Does Semantic Context Benefit Speech Understanding through "Top-Down" Processes? Evidence from Time-resolved Sparse fMRI.

When speech is degraded, word report is higher for semantically coherent sentences (e.g., her new skirt was made of denim) than for anomalous sentences (e.g., her good slope was done in carrot). Such increased intelligibility is often described as resulting from "top-down" processes, reflecting an assumption that higher-level (semantic) neural processes support lower-level (perceptual) mechanisms. We used time-resolved sparse fMRI to test for top-down neural mechanisms, measuring activity while participants heard coherent and anomalous sentences presented in speech envelope/spectrum noise at varying signal-to-noise ratios (SNR). The timing of BOLD responses to more intelligible speech provides evidence of hierarchical organization, with earlier responses in peri-auditory regions of the posterior superior temporal gyrus than in more distant temporal and frontal regions. Despite Sentence content × SNR interactions in the superior temporal gyrus, prefrontal regions respond after auditory/perceptual regions. Although we cannot rule out top-down effects, this pattern is more compatible with a purely feedforward or bottom-up account, in which the results of lower-level perceptual processing are passed to inferior frontal regions. Behavioral and neural evidence that sentence content influences perception of degraded speech does not necessarily imply "top-down" neural processes.

Where is the economics in spatial econometrics?

Spatial econometrics has been criticized by some economists because some model specifications have been driven by data-analytic considerations rather than having a firm foundation in economic theory. In particular this applies to the so-called W matrix, which is integral to the structure of endogenous and exogenous spatial lags, and to spatial error processes, and which are almost the sine qua non of spatial econometrics. Moreover it has been suggested that the significance of a spatially lagged dependent variable involving W may be misleading, since it may be simply picking up the effects of omitted spatially dependent variables, incorrectly suggesting the existence of a spillover mechanism. In this paper we review the theoretical and empirical rationale for network dependence and spatial externalities as embodied in spatially lagged variables, arguing that failing to acknowledge their presence at least leads to biased inference, can be a cause of inconsistent estimation, and leads to an incorrect understanding of true causal processes.

Regional productivity variation and the impact of public capital stock: an analysis with spatial interaction, with reference to Spain

In this paper we examine whether variations in the level of public capital across Spain‟s Provinces affected productivity levels over the period 1996-2005. The analysis is motivated by contemporary urban economics theory, involving a production function for the competitive sector of the economy („industry‟) which includes the level of composite services derived from „service‟ firms under monopolistic competition. The outcome is potentially increasing returns to scale resulting from pecuniary externalities deriving from internal increasing returns in the monopolistic competition sector. We extend the production function by also making (log) labour efficiency a function of (log) total public capital stock and (log) human capital stock, leading to a simple and empirically tractable reduced form linking productivity level to density of employment, human capital and public capital stock. The model is further extended to include technological externalities or spillovers across provinces. Using panel data methodology, we find significant elasticities for total capital stock and for human capital stock, and a significant impact for employment density. The finding that the effect of public capital is significantly different from zero, indicating that it has a direct effect even after controlling for employment density, is contrary to some of the earlier research findings which leave the question of the impact of public capital unresolved.

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to different forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial effects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches.

Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.

Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.

A bayesian spatial individual effects probit model of the 2010 UK general election

The Conservative Party emerged from the 2010 United Kingdom General Election as the largest single party, but their support was not geographically uniform. In this paper, we estimate a hierarchical Bayesian spatial probit model that tests for the presence of regional voting effects. This model allows for the estimation of individual region-specic effects on the probability of Conservative Party success, incorporating information on the spatial relationships between the regions of the mainland United Kingdom. After controlling for a range of important covariates, we find that these spatial relationships are significant and that our individual region-specic effects estimates provide additional evidence of North-South variations in Conservative Party support.

Personal Indebtedness, Spatial Effects and Crime

There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

Personal Indebtedness, Spatial Effects and Crime

There is a long and detailed history of attempts to understand what causes crime. One of the most prominent strands of this literature has sought to better understand the relationship between economic conditions and crime. Following Becker (1968), the economic argument is that in an attempt to maintain consumption in the face of unemployment, people may resort to sources of illicit income. In a similar manner, we might expect ex–ante, that increases in the level of personal indebtedness would be likely to provide similar incentives to engage in criminality. In this paper we seek to understand the spatial pattern of property and theft crimes using a range of socioeconomic variables, including data on the level of personal indebtedness.

Personal indebtedness, community characteristics and theft crimes

Becker (1968) and Stigler (1970) provide the germinal works for an economic analysis of crime, and their approach has been utilised to consider the response of crime rates to a range of economic, criminal and socioeconomic factors. Until recently however this did not extend to a consideration of the role of personal indebtedness in explaining the observed pattern of crime. This paper uses the Becker (1968) and Stigler (1970) framework, and extends to a fuller consideration of the relationship between economic hardship and theft crimes in an urban setting. The increase in personal debt in the past decade has been significant, which combined with the recent global recession, has led to a spike in personal insolvencies. In the context of the recent recession it is important to understand how increases in personal indebtedness may spillover into increases in social problems like crime. This paper uses data available at the neighbourhood level for London, UK on county court judgments (CCJ's) granted against residents in that neighbourhood, this is our measure of personal indebtedness, and examines the relationship between a range of community characteristics (economic, socio-economic, etc), including the number of CCJ's granted against residents, and the observed pattern of theft crimes for three successive years using spatial econometric methods. Our results confirm that theft crimes in London follow a spatial process, that personal indebtedness is positively associated with theft crimes in London, and that the covariates we have chosen are important in explaining the spatial variation in theft crimes. We identify a number of interesting results, for instance that there is variation in the impact of covariates across crime types, and that the covariates which are important in explaining the pattern of each crime type are largely stable across the three periods considered in this analysis.

Spatial Takeoff in the First Industrial Revolution

Using the framework of Desmet and Rossi-Hansberg (forthcoming), we present a model of spatial takeoff that is calibrated using spatially-disaggregated occupational data for England in c.1710. The model predicts changes in the spatial distribution of agricultural and manufacturing employment which match data for c.1817 and 1861. The model also matches a number of aggregate changes that characterise the first industrial revolution. Using counterfactual geographical distributions, we show that the initial concentration of productivity can matter for whether and when an industrial takeoff occurs. Subsidies to innovation in either sector can bring forward the date of takeoff while subsidies to the use of land by manufacturing firms can significantly delay a takeoff because it decreases spatial concentration of activity.

Spatial Takeoff in the First Industrial Revolution

Using the framework of Desmet and Rossi-Hansberg (forthcoming), we present a model of spatial takeoff that is calibrated using spatially-disaggregated occupational data for England in c.1710. The model predicts changes in the spatial distribution of agricultural and manufacturing employment which match data for c.1817 and 1861. The model also matches a number of aggregate changes that characterise the first industrial revolution. Using counterfactual geographical distributions, we show that the initial concentration of productivity can matter for whether and when an industrial takeoff occurs. Subsidies to innovation in either sector can bring forward the date of takeoff while subsidies to the use of land by manufacturing firms can significantly delay a takeoff because it decreases spatial concentration of activity.

Consumer Behaviour in a Social Context: Implications for Environmental Policy

In this paper we summarise some of our recent work on consumer behaviour, drawing on recent developments in behavioural economics, in which consumers are embedded in a social context, so their behaviour is shaped by their interactions with other consumers. For the purpose of this paper we also allow consumption to cause environmental damage. Analysing the social context of consumption naturally lends itself to the use of game theoretic tools, and indicates that we seek to develop links between economics and sociology rather than economics and psychology, which has been the more predominant field for work in behavioural economics. We shall be concerned with three sets of issues: conspicuous consumption, consumption norms and altruistic behaviour. Our aim is to show that building links between sociological and economic approaches to the study of consumer behaviour can lead to significant and surprising implications for conventional economic policy prescriptions, especially with respect to environmental policy.

Testing low-altitude infrared digital photography from a mini-UAV to retrieve information for biological conservation

Satellite remote sensing imagery is used for forestry, conservation and environmental applications, but insufficient spatial resolution, and, in particular, unavailability of images at the precise timing required for a given application, often prevent achieving a fully operational stage. Airborne remote sensing has the advantage of custom-tuned sensors, resolution and timing, but its price prevents using it as a routine technique for the mentioned fields. Some Unmanned Aerial Vehicles might provide a “third way” solution as low-cost techniques for acquiring remotely sensed information, under close control of the end-user, albeit at the expense of lower quality instrumentation and instability. This report evaluates a light remote sensing system based on a remotely-controlled mini-UAV (ATMOS-3) equipped with a color infra-red camera (VEGCAM-1) designed and operated by CATUAV. We conducted a testing mission over a Mediterranean landscape dominated by an evergreen woodland of Aleppo pine (Pinus halepensis) and (Holm) oak (Quercus ilex) in the Montseny National Park (Catalonia, NE Spain). We took advantage of state-of-the-art ortho-rectified digital aerial imagery (acquired by the Institut Cartogràfic de Catalunya over the area during the previous year) and used it as quality reference. In particular, we paid attention to: 1) Operationality of flight and image acquisition according to a previously defined plan; 2) Radiometric and geometric quality of the images; and 3) Operational use of the images in the context of applications. We conclude that the system has achieved an operational stage regarding flight activities, although with meteorological limits set by wind speed and turbulence. Appropriate landing areas can be sometimes limiting also, but the system is able to land on small and relatively rough terrains such as patches of grassland or short matorral, and we have operated the UAV as far as 7 km from the control unit. Radiometric quality is sufficient for interactive analysis, but probably insufficient for automated processing. A forthcoming camera is supposed to greatly improve radiometric quality and consistency. Conventional GPS positioning through time synchronization provides coarse orientation of the images, with no roll information.