Does the Shape of a Territory Influence the Locations of Human Activities? a Numerical Geography Approach

This paper aims at showing how far the shape of a studied area influences the results of optimal location-allocation models. Simulations are performed on rectangular toy-networks with an equal number of vertices but with different length/width ratios. The case of merging two such networks into a common market is also considered. We limit our experience to the Simple Plant Location Problem (SPLP) which captures the fundamental trade-off of economic geography between accessibility and economies-of-scales. Results are analysed in terms of locations, allocations and costs. The results help at understanding how far an area (country/region) has larger development problems than others just because of its shape and/or of the way this area is linked within a common market (elongation of the country and length of the common border). Several real world examples are discussed when interpreting of the results.

Transportation networks and the location of human activities

The impact of transportation networks on the location of human activities is a surprisingly neglected topic in economic geography. Using the simple plant location problem, this paper investigates such an impact in the case of a few idealized networks. It is seen that a grid network tends to foster a dispersed pattern of activities, while the center of a radial network acts as an attractor. The case of two economies characterized by different network configurations that form a custom union is then analyzed. It is shown that the structural properties of the networks still hold, though some locations are pulled toward the common border. This suggests that no much relocation should be expected within the European Union if the state members endorse similar fiscal and social policies after the formation of the single market.

Neighbourhood effects and endogeneity issues

A recent body of research suggests that the spatial structure of cities might influence the socioeconomic characteristics and outcomes of their residents. In particular, the literature on neighbourhood effects emphasizes the potential influence of the socioeconomic composition of neighbourhoods in shaping individual’s behaviours and outcomes, through social networks, peer influences or socialization effects. However, empirical work still has not reached a consensus regarding the existence and magnitude of such effects. This is mainly because the study of neighbourhood effects raises important methodological concerns that have not often been taken into account. Notably, as individuals with similar socio-economic characteristics tend to sort themselves into certain parts of the city, the estimation of neighbourhood effects raises the issue of location choice endogeneity. Indeed, it is difficult to distinguish between neighbourhood effects and correlated effects, i.e. similarities in behaviours and outcomes arising from individuals having similar characteristics. This problem, if not dequately corrected for, may yield biased results. In the first part of this paper, neighbourhood effects are defined and some methodological problems involved in measuring such effects are identified. Particular attention is paid to the endogeneity issue, giving a formal definition of the problem and reviewing the main methods that have been used in the literature to try to solve it. The second part is devoted to an empirical illustration of the study of neighbourhood effects, in the case of labour-market outcomes of young adults in Brussels. The effect of living in a deprived neighbourhood on the unemployment probability of young adults residing in Brussels is estimated using logistic regressions. The endogeneity of neighbourhood is addressed by restricting the sample to young adults residing with their parents. Then, a ensitivity analysis is used to assess the robustness of the results to the presence of both observed and unobserved parental covariates.neighbourhood effects, endogeneity, self-selection, sensitivity analysis, Brussels

Coherent backscattering in nonlinear atomic media: quantum Langevin approach

In this theoretical paper, we investigate coherence properties of the near-resonant light scattered by two atoms exposed to a strong monochromatic field. To properly incorporate saturation effects, we use a quantum Langevin approach. In contrast to the standard optical Bloch equations, this method naturally provides the inelastic spectrum of the radiated light induced by the quantum electromagnetic vacuum fluctuations. However, to get the right spectral properties of the scattered light, it is essential to correctly describe the statistical properties of these vacuum fluctuations. Because of the presence of the two atoms, these statistical properties are not Gaussian : (i) the spatial two-points correlation function displays a speckle-like behavior and (ii) the three-points correlation function does not vanish. We also explain how to incorporate in a simple way propagation with a frequency-dependent scattering mean-free path, meaning that the two atoms are embedded in an average scattering dispersive medium. Finally we show that saturation-induced nonlinearities strongly modify the atomic scattering properties and, as a consequence, provide a source of decoherence in multiple scattering. This is exemplified by considering the coherent backscattering configuration where interference effects are blurred by this decoherence mechanism. This leads to a decrease of the so-called coherent backscattering enhancement factor.Comment: 19 pages, 1 figur

Needlet algorithms for estimation in inverse problems

We provide a new algorithm for the treatment of inverse problems which combines the traditional SVD inversion with an appropriate thresholding technique in a well chosen new basis. Our goal is to devise an inversion procedure which has the advantages of localization and multiscale analysis of wavelet representations without losing the stability and computability of the SVD decompositions. To this end we utilize the construction of localized frames (termed "needlets") built upon the SVD bases. We consider two different situations: the "wavelet" scenario, where the needlets are assumed to behave similarly to true wavelets, and the "Jacobi-type" scenario, where we assume that the properties of the frame truly depend on the SVD basis at hand (hence on the operator). To illustrate each situation, we apply the estimation algorithm respectively to the deconvolution problem and to the Wicksell problem. In the latter case, where the SVD basis is a Jacobi polynomial basis, we show that our scheme is capable of achieving rates of convergence which are optimal in the $L_2$ case, we obtain interesting rates of convergence for other $L_p$ norms which are new (to the best of our knowledge) in the literature, and we also give a simulation study showing that the NEED-D estimator outperforms other standard algorithms in almost all situations.Comment: Published at http://dx.doi.org/10.1214/07-EJS014 in the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving

In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used to model the cognitive state of subjects, and to explore the integration of multiple sensor modalities to improve the reliability of detection of human displays of awareness and emotion. We observed chess players engaged in problems of increasing difficulty while recording their behavior. Such recordings can be used to estimate a participant's awareness of the current situation and to predict ability to respond effectively to challenging situations. Results show that a multimodal approach is more accurate than a unimodal one. By combining body posture, visual attention and emotion, the multimodal approach can reach up to 93% of accuracy when determining player's chess expertise while unimodal approach reaches 86%. Finally this experiment validates the use of our equipment as a general and reproducible tool for the study of participants engaged in screen-based interaction and/or problem solving

Deep learning investigation for chess player attention prediction using eye-tracking and game data

This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the probability fixation for individual pixels Using a skip-layer architecture of an autoencoder, with a unified decoder, we are able to use multiscale features to predict saliency of part of the board at different scales, showing multiple relations between pieces. We have used scan path and fixation data from players engaged in solving chess problems, to compute 6600 saliency maps associated to the corresponding chess piece configurations. This corpus is completed with synthetically generated data from actual games gathered from an online chess platform. Experiments realized using both scan-paths from chess players and the CAT2000 saliency dataset of natural images, highlights several results. Deep features, pretrained on natural images, were found to be helpful in training visual attention prediction for chess. The proposed neural network architecture is able to generate meaningful saliency maps on unseen chess configurations with good scores on standard metrics. This work provides a baseline for future work on visual attention prediction in similar contexts

Preparation for Bias as a Buffer Against the Effect of Racial Discrimination on Academic Attitudes of African American College Students

Racial inequalities in the education system are an issue that has yet to be adequately addressed. Given how discriminatory experiences adversely impact African American students, it is important to understand how their educational attitudes are impacted and ways that students can be protected from these harmful experiences. The study aims to answer six research questions: 1) How does racial discrimination predict African American college students’ value placed in education? 2) How does racial discrimination predict African American college students’ expectations for success? 3) How do preparation for bias messages predict the value they place in education? 4) How do preparation for bias messages predict African American college students’ expectations for success? 5) Do preparation for bias messages buffer the effect of racial discrimination on value placed in education? 6) Do preparation for bias messages buffer the effect of racial discrimination on expectations for success