Segmentation algorithm for non-stationary compound Poisson processes. With an application to inventory time series of market members in a financial market

We introduce an algorithm for the segmentation of a class of regime switching processes. The segmentation algorithm is a non parametric statistical method able to identify the regimes (patches) of a time series. The process is composed of consecutive patches of variable length. In each patch the process is described by a stationary compound Poisson process, i.e. a Poisson process where each count is associated with a fluctuating signal. The parameters of the process are different in each patch and therefore the time series is non-stationary. Our method is a generalization of the algorithm introduced by Bernaola-Galván, et al. [Phys. Rev. Lett. 87, 168105 (2001)]. We show that the new algorithm outperforms the original one for regime switching models of compound Poisson processes. As an application we use the algorithm to segment the time series of the inventory of market members of the London Stock Exchange and we observe that our method finds almost three times more patches than the original one. © 2010 EDP Sciences, Società Italiana di Fisica, Springer-Verlag

Strategies used as spectroscopy of financial markets reveal new stylized facts

We propose a new set of stylized facts quantifying the structure of financial markets. The key idea is to study the combined structure of both investment strategies and prices in order to open a qualitatively new level of understanding of financial and economic markets. We study the detailed order flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This enormous dataset allows us to compare (i) a closed national market (A-shares) with an international market (B-shares), (ii) individuals and institutions and (iii) real investors to random strategies with respect to timing that share otherwise all other characteristics. We find that more trading results in smaller net return due to trading frictions. We unveiled quantitative power laws with non-trivial exponents, that quantify the deterioration of performance with frequency and with holding period of the strategies used by investors. Random strategies are found to perform much better than real ones, both for winners and losers. Surprising large arbitrage opportunities exist, especially when using zero-intelligence strategies. This is a diagnostic of possible inefficiencies of these financial markets.Comment: 13 pages including 5 figures and 1 tabl

Joining the conspiracy? Negotiating ethics and emotions in researching (around) AIDS in southern Africa

AIDS is an emotive subject, particularly in southern Africa. Among those who have been directly affected by the disease, or who perceive themselves to be personally at risk, talking about AIDS inevitably arouses strong emotions - amongst them fear, distress, loss and anger. Conventionally, human geography research has avoided engagement with such emotions. Although the ideal of the detached observer has been roundly critiqued, the emphasis in methodological literature on 'doing no harm' has led even qualitative researchers to avoid difficult emotional encounters. Nonetheless, research is inevitably shaped by emotions, not least those of the researchers themselves. In this paper, we examine the role of emotions in the research process through our experiences of researching the lives of 'Young AIDS migrants' in Malawi and Lesotho. We explore how the context of the research gave rise to the production of particular emotions, and how, in response, we shaped the research, presenting a research agenda focused more on migration than AIDS. This example reveals a tension between universalised ethics expressed through ethical research guidelines that demand informed consent, and ethics of care, sensitive to emotional context. It also demonstrates how dualistic distinctions between reason and emotion, justice and care, global and local are unhelpful in interpreting the ethics of research practice

Manipulating infrared photons using plasmons in transparent graphene superlattices

Superlattices are artificial periodic nanostructures which can control the flow of electrons. Their operation typically relies on the periodic modulation of the electric potential in the direction of electron wave propagation. Here we demonstrate transparent graphene superlattices which can manipulate infrared photons utilizing the collective oscillations of carriers, i.e., plasmons of the ensemble of multiple graphene layers. The superlattice is formed by depositing alternating wafer-scale graphene sheets and thin insulating layers, followed by patterning them all together into 3-dimensional photonic-crystal-like structures. We demonstrate experimentally that the collective oscillation of Dirac fermions in such graphene superlattices is unambiguously nonclassical: compared to doping single layer graphene, distributing carriers into multiple graphene layers strongly enhances the plasmonic resonance frequency and magnitude, which is fundamentally different from that in a conventional semiconductor superlattice. This property allows us to construct widely tunable far-infrared notch filters with 8.2 dB rejection ratio and terahertz linear polarizers with 9.5 dB extinction ratio, using a superlattice with merely five graphene atomic layers. Moreover, an unpatterned superlattice shields up to 97.5% of the electromagnetic radiations below 1.2 terahertz. This demonstration also opens an avenue for the realization of other transparent mid- and far-infrared photonic devices such as detectors, modulators, and 3-dimensional meta-material systems.Comment: under revie

Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

Artificial immune systems

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self or nonself substances. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

The international synchronisation of business cycles: the role of animal spirits

Business cycles among industrial countries are highly correlated. We develop a two-country behavioral macroeconomic model where the synchronization of the business cycle is produced endogenously. The main channel of synchronization occurs through a propagation of “animal spirits”, i.e. waves of optimism and pessimism that become correlated internationally. We find that this propagation occurs with relatively low levels of trade integration. We do not need a correlation of exogenous shocks to generate synchronization. We also empirically test the main predictions of the model

Change or control? Developing dialogues between research and public protection

This paper aims to scope out some of the implications of desistance research for the community management of high risk offenders. Acknowledging the limited empirical research exploring this interface, this paper outlines the evolving evidence base and what this tells us about the process of desistance and what supports it. The evidence as to whether 'high risk offenders' desist and what we know about this process is discussed prior to outlining the landscape of current and principal practice approaches which can be located in the community/public protection model. Potential dialogues between desistance research and public protection practices are discussed to explore ensuing implications and opportunities for practice