Thermodynamic properties of tetrameric bond-alternating spin chains

Thermodynamic properties of a tetrameric bond-alternating Heisenberg spin chain with ferromagnetic-ferromagnetic-antiferromagnetic-antiferromagnetic exchange interactions are studied using the transfer-matrix renormalization group and compared to experimental measurements. The temperature dependence of the uniform susceptibility exhibits typical ferrimagnetic features. Both the uniform and staggered magnetic susceptibilities diverge in the limit $T\to 0$, indicating that the ground state has both ferromagnetic and antiferromagnetic long-range orders. A double-peak structure appears in the temperature dependence of the specific heat. Our numerical calculation gives a good account for the temperature and field dependence of the susceptibility, the magnetization, and the specific heat for Cu(3-Clpy)$_{2}$(N$_{3}$)$_{2}$ (3-Clpy=3-Chloroyridine).Comment: 8 pages, 12 figures; Replaced with final version accepted in Phys. Rev.

Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning

© 2018 IEEE. Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations

Bosonic Super Liouville System: Lax Pair and Solution

We study the bosonic super Liouville system which is a statistical transmutation of super Liouville system. Lax pair for the bosonic super Liouville system is constructed using prolongation method, ensuring the Lax integrability, and the solution to the equations of motion is also considered via Leznov-Saveliev analysis.Comment: LaTeX, no figures, 11 page

Supergravities with Minkowski x Sphere Vacua

Recently the authors have introduced a new gauged supergravity theory with a positive definite potential in D=6, obtained through a generalised Kaluza-Klein reduction from D=7. Of particular interest is the fact that this theory admits certain Minkowski x Sphere vacua. In this paper we extend the previous results by constructing gauged supergravities with positive definitive potentials in diverse dimensions, together with their vacuum solutions. In addition, we prove the supersymmetry of the generalised reduction ansatz. We obtain a supersymmetric solution with no form-field fluxes in the new gauged theory in D=9. This solution may be lifted to D=10, where it acquires an interpretation as a time-dependent supersymmetric cosmological solution supported purely by the dilaton. A further uplift to D=11 yields a solution describing a pp-wave.Comment: Latex, 26 pages, typos correcte

Blind modulation format identification using nonlinear power transformation

This paper proposes and experimentally demonstrates a blind modulation format identification (MFI) method delivering high accuracy (> 99%) even in a low OSNR regime (< 10 dB). By using nonlinear power transformation and peak detection, the proposed MFI can recognize whether the signal modulation format is BPSK, QPSK, 8-PSK or 16-QAM. Experimental results demonstrate that the proposed MFI can achieve a successful identification rate as high as 99% when the incoming signal OSNR is 7 dB. Key parameters, such as FFT length and laser phase noise tolerance of the proposed method, have been characterized

Time variable cosmological constant of holographic origin with interaction in Brans-Dicke theory

Time variable cosmological constant (TVCC) of holographic origin with interaction in Brans-Dicke theory is discussed in this paper. We investigate some characters for this model, and show the evolutions of deceleration parameter and equation of state (EOS) for dark energy. It is shown that in this scenario an accelerating universe can be obtained and the evolution of EOS for dark energy can cross over the boundary of phantom divide. In addition, a geometrical diagnostic method, jerk parameter is applied to this model to distinguish it with cosmological constant.Comment: 10 pages, 9 figure

Enhanced word embedding similarity measures using fuzzy rules for query expansion

© 2017 IEEE. Query expansion has been widely used to select additional words that are related to the original query words in the field of information retrieval. In this paper, we present a novel query expansion method that jointly uses fuzzy rules and a word embedding similarity calculation. The expansion words are generated using a word embedding method and selected according to their semantic similarity to the original query. Fuzzy rules are used to enhance the word similarity calculations and reweight expansion words. When measuring and ranking the relevance of a retrieved document, the original query and the expansion words with their weights are considered. We conduct experiments on the query expansion in document ranking tasks. Experimental results from the document ranking task show that the proposed method is able to significantly outperform state-of-the-art baseline methods

Biodegradation of phenanthrene in artificial seawater by using free and immobilized strain of Sphingomonas sp. GY2B

Biodegradation has been suggested as an alternative way to remove polycyclic aromatic hydrocarbons (PAHs) from contaminated environment. Phenanthrene is a representative carcinogenic PAHs containing&#8220;bay-region&#8221; and &#8220;K-region&#8221;. Strain Sphingomonas sp. GY2B is a high efficient phenanthrene-degrading strain isolated from crude oil contaminated soils and had a broad-spectrum degradation ability on PAHsand related aromatic compounds. This paper reports the domestication of strain Sphingomonas sp. GY2B in artificial seawater (AS) and the immobilization of the strain onto rice straw. Results showed that adding 85% artificial seawater had very low impact on the growth and&#160; phenanthrene degradation ability of strain GY2B being domesticated for five generations. Phenanthrene was rapidly degraded when thegrowth of strain GY2B was in the exponential phase that the initial added 100 mgL-1 phenanthrene had been almost completely degraded within 66 h. The optimal immobilization carrier weight and length of rice straw were 25 gL-1 and 0.5 cm, respectively. The immobilized strain GY2B had high degradation rate both in mineral salts medium and 80% artificial seawater, and was higher than that of the free strain GY2B. More than 95% phenanthrene (100 mgL-1) was degraded within 32 h, and the phenanthrene degradation percentages were &gt; 99.5% after 67 h for immobilized strains. Immobilization of strain GY2B with rice straw possesses a good application potential in the treatment of wastewater and bioremediation of estuary and offshore environment contaminated by phenanthrene

Does deep learning help topic extraction? A kernel k-means clustering method with word embedding

© 2018 All rights reserved. Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method's ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities