Ontology mapping neural network: An approach to learning and inferring correspondences among ontologies

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The output of one network in response to a stimulus to another network can be interpreted as an analogical mapping. In a similar fashion, the networks can be explicitly trained to map specific items in one domain to specific items in another domain. Representation layer helps the network learn relationship mapping with direct training method. OMNN is applied to several OAEI benchmark test cases to test its performance on ontology mapping. Results show that OMNN approach is competitive to the top performing systems that participated in OAEI 2009

Ferromagnetism in 2p Light Element-Doped II-oxide and III-nitride Semiconductors

II-oxide and III-nitride semiconductors doped by nonmagnetic 2p light elements are investigated as potential dilute magnetic semiconductors (DMS). Based on our first-principle calculations, nitrogen doped ZnO, carbon doped ZnO, and carbon doped AlN are predicted to be ferromagnetic. The ferromagnetism of such DMS materials can be attributed to a p-d exchange-like p-p coupling interaction which is derived from the similar symmetry and wave function between the impurity (p-like t_2) and valence (p) states. We also propose a co-doping mechanism, using beryllium and nitrogen as dopants in ZnO, to enhance the ferromagnetic coupling and to increase the solubility and activity

Color-flavor locked strangelets in a quark mass density-dependent model

The color-flavor locked (CFL) phase of strangelets is investigated in a quark mass density-dependent model. Parameters are determined by stability arguments. It is concluded that three solutions to the system equations can be found, corresponding, respectively, to positively charged, negatively charged, and nearly neutral CFL strangelets. The charge to baryon number of the positively charged strangelets is smaller than the previous result, while the charge of the negatively charged strangelets is nearly proportional in magnitude to the cubic-root of the baryon number. However, the positively charged strangelets are more stable compared to the other two solutions.Comment: 11 pages,7 figures, Accepted for publication in Int. J. Mod. Phys.

Equilibrium Times for the Multicanonical Method

This work measures the time to equilibrium for the multicanonical method on the 2D-Ising system by using a new criterion, proposed here, to find the time to equilibrium, teq, of any sampling procedure based on a Markov process. Our new procedure gives the same results that the usual one, based on the magnetization, for the canonical Metropolis sampling on a 2D-Ising model at several temperatures. For the multicanonical method we found a power-law relationship with the system size, L, of teq=0.27(15) L^2.80(13), and with the number of energy levels to explore, kE, of teq=0.7(13) kE^1.40(11), in perfect agreement with the result just above. In addition, some kind of critical slowing down was observed around the critical energy. Our new procedure is completely general, and can be applied to any sampling method based on a Markov process.Comment: 7 pages, 5 eps figures, to be published in Int. J. Mod. Phys.

Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning

This work is partially supported by grants from the National Natural Science Foundation of China under contract No. 61390515, No. U1611461, and No. 61425025, and the National Basic Research Program of China under Grant No. 2015CB351806

Effect of nonstationarities on detrended fluctuation analysis

Detrended fluctuation analysis (DFA) is a scaling analysis method used to quantify long-range power-law correlations in signals. Many physical and biological signals are ``noisy'', heterogeneous and exhibit different types of nonstationarities, which can affect the correlation properties of these signals. We systematically study the effects of three types of nonstationarities often encountered in real data. Specifically, we consider nonstationary sequences formed in three ways: (i) stitching together segments of data obtained from discontinuous experimental recordings, or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts -- a ``cutting'' procedure commonly used in preparing data prior to signal analysis; (ii) adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitude, and (iii) generating a signal comprised of segments with different properties -- e.g. different standard deviations or different correlation exponents. We compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities.Comment: 17 pages, 10 figures, corrected some typos, added one referenc

Dispersion, damping, and intensity of spin excitations in the single-layer (Bi,Pb)2_{2}(Sr,La)2_{2}CuO6+δ_{6+\delta} cuprate superconductor family

Using Cu-$L_3$ edge resonant inelastic x-ray scattering (RIXS) we measured the dispersion and damping of spin excitations (magnons and paramagnons) in the high-$T_\mathrm{c}$ superconductor (Bi,Pb)$_{2}$(Sr,La)$_{2}$CuO$_{6+\delta}$ (Bi2201), for a large doping range across the phase diagram ($0.03\lesssim p\lesssim0.21$). Selected measurements with full polarization analysis unambiguously demonstrate the spin-flip character of these excitations, even in the overdoped sample. We find that the undamped frequencies increase slightly with doping for all accessible momenta, while the damping grows rapidly, faster in the (0,0)$\rightarrow$(0.5,0.5) nodal direction than in the (0,0)$\rightarrow$(0.5,0) antinodal direction. We compare the experimental results to numerically exact determinant quantum Monte Carlo (DQMC) calculations that provide the spin dynamical structure factor $S(\textbf{Q},\omega)$ of the three-band Hubbard model. The theory reproduces well the momentum and doping dependence of the dispersions and spectral weights of magnetic excitations. These results provide compelling evidence that paramagnons, although increasingly damped, persist across the superconducting dome of the cuprate phase diagram; this implies that long range antiferromagnetic correlations are quickly washed away, while short range magnetic interactions are little affected by doping.Comment: 11 pages, 9 figure