A mutual friend: poems for Charles Dickens

An anthology of poems by contemporary poets celebrating Charles Dickens' bicentenary

Separating skill from luck in REIT mutual funds

This study uses a bootstrap methodology to explicitly distinguish between skill and luck for 80 Real Estate Investment Trust Mutual Funds in the period January 1995 to May 2008. The methodology successfully captures non-normality in the idiosyncratic risk of the funds. Using unconditional, beta conditional and alpha-beta conditional estimation models, the results indicate that all but one fund demonstrates poor skill. Tests of robustness show that this finding is largely invariant to REIT market conditions and maturity.

Synchronization of digital data decoders

Burst timing synchronisation is maintained in a digital data decoder during multiple burst reception in a TDMA system. The data within a multiple burst are streamed into memory storage and data corresponding to a first burst in the series of bursts are selected on the basis of a current timing estimate derived from a synchronisation burst. Selections of data corresponding to other bursts in the series of bursts are modified in accordance with updated timing estimates derived from previously processed bursts.

Domestic and foreign bias in global real estate mutual fund investment allocations

The purpose of the study is to seek a better understanding of the investment allocation behaviour of the real estate mutual funds by focusing on asset allocation at the country level. Analysing the country allocation of 553 real estate mutual funds domiciled in 20 countries, we attempt to trace how investment bias exists across countries and affects their country allocations. Our results evidence the existence of disproportionate country allocation to their domestic markets (domestic bias) and to each foreign market (foreign bias). We also find each bias is influenced by different sets of variables: real estate market influences for domestic bias and familiarity influences for foreign bias. This difference in factors influential for each bias in part explains the conflated relationship between the two biases.

Real estate mutual funds: herding, momentum-trading and performance

This study jointly examines herding, momentum trading and performance in real estate mutual funds (REMFs). We do this using trading and performance data for 159 REMFs across the period 1998–2008. In support of the view that Real Estate Investment Trust (REIT) stocks are relatively more transparent, we find that stock herding by REMFs is lower in REIT stocks than other stock. Herding behavior in our data reveals a tendency for managers to sell winners, reflective of the “disposition effect.” We find low overall levels of REMF momentum trading, but further evidence of the disposition effect when momentum trading is segregated into buy–sell dimensions. We test the robustness of our analysis using style analysis, and by reference to the level of fund dividend distribution. Our results for this are consistent with our conjecture about the role of transparency in herding, but they provide no new insights in relation to the momentum-trading dimensions of our analysis. Summarizing what are complex interrelationships, we find that neither herding nor momentum trading are demonstrably superior investment strategies for REMFs.

Mutual binding of polymer end-groups by complementary π–π-stacking: a molecular “Roman Handshake”

Self-complementary tweezer-molecules based on a naphthalenediimide core self-assemble into supramolecular dimers through mutual π–π-stacking and hydrogen bonding. The resulting motif is extremely stable in solution (Ka = 105 M−1), and its attachment to one terminal position of a poly(ethylene glycol) chain leads to a doubling of the polymer's apparent molecular weight.

Mutual dependency grid for stakeholder mapping: a component-based approach to supply chain participant analysis

Stakeholder analysis plays a critical role in business analysis. However, the majority of the stakeholder identification and analysis methods focus on the activities and processes and ignore the artefacts being processed by human beings. By focusing on the outputs of the organisation, an artefact-centric view helps create a network of artefacts, and a component-based structure of the organisation and its supply chain participants. Since the relationship is based on the components, i.e. after the stakeholders are identified, the interdependency between stakeholders and the focal organisation can be measured. Each stakeholder is associated with two types of dependency, namely the stakeholder’s dependency on the focal organisation and the focal organisation’s dependency on the stakeholder. We identify three factors for each type of dependency and propose the equations that calculate the dependency indexes. Once both types of the dependency indexes are calculated, each stakeholder can be placed and categorised into one of the four groups, namely critical stakeholder, mutual benefits stakeholder, replaceable stakeholder, and easy care stakeholder. The mutual dependency grid and the dependency gap analysis, which further investigates the priority of each stakeholder by calculating the weighted dependency gap between the focal organisation and the stakeholder, subsequently help the focal organisation to better understand its stakeholders and manage its stakeholder relationships.

When long-range zero-lag synchronization is feasible in cortical networks

Many studies have reported long-range synchronization of neuronal activity between brain areas, in particular in the beta and gamma bands with frequencies in the range of 14–30 and 40–80 Hz, respectively. Several studies have reported synchrony with zero phase lag, which is remarkable considering the synaptic and conduction delays inherent in the connections between distant brain areas. This result has led to many speculations about the possible functional role of zero-lag synchrony, such as for neuronal communication, attention, memory, and feature binding. However, recent studies using recordings of single-unit activity and local field potentials report that neuronal synchronization may occur with non-zero phase lags. This raises the questions whether zero-lag synchrony can occur in the brain and, if so, under which conditions. We used analytical methods and computer simulations to investigate which connectivity between neuronal populations allows or prohibits zero-lag synchrony. We did so for a model where two oscillators interact via a relay oscillator. Analytical results and computer simulations were obtained for both type I Mirollo–Strogatz neurons and type II Hodgkin–Huxley neurons. We have investigated the dynamics of the model for various types of synaptic coupling and importantly considered the potential impact of Spike-Timing Dependent Plasticity (STDP) and its learning window. We confirm previous results that zero-lag synchrony can be achieved in this configuration. This is much easier to achieve with Hodgkin–Huxley neurons, which have a biphasic phase response curve, than for type I neurons. STDP facilitates zero-lag synchrony as it adjusts the synaptic strengths such that zero-lag synchrony is feasible for a much larger range of parameters than without STDP.

Self-organized 40Hz synchronization in a physiological theory of EEG

We present evidence that large-scale spatial coherence of 40 Hz oscillations can emerge dynamically in a cortical mean field theory. The simulated synchronization time scale is about 150 ms, which compares well with experimental data on large-scale integration during cognitive tasks. The same model has previously provided consistent descriptions of the human EEG at rest, with tranquilizers, under anesthesia, and during anesthetic-induced epileptic seizures. The emergence of coherent gamma band activity is brought about by changing just one physiological parameter until cortex becomes marginally unstable for a small range of wavelengths. This suggests for future study a model of dynamic computation at the edge of cortical stability.

Islamic mutual funds' financial performance and international investment style: evidence from 20 countries

We pursue the first large-scale investigation of a strongly growing mutual fund type: Islamic funds. Based on an unexplored, survivorship bias-adjusted data set, we analyse the financial performance and investment style of 265 Islamic equity funds from 20 countries. As Islamic funds often have diverse investment regions, we develop a (conditional) three-level Carhart model to simultaneously control for exposure to different national, regional and global equity markets and investment styles. Consistent with recent evidence for conventional funds, we find Islamic funds to display superior learning in more developed Islamic financial markets. While Islamic funds from these markets are competitive to international equity benchmarks, funds from especially Western nations with less Islamic assets tend to significantly underperform. Islamic funds’ investment style is somewhat tilted towards growth stocks. Funds from predominantly Muslim economies also show a clear small cap preference. These results are consistent over time and robust to time varying market exposures and capital market restrictions.

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

Study of optimization for cooperative motion between two humans in mutual tracking

Verbal communication is essential for human society and human civilization. Non-verbal communication, on the other hand, is more widely used not only by human but also other kind of animals, and the content of information is estimated even larger than the verbal communication. Among the non-verbal communication mutual motion is the simplest and easiest to study experimentally and analytically. We measured the power spectrum of the hand velocity in various conditions and clarified the following points on the feed-back and feed- forward mechanism as basic knowledge to understand the condition of good communication.