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Nigrostriatal dopamine (DA) is critical to action selection and learning. Axonal DA release is locally influenced by striatal neurotransmitters. Striatal neurons are principally GABAergic projection neurons and interneurons, and a small minority of other neurons are cholinergic interneurons (ChIs). ChIs strongly gate striatal DA release via nicotinic receptors (nAChRs) identified on DA axons. Striatal GABA is thought to modulate DA, but GABA receptors have not been documented conclusively on DA axons. However, ChIs express GABA receptors and are therefore candidates for potential mediators of GABA regulation of DA. We addressed whether striatal GABA and its receptors can modulate DA release directly, independently from ChI regulation, by detecting DA in striatal slices from male mice using fast-scan cyclic voltammetry in the absence of nAChR activation. DA release evoked by single electrical pulses in the presence of the nAChR antagonist dihydro-β-erythroidine was reduced by GABA or agonists of GABAA or GABAB receptors, with effects prevented by selective GABA receptor antagonists. GABA agonists slightly modified the frequency sensitivity of DA release during short stimulus trains. GABA agonists also suppressed DA release evoked by optogenetic stimulation of DA axons. Furthermore, antagonists of GABAA and GABAB receptors together, or GABAB receptors alone, significantly enhanced DA release evoked by either optogenetic or electrical stimuli. These results indicate that striatal GABA can inhibit DA release through GABAA and GABAB receptors and that these actions are not mediated by cholinergic circuits. Furthermore, these data reveal that there is a tonic inhibition of DA release by striatal GABA operating through predominantly GABAB receptors. SIGNIFICANCE STATEMENT The principal inhibitory transmitter in the mammalian striatum, GABA, is thought to modulate striatal dopamine (DA) release, but definitive evidence for GABA receptors on DA axons is lacking. Striatal cholinergic interneurons regulate DA release via axonal nicotinic receptors (nAChRs) and also express GABA receptors, but they have not been eliminated as potentially critical mediators of DA regulation by GABA. Here, we found that GABAA and GABAB receptors inhibit DA release without requiring cholinergic interneurons. Furthermore, ambient levels of GABA inhibited DA release predominantly through GABAB receptors. These findings provide further support for direct inhibition of DA release by GABA receptors and reveal that striatal GABA operates a tonic inhibition on DA output that could critically influence striatal output.

In recent work, we have developed a theory of economic forecasting for empirical econometric models when there are structural breaks. This research shows that wellspecified models may forecast poorly, whereas it is possible to design forecasting devices more immune to the effects of breaks. In this chapter, we summarize key aspects of that theory, describe the models and data, then provide an empirical illustration of some of these developments when the goal is to generate sequences of inflation forecasts over a long historical period, starting with the model of annual inflation in the UK over 1875– 1991 in Hendry (2001b).

Behavioural interactions between ecosystem engineers may strongly influence community structure. We tested whether an invasive ecosystem engineer, the alga Caulerpa taxifolia, indirectly facilitated community diversity by modifying the behaviour of a native ecosystem engineer, the clam Anadara trapezia, in southeastern Australia. In this study, clams in Caulerpa-invaded sediments partially unburied themselves, extending >30% of their shell surface above the sediment, providing rare, hard substrata for colonization. Consequently, clams in Caulerpa had significantly higher diversity and abundance of epibiota compared with clams in unvegetated sediments. To isolate the role of clam burial depth from direct habitat influences or differential predation by habitat, we manipulated clam burial depth, predator exposure and habitat (Caulerpa or unvegetated) in an orthogonal experiment. Burial depth overwhelmingly influenced epibiont species richness and abundance, resulting in a behaviourally mediated facilitation cascade. That Caulerpa controls epibiont communities by altering Anadara burial depths illustrates that even subtle behavioural responses of one ecosystem engineer to another can drive extensive community-wide facilitation. © 2009 Blackwell Publishing Ltd/CNRS.

Purpose To examines strategies that have locked the Australian wine industry into a price-sensitive, commodity wine market. The paper seeks to explain the inherent weakness of these strategies and their inability to address current challenges and opportunities. Design/methodology/approach The paper uses empirical research data gathered from 100 SME wine firms. These firms were selected using a stratified random sampling technique. Four states were covered New South Wales, Victoria, South Australia, and Western Australia with all major wine regions in these states equally represented. There was careful sampling according to size, so that boutique, and small- and medium-sized firms were represented. Non-exporting firms were excluded. In each case either the CEO or the marketing manager was interviewed. Findings The findings confirm the paper's hypothesis, that increasingly the Australian wine industry has become risk-averse and locked into paradigms and organizational frameworks that are disconnecting from users and their requirements. The findings concur that greater differentiation of place, product, supply chains, and markets is required if the industry is to operate effectively within a multi-dimensional landscape and continue to attract sustainable returns.

Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations illustrate the analysis, to evaluate the biases in conventional standard errors when each model is mis-specified, compute the relative mean-square forecast errors of the two models for both DGPs, and investigate autocorrelated errors, so both models can better approximate the converse GDP. The outcomes are surprisingly different from established results.

This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.

We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition. QC 20140117

This article presents a statistical framework for estimating the current state of the economy (together with the recent past and near future) in a way in which the latest releases of high-frequency economic data can be incorporated, and in a way in which the impact of the latest release on the forecast can be readily assessed (providing a narrative to the changes in the estimate/forecast over time as more information accrues). It is organized as follows. Section 2 defines the problem of nowcasting in general and relates it to the concept of news in macroeconomic data releases. Section 3 explains the details of the approach. Section 4 discusses related literature. Section 5 illustrates the characteristics of the model via an application to the nowcast of GDP and inflation in the euro area. Section 6 discusses issues for further research, while Section 7 concludes.

Chapter 6

Speech recognition has become common in many application domains. Incorporating acoustic-phonetic knowledge into Automatic Speech Recognition (ASR) systems design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper, a set of six detectors for the above mentioned attributes is designed based on the E-αNet model of neural networks. This model was chosen for its capability to learn hidden activation functions that results in better generalization properties. Experimental set-up and results are presented that show an average 3.5% improvement over a baseline neural network implementation. Speech recognition has become common in many application domains. Incorporating acoustic-phonetic knowledge into Automatic Speech Recognition (ASR) systems design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper, a set of six detectors for the above mentioned attributes is designed based on the E-αNet model of neural networks. This model was chosen for its capability to learn hidden activation functions that results in better generalization properties. Experimental set-up and results are presented that show an average 3.5% improvement over a baseline neural network implementation