Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models

This paper addresses the problem of determining optimal designs for biological process models with intractable likelihoods, with the goal of parameter inference. The Bayesian approach is to choose a design that maximises the mean of a utility, and the utility is a function of the posterior distribution. Therefore, its estimation requires likelihood evaluations. However, many problems in experimental design involve models with intractable likelihoods, that is, likelihoods that are neither analytic nor can be computed in a reasonable amount of time. We propose a novel solution using indirect inference (II), a well established method in the literature, and the Markov chain Monte Carlo (MCMC) algorithm of Müller et al. (2004). Indirect inference employs an auxiliary model with a tractable likelihood in conjunction with the generative model, the assumed true model of interest, which has an intractable likelihood. Our approach is to estimate a map between the parameters of the generative and auxiliary models, using simulations from the generative model. An II posterior distribution is formed to expedite utility estimation. We also present a modification to the utility that allows the Müller algorithm to sample from a substantially sharpened utility surface, with little computational effort. Unlike competing methods, the II approach can handle complex design problems for models with intractable likelihoods on a continuous design space, with possible extension to many observations. The methodology is demonstrated using two stochastic models; a simple tractable death process used to validate the approach, and a motivating stochastic model for the population evolution of macroparasites.

Plasma–surface interactions at nanoscales : a combinatorial theoretical, process diagnostics and surface microanalysis approach

This paper introduces an integral approach to the study of plasma-surface interactions during the catalytic growth of selected nanostructures (NSs). This approach involves basic understanding of the plasma-specific effects in NS nucleation and growth, theoretical modelling, numerical simulations, plasma diagnostics, and surface microanalysis. Using an example of plasma-assisted growth of surface-supported single-walled carbon nanotubes, we discuss how the combination of these techniques may help improve the outcomes of the growth process. A specific focus here is on the effects of nanoscale plasma-surface interactions on the NS growth and how the available techniques may be used, both in situ and ex situ to optimize the growth process and structural parameters of NSs.

Highly efficient silicon nanoarray solar cells by a single-step plasma-based process

Highly efficient solar cells (conversion efficiency 11.9%, fill factor 70%) based on the vertically aligned single-crystalline nanostructures are fabricated without any pre-fabricated p-n junctions in a very simple, single-step process of Si nanoarray formation by etching p-type Si(100) wafers in low-temperature environment-friendly plasmas of argon and hydrogen mixtures.

Nanopore processing in dielectric materials and dielectric template-assisted nanoarray synthesis : using pulsed bias to enhance process throughput and precision

An effective technique to improve the precision and throughput of energetic ion condensation through dielectric nanoporous templates and reduce nanopore clogging by using finely tuned pulsed bias is proposed. Multiscale numerical simulations of ion deposition show the possibility of controlling the dynamic charge balance on the upper template's surface to minimize ion deposition on nanopore sidewalls and to deposit ions selectively on the substrate surface in contact with the pore opening. In this way, the shapes of nanodots in template-assisted nanoarray fabrication can be effectively controlled. The results are applicable to various processes involving porous dielectric nanomaterials and dense nanoarrays.

From nucleation to nanowires : a single-step process in reactive plasmas

This feature article introduces a deterministic approach for the rapid, single-step, direct synthesis of metal oxide nanowires. This approach is based on the exposure of thin metal samples to reactive oxygen plasmas and does not require any intervening processing or external substrate heating. The critical roles of the reactive oxygen plasmas, surface processes, and plasma-surface interactions that enable this growth are critically examined by using a deterministic viewpoint. The essentials of the experimental procedures and reactor design are presented and related to the key process requirements. The nucleation and growth kinetics is discussed for typical solid-liquid-solid and vapor-solid-solid mechanisms related to the synthesis of the oxide nanowires of metals with low (Ga, Cd) and high (Fe) melting points, respectively. Numerical simulations are focused on the possibility to predict the nanowire nucleation points through the interaction of the plasma radicals and ions with the nanoscale morphological features on the surface, as well as to control the localized 'hot spots' that in turn determine the nanowire size and shape. This generic approach can be applied to virtually any oxide nanoscale system and further confirms the applicability of the plasma nanoscience approaches for deterministic nanoscale synthesis and processing.

Disentangling fluxes of energy and matter in plasma-surface interactions : effect of process parameters

The possibility to discriminate between the relative importance of the fluxes of energy and matter in plasma-surface interaction is demonstrated by the energy flux measurements in low-temperature plasmas ignited by the radio frequency discharge (power and pressure ranges 50-250 W and 8-11.5 Pa) in Ar, Ar+ H2, and Ar+ H2 + CH4 gas mixtures typically used in nanoscale synthesis and processing of silicon- and carbon-based nanostructures. It is shown that by varying the gas composition and pressure, the discharge power, and the surface bias one can effectively control the surface temperature and the matter supply rates. The experimental findings are explained in terms of the plasma-specific reactions in the plasma bulk and on the surface.

Tailoring carbon nanotips in the plasma-assisted chemical vapor deposition: Effect of the process parameters

Carbon nanotips have been synthesized from a thin carbon film deposited on silicon by bias-enhanced hot filament chemical vapor deposition under different process parameters. The results of scanning electron microscopy indicate that high-quality carbon nanotips can only be obtained under conditions when the ion flux is effectively drawn from the plasma sustained in a CH4 + NH3 + H2 gas mixture. It is shown that the morphology of the carbon nanotips can be controlled by varying the process parameters such as the applied bias, gas pressure, and the NH3 / H2 mass flow ratios. The nanotip formation process is examined through a model that accounts for surface diffusion, in addition to sputtering and deposition processes included in the existing models. This model makes it possible to explain the major difference in the morphologies of the carbon nanotips formed without and with the aid of the plasma as well as to interpret the changes of their aspect ratio caused by the variation in the ion/gas fluxes. Viable ways to optimize the plasma-based process parameters to synthesize high-quality carbon nanotips are suggested. The results are relevant to the development of advanced plasma-/ion-assisted methods of nanoscale synthesis and processing.

Low-phosporous nickel-coated carbon microcoils : Controlling microstructure through an electroless plating process

Carbon microcoils (CMCs) have been coated with a nickel-phosphorus (Ni-P) film using an electroless plating process, with sodium hypophosphite as a reducing agent in an alkaline bath. CMC composites have potential applications as microwave absorption materials. The morphology, elemental composition and phases in the coating layer of the CMCs and Ni-coated CMCs were investigated by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD), respectively. The effects of process parameters such as pH, temperature and coating time of the plating bath on the phosphorus content and deposition rate of the electroless Ni-P coating were studied. The results revealed that a continuous, uniform and low-phosphorous nickel coating was deposited on the surface of the CMCs for 20 min at pH 9.0, plating bath temperature 70 °C. The as-deposited coatings with approximately 4.5 wt.% phosphorus were found to consist of a mix of nano- and microcrystalline phases. The mean particle size of Ni-P nanoparticles on the outer surface of the CMCs was around 11.9 nm. The deposition rate was found to moderately increase with increasing pH, whereas, the phosphorous content of the deposit exhibited a significant decrease. Moreover, the material of the coating underwent a phase transition between an amorphous and a crystalline structure. The thickness of the deposit and the deposition rate may be controlled through careful variation of the coating time and plating bath temperature.

Angular distribution of carbon ion flux in a nanotube array during the plasma process by the Monte Carlo technique

Angular distribution of microscopic ion fluxes around nanotubes arranged into a dense ordered pattern on the surface of the substrate is studied by means of multiscale numerical simulation. The Monte Carlo technique was used to show that the ion current density is distributed nonuniformly around the carbon nanotubes arranged into a dense rectangular array. The nonuniformity factor of the ion current flux reaches 7 in dense (5× 1018 m-3) plasmas for a nanotube radius of 25 nm, and tends to 1 at plasma densities below 1× 1017 m-3. The results obtained suggest that the local density of carbon adatoms on the nanotube side surface, at areas facing the adjacent nanotubes of the pattern, can be high enough to lead to the additional wall formation and thus cause the single- to multiwall structural transition, and other as yet unexplained nanoscience phenomena.

Fusiform activation to animals is driven by the process, not the stimulus

Previous studies have found that the lateral posterior fusiform gyri respond more robustly to pictures of animals than pictures of manmade objects and suggested that these regions encode the visual properties characteristic of animals. We suggest that such effects actually reflect processing demands arising when items with similar representations must be finely discriminated. In a positron emission tomography (PET) study of category verification with colored photographs of animals and vehicles, there was robust animal-specific activation in the lateral posterior fusiform gyri when stimuli were categorized at an intermediate level of specificity (e.g., dog or car). However, when the same photographs were categorized at a more specific level (e.g., Labrador or BMW), these regions responded equally strongly to animals and vehicles. We conclude that the lateral posterior fusiform does not encode domain-specific representations of animals or visual properties characteristic of animals. Instead, these regions are strongly activated whenever an item must be discriminated from many close visual or semantic competitors. Apparent category effects arise because, at an intermediate level of specificity, animals have more visual and semantic competitors than do artifacts.

Supporting process model validation through natural language generation

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation.

Weaknesses in the initialisation process of the Common Scrambling Algorithm Stream Cipher

The Common Scrambling Algorithm Stream Cipher (CSASC) is a shift register based stream cipher designed to encrypt digital video broadcast. CSA-SC produces a pseudo-random binary sequence that is used to mask the contents of the transmission. In this paper, we analyse the initialisation process of the CSA-SC keystream generator and demonstrate weaknesses which lead to state convergence, slid pairs and shifted keystreams. As a result, the cipher may be vulnerable to distinguishing attacks, time-memory-data trade-off attacks or slide attacks.

Towards a multiple-goal pursuit and multilevel view of continuing information technology usage : a process model integration and a research agenda

The value of information technology (IT) is often realized when continuously being used after users’ initial acceptance. However, previous research on continuing IT usage is limited for dismissing the importance of mental goals in directing users’ behaviors and for inadequately accommodating the group context of users. This in-progress paper offers a synthesis of several literature to conceptualize continuing IT usage as multilevel constructs and to view IT usage behavior as directed and energized by a set of mental goals. Drawing from the self-regulation theory in the social psychology, this paper proposes a process model, positioning continuing IT usage as multiple-goal pursuit. An agent-based modeling approach is suggested to further explore causal and analytical implications of the proposed process model.

Whose story is it anyway? : the screenwriter as author in the process of adaptation

This project explores issues confronted when authoring a previously authored story, one received from history. Using the defection of Soviet spies, Vladimir and Evdokia Petrov as its focal point, it details how a screenwriter addresses issues arising in the adaptation of both fictional and biographical representations suitable for contemporary cinema. Textual fidelity and concepts of interpretation, aesthetics and audience, negotiating factual and fictional imperatives, authorial visibility and invisibility, moral and ethical conundrums are negotiated and a set of guiding principles emerge from this practice-led investigation.

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.