Dynamic Niche Clustering: a fuzzy variable radius niching technique for multimodal optimisation in GAs

This paper describes the recent developments and improvements made to the variable radius niching technique called Dynamic Niche Clustering (DNC). DNC is fitness sharing based technique that employs a separate population of overlapping fuzzy niches with independent radii which operate in the decoded parameter space, and are maintained alongside the normal GA population. We describe a speedup process that can be applied to the initial generation which greatly reduces the complexity of the initial stages. A split operator is also introduced that is designed to counteract the excessive growth of niches, and it is shown that this improves the overall robustness of the technique. Finally, the effect of local elitism is documented and compared to the performance of the basic DNC technique on a selection of 2D test functions. The paper is concluded with a view to future work to be undertaken on the technique.

View-based modelling of human visual navigation errors

View-based and Cartesian representations provide rival accounts of visual navigation in humans, and here we explore possible models for the view-based case. A visual “homing” experiment was undertaken by human participants in immersive virtual reality. The distributions of end-point errors on the ground plane differed significantly in shape and extent depending on visual landmark configuration and relative goal location. A model based on simple visual cues captures important characteristics of these distributions. Augmenting visual features to include 3D elements such as stereo and motion parallax result in a set of models that describe the data accurately, demonstrating the effectiveness of a view-based approach.

An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps

The application of automatic segmentation methods in lesion detection is desirable. However, such methods are restricted by intensity similarities between lesioned and healthy brain tissue. Using multi-spectral magnetic resonance imaging (MRI) modalities may overcome this problem but it is not always practicable. In this article, a lesion detection approach requiring a single MRI modality is presented, which is an improved method based on a recent publication. This new method assumes that a low similarity should be found in the regions of lesions when the likeness between an intensity based fuzzy segmentation and a location based tissue probabilities is measured. The usage of a normalized similarity measurement enables the current method to fine-tune the threshold for lesion detection, thus maximizing the possibility of reaching high detection accuracy. Importantly, an extra cleaning step is included in the current approach which removes enlarged ventricles from detected lesions. The performance investigation using simulated lesions demonstrated that not only the majority of lesions were well detected but also normal tissues were identified effectively. Tests on images acquired in stroke patients further confirmed the strength of the method in lesion detection. When compared with the previous version, the current approach showed a higher sensitivity in detecting small lesions and had less false positives around the ventricle and the edge of the brain

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

Can we measure terrestrial photosynthesis from space directly, using spectral reflectance and fluorescence?

Attempts to estimate photosynthetic rate or gross primary productivity from remotely sensed absorbed solar radiation depend on knowledge of the light use efficiency (LUE). Early models assumed LUE to be constant, but now most researchers try to adjust it for variations in temperature and moisture stress. However, more exact methods are now required. Hyperspectral remote sensing offers the possibility of sensing the changes in the xanthophyll cycle, which is closely coupled to photosynthesis. Several studies have shown that an index (the photochemical reflectance index) based on the reflectance at 531 nm is strongly correlated with the LUE over hours, days and months. A second hyperspectral approach relies on the remote detection of fluorescence, which is a directly related to the efficiency of photosynthesis. We discuss the state of the art of the two approaches. Both have been demonstrated to be effective, but we specify seven conditions required before the methods can become operational.

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Synthesis, characterization, crystal structure, and DNA-binding of ruthenium(II) complexes of heterocyclic nitrogen ligands resulting from a benzimidazole-based quinazoline derivative

The reaction of cis-[RuCl2(dmso)(4)] with [6-(2-pyridinyl)-5,6-dihydrobenzimidazo[1,2-c] quinazoline] (L) afforded in pure form a blue ruthenium(II) complex, [Ru(L-1)(2)] (1), where the original L changed to [2-(1H-benzoimidazol-2-yl)-phenyl]-pyridin-2-ylmethylene-amine (HL1). Treatment of RuCl3 center dot 3H(2)O with L in dry tetrahydrofuran in inert atmosphere led to a green ruthenium(II) complex, trans-[RuCl2(L-2)(2)] (2), where L was oxidized in situ to the neutral species 6-pyridin-yl-benzo[4,5]imidazo[1,2-c] quinazoline (L-2). Complex 2 was also obtained from the reaction of RuCl3 center dot 3H(2)O with L-2 in dry ethanol. Complexes 1 and 2 have been characterized by physico-chemical and spectroscopic tools, and 1 has been structurally characterized by single-crystal X-ray crystallography. The electrochemical behavior of the complexes shows the Ru(III)/Ru(II) couple at different potentials with quasi-reversible voltammograms. The interaction of these complexes with calf thymus DNA by using absorption and emission spectral studies allowed determination of the binding constant K-b and the linear Stern-Volmer quenching constant K-SV

Ruthenium mediated C–H activation of benzaldehyde thiosemicarbazones: Synthesis, structure and, spectral and electrochemical properties of the resulting complexes

Reaction of five 4R-benzaldehyde thiosemicarbazones (R = OCH3, CH3, H, Cl and NO2) with [ Ru(PPh3)(3)(-CO)(H) Cl] in refluxing methanol in the presence of a base (NEt3) affords complexes of two different types, viz. 1-R and 2-R. In the 1-R complexes the thiosemicarbazone is coordinated to ruthenium as a dianionic tridentate C,N,S-donor via C-H bond activation. Two triphenylphosphines and a carbonyl are also coordinated to ruthenium. The tricoordinated thiosemicarbazone ligand is sharing the same equatorial plane with ruthenium and the carbonyl, and the PPh3 ligands are mutually trans. In the 2-R complexes the thiosemicarbazone ligand is coordinated to ruthenium as a monoanionic bidentate N, S-donor forming a four-membered chelate ring with a bite angle of 63.91(11)degrees. Two triphenylphosphines, a carbonyl and a hydride are also coordinated to ruthenium. The coordinated thiosemicarbazone ligand, carbonyl and hydride constitute one equatorial plane with the metal at the center, where the carbonyl is trans to the coordinated nitrogen of the thiosemicarbazone and the hydride is trans to the sulfur. The two triphenylphosphines are trans. Structures of the 1-CH3 and 2-CH3 complexes have been determined by X-ray crystallography. All the complexes show intense transitions in the visible region, which are assigned, based on DFT calculations, to transitions within orbitals of the thiosemicarbazone ligand. Cyclic voltammetry on the complexes shows two oxidations of the coordinated thiosemicarbazone on the positive side of SCE and a reduction of the same ligand on the negative side.

Playing with history: negotiating subjectivity in contemporary lens based work

Contemporary artists exploring Jewish identity in the UK are caught between two exclusions, broadly speaking: an art community that that sees itself as ‘post –identity’ and a ‘black’ art scene that revolves around the organizations that emerged out of the Identity debates of the 1980s and 1990s, namely Iniva, Third Text, Autograph. These organizations and those debates, don’t usually include Jewish identity within their remit as Jewish artists are considered to be well represented in the British art scene and, in any case, white. Out of these assumptions, questions arise in relation to the position of Jews in Britain and what is at stake for an artist in exploring Jewish Identity in their work. There is considerable scholarship, relatively speaking on art and Jewish Identity in the US (such as Lisa Bloom; Norman Kleeblatt; Catherine Sousslouf), which inform the debates on visual culture and Jews. In this chapter, I will be drawing out some of the distinctions between the US and the UK debates within my analysis, building on my own writing over the last ten years as well as the work of Juliet Steyn, Jon Stratton and Griselda Pollock. In short, this chapter aims to explore the problematic of what Jewish Identity can offer the viewer as art; what place such art inhabits within a wider artistic context and how, if at all, it is received. There is a predominance of lens based work that explores Identity arising out of the provenance of feminist practices and the politics of documentary that will be important in the framing of the work. I do not aim to consider what constitutes a Jewish artist, that has been done elsewhere and is an inadequate and somewhat spurious conversation . I will also not be focusing on artists whose intention is to celebrate an unproblematised Jewishness (however that is constituted in any given work). Recent artworks and scholarship has in any case rendered the trumpeting of attachment to any singular identity anachronistic at best. I will focus on artists working in the UK who incorporate questions of Jewishness into a larger visual enquiry that build on Judith Butler’s notion of identity as process or performative as well as the more recent debates and artwork that consider the intersectionality of identifications that co-constitute provisional identities (Jones, Modood, Sara Ahmed, Braidotti/Nikki S Lee, Glenn Ligon). The case studies to think through these questions of identity, will be artworks by Susan Hiller, Doug Fishbone and Suzanne Triester. In thinking through works by these artists, I will also serve to contextualise them, situating them briefly within the history of the landmark exhibition in the UK, Rubies and Rebels and the work of Ruth Novaczek, Lily Markewitz, Oreet Ashery and myself.

Mobility based energy efficient and multi-sink algorithms for consumer home networks

With the fast development of the Internet, wireless communications and semiconductor devices, home networking has received significant attention. Consumer products can collect and transmit various types of data in the home environment. Typical consumer sensors are often equipped with tiny, irreplaceable batteries and it therefore of the utmost importance to design energy efficient algorithms to prolong the home network lifetime and reduce devices going to landfill. Sink mobility is an important technique to improve home network performance including energy consumption, lifetime and end-to-end delay. Also, it can largely mitigate the hot spots near the sink node. The selection of optimal moving trajectory for sink node(s) is an NP-hard problem jointly optimizing routing algorithms with the mobile sink moving strategy is a significant and challenging research issue. The influence of multiple static sink nodes on energy consumption under different scale networks is first studied and an Energy-efficient Multi-sink Clustering Algorithm (EMCA) is proposed and tested. Then, the influence of mobile sink velocity, position and number on network performance is studied and a Mobile-sink based Energy-efficient Clustering Algorithm (MECA) is proposed. Simulation results validate the performance of the proposed two algorithms which can be deployed in a consumer home network environment.

Centrality and spectral radius in dynamic communication networks

We explore the influence of the choice of attenuation factor on Katz centrality indices for evolving communication networks. For given snapshots of a network observed over a period of time, recently developed communicability indices aim to identify best broadcasters and listeners in the network. In this article, we looked into the sensitivity of communicability indices on the attenuation factor constraint, in relation to spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We proposed relaxed communicability measures where the spectral radius bound on attenuation factor is relaxed and the adjacency matrix is normalised in order to maintain the convergence of the measure. Using a vitality based measure of both standard and relaxed communicability indices we looked at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We illustrated our findings with two examples of real-life networks, MIT reality mining data set of daily communications between 106 individuals during one year and UK Twitter mentions network, direct messages on Twitter between 12.4k individuals during one week.

A high-resolution near-infrared extraterrestrial solar spectrum derived from ground-based Fourier transform spectrometer measurements

A detailed spectrally-resolved extraterrestrial solar spectrum (ESS) is important for line-by-line radiative transfer modeling in the near-infrared (near-IR). Very few observationally-based high-resolution ESS are available in this spectral region. Consequently the theoretically-calculated ESS by Kurucz has been widely adopted. We present the CAVIAR (Continuum Absorption at Visible and Infrared Wavelengths and its Atmospheric Relevance) ESS which is derived using the Langley technique applied to calibrated observations using a ground-based high-resolution Fourier transform spectrometer (FTS) in atmospheric windows from 2000–10000 cm-1 (1–5 μm). There is good agreement between the strengths and positions of solar lines between the CAVIAR and the satellite-based ACE-FTS (Atmospheric Chemistry Experiment-FTS) ESS, in the spectral region where they overlap, and good agreement with other ground-based FTS measurements in two near-IR windows. However there are significant differences in the structure between the CAVIAR ESS and spectra from semi-empirical models. In addition, we found a difference of up to 8 % in the absolute (and hence the wavelength-integrated) irradiance between the CAVIAR ESS and that of Thuillier et al., which was based on measurements from the Atmospheric Laboratory for Applications and Science satellite and other sources. In many spectral regions, this difference is significant, as the coverage factor k = 2 (or 95 % confidence limit) uncertainties in the two sets of observations do not overlap. Since the total solar irradiance is relatively well constrained, if the CAVIAR ESS is correct, then this would indicate an integrated “loss” of solar irradiance of about 30 W m-2 in the near-IR that would have to be compensated by an increase at other wavelengths.

A spectral view of nonlinear fluxes and stationary-transient interaction in the atmosphere

Nonlinear spectral transfers of kinetic energy and enstrophy, and stationary-transient interaction, are studied using global FGGE data for January 1979. It is found that the spectral transfers arise primarily from a combination, in roughly equal measure, of pure transient and mixed stationary-transient interactions. The pure transient interactions are associated with a transient eddy field which is approximately locally homogeneous and isotropic, and they appear to be consistently understood within the context of two-dimensional homogeneous turbulence. Theory based on spatial wale separation concepts suggests that the mixed interactions may be understood physically, to a first approximation, as a process of shear-induced spectral transfer of transient enstrophy along lines of constant zonal wavenumber. This essentially conservative enstrophy transfer generally involves highly nonlocal stationary-transient energy conversions. The observational analysis demonstrates that the shear-induced transient enstrophy transfer is mainly associated with intermediate-scale (zonal wavenumber m > 3) transients and is primarily to smaller (meridional) scales, so that the transient flow acts as a source of stationary energy. In quantitative terms, this transient-eddy rectification corresponds to a forcing timescale in the stationary energy budget which is of the same order of magnitude as most estimates of the damping timescale in simple stationary-wave models (5 to 15 days). Moreover, the nonlinear interactions involved are highly nonlocal and cover a wide range of transient scales of motion.

Graph-based algorithms for comparison and prediction of household-level energy use profiles

We present an efficient graph-based algorithm for quantifying the similarity of household-level energy use profiles, using a notion of similarity that allows for small time–shifts when comparing profiles. Experimental results on a real smart meter data set demonstrate that in cases of practical interest our technique is far faster than the existing method for computing the same similarity measure. Having a fast algorithm for measuring profile similarity improves the efficiency of tasks such as clustering of customers and cross-validation of forecasting methods using historical data. Furthermore, we apply a generalisation of our algorithm to produce substantially better household-level energy use forecasts from historical smart meter data.