Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribution are also proposed. The proposed approach is tested using SCM-based model for testing the consequences of applying the truncated normal distribution in the RRC model. The experimental evaluation is performed using four different base classifiers and seven quality measures. The results showed that the proposed approach is comparable to the RRC model built using beta distribution. What is more, for some base classifiers, the truncated-normal-based SCM algorithm turned out to be better at discovering objects coming from minority classes.Comment: arXiv admin note: text overlap with arXiv:1901.0882

Blue luminescence of Au nanoclusters embedded in silica matrix

Photoluminescence study using the 325 nm He-Cd excitation is reported for the Au nanoclusters embedded in SiO2 matrix. Au clusters are grown by ion beam mixing with 100 KeV Ar+ irradiation on Au [40 nm]/SiO2 at various fluences and subsequent annealing at high temperature. The blue bands above ~3 eV match closely with reported values for colloidal Au nanoclusters and supported Au nanoislands. Radiative recombination of sp electrons above Fermi level to occupied d-band holes are assigned for observed luminescence peaks. Peaks at 3.1 eV and 3.4 eV are correlated to energy gaps at the X- and L-symmetry points, respectively, with possible involvement of relaxation mechanism. The blue shift of peak positions at 3.4 eV with decreasing cluster size is reported to be due to the compressive strain in small clusters. A first principle calculation based on density functional theory using the full potential linear augmented plane wave plus local orbitals (FP-LAPW+LO) formalism with generalized gradient approximation (GGA) for the exchange correlation energy is used to estimate the band gaps at the X- and L-symmetry points by calculating the band structures and joint density of states (JDOS) for different strain values in order to explain the blueshift of ~0.1 eV with decreasing cluster size around L-symmetry point.Comment: 13 pages, 7 Figures Only in PDF format; To be published in J. of Chem. Phys. (Tentative issue of publication 8th December 2004

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases

Combination of linear classifiers using score function -- analysis of possible combination strategies

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the proposed approach was applied to the heterogeneous ensemble and it was compared to two reference methods -- majority voting and model averaging respectively. The comparison was made in terms of seven different quality criteria. The result shows that combination strategies based on simple average, and trimmed average are the best combination strategies of the geometrical combination

Pyrite nanocrystals: shape-controlled synthesis and tunable optical properties via reversible self-assembly

Nanocrystals from non-toxic, earth abundant materials have recently received great interest for their potential large-scale application in photovoltaics and photocatalysis. Here, we report for the first time on the shape-controlled and scalable synthesis of phase-pure pyrite (FeS2) nanocrystals employing the simple, inexpensive, thermal reaction of iron–oleylamine complexes with sulfur in oleylamine. Either dendritic nanocrystals (nanodendrites) or nanocubes are obtained by adjusting the iron-oleylamine concentration and thereby controlling the nucleus concentration and kinetics of the nanocrystal growth. Pyrite nanodendrites are reversibly assembled by washing with toluene and redispersed by adding the ligand oleylamine. The assembly–redispersion-process is accompanied by an increased absorption in the red/near-infrared spectral region for the aggregated state. This increased low-energy absorption is due to interactions between the closed-packed nanocrystals. High-concentration nanodendrite dispersions are used to prepare pyrite thin films with strong broadband extinction in the visible and near-infrared. These films are attractive candidates for light harvesting in all inorganic solar cells based on earth abundant, non-toxic materials as well as for photocatalytic applications

Global Networks of Trade and Bits

Considerable efforts have been made in recent years to produce detailed topologies of the Internet. Although Internet topology data have been brought to the attention of a wide and somewhat diverse audience of scholars, so far they have been overlooked by economists. In this paper, we suggest that such data could be effectively treated as a proxy to characterize the size of the "digital economy" at country level and outsourcing: thus, we analyse the topological structure of the network of trade in digital services (trade in bits) and compare it with that of the more traditional flow of manufactured goods across countries. To perform meaningful comparisons across networks with different characteristics, we define a stochastic benchmark for the number of connections among each country-pair, based on hypergeometric distribution. Original data are thus filtered by means of different thresholds, so that we only focus on the strongest links, i.e., statistically significant links. We find that trade in bits displays a sparser and less hierarchical network structure, which is more similar to trade in high-skill manufactured goods than total trade. Lastly, distance plays a more prominent role in shaping the network of international trade in physical goods than trade in digital services.Comment: 25 pages, 6 figure

What is a Cool-Core Cluster? A Detailed Analysis of the Cores of the X-ray Flux-Limited HIFLUGCS Cluster Sample

We use the largest complete sample of 64 galaxy clusters (HIghest X-ray FLUx Galaxy Cluster Sample) with available high-quality X-ray data from Chandra, and apply 16 cool-core diagnostics to them, some of them new. We also correlate optical properties of brightest cluster galaxies (BCGs) with X-ray properties. To segregate cool core and non-cool-core clusters, we find that central cooling time, t_cool, is the best parameter for low redshift clusters with high quality data, and that cuspiness is the best parameter for high redshift clusters. 72% of clusters in our sample have a cool core (t_cool < 7.7 h_{71}^{-1/2} Gyr) and 44% have strong cool cores (t_cool <1.0 h_{71}^{-1/2} Gyr). For the first time we show quantitatively that the discrepancy in classical and spectroscopic mass deposition rates can not be explained with a recent formation of the cool cores, demonstrating the need for a heating mechanism to explain the cooling flow problem. [Abridged]Comment: 45 pages, 19 figures, 7 tables. Accepted for publication in A&A. Contact Person: Rupal Mittal ([email protected]

Atomistic simulations of self-trapped exciton formation in silicon nanostructures: The transition from quantum dots to nanowires

Using an approximate time-dependent density functional theory method, we calculate the absorption and luminescence spectra for hydrogen passivated silicon nanoscale structures with large aspect ratio. The effect of electron confinement in axial and radial directions is systematically investigated. Excited state relaxation leads to significant Stokes shifts for short nanorods with lengths less than 2 nm, but has little effect on the luminescence intensity. The formation of self-trapped excitons is likewise observed for short nanostructures only; longer wires exhibit fully delocalized excitons with neglible geometrical distortion at the excited state minimum.Comment: 10 pages, 4 figure

Mechanical and Electronic Properties of MoS2_2 Nanoribbons and Their Defects

We present our study on atomic, electronic, magnetic and phonon properties of one dimensional honeycomb structure of molybdenum disulfide (MoS$_2$) using first-principles plane wave method. Calculated phonon frequencies of bare armchair nanoribbon reveal the fourth acoustic branch and indicate the stability. Force constant and in-plane stiffness calculated in the harmonic elastic deformation range signify that the MoS$_2$ nanoribbons are stiff quasi one dimensional structures, but not as strong as graphene and BN nanoribbons. Bare MoS$_2$ armchair nanoribbons are nonmagnetic, direct band gap semiconductors. Bare zigzag MoS$_2$ nanoribbons become half-metallic as a result of the (2x1) reconstruction of edge atoms and are semiconductor for minority spins, but metallic for the majority spins. Their magnetic moments and spin-polarizations at the Fermi level are reduced as a result of the passivation of edge atoms by hydrogen. The functionalization of MoS$_2$ nanoribbons by adatom adsorption and vacancy defect creation are also studied. The nonmagnetic armchair nanoribbons attain net magnetic moment depending on where the foreign atoms are adsorbed and what kind of vacancy defect is created. The magnetization of zigzag nanoribbons due to the edge states is suppressed in the presence of vacancy defects.Comment: 11 pages, 5 figures, first submitted at November 23th, 200

An evaluative baseline for geo-semantic relatedness and similarity

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer’s semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity