Coexistence of Localized and Extended States in Disordered Systems

It is commonly believed that Anderson localized states and extended states do not coexist at the same energy. Here we propose a simple mechanism to achieve the coexistence of localized and extended states in a band in a class of disordered quasi-1D and quasi-2D systems. The systems are partially disordered in a way that a band of extended states always exists, not affected by the randomness, whereas the states in all other bands become localized. The extended states can overlap with the localized states both in energy and in space, achieving the aforementioned coexistence. We demonstrate such coexistence in disordered multi-chain and multi-layer systems.Comment: 5 pages, 3 figure

Carbon nanotube template-assisted synthesis of zinc ferrite nanochains

We synthesized zinc ferrite nanochains assembled from nanoparticles using a carbon nanotubes (CNTs) template method The resulting nanochains were systematically characterized with respect to crystal structure morphology elemental composition magnetic properties and specific surface area by X-ray diffraction (XRD) transmission electron microscopy (TEM) field emission scanning electron microscopy (FESEM) X-ray photoelectron spectroscopy (XPS) superconducting quantum interference device (SQUID) magnetometry and the N(2) adsorption method The morphology results showed that the zinc ferrite particles with diameters of 10-20 rim were structurally linked to form nanochains The magnetic property investigation indicated that the zinc ferrite nanochains exhibited ferromagnetic behavior and possessed a saturation magnetization of 45 4 emu g(-1) at 300K We addressed the growth mechanism by analyzing the experimental conditions and characterization results This method may be applicable to synthesizing other metal oxide nanochains as wellArticleMATERIALS CHEMISTRY AND PHYSICS. 124(2-3):1029-1033 (2010)journal articl

A hybrid representation based simile component extraction

Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models

Towards a temporal network analysis of interactive WiFi users

Complex networks are used to depict topological features of complex systems. The structure of a network characterizes the interactions among elements of the system, and facilitates the study of many dynamical processes taking place on it. In previous investigations, the topological infrastructure underlying dynamical systems is simplified as a static and invariable skeleton. However, this assumption cannot cover the temporal features of many time-evolution networks, whose components are evolving and mutating. In this letter, utilizing the log data of WiFi users in a Chinese university campus, we infuse the temporal dimension into the construction of dynamical human contact network. By quantitative comparison with the traditional aggregation approach, we find that the temporal contact network differs in many features, e.g., the reachability, the path length distribution. We conclude that the correlation between temporal path length and duration is not only determined by their definitions, but also influenced by the microdynamical features of human activities under certain social circumstance as well. The time order of individuals' interaction events plays a critical role in understanding many dynamical processes via human close proximity interactions studied in this letter. Besides, our study also provides a promising measure to identify the potential superspreaders by distinguishing the nodes functioning as the relay hub.Comment: 6 pages, 6 figure