A peer-peer particle swarm optimizer

Particle Swarm Optimization is a parallel algorithm that spawns particles across a search space searching for an optimized solution. Though inherently parallel, they have distinct synchronizations points which stumbles attempts to create completely distributed versions of it. In this paper, we attempt to create a completely distributed peer-peer particle swarm optimization in a cluster of heterogeneous nodes. Since, the original algorithm requires explicit synchronization points we modified the algorithm in multiple ways to support a peer-peer system of nodes. We also modify certain aspect of the basic PSO algorithm and show how certain numerical problems can take advantage of the same thereby yielding fast convergence.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Investigating proline puckering states in diproline segments in proteins

In the current study, the puckering states of the Proline ring occurring in diproline segments (LPro-LPro) in proteins has been investigated with a segregation made on the basis of cis and trans states for the Pro-Pro peptide bond and the conformational states for the diproline segment to investigate the effects of conformation of the diproline segment on the corresponding puckering state of the Proline ring in the segment if any. The value of the endocyclic ring torsional angles of the pyrrolidine ring has been used for calculating and visualizing various puckering states using a proposed new sign convention (+/-) nomenclature. The results have been compared to that obtained in a previous study on peptides from this group. In this study, quite interestingly, the Planar (G) conformation that was present in 14.3% of the cases in peptides, appears to be nearly a rare conformation in the case of proteins (1.9%). The present study indicates that the (C-exo/C-exo), (C-exo/Twisted C-exo-C-endo) and (Twisted C-endo-C-exo/Twisted C-endo-C-exo) categories are the most preferred combinations. For Proline rings in proteins, the states C-exo, Twisted C-exo-C-endo and Twisted C-endo-C-exo are the most preferred states. Within diproline segments, the pyrrolidine ring conformations do not show a strong co-relation to the backbone conformation in which they are observed. It is likely that five-membered rings have a considerable plasticity of structure and are readily deformed to accommodate a variety of energetically preferred backbone conformations.

Multiobjective discrete particle swarm optimization for multisensor image alignment

A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient.

Multi-resistance states in the electrical switching behavior of amorphous Si15Te75Ge10 thin films: Possibility of multi-bit storage

Electrical switching studies on amorphous Si15Te75Ge10 thin film devices reveal the existence of two distinct, stable low-resistance, SET states, achieved by varying the electrical input to the device. The multiple resistance levels can be attributed to multi-stage crystallization, as observed from temperature dependant resistance studies. The devices are tested for their ability to be RESET with minimal resistance degradation; further, they exhibit a minimal drift in the SET resistance value even after several months of switching. (c) 2013 Elsevier B.V. All rights reserved.

Fermions in synthetic non-Abelian gauge potentials: rashbon condensates to novel Hamiltonians

Recent advances in the generation of synthetic gauge fields in cold atomic systems have stimulated interest in the physics of interacting bosons and fermions in them. In this paper, we discuss interacting two-component fermionic systems in uniform non-Abelian gauge fields that produce a spin-orbit interaction and uniform spin potentials. Two classes of gauge fields discussed include those that produce a Rashba spin-orbit interaction and the type of gauge fields (SM gauge fields) obtained in experiments by the Shanxi and MIT groups. For high symmetry Rashba gauge fields, a two-particle bound state exists even for a vanishingly small attractive interaction described by a scattering length. Upon increasing the strength of a Rashba gauge field, a finite density of weakly interacting fermions undergoes a crossover from a BCS like ground state to a BEC state of a new kind of boson called the rashbon whose properties are determined solely by the gauge field and not by the interaction between the fermions. The rashbon Bose-Einstein condensate (RBEC) is a quite intriguing state with the rashbon-rashbon interactions being independent of the fermion-fermion interactions (scattering length). Furthermore, we show that the RBEC has a transition temperature of the order of the Fermi temperature, suggesting routes to enhance the transition temperatures of weakly interacting superfluids by tuning the spin-orbit coupling. For the SM gauge fields, we show that in a regime of parameters, a pair of particles with finite centre-of-mass momentum is the most strongly bound. In other regimes of centre-of-mass momenta, there is no two-body bound state, but a resonance like feature appears in the scattering continuum. In the many-body setting, this results in flow enhanced pairing. Also, strongly interacting normal states utilizing the scattering resonance can be created opening the possibility of studying properties of helical Fermi liquids. This paper contains a general discussion of the physics of Feshbach resonance in a non-Abelian gauge field, where several novel features such as centre-of-mass-momentum-dependent effective interactions are shown. It is also shown that a uniform non-Abelian gauge field in conjunction with a spatial potential can be used to generate novel Hamiltonians; we discuss an explicit example of the generation of a monopole Hamiltonian.

Remoulded shear strength at plastic and semi-solid states

The undrained shear strength of remoulded soils is of great concern in geotechnical engineering applications. This study aims to develop a reliable approach for determining the undrained shear strength of remoulded fine-grained soils, through the use of index test results, at both the plastic and semi-solid states of consistency. Experimental investigation and subsequent analysis involving a number of fine-grained soils of widely varying plasticity and geological origin have led to a two-parameter linear model of the relationship between logarithm of remoulded undrained shear strength and liquidity index. The numerical values of the parameters are found to be dependent to a lesser extent on the soil group and to a greater extent on the soil state. Based on the values of regression coefficient, ranking index and ranking distance, it seems that the relationship represents the experimental results well. It may be pointed out that the possibility of such a relationship in the semi-solid state of a soil has not been explored in the past. It is also shown that the shear strength at the plastic limit is about 32-34 times that at the liquid limit.

Discussion: Remoulded shear strength at plastic and semi-solid states

The undrained shear strength of remoulded soils is of great concern in geotechnical engineering applications. This study aims to develop a reliable approach for determining the undrained shear strength of remoulded fine-grained soils, through the use of index test results, at both the plastic and semi-solid states of consistency. Experimental investigation and subsequent analysis involving a number of fine-grained soils of widely varying plasticity and geological origin have led to a two-parameter linear model of the relationship between logarithm of remoulded undrained shear strength and liquidity index. The numerical values of the parameters are found to be dependent to a lesser extent on the soil group and to a greater extent on the soil state. Based on the values of regression coefficient, ranking index and ranking distance, it seems that the relationship represents the experimental results well. It may be pointed out that the possibility of such a relationship in the semi-solid state of a soil has not been explored in the past. It is also shown that the shear strength at the plastic limit is about 32–34 times that at the liquid limit.

Entropy of quantum states: Ambiguities

The von Neumann entropy of a generic quantum state is not unique unless the state can be uniquely decomposed as a sum of extremal or pure states. As pointed out to us by Sorkin, this happens if the GNS representation (of the algebra of observables in some quantum state) is reducible, and some representations in the decomposition occur with non-trivial degeneracy. This non-unique entropy can occur at zero temperature. We will argue elsewhere in detail that the degeneracies in the GNS representation can be interpreted as an emergent broken gauge symmetry, and play an important role in the analysis of emergent entropy due to non-Abelian anomalies. Finally, we establish the analogue of an H-theorem for this entropy by showing that its evolution is Markovian, determined by a stochastic matrix.

Dynamic state estimation for identifying earthquake support motions in instrumented structures

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

Modified particle swarm optimization for pattern clustering

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Cooperative Particle Swarm Optimization Based Receiver for Large-Dimension MIMO-ZPSC Systems

In this paper, we propose a cooperative particle swarm optimization (CPSO) based channel estimation/equalization scheme for multiple-input multiple-output zero-padded single-carrier (MIMO-ZPSC) systems with large dimensions in frequency selective channels. We estimate the channel state information at the receiver in time domain using a PSO based algorithm during training phase. Using the estimated channel, we perform information symbol detection in the frequency domain using FFT based processing. For this detection, we use a low complexity OLA (OverLap Add) likelihood ascent search equalizer which uses minimum mean square (MMSE) equalizer solution as the initial solution. Multiple iterations between channel estimation and data detection are carried out which significantly improves the mean square error and bit error rate performance of the receiver.

Evolution of ferromagnetism from a frustrated state in LixNi(2-x)O2 (0.67 < x < 0.98)

Two-dimensional triangular-lattice antiferromagnetic systems continue to be an interesting area in condensed matter physics and LiNiO2 is one such among them. Here we present a detailed experimental magnetic study of the quasi-stoichiometric LixNi2-xO2 system (0.67states-namely spin glass, cluster glass, re-entrant spin glass and ferromagnetic. This study deals with the magnetic properties of these four distinct ground states. The spin glass state is evidenced by the frequency-dependent peak shift as well as the time-dependent slow dynamics (magnetic relaxation, magnetic memory effect etc). By tuning the Li deficiency in a controlled manner, an increase in the ordering temperature is observed. Most strikingly, with the Li deficiency the nature of the magnetic ground state is changed from spin glass to ferromagnetic, with two intermediate states-namely cluster glass and re-entrant spin glass. The critical behaviour of the re-entrant spin glass is also studied here. The critical exponents (beta, gamma and delta) are extracted from the modified Arrot plot, Kouvel-Fisher method, and critical isotherm analysis. The critical exponents match with the long-range mean-field model. The values of the critical exponents are confirmed by the Widom scaling law: delta = 1 + gamma beta(-1). Furthermore, the universality class of the scaling relations is verified, where the scaled m and scaled h collapse into two branches. Finally, based on our observations, a phase diagram is constructed.