valley currents is also studied, opening up new possibilities for the design of valleytronics devices

Reverse strain-induced snake states in graphene nanoribbons

Strain can tailor the band structures and properties of graphene nanoribbons (GNRs) with the well-known emergent pseudo-magnetic fields and the corresponding pseudo-Landau levels (pLLs). We design one type of the zigzag GNR (ZGNR) with reverse strains, producing pseudo-magnetic fields with opposite signs in the lower and upper half planes. Therefore, electrons propagate along the interface as "snake states", experiencing opposite Lorentz forces as they cross the zero field border line. By using the Landauer-Buttiker formalism combined with the nonequilibrium Green's function method, the existence and robustness of the reverse strain-induced snake states are further studied. Furthermore, the realization of long-thought pure valley currents in monolayer graphene systems is also proposed in our device.Comment: 6 figure

Tests of conservation laws in post-Newtonian gravity with binary pulsars

General relativity is a fully conservative theory, but there exist other possible metric theories of gravity. We consider non-conservative ones with a parameterized post-Newtonian (PPN) parameter, $\zeta_2$. A non-zero $\zeta_2$ induces a self-acceleration for the center of mass of an eccentric binary pulsar system, which contributes to the second time derivative of the pulsar spin frequency, $\ddot{\nu}$. In our work, using the method in Will (1992), we provide an improved analysis with four well-timed, carefully-chosen binary pulsars. In addition, we extend Will's method and derive $\zeta_2$'s effect on the third time derivative of the spin frequency, \dddot{\nu}. For PSR B1913+16, the constraint from \dddot{\nu} is even tighter than that from $\ddot{\nu}$. We combine multiple pulsars with Bayesian inference, and obtain an upper limit, $\left|\zeta_{2}\right|<1.3\times10^{-5}$ at 95% confidence level, assuming a flat prior in $\log_{10} \left| \zeta_{2}\right|$. It improves the existing bound by a factor of three. Moreover, we propose an analytical timing formalism for $\zeta_2$. Our simulated times of arrival with simplified assumptions show binary pulsars' capability in limiting $\zeta_{2}$, and useful clues are extracted for real data analysis in future. In particular, we discover that for PSRs B1913+16 and J0737$-$3039A, \dddot{\nu} can yield more constraining limits than $\ddot{\nu}$.Comment: 14 pages, 5 figures, 4 tables; accepted by Ap