We theoretically study the high-harmonic generation (HHG) in one-dimensional spin systems. While in electronic systems the driving by ac electric fields produces radiation from the dynamics of excited charges, we consider here the situation where spin systems excited by a magnetic field pulse generate radiation via a time-dependent magnetization. Specifically, we study the magnetic dipole radiation in two types of ferromagnetic spin chain models, the Ising model with static longitudinal field and the XXZ model, and reveal the structure of the spin HHG and its relation to spin excitations. For weak laser amplitude, a peak structure appears which can be explained by time-dependent perturbation theory. With increasing amplitude, plateaus with well-defined cutoff energies emerge. In the Ising model with longitudinal field, the thresholds of the multiple plateaus in the radiation spectra can be explained by the annihilation of multiple magnons. In the XXZ model, which retains the Z 2 symmetry, the laser magnetic field can induce a phase transition of the ground state when it exceeds a critical value, which results in a drastic change of the spin excitation character. As a consequence, the first cutoff energy in the HHG spectrum changes from a single-magnon to a two-magnon energy at this transition. Our results demonstrate the possibility of generating high-harmonic radiation from magnetically ordered materials and the usefulness of high-harmonic signals for extracting information on the spin excitation spectrum.
+A large fraction of online queries target entities. For this reason, Search Engine Result Pages (SERPs) increasingly contain information about the searched entities such as pictures, short summaries, related entities, and factual information. A key facet that is often displayed on the SERPs and that is instrumental for many applications is the entity type. However, an entity is usually not associated to a single generic type in the background knowledge graph but rather to a set of more specific types, which may be relevant or not given the document context. For example, one can find on the Linked Open Data cloud the fact that Tom Hanks is a person, an actor, and a person from Concord, California. All these types are correct but some may be too general to be interesting (e.g., person), while other may be interesting but already known to the user (e.g., actor), or may be irrelevant given the current browsing context (e.g., person from Concord, California). In this paper, we define the new task of ranking entity types given an entity and its context. We propose and evaluate new methods to find the most relevant entity type based on collection statistics and on the knowledge graph structure interconnecting entities and types. An extensive experimental evaluation over several document collections at different levels of granularity (e.g., sentences, paragraphs) and different type hierarchies (including DBpedia, Freebase, and schema.org) shows that hierarchy-based approaches provide more accurate results when picking entity types to be displayed to the end-user.
+Webpages are an abundant source of textual information with manually annotated entity links, and are often used as a source of training data for a wide variety of machine learning NLP tasks. However, manual annotations such as those found on Wikipedia are sparse, noisy, and biased towards popular entities. Existing entity linking systems deal with those issues by relying on simple statistics extracted from the data. While such statistics can effectively deal with noisy annotations, they introduce bias towards head entities and are ineffective for long tail (e.g., unpopular) entities. In this work, we first analyze statistical properties linked to manual annotations by studying a large annotated corpus composed of all English Wikipedia webpages, in addition to all pages from the Com-monCrawl containing English Wikipedia annotations. We then propose and evaluate a series of entity linking approaches, with the explicit goal of creating highly-accurate (precision > 95%) and broad annotated corpuses for machine learning tasks. Our results show that our best approach achieves maximal-precision at usable recall levels, and outperforms both state-of-the-art entity-linking systems and human annotators.
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