A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.

An object-centred approach to encourage online participation in the context of behaviour change

Online groups rely on contributions from their members to flourish, but in the context of behaviour change individuals are typically reluctant to participate actively before they have changed successfully. We took inspiration from CSCW research on objects to address this problem by shifting the focus of online participation from the exchange of personal experiences to more incidental interactions mediated by objects that offer support for change. In this article we describe how we designed, deployed and studied a smartphone application that uses different objects, called distractions and tips, to facilitate social interaction amongst people trying to quit smoking. A field study with 18 smokers revealed different forms of interaction: purely instrumental interactions with the objects, subtle engagement with other users through receptive and covert interactions, as well as explicit interaction with other users through disclosure and mutual support. The distraction objects offered a stepping-stone into interaction, whereas the tips encouraged interaction with the people behind the objects. This understanding of interaction through objects complements existing frameworks of online participation and adds to the current discourse on object-centred sociality. Furthermore, it provides an alternative approach to the design of online support groups, which offers the users enhanced control about the information they share with other users. We conclude by discussing how researchers and practitioners can apply the ideas of interaction around objects to other domains where individuals may have a simultaneous desire and reluctance to interact.

Organizational affordances: A structuration theory approach to affordances

Affordance is an important concept in the field of human–computer interaction. There are various interpretations of affordances, often extending the original notion of James J. Gibson. Often the treatment of affordances in the current human–computer interaction literature has been a one-to-one relationship between a user and an artefact. We believe that the social and cultural contexts within which an artefact is situated affect the way in which the artefact is used and the notion of affordance needs to be seen as a dynamic, always emerging relationship between people and their environment. Using a Structuration Theory approach, we conceptualize the notion of affordance at a much broader level, encompassing social and cultural aspects. We suggest that affordances should be seen at three levels: single user, organizational (or work group) and societal. Focusing on the organizational level affordances, we provide details of several important factors that affect the emergence of affordances. - This article provides a new perspective on the discourse of affordance with the use of Structuration Theory. - It shows how affordance can be understood as ‘use’ in situated practices (i.e. ‘technology-in-practice’) - The Structuration Theory approach to affordances is showcased using two case studies.

A relational approach to direct mail consumption

- Purpose This paper aims to investigate how direct mail consumption contributes to brand relationship quality. Store flyers and other direct mailings continue to play a significant role in many companies’ communication strategies. Research on this topic predominantly investigates driving store traffic and sales. Less is known regarding the consumer side, such as the value that consumers may derive from the consumption of direct mailings and the effects of such a value on brand relationship quality. To address this limitation, this paper tests a causal model of the contribution of direct mail value to brand commitment, drawing on a value framework that integrates social theory of engagement regimes and literature on experiential customer value. - Design/methodology/approach The empirical work of this paper is based on a rigorous four-study mixed methods design, involving qualitative study, confirmatory factor analysis and partial least squares structural modeling. - Findings The authors develop two second-order formatively designed scales – familiar value and planned value scales – that illustrate the role of engagement regimes in consumer behavior. Although both types of value contribute equally to direct mail attachment, they exert contrasting effects on other mediational consumer responses, such as reading and gratitude. Finally, the proposed theoretical model appears to be robust in predicting customers’ brand commitment. - Research limitations/implications This study provides new insights into the research on consumer value and brand relational communication. - Originality/value This study is the first to consider consumer benefits from the social perspective of engagement regimes.

Genomic approach to organ-specific growth control

Growth is a fundamental aspect of life cycle of all organisms. Body size varies highly in most animal groups, such as mammals. Moreover, growth of a multicellular organism is not uniform enlargement of size, but different body parts and organs grow to their characteristic sizes at different times. Currently very little is known about the molecular mechanisms governing this organ-specific growth. The genome sequencing projects have provided complete genomic DNA sequences of several species over the past decade. The amount of genomic sequence information, including sequence variants within species, is constantly increasing. Based on the universal genetic code, we can make sense of this sequence information as far as it codes proteins. However, less is known about the molecular mechanisms that control expression of genes, and about the variations in gene expression that underlie many pathological states in humans. This is caused in part by lack of information about the second genetic code that consists of the binding specificities of transcription factors and the combinatorial code by which transcription factor binding sites are assembled to form tissue-specific and/or ligand-regulated enhancer elements. This thesis presents a high-throughput assay for identification of transcription factor binding specificities, which were then used to measure the DNA binding profiles of transcription factors involved in growth control. We developed ‘enhancer element locator’, a computational tool, which can be used to predict functional enhancer elements. A genome-wide prediction of human and mouse enhancer elements generated a large database of enhancer elements. This database can be used to identify target genes of signaling pathways, and to predict activated transcription factors based on changes in gene expression. Predictions validated in transgenic mouse embryos revealed the presence of multiple tissue-specific enhancers in mouse c- and N-Myc genes, which has implications to organ specific growth control and tumor type specificity of oncogenes. Furthermore, we were able to locate a variation in a single nucleotide, which carries a susceptibility to colorectal cancer, to an enhancer element and propose a mechanism by which this SNP might be involved in generation of colorectal cancer.

A Generalized Approach to Multiresolution Complex SAR Signal Processing

Multiresolution synthetic aperture radar (SAR) image formation has been proven to be beneficial in a variety of applications such as improved imaging and target detection as well as speckle reduction. SAR signal processing traditionally carried out in the Fourier domain has inherent limitations in the context of image formation at hierarchical scales. We present a generalized approach to the formation of multiresolution SAR images using biorthogonal shift-invariant discrete wavelet transform (SIDWT) in both range and azimuth directions. Particularly in azimuth, the inherent subband decomposition property of wavelet packet transform is introduced to produce multiscale complex matched filtering without involving any approximations. This generalized approach also includes the formulation of multilook processing within the discrete wavelet transform (DWT) paradigm. The efficiency of the algorithm in parallel form of execution to generate hierarchical scale SAR images is shown. Analytical results and sample imagery of diffuse backscatter are presented to validate the method.

A systematic-ecological approach to Baltic Sea ice studies of algae and protists

The seasonal occurrence of sea ice that annually covers almost half the Baltic Sea area provides a unique habitat for halo- and cold temperature-tolerant extremophiles. Baltic Sea ice biology has more than 100 years of tradition that began with the floristic observation of species by the early pioneers using light microscopic techniques that were the only thing available at the time. Since the discovery of life within sea ice, more technologies have become available for taxonomy. Electron microscopy and genetic evidence have been used to identify sea ice biota revealing increased numbers of taxa. Meanwhile ecologists have used light microscopic cell enumeration in addition to the chemical and physical properties of sea ice in attempts to explain the food web structure of sea ice and its functions. Thus, during the Baltic winter, the sea ice hosts more abundant and diverse microbial communities than the water column beneath it. These communities are typically dominated by autotrophic diatoms together with a diverse assortment of dinoflagellates, auto- and heterotrophic flagellates, ciliates, metazoan rotifers and bacteria, which are mostly responsible for the recycling of nutrients. This thesis comprises ecological and systematic studies. In addition to the results of the previous studies carried out on landfast ice, the data presented here provide new insight into the spatial distribution of pelagial sea ice, which has remained largely unexplored. The studies reveal spatial heterogeneity in the pelagial sea ice of the Gulf of Bothnia. There were mismatches in chlorophyll-a concentrations and in photosynthetic efficiencies of the communities studied. The temporal succession was followed and experimental studies performed investigating the community responses towards increased or decreased light in landfast ice in the Gulf of Finland. The systematic studies carried out with established dinoflagellate cultures revealed a new resting cyst belonging to common sea ice dinoflagellate, Scrippsiella hangoei (Schiller) Larsen 1995. The cyst can be used to explain the overwintering of this species during prolonged periods of darkness. The dissimilarities and similarities in the material isolated from the sea ice called for description of a new subspecies Heterocapsa arctica ssp. frigida. The cells obtained in the cultured material were unlike those of the previously described species, necessitating description of ssp. frigida. As a result of its own unique habitus, the subspecies had been noted by Finnish taxonomists during the past three decades and thus its annual occurrence and geographical distribution in the Baltic Sea. This illustrates how combining ecology and systematics increases our understanding of organisms.

Low carbon fuels and commodity chemicals from waste gases – systematic approach to understand energy metabolism in a model acetogen

Gas fermentation using acetogenic bacteria offers a promising route for the sustainable production of low carbon fuels and commodity chemicals from abundant, inexpensive C1 feedstocks including industrial waste gases, syngas, reformed methane or methanol. Clostridium autoethanogenum is a model gas fermenting acetogen that produces fuel ethanol and 2,3-butanediol, a precursor for nylon and rubber. Acetogens have already been used in large scale industrial fermentations, they are ubiquitous and known to play a prominent role in the global carbon cycle. Still, they are considered to live on the thermodynamic edge of life and potential energy constraints when growing on C1 gases pose a major challange for the commercial production of fuels and chemicals. We have developed a systematic platform to investigate acetogenic energy metabolism, exemplified here by experiments contrasting heterotrophic and autotrophic metabolism. The platform is built from complete omics technologies, augmented with genetic tools and complemented by a manually curated genome-scale mathematical model. Together the tools enable the design and development of new, energy efficient pathways and strains for the production of chemicals and advanced fuels via C1 gas fermentation. As a proof-of-platform, we investigated heterotrophic growth on fructose versus autotrophic growth on gas that demonstrate the role of the Rnf complex and Nfn complex in maintaining growth using the Wood–Ljungdahl pathway. Pyruvate carboxykinase was found to control the rate-limiting step of gluconeogenesis and a new specialized glyceraldehyde-3-phosphate dehydrogenase was identified that potentially enhances anabolic capacity by reducing the amount of ATP consumed by gluconeogenesis. The results have been confirmed by the construction of mutant strains.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Who really ate the fruit? A novel approach to camera trapping for quantifying frugivory by ruminants

Tropical forest ruminants disperse several plants; yet, their effectiveness as seed dispersers is not systematically quantified. Information on frequency and extent of frugivory by ruminants is lacking. Techniques such as tree watches or fruit traps adapted from avian frugivore studies are not suitable to study terrestrial frugivores, and conventional camera traps provide little quantitative information. We used a novel time-delay camera-trap technique to assess the effectiveness of ruminants as seed dispersers for Phyllanthus emblica at Mudumalai, southern India. After being triggered by animal movement, cameras were programmed to take pictures every 2 min for the next 6 min, yielding a sequence of four pictures. Actual frugivores were differentiated from mere visitors, who did not consume fruit, by comparing the number of fruit remaining across the time-delay photograph sequence. During a 2-year study using this technique, we found that six terrestrial mammals consumed fallen P. emblica fruit. Additionally, seven mammals and one bird species visited fruiting trees but did not consume fallen fruit. Two ruminants, the Indian chevrotain Moschiola indica and chital Axis axis, were P. emblica's most frequent frugivores and they accounted for over 95% of fruit removal, while murid rodents accounted for less than 1%. Plants like P. emblica that are dispersed mainly by large mammalian frugivores are likely to have limited ability to migrate across fragmented landscapes in response to rapidly changing climates. We hope that more quantitative information on ruminant frugivory will become available with a wider application of our time-delay camera-trap technique.

A discrete wavelet transform approach to multiresolution complex SAR image generation

We present a signal processing approach using discrete wavelet transform (DWT) for the generation of complex synthetic aperture radar (SAR) images at an arbitrary number of dyadic scales of resolution. The method is computationally efficient and is free from significant system-imposed limitations present in traditional subaperture-based multiresolution image formation. Problems due to aliasing associated with biorthogonal decomposition of the complex signals are addressed. The lifting scheme of DWT is adapted to handle complex signal approximations and employed to further enhance the computational efficiency. Multiresolution SAR images formed by the proposed method are presented.

A geometric approach to stationary defect solutions in one space dimension

In this manuscript, we consider the impact of a small jump-type spatial heterogeneity on the existence of stationary localized patterns in a system of partial dierential equations in one spatial dimension...

GMM based Bayesian approach to speech enhancement in signal transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.