Human pavlovian-instrumental transfer.

The vigor with which a participant performs actions that produce valuable outcomes is subject to a complex set of motivational influences. Many of these are believed to involve the amygdala and the nucleus accumbens, which act as an interface between limbic and motor systems. One prominent class of influences is called pavlovian-instrumental transfer (PIT), in which the motivational characteristics of a predictor influence the vigor of an action with respect to which it is formally completely independent. We provide a demonstration of behavioral PIT in humans, with an audiovisual predictor of the noncontingent delivery of money inducing participants to perform more avidly an action involving squeezing a handgrip to earn money. Furthermore, using functional magnetic resonance imaging, we show that this enhanced motivation was associated with a trial-by-trial correlation with the blood oxygenation level-dependent (BOLD) signal in the nucleus accumbens and a subject-by-subject correlation with the BOLD signal in the amygdala. Our data dovetails well with the animal literature and sheds light on the neural control of vigor.

Energy scattering in weakly non-linear systems.

The Chinese Tam-Tam exhibits non-linear behavior in its vibro-acoustic response. The frequency content of the response during free, unforced vibration smoothly changes, with energy being progressively smeared out over a greater bandwidth with time. This is used as a motivating case for the general study of the phenomenon of energy cascading through weak nonlinearity. Numerical models based upon the Fermi-Pasta-Ulam system of non-linearly coupled oscillators, modified with the addition of damping, have been developed. These were used to study the response of ensembles of systems with randomized natural frequencies. Results from simulations will be presented here. For un-damped systems, individual ensemble members exhibit cyclical energy exchange between linear modes, but the ensemble average displays a steady state. For the ensemble response of damped systems, lightly damped modes can exhibit an effective damping which is higher than predicated by linear theory. The presence of a non-linearity provides a path for energy flow to other modes, increasing the apparent damping spectrum at some frequencies and reducing it at others. The target of this work is a model revealing the governing parameters of a generic system of this type and leading to predictions of the ensemble response.

The intelligent product driven supply chain

Establishing connectivity of products with real-time information about themselves can at one level provide accurate data, and at another, allow products to assess and influence their own destiny. In this way, the specification for an intelligent product is being built - one whose information content is permanently bound to its material content. This paper explores the impact of such development on supply chains, contrasting between simple and complex product supply chains. The Auto-ID project is on track to enable such connectivity between products and information using a single, open-standard, data repository for storage and retrieval of product information. The potential impact on the design and management of supply chains is immense. This paper provides an introduction to of some of these changes, demonstrating that by enabling intelligent products, Auto ID systems will be instrumental in driving future supply chains. The paper also identifies specific application areas for this technology in the product supply chain.

Ultrashort pulse generation in diode laser devices

The generation of ultrashort optical pulses by semiconductor lasers has been extensively studied for many years. A number of methods, including gain-/Q-switching and different types of mode locking, have been exploited for the generation of picosecond and sub-picosecond pulses [1]. However, the shortest pulses produced by diode lasers are still much longer and weaker than those that are generated by advanced mode-locked solid-state laser systems [2]. On the other hand, an interesting class of devices based on superradiant emission from multiple contact diode laser structures has also been recently reported [3]. Superradiance (SR) is a transient quantum optics phenomenon based on the cooperative radiative recombination of a large number of oscillators, including atoms, molecules, e-h pairs, etc. SR in semiconductors can be used for the study of fundamental properties of e-h ensembles such as photon-mediated pairing, non-equilibrium e-h condensation, BSC-like coherent states and related phenomena. Due to the intrinsic parameters of semiconductor media, SR emission typically results in the generation of a high-power optical pulse or pulse train, where the pulse duration can be much less than 1 ps, under optimised bias conditions. Advantages of this technique over mode locking in semiconductor laser structures include potentially shorter pulsewidths and much larger peak powers. Moreover, the pulse repetition rate of mode-locked pulses is fixed by the cavity round trip time, whereas the repetition rate of SR pulses is controlled by the current bias and can be varied over a wide range. © 2012 IEEE.

The misbehavior of value and the discipline of the will.

Most reinforcement learning models of animal conditioning operate under the convenient, though fictive, assumption that Pavlovian conditioning concerns prediction learning whereas instrumental conditioning concerns action learning. However, it is only through Pavlovian responses that Pavlovian prediction learning is evident, and these responses can act against the instrumental interests of the subjects. This can be seen in both experimental and natural circumstances. In this paper we study the consequences of importing this competition into a reinforcement learning context, and demonstrate the resulting effects in an omission schedule and a maze navigation task. The misbehavior created by Pavlovian values can be quite debilitating; we discuss how it may be disciplined.

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans.

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.

Lyapunov functions for stable cascades and applications to global stabilization

This paper generalizes recent Lyapunov constructions for a cascade of two nonlinear systems, one of which is stable rather than asymptotically stable. A new cross-term construction in the Lyapunov function allows us to replace earlier growth conditions by a necessary boundedness condition. This method is instrumental in the global stabilization of feedforward systems, and new stabilization results are derived from the generalized construction.

Direct observation of charge-carrier heating at WZ-ZB InP nanowire heterojunctions.

We have investigated the dynamics of hot charge carriers in InP nanowire ensembles containing a range of densities of zinc-blende inclusions along the otherwise wurtzite nanowires. From time-dependent photoluminescence spectra, we extract the temperature of the charge carriers as a function of time after nonresonant excitation. We find that charge-carrier temperature initially decreases rapidly with time in accordance with efficient heat transfer to lattice vibrations. However, cooling rates are subsequently slowed and are significantly lower for nanowires containing a higher density of stacking faults. We conclude that the transfer of charges across the type II interface is followed by release of additional energy to the lattice, which raises the phonon bath temperature above equilibrium and impedes the carrier cooling occurring through interaction with such phonons. These results demonstrate that type II heterointerfaces in semiconductor nanowires can sustain a hot charge-carrier distribution over an extended time period. In photovoltaic applications, such heterointerfaces may hence both reduce recombination rates and limit energy losses by allowing hot-carrier harvesting.

Values and actions in aversion

Model-based and model-free controllers can, in principle, learn arbitrary actions to optimize their behavior, at least those actions that can be expressed and explored. Indeed, these are often referred to as instrumental controllers because their choices are learned to be instrumental for the delivery of desired outcomes. Although this flexibility is very powerful, it comes with an attendant cost of learning. Evolution appears to have endowed everything from the simplest organisms to us with powerful, pre-specified, but inflexible alternatives. These responses are termed Pavlovian, after the famous Russian physiologist and psychologist Pavlov. The responses of the Pavlovian controller are determined by evolutionary (phylogenetic) considerations rather than (ontogenetic) aspects of the contingent development or learning of an individual. These responses directly interact with instrumental choices arising from goal-directed and habitual controllers. This interaction has been studied in a wealth of animal paradigms, and can be helpful, neutral, or harmful, according to circumstance. Although there has been less careful or analytical study of it in humans, it can be interpreted as underpinning a wealth of behavioral aberrations. © 2009 Elsevier Inc. All rights reserved.

Efficient parametric uncertainty analysis within the hybrid Finite Element/Statistical Energy Analysis method

This paper is concerned with the development of efficient algorithms for propagating parametric uncertainty within the context of the hybrid Finite Element/Statistical Energy Analysis (FE/SEA) approach to the analysis of complex vibro-acoustic systems. This approach models the system as a combination of SEA subsystems and FE components; it is assumed that the FE components have fully deterministic properties, while the SEA subsystems have a high degree of randomness. The method has been recently generalised by allowing the FE components to possess parametric uncertainty, leading to two ensembles of uncertainty: a non-parametric one (SEA subsystems) and a parametric one (FE components). The SEA subsystems ensemble is dealt with analytically, while the effect of the additional FE components ensemble can be dealt with by Monte Carlo Simulations. However, this approach can be computationally intensive when applied to complex engineering systems having many uncertain parameters. Two different strategies are proposed: (i) the combination of the hybrid FE/SEA method with the First Order Reliability Method which allows the probability of the non-parametric ensemble average of a response variable exceeding a barrier to be calculated and (ii) the combination of the hybrid FE/SEA method with Laplace's method which allows the evaluation of the probability of a response variable exceeding a limit value. The proposed approaches are illustrated using two built-up plate systems with uncertain properties and the results are validated against direct integration, Monte Carlo simulations of the FE and of the hybrid FE/SEA models. © 2013 Elsevier Ltd.

Atom probe tomography for advanced metallization

In microelectronics, the increase in complexity and the reduction of devices dimensions make essential the development of new characterization tools and methodologies. Indeed advanced characterization methods with very high spatial resolution are needed to analyze the redistribution at the nanoscale in devices and interconnections. The atom probe tomography has become an essential analysis to study materials at the nanometer scale. This instrument is the only analytical microscope capable to produce 3D maps of the distribution of the chemical species with an atomic resolution inside a material. This technique has benefit from several instrumental improvements during last years. In particular, the use of laser for the analysis of semiconductors and insulating materials offers new perspectives for characterization. The capability of APT to map out elements at the atomic scale with high sensitivity in devices meets the characterization requirements of semiconductor devices such as the determination of elemental distributions for each device region. In this paper, several examples will show how APT can be used to characterize and understand materials and process for advanced metallization. The possibilities and performances of APT (chemical analysis of all the elements, atomic resolution, planes determination, crystallographic information...) will be described as well as some of its limitations (sample preparation, complex evaporation, detection limit, ...). The examples illustrate different aspect of metallization: dopant profiling and clustering, metallic impurities segregation on dislocation, silicide formation and alloying, high K/metal gate optimization, SiGe quantum dots, as well as analysis of transistors and nanowires. © 2013 Elsevier B.V. All rights reserved.