Ensemble effects in the coupling of acetylene to benzene on a bimetallic surface: A study with Pd{111}/Au

Acetylene coupling to benzene on the Pd(lll) surface is greatly enhanced by the presence of catalytically inert Au atoms. LEED and Auger spectroscopy show that progressive annealing of Au overlayers on Pd(lll) leads to the formation of a series of random surface alloys with continuously varying composition. Cyclization activity is a strong function of surface composition-the most efficient catalyst corresponds to a surface of composition similar to 85% Pd. CO TPD and HREELS data show that acetylene cyclization activity is not correlated with the availability of singleton Pd atoms, nor just with the presence of 3-fold pure Pd sites-the preferred chemisorption site for C2H2 on Pd{111}. The data can be quantitatively rationalized in terms of a simple model in which catalytic activity is dominated by Pd6Au and Pd-7 surface ensembles, allowance being made for the known degree to which pure Pd{111} decomposes the reactant and product molecules.

The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results. © 1964-2012 IEEE.

A Hundred Comets: The Visual-Wavelength Observations of the Survey of Ensemble Physical Properties of Cometary Nuclei (SEPPCoN)

We present new results from SEPPCoN, a Survey of Ensemble Physical Properties of Cometary Nuclei. This project is currently surveying 100 Jupiter-family comets (JFCs) to measure the mid-infrared thermal emission and visible reflected sunlight of the nuclei. The scientific goal is to determine the distributions of radius, geometric albedo, thermal inertia, axial ratio, and color among the JFC nuclei. In the past we have presented results from the completed mid-IR observations of our sample [1]; here we present preliminary results from ongoing, broadband visible-wavelength observations of nuclei obtained from a variety of ground-based facilities (Mauna Kea, Cerro Pachon, La Silla, La Palma, Apache Point, Table Mtn., and Palomar Mtn.), including contributions from the Near Earth Asteroid Telescope project (NEAT) archive. The nuclei were observed at high heliocentric distance (usually over 4 AU) and so many comets show either no or little contamination from dust coma. While several nuclei have been observed as snapshots, we have multiepoch photometry for many of our targets. With our datasets we are building a large database of photometry, and such a database is essential to the derivation of albedo and shape of a large number of nuclei, and to the understanding of biases in the survey. Support for this work was provided by NSF and the NASA Planetary Astronomy program. Reference: [1] Fernandez, Y.R., et al. 2007, BAAS 39, 827.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.

A reactive force field simulation of liquid-liquid phase transitions in phosphorus

A force field model of phosphorus has been developed based on density functional (DF) computations and experimental results, covering low energy forms of local tetrahedral symmetry and more compact (simple cubic) structures that arise with increasing pressure. Rules tailored to DF data for the addition, deletion, and exchange of covalent bonds allow the system to adapt the bonding configuration to the thermodynamic state. Monte Carlo simulations in the N-P-T ensemble show that the molecular (P-4) liquid phase, stable at low pressure P and relatively low temperature T, transforms to a polymeric (gel) state on increasing either P or T. These phase changes are observed in recent experiments at similar thermodynamic conditions, as shown by the close agreement of computed and measured structure factors in the molecular and polymer phases. The polymeric phase obtained by increasing pressure has a dominant simple cubic character, while the polymer obtained by raising T at moderate pressure is tetrahedral. Comparison with DF results suggests that the latter is a semiconductor, while the cubic form is metallic. The simulations show that the T-induced polymerization is due to the entropy of the configuration of covalent bonds, as in the polymerization transition in sulfur. The transition observed with increasing P is the continuation at high T of the black P to arsenic (A17) structure observed in the solid state, and also corresponds to a semiconductor to metal transition. (C) 2004 American Institute of Physics.

Monte Carlo simulation of properties of monolayers and metal islands adsorbed on metallic (111) surfaces

To obtain the surface stress changes due to the adsorption of metal monolayers onto metallic surfaces, a new model derived from thermodynamic considerations is presented. Such a model is based on continuum Monte Carlo simulations with embedded atom method potentials in the canonical ensemble, and it is extended to consider the behavior on different islands adsorbed onto (111) substrate surfaces. Homoepitaxial and heteroepitaxial systems are studied. Pseudomorphic growth is not observed for small metal islands with considerable positive misfit with the substrate. Instead, the islands become compressed upon increase of their size. A simple model is proposed to interpolate between the misfits of atoms in small islands and the pseudomorphic behavior of the monolayer.

On the generation of metal clusters with the electrochemical scanning tunneling microscope

In the present work we consider two aspects of the deposition of metal clusters on an electrode surface. The formation of such clusters with the tip of a scanning tunneling microscope is simulated by atom dynamics. Subsequently the stability of these clusters is investigated by Monte Carlo simulations in a grand-canonical ensemble. In particular, the following systems were considered explicitly: Pd clusters on Au(111), Cu on Au(111), Ag on Au(111), Pb on Au(111) and Cu on Ag(111). The analysis of the results obtained for the different systems leads to the conclusion that optimal systems for nanostructuring are those where the metals participating have similar cohesive energies and negative heats of alloy formation. In this respect, the system Cu-Pd(111) is predicted as a good candidate for the formation of stable clusters. (c) 2005 Elsevier B.V. All rights reserved.

The Combination of Multiple Classifiers Using an Evidential Reasoning Approach

In many domains when we have several competing classifiers available we want to synthesize them or some of them to get a more accurate classifier by a combination function. In this paper we propose a ‘class-indifferent’ method for combining classifier decisions represented by evidential structures called triplet and quartet, using Dempster's rule of combination. This method is unique in that it distinguishes important elements from the trivial ones in representing classifier decisions, makes use of more information than others in calculating the support for class labels and provides a practical way to apply the theoretically appealing Dempster–Shafer theory of evidence to the problem of ensemble learning. We present a formalism for modelling classifier decisions as triplet mass functions and we establish a range of formulae for combining these mass functions in order to arrive at a consensus decision. In addition we carry out a comparative study with the alternatives of simplet and dichotomous structure and also compare two combination methods, Dempster's rule and majority voting, over the UCI benchmark data, to demonstrate the advantage our approach offers. (A continuation of the work in this area that was published in IEEE Trans on KDE, and conferences)

Short pulse laser-induced dissociation of vibrationally cold, trapped molecular ions

An electrostatic trapping scheme for use in the study of light-induced dissociation of molecular ions is outlined. We present a detailed description of the electrostatic reflection storage device and specifically demonstrate its use in the preparation of a vibrationally cold ensemble of deuterium hydride (HD+) ions. By interacting an intense femtosecond laser with this target and detecting neutral fragmentation products, we are able to elucidate previously inaccessible dissociation dynamics for fundamental diatomics in intense laser fields. In this context, we present new results of intense field dissociation of HD+ which are interpreted in terms of recent theoretical calculations.

Quasi-classical model of non-destructive wavepacket manipulation by intense ultrashort nonresonant laser pulses

A quasi-classical model (QCM) of nuclear wavepacket generation, modification and imaging by three intense ultrafast near-infrared laser pulses has been developed. Intensities in excess of 10(13) W cm(-2) are studied, the laser radiation is non-resonant and pulse durations are in the few-cycle regime, hence significantly removed from the conditions typical of coherent control and femtochemistry. The 1s sigma ground state of the D-2 precursor is projected onto the available electronic states in D-2(+) (1s sigma(g) ground and 2p sigma(u) dissociative) and D+ + D+ (Coulomb explosion) by tunnel ionization by an ultrashort 'pump' pulse, and relative populations are found numerically. A generalized non-adiabatic treatment allows the dependence of the initial vibrational population distribution on laser intensity to be calculated. The wavepacket is approximated as a classical ensemble of particles moving on the 1s sigma(g) potential energy surface (PES), and hence follow trajectories of different amplitudes and frequencies depending on the initial vibrational state. The 'control' pulse introduces a time-dependent polarization of the molecular orbital, causing the PES to be modified according to the dynamic Stark effect and the transition dipole. The trajectories adjust in amplitude, frequency and phase-offset as work is done on or by the resulting force; comparing the perturbed and unperturbed trajectories allows the final vibrational state populations and phases to be determined. The action of the 'probe' pulse is represented by a discrete internuclear boundary, such that elements of the ensemble at a larger internuclear separation are assumed to be photodissociated. The vibrational populations predicted by the QCM are compared to recent quantum simulations (Niederhausen and Thumm 2008 Phys. Rev. A 77 013404), and a remarkable agreement has been found. The applicability of this model to femtosecond and attosecond time-scale experiments is discussed and the relation to established femtochemistry and coherent control techniques are explored.

Indicators of the impact of climate change on migratory species

The Bonn Convention on the Conservation of Migratory Species of Wild Animals adopted a Resolution in 2005 recognising the impacts of climate change on migratory species. It called on Contracting Parties to undertake more research to improve our understanding of these impacts and to implement adaptation measures to reduce foreseeable adverse effects. Given the large diversity of taxa and species affected by climate change, it is impossible to monitor all species and effects thereof. However, it is likely that many of the key ecological and physical processes through which climate change may impact wildlife could be monitored using a suite of indicators, each comprising parameters of species/populations or groups of species as proxies for wider assemblages, habitats and ecosystems. Herein, we identify a suite of 17 indicators whose attributes could reveal negative impacts of climate change on the global status of migratory species: 4 for birds, 4 for marine mammals, 2 for sea turtles, 1 for fish, 3 for land mammals and 3 for bats. A few of these indicators would be relatively straightforward to develop, but most would require additional data collation, and in many cases methodological development. Choosing and developing indicators of the impacts of climate change on migratory species is a challenge, particularly with endangered species, which are subject to many other pressures. To identify and implement conservation measures for these species, indicators must account for the full ensemble of pressures, and link to a system of alerts and triggers for action.