Large Neighbor Research Articles

Schwinger boson study of superconductivity mediated by antiferromagnetic spin fluctuations

We study superconductivity in a normal metal, arising from effective electron-electron interactions mediated by spin-fluctuations in a neighboring antiferromagnetic insulator. Introducing a frustrating next-nearest neighbor interaction in a N\'eel antiferromagnet with an uncompensated interface, the superconducting critical temperature is found to be enhanced as the frustration is increased. Further, for sufficiently large next-nearest neighbor interaction, the antiferromagnet is driven into a stripe phase, which can also give rise to attractive electron-electron interactions. For the stripe phase, as previously reported for the N\'eel phase, the superconducting critical temperature is found to be amplified for an uncompensated interface where the normal metal conduction electrons are coupled to only one of the two sublattices of the magnet. The superconducting critical temperature arising from fluctuations in the stripe phase antiferromagnet can be further enhanced by approaching the transition back to the N\'eel phase.

Energiewende, 20 years later - [CrossTalk

IN 2000, GERMANY launched a deliberately targeted program to decarbonize its primary energy supply, a plan more ambitious than anything seen anywhere else. The policy, called the Energiewende, is rooted in Germany's naturalistic and romantic tradition, reflected in the rise of the Green Party and, more recently, in public opposition to nuclear electricity generation. These attitudes are not shared by the country's two large neighbors: France built the world's leading nuclear industrial complex with hardly any opposition, and Poland is content burning its coal.

Dependences of local density of state on temperature, size, and shape in two-dimensional nano-structured superconductors

In this paper, we investigate a local density of state (LDOS) in a two-dimensional nano-structured superconductor. We solve the Bogoliubov-de Gennes equations self-consistently with the two-dimensional finite element method. In the nano-structured superconductor, the LDOS as a function of energy has many discrete peaks. Discretization of the LDOS comes from discretization of energy levels due to the quantum confinement effect in the nano-structured system. When temperature increases, a width of a peak in the LDOS is spread to a large energy range and neighbor peaks are overlapped. On the other hand, for the fixed temperature, the behavior of the LDOS is different between nano-scaled rectangular and square systems. In the nano-scaled rectangular system, when only a length of a long side increases, a contribution of the quantum confinement effect from the long side is suppressed, while the contribution from a length of a short side remains large. Then, some peaks are left in the LDOS even when the length of the long side is very large. These peaks form a periodic structure and can be regarded as gaps in a multi gap structure due to the quantum confinement effect. On the other hand, energy levels in the square system tend to arrange equally. Then, in the square system, peaks in the LDOS which exist in the rectangular system are small. Also, the period between peaks in the LDOS in the square system is smaller than that in the rectangular system.

On novelty detection for multi-class classification using non-linear metric learning

Novelty detection is a binary task aimed at identifying whether a test sample is novel or unusual compared to a previously observed training set. A typical approach is to consider distance as a criterion to detect such novelties. However, most previous work does not focus on finding an optimum distance for each particular problem. In this paper, we propose to detect novelties by exploiting non-linear distances learned from multi-class training data. For this purpose, we adopt a kernelization technique jointly with the Large Margin Nearest Neighbor (LMNN) metric learning algorithm. The optimum distance tries to keep each known class’ instances together while pushing instances from different known classes to remain reasonably distant. We propose a variant of the K-Nearest Neighbors (KNN) classifier that employs the learned distance to detect novelties. Besides, we use the learned distance to perform multi-class classification. We show quantitative and qualitative experiments conducted on synthetic and real data sets, revealing that the learned metrics are effective in improving novelty detection compared to other metrics. Our method also outperforms previous work regularly used for novelty detection.

How one language became four: the impact of different contact-scenarios between “Sadani” and the tribal languages of Jharkhand

Abstract Four Indo-Aryan linguistic varieties are spoken in the state of Jharkhand in eastern central India, Sadri/Nagpuri, Khortha, Kurmali and Panchparganiya, which are considered by most linguists to be dialects of other, larger languages of the region, such as Bhojpuri, Magahi and Maithili, although their speakers consider them to be four distinct but closely related languages, collectively referred to as “Sadani”. In the present paper, we first make use of the program COG by the Summer Institute of Linguistics (SIL) to show that these four varieties do indeed form a distinct, compact genealogical group within the Magadhan language group of Indo-Aryan. We then go on to argue that the traditional classification of these languages as dialects of other languages appears to be based on morphosyntactic differences between these four languages and similarities with their larger neighbors such as Bhojpuri and Magahi, differences which have arisen due to the different contact situations in which they are found.

Triplet grain growth in a-texture polycrystalline ZnO thin films

A growth of grain triplets is identified in vapor deposited, a-texture polycrystalline zinc oxide thin films, using a combination of transmission electron microscopy-based automated crystal orientation mapping and high-resolution imaging. Each triplet consists of three wurtzite phase grains, coordinated to near tetrahedral relative growth angles by the close-packed planes of a few nanometer diameter core of metastable zinc blende phase ZnO located at their triple junction. The triplets are aligned such that two of the grains have their fast growing [21¯1¯0] axes (near-) parallel to the substrate normal, and so coarsen under the principle of competitive grain growth. In contrast, growth of the third grain with a (101¯3) texture is impeded, such that it forms a small wedge between the ends of its two larger neighbors. Remarkably, all three boundaries between the grains adhere to a [21¯1¯0]/(011¯3) coherent twin orientation relationship, which corresponds to a grain geometry that is incommensurate with a perfect crystallographic nature. This coherency is primarily achieved via the small grain having an orientation that rotates internally by 10±2° across its volume, when going between the two interfaces formed with the neighboring large grains. The energy associated with this structural distortion is compensated by the formation of the coherent twin boundaries, such that the triplets are a stable or semi-stable growth form that are abundant in the film. The small (101¯3) texture grains may renucleate persistently, taking either of two possible orientations from the underlying tetrahedral coordination. By developing a phenomenological model for the triplet geometry and form, we associate these two orientations to small hexagonal caps and angled wedges seen in the surface morphology of the film. These findings potentially carry relevance for a wide range of vapor deposited compact films and free-standing nanostructures of wurtzite phase octet binary semiconductor compounds. We therefore believe that these insights can stimulate new research, for instance to obtain a more fundamental understanding of the growth mechanisms at the atomistic level.

Caracterização epidemiológica dos principais indicadores de saúde de COVID-19 em Teresina-PI, Brasil: uma breve análise

Este estudo objetivou analisar o perfil epidemiológico dos principais indicadores de saúde de COVID-19 em Teresina-PI. Trata-se de um levantamento epidemiológico, descritivo e quantitativo de casos por COVID-19 notificados na cidade de Teresina-PI, no período de 13de março a12 de julho de 2020. Os dados foram analisados a partir dos informes do boletim epidemiológico de saúde na cidade disponibilizada pela Fundação Municipal de Saúde (FMS). As variáveis avaliadas foram a faixa-etária, gênero, taxa de letalidade, comorbidades, óbitos, casos confirmados, ao dez maiores bairros com casos confirmados, média, mediana, desvio padrão, variância e análise da evolução no número de infecções e óbitos pela COVID-19. Tabularam-se os dados, utilizando os programas TABNET e Microsoft Office Excel 2019. Foram confirmados 10.914 casos de COVID-19 na cidade de Teresina-PI e observou-se uma tendência de aumento na frequência de notificação. Houve predominância nos casos confirmados no sexo feminino (n=6058; 55,51%), na faixa etária de 30 a 39 anos (n=2.799;25,6%) e de 40 a 49 anos (n=2.012; 18,5%). Enquanto, em relação aos que chegaram a óbito, foi no sexo masculino (n=303; 55,9%), com letalidade de 6,23%, e na faixa etária de 70 anos a 79 (n=134; 25,0%) e 80 a 89 (n=126; 23,2%), com letalidade de 45,3% e de 62% respectivamente. E 82% dos pacientes apresentavam comorbidades. Percebeu-se ainda a maior incidência de óbitos nos bairros do Itararé 27,50%, seguida por Promorar com 15,50%. Porém, de confirmações de casos 20% Itararé e 14% Mocambinho. O comportamento da pandemia é ascendente na cidade de Teresina-PI, o que evidencia a necessidade de mais medidas de contenção.

An efficient kernel-based feature extraction using a pull–push method

Supervised dimensionality reduction methods using nonlinear mappings for pattern recognition tasks are more appropriate for nonlinearly distributed data. Generally, for most algorithms, these samples (called hard samples) located at the edge or in other classes influence the performance of the proposed methods. In this study, the large margin nearest neighbor (LMNN) and weighted local modularity (WLM) in the complex network are introduced to deal with these hard samples to push and pull them rapidly toward the center of the class, and the samples with the same label shrink, as a whole, into the center of the class. A novel feature extraction method named KWLM–LMNN, which uses the kernel trick and combining WLM with LMNN, is proposed. Comparative experiments with state-of-the-art feature extraction methods demonstrate the effectiveness of the proposed method.

Balancing large margin nearest neighbours for imbalanced data

It is critical to learn and obtain a good distance metric that can precisely measure the distance between samples in imbalanced data. However, traditional metric learning algorithms, e.g. large margin nearest neighbour (LMNN), information-theoretic metric learning, neighbourhood component analysis, do not take imbalanced distributions of classes into consideration. The traditional methods are apt to be affected by the majority samples, so those important minority samples are often ignored during the learning phase of distance metrics matrix, this may gravely confuse decision-making systems on classifying samples. In order to resolve this problem, the authors propose a novel metric-learning method named balancing large margin nearest neighbour (BLMNN) for imbalanced data. BLMNN can improve the objective function according to the distribution of classes, which treats the minority and majority classes equally during the optimisation process. Thus, the contribution of minority class is taken into full consideration, which can greatly improve the accuracy of classification. Substantial experiments were performed on real-world imbalanced datasets. The experiments results in various evaluation indexes of the proposed method comparing it with other metric-learning methods show the advantages of the proposed method.

Cross-border effects in interconnected electricity markets - an analysis of the Swiss electricity prices

Electricity markets in Europe become increasingly interconnected due to new grid connections and market coupling regulations. This paper examines the interdependencies between the Swiss electricity market and those of neighbouring countries. The Swiss market serves as a good example for a smaller electricity market which is increasingly affected by developments in the large neighbouring countries. To study these cross-border effects, especially those on Swiss electricity prices, we apply two different methodologies, an econometric and a Nash-Cournot equilibrium model. The analyses show that the Swiss electricity price correlates strongly with the German electricity price in the summer, but tends to follow the French electricity price in the winter. Another finding is that gas prices and the electricity load of neighbouring countries have a significant influence on prices. In particular, the load of France and Italy is driving up Swiss prices in the winter, while the German electricity demand and renewable energy generation have a larger influence on Swiss prices in the summer.

Do boundary consolidations alter the relationship between politicians and voters? The case of municipal mergers in Japan

ABSTRACT This paper examines whether boundary changes lead to changes in the relationship between voters and politicians. We focus on the wave of municipal mergers in Japan that took place in the 2000s in order to examine this question. Municipalities with small population size, once they merge with their larger neighbours, would have a small number of voters relative to the size of the electorate in the post-merger municipalities. Therefore, municipal politicians in the post-merger municipalities do not have strong electoral incentives to receive support from geographic areas corresponding with pre-merger municipalities of small population size. Using a survey of voters in 89 locations corresponding with pre-merger municipalities, we demonstrate that voters who live in small municipalities that experienced mergers interact with politicians less frequently and have less favourable impressions of politicians than before the mergers. These patterns are not observed in municipalities that did not experience mergers.

Competing Magnetic Interactions in the Antiferromagnetic Topological Insulator MnBi_{2}Te_{4}.

The antiferromagnetic (AFM) compound MnBi_{2}Te_{4} is suggested to be the first realization of an AFM topological insulator. We report on inelastic neutron scattering studies of the magnetic interactions in MnBi_{2}Te_{4} that possess ferromagnetic triangular layers with AFM interlayer coupling. The spin waves display a large spin gap and pairwise exchange interactions within the triangular layer are long ranged and frustrated by large next-nearest neighbor AFM exchange. The degree of frustration suggests proximity to a variety of magnetic phases, potentially including skyrmion phases, which could be accessed in chemically tuned compounds or upon the application of symmetry-breaking fields.

The Domestic and International Tax Considerations When Structuring Investments into Laos

The People’s Democratic Republic of Laos (Laos) is a relatively small ASEAN economy especially when compared to its larger neighbours. However, with an impressive average annual growth rate in GDP of 8% per year it has high investment potential especially as it starts to diversify into other sectors, such as hydropower and transportation. When planning an investment into Laos there are many things for investors to consider in addition to the Laos domestic tax issues and the application of tax treaties, an investor should also examine the tax efficiency and benefits of using a holding company and the costs of maintaining such a company. These are the key domestic and international tax considerations discussed in this article.

Linear and nonlinear growth determinants: The case of Mongolia and its connection to China

We investigate growth determinants for Mongolia as a small emerging economy with respect to China as its large neighbor. Our causality analysis during 1992 to 2017 reveals significant linear as well as nonlinear relationships in growth explanation. China's GDP and coal prices, together with some of their linear and nonlinear lagged components, predict Mongolia's GDP, where a 1 % increase in China's GDP relates to a 1.5% increase in that of Mongolia. Current exchange rates and the nonlinear components of lagged consumer prices also explain growth. Our results underline the role of macroeconomic drivers of growth in emerging economies.

Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach

Gesture recognition by surface electromyography (sEMG) signals is used for several applications as prosthesis control and human-machines interfaces. One of trending approaches to sEMG acquisition is the multiple-channel armband with equidistant electrodes. Several efforts are made to improve the performance of these devices in gestures recognition applications, especially in feature selection. It is necessary choose one approach for feature selection due to the great number of electromyography features available to use. In this work, an extensive comparison of feature reduction techniques and their influences in the classification process is presented. Unlike other works, we presents the comparison between methods of feature selection and classification; this main contribution is show how the feature reduction process can aid and increase the performance of classification of sEMG in armband acquisition approach. Two general methods were employed, feature selection by wrapper forward stepwise and dimensionality reduction, resulting in eight different techniques. The following dimensionality reduction techniques were used: Principal Component Analysis, Linear Discriminant Analysis, Isomap, Manifold Charting, Autoencoder, t-distributed Stochastic Neighbor Embedding, and Large Margin Nearest Neighbor (LMNN). Seven classifiers were used, aiming at recognize six gestures acquired from an 8-channel armband of 13 subjects. An average accuracy of 89.4 % was obtained with 5 features and an Extreme Learning Machine classifier, in the feature selection approach. On other hand, considering 40 dimensions, an average accuracy of 94 % was obtained, regarding a combination between Support Vector Machine with Gaussian kernel and a LMNN technique. These results showed significant differences in statistical tests.

BATWA – STO LAT PRZEMIAN. MIĘDZY GLOBALIZACJĄ, PAŃSTWEM A TURYSTYKĄ

The Batwa – A Hundred Years of Transformation. Between Globalisation, State and Tourism The article is a deep refl ection on changes taking place in minority cultures, not only in contact with their larger neighbours, but also, and above all, under the infl uence of globalisation processes and the rapid development of tourism. The research is structured in order to present the Batwa people living in the African Great Lakes region from a broader time perspective. The juxtaposition of two research periods, one from the early 20th century (Jan Czekanowski) and the other from the 21st century (Lucjan Buchalik), made it possible to track the changes in the everyday life of this community. On the one hand, the Batwa are marginalised, and on the other hand, they are being absorbed by the surrounding, more dominant peoples. Studying the transformation process, one can notice that the Batwa accepted many changes resulting from their contacts with the outside world. It was the process of forcible displacement from their historical territories that threatened the existence of this community.

Against all odds: Numerical assessment by tufted capuchin monkeys

When competitors are able to assess the asymmetry in the resource holding potential before interacting, individuals or groups should avoid interacting with stronger opponents, thereby avoiding the energy costs and risk of injury associated with aggressive intergroup encounters. Thus, escalated aggression is expected only between closely matched competitors. Among Argentine tufted capuchin monkeys (Sapajus nigritus), intergroup dominance is decided by the asymmetry in male group size. Using playback experiments, I simulated intergroup encounters with neighboring groups, manipulating both the apparent numerical asymmetry and the resource context. During experimental trials, I recorded the approach behavior of the focal individual, as well as changes in neighbor density and individual travel speed following the presentation of the playback stimulus, to assess whether individual willingness to participate in resource defense was affected by the probability of winning the encounter. In spite of the competitive disadvantage, neither males nor females showed a decreased probability of approach when the numerical odds strongly favored the opposing group. Instead decisions regarding whether to participate appear to be driven primarily by the resource context. Nevertheless, changes in individual behavior during approaches suggest that tufted capuchin monkeys are sensitive to the relative odds. Individuals accelerated less when approaching a larger group, although no changes in neighbor density were apparent. The absence of an effect of the numerical asymmetry on willingness to approach the playback speaker suggests that subordinate groups benefit from engaging in intergroup aggression with larger neighbors, despite the high probability of losing. These encounters may serve to assess the current subjective resource valuation of the neighboring group or limit territorial expansion by large groups by decreasing the marginal value of home range exclusivity. Because these encounters are riskier, however, individuals appear to alter their approaches, becoming more tentative as the numerical odds increasingly favor the opposing group.

Spatio-temporal metric learning for individual recognition from locomotion

Individual recognition from locomotion is a challenging task owing to large intra-class and small inter-class variations. In this article, we present a novel metric learning method for individual recognition from skeleton sequences. Firstly, we propose to model articulated body on Riemannian manifold to describe the essence of human motion, which can reflect biometric signatures of the enrolled individuals. Then two spatia-temporal metric learning approaches are proposed, namely Spatio-Temporal Large Margin Nearest Neighbor (ST-LMNN) and Spatio-Temporal Multi-Metric Learning (STMM), to learn discriminant bilinear metrics which can encode the spatio-temporal structure of human motion. Specifically, the ST-LMNN algorithm extends the bilinear model into classical Large Margin Nearest Neighbor method, which learns a low-dimensional local linear embedding in the spatial and temporal domain, respectively. To further capture the unique motion pattern for each individual, the proposed STMM algorithm learns a set of individual-specific spatio-temporal metrics, which make the projected features of the same person closer to its class mean than that of different classes by a large margin. Beyond that, we present a new publicly available dataset for locomotion recognition to evaluate the influence of both internal and external covariant factors. According to the experimental results from the three public datasets, we believe that the proposed approaches are both able to achieve competitive results in individual recognition.

SqueezeFit: Label-Aware Dimensionality Reduction by Semidefinite Programming

Given labeled points in a high-dimensional vector space, we seek a low-dimensional subspace such that projecting onto this subspace maintains some prescribed distance between points of differing labels. Intended applications include compressive classification. Taking inspiration from large margin nearest neighbor classification, this paper introduces a semidefinite relaxation of this problem. Unlike its predecessors, this relaxation is amenable to theoretical analysis, allowing us to provably recover a planted projection operator from the data.

Austria Made in Hollywood by Jacqueline Vansant

Reviewed by: Austria Made in Hollywood by Jacqueline Vansant Laura A. Detre Jacqueline Vansant, Austria Made in Hollywood. Rochester, NY: Camden House, 2019. 196 pp. For a country of its size, Austria receives a lot of attention from American filmmakers. Not only has Hollywood made many movies set in both Imperial Austria and the Republic of Austria, but filmmakers' interest in the country has remained somewhat steady throughout the history of the industry. Whether [End Page 115] writers and directors were interested in documenting Nazism, Austria's role in the Cold War, or simply the myths of grandeur and elegance surrounding the former imperial capital, Vienna, they have been using the country as a setting for American films since the early days of moviemaking and continue to find inspiration there. This is the focus of Jacqueline Vansant's book Austria Made in Hollywood. She shows how Hollywood's Austrian films began in 1923 with Erich Stroheim's film Merry-Go-Round and remain an important part of American filmmaking through the 1960s with movies such as The Cardinal and The Sound of Music. Vansant makes the argument that these films were often vehicles for working through American social issues, particularly around social class and ethnicity, as well as global political movements that transcended both the United States and Austria. Vansant's book is organized into five chapters, which are essentially chronological but also focus on discrete topics in the history of this filmmaking. Chapter 1 looks closely at Erich Stroheim and his films that were set in Austria. She notes that other scholars' focus on Stroheim's rocky relationships with studio heads gives short shrift to the significance of his nostalgia for pre–World War I Austria and the way he simultaneously challenged contemporary mores. The topic of Chapter 2 spans a broader period, looking at films that depict romantic relationships between Americans and Austrians between 1932 and 1960. In particular, Vansant writes about four Paramount-produced comedies, Evenings for Sale, Champagne Waltz, The Emperor Waltz, and A Breath of Scandal. All four of these films use their Austrian settings to discuss social issues such as poverty, racism and eugenics, women's sexual empowerment, and the positive contributions to be made by immigrants. They were also deeply impacted by world events, such as the Great Depression, World War II and the Holocaust, and the Cold War. Chapter 3 looks at films set in the Austro-Hungarian Empire and made just before or during the Second World War, many of which contrasted Austria with Nazi Germany and posited the benefits of multicultural societies. Unfortunately, Vansant also notes that the subtleties of these films may have been lost on American audiences. She writes that the reviewers for the New York Times dismissed these films without connecting them to contemporary issues, and we can presume that most audiences did the same. Sticking with the theme of a general public that was less than enthusiastic about filmic delves into contemporary issues, Chapter 4 looks specifically at three films, So Ends Our [End Page 116] Night, They Dare Not Love, and Once Upon a Honeymoon, all of which depict the Anschluss. These films came in the context of American isolationism and were presented to a public that had little interest in international affairs. They also presented the true story of the annexation of the independent state of Austria to its larger neighbor through the particular lenses of their respective filmmakers' biases. In her final chapter, Vansant examines the depiction of Austria in The Cardinal and The Sound of Music, both of which came approximately twenty years after the majority of the other films discussed in this text. Because of this chronological difference, these two films view the country in significantly different ways. As she says, "unlike the films discussed in the previous chapter, The Cardinal and The Sound of Music were neither a call to arms nor justification for US involvement in the war" (113). Both films used Austria's recent history as material to primarily entertain audiences. An argument could be made for The Cardinal as a cautionary tale about silence in the face of evil, but ultimately it was made to get viewers...