Compositional analysis of thin film amorphous semiconductors and insulators using LIMA

LIMA (Laser-induced Ion Mass Analysis) is a new technique capable of compositional analysis of thin films and surface regions. Under UHV conditions a focused laser beam evaporates and ionizes a microvolume of specimen material from which a mass spectrum is obtained. LIMA has been used to examine a range of thin film materials with applications in electronic devices. The neutral photon probe avoids charging problems, and low conductivity materials are examined without prior metallization. Analyses of insulating silicon oxides, nitrides, and oxynitrides confirm estimates of composition from infrared measurements. However, the hydrogen content of hydrogenated amorphous silicon (a-Si : H) found by LIMA shows no correlation with values given by infrared absorption analysis. Explanations are proposed and discussed. © 1985.

Neural mechanisms of belief inference during cooperative games.

Humans have the arguably unique ability to understand the mental representations of others. For success in both competitive and cooperative interactions, however, this ability must be extended to include representations of others' belief about our intentions, their model about our belief about their intentions, and so on. We developed a "stag hunt" game in which human subjects interacted with a computerized agent using different degrees of sophistication (recursive inferences) and applied an ecologically valid computational model of dynamic belief inference. We show that rostral medial prefrontal (paracingulate) cortex, a brain region consistently identified in psychological tasks requiring mentalizing, has a specific role in encoding the uncertainty of inference about the other's strategy. In contrast, dorsolateral prefrontal cortex encodes the depth of recursion of the strategy being used, an index of executive sophistication. These findings reveal putative computational representations within prefrontal cortex regions, supporting the maintenance of cooperation in complex social decision making.

Encapsluation of Co and Pd multi-metal nanowires inside multiwalled carbon nanotubes by microwave plasma chemical vapor deposition

We report the synthesis of multiwalled carbon nanotubes (MWCNTs) encapsulated with Co/Pd magnetic and nonmagnetic multi-metal nanowires using Co and Pd thin-layers deposited on Si substrate by microwave plasma enhanced chemical vapor deposition using a bias-enhanced growth method. Detailed structural and compositional investigations of these metal nanowires inside MWCNTs were carried out by scanning electron microscopy and transmission electron microscopy to elucidate the growth mechanisms. Energy dispersive X-ray spectroscopy revealed that MWCNTs were encapsulated with Co and Pd nanowires, separately, at the tube top and the bottom of Co nanowire, respectively. The face-centered-cubic (fcc) structure of Co nanowires was confirmed by a selected area diffraction pattern. We proposed a fruitful description for the encapsulating mechanisms of both Co and Pd multi-metal nanowires. © 2006 Elsevier B.V. All rights reserved.

Methodological implications of critical realism for mixed-methods research

Building on recent developments in mixed methods, we discuss the methodological implications of critical realism and explore how these can guide dynamic mixed-methods research design in information systems. Specifically, we examine the core ontological assumptions of CR in order to gain some perspective on key epistemological issues such as causation and validity, and illustrate how these shape our logic of inference in the research process through what is known as retroduction. We demonstrate the value of a CR-led mixed-methods research approach by drawing on a study that examines the impact of ICT adoption in the financial services sector. In doing so, we provide insight into the interplay between qualitative and quantitative methods and the particular value of applying mixed methods guided by CR methodological principles. Our positioning of demi-regularities within the process of retroduction contributes a distinctive development in this regard. We argue that such a research design enables us to better address issues of validity and the development of more robust meta-inferences.

Nature of heterointerfaces in GaAs/InAs and InAs/GaAs axial nanowire heterostructures

The structural and compositional characteristics of heterointerfaces of Au-catalyzed GaAs/InAs and InAs/GaAs axial nanowire heterostructures were comprehensively investigated by transmission electron microscopy. It has been found that the GaAs/InAs interface is not sharp and contains an InGaAs transition segment, and in contrast, the InAs/GaAs interface is atomically sharp. This difference in the nature of heterointerfaces can be attributed to the difference in the affinity of the group III elements with the catalyst material. © 2008 American Institute of Physics.

Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Gaussian processes for machine learning (GPML) toolbox

The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

Altruistic learning.

The origin of altruism remains one of the most enduring puzzles of human behaviour. Indeed, true altruism is often thought either not to exist, or to arise merely as a miscalculation of otherwise selfish behaviour. In this paper, we argue that altruism emerges directly from the way in which distinct human decision-making systems learn about rewards. Using insights provided by neurobiological accounts of human decision-making, we suggest that reinforcement learning in game-theoretic social interactions (habitisation over either individuals or games) and observational learning (either imitative of inference based) lead to altruistic behaviour. This arises not only as a result of computational efficiency in the face of processing complexity, but as a direct consequence of optimal inference in the face of uncertainty. Critically, we argue that the fact that evolutionary pressure acts not over the object of learning ('what' is learned), but over the learning systems themselves ('how' things are learned), enables the evolution of altruism despite the direct threat posed by free-riders.