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We present the mean-field phase diagram of electrons in a kagome flat band with repulsive interactions. In addition to flat-band ferromagnetism, the Hartree-Fock analysis yields cascades of unconventional magnetic orders driven by onsite repulsion as filling changes. These include a series of antiferromagnetic (AFM) spin-charge stripe orders, as well as an evolution from $120^\circ$AFM to intriguing noncoplanar spin orders with tetrahedral structures. We also map out the phase diagram under extended repulsion at half and empty fillings of the flat band. To examine the possibilities beyond the mean-field level, we conduct a projective symmetry group analysis and identify the feasible $\mathbb Z_2$ spin liquids and the magnetic orders derivable from them. The theoretical phase diagrams are compared with recent experiments on FeSn and FeGe, enabling a determination of the most likely magnetic instabilities in these and similar flat-band kagome materials.
Abstract This study investigated (a) the sensitivity of a modified Stroop Colour Word Test to index attentional deficits in mild head-injured patients and (b) the influence of anxiety on attentional performance. Patients (N = 35) were individually matched with controls for age, sex, education, and IQ. Mild head-injured patients performed more poorly than did controls on the original, modified, and interference conditions of the Stroop Test. Although state anxiety influenced performance on the Stroop Test, deficits in performance were not explained simply by anxiety. The findings support the hypothesis that mild head injury does result in an identifiable impairment of focused attention.
No abstract is provided for this article.
Abstract Potentiodynamische Polarisationskurven wurden für 5 Legierungen mit dem Elektrolyten 0.15 N‐H3BO3 + O. 1 5 N Na2B4O‐,′ l0 H 2 O (pH=8.7) aufgenommen.
There are billions of images on the Internet. Today, searching for a desired image is largely based on textual data such as filename or associated text on the web page; not much use is made of the image content. There are good reasons for this. The field of content-based image retrieval, which emerged during the 1990s, focused primarily on color and texture cues. These were easier to model than shape, but they turned out to be much less useful than originally hoped. I shall review some of the recent developments in the field of visual object recognition in the computer vision community that offer greater promise. Much better image features for characterizing shape, advances in machine learning techniques, and the availability of large amounts of training data lie at the heart of these approaches.
Today, big and small organizations alike collect huge amounts of data, and they do so with one goal in mind: extract "value" through sophisticated exploratory analysis, and use it as the basis to make decisions as varied as personalized treatment and ad targeting. Unfortunately, existing data analytics tools are slow in answering queries, as they typically require to sift through huge amounts of data stored on disk, and are even less suitable for complex computations, such as machine learning algorithms. These limitations leave the potential of extracting value of big data unfulfilled.