Tourism agglomeration and its impact on social welfare: an empirical approach to the Spanish case.

This paper measures two descriptors of tourism namely, its scale and agglomeration level and subsequently evaluates both descriptors according to their direct and joint impacts on the host communities' quality of life. The key constructs for this research are the following: (1) a tourism evaluation function that incorporates the scale and agglomeration of tourism, which is constructed for each one of the 50 Spanish provinces; and (2) a measure of the host communities' quality of life that comprises 12 objective partial indicators and an overall indicator that integrates them all. Results show the existence of carrying capacity frontiers or maximum thresholds that tourist destinations can sustain without damaging the economic, socio cultural, or environmental systems of the communities they belong.Communities; Sustainable tourism; Carrying capacity; Spain;

Hotel Location in Tourism Cities: Madrid 1936-1998.

To determine how the positioning of new hotels is affected by the distribution of similar incumbent competitors, this paper investigates geographic location, price, size, and services. With data on all 240 hotels operating in the city of Madrid between 1936 and 1998, a model of geographic and product location at the time of the hotels’ foundings is estimated based on the above mentioned variables. These are simultaneously determined and contingent upon the changing socioeconomic and urban circumstances of the city. The findings suggest that agglomeration occurs only among differentiated establishments. In the balance between agglomeration and differentiation strategies, particularly significant is the trade-off between price and geographic dimensions.Emplacement des hôtels dans les villes touristiques: Madrid 1936–1998. Pour déterminer comment le positionnement des nouveaux hôtels est affecté par la distribution des concurrents similaires et déjà établis, cet article examine situation géographique, prix, grandeur et services. Avec des données sur tous les 240 hôtels en opération à Madrid entre 1936 et 1998, on calcule un modèle de la situation géographique et des services au moment de la fondation des hôtels, en se basant sur les variables surmentionnées. Celles-ci dépendent au même temps des circonstances urbaines et socioéconomiques changeantes de la ville. Les résultats suggèrent que l’agglomération a lieu seulement parmi les établissements différenciés. Dans l’équilibre entre les stratégies d’agglomération et de différentiation, le compromis entre prix et situation est particulièrement significatif.Hotels; Location; Madrid; Hotels; Situation;

PIXOR: Real-time 3D Object Detection from Point Clouds

We address the problem of real-time 3D object detection from point clouds in the context of autonomous driving. Computation speed is critical as detection is a necessary component for safety. Existing approaches are, however, expensive in computation due to high dimensionality of point clouds. We utilize the 3D data more efficiently by representing the scene from the Bird's Eye View (BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions. The input representation, network architecture, and model optimization are especially designed to balance high accuracy and real-time efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets we show that the proposed detector surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at >28 FPS.Comment: Update of CVPR2018 paper: correct timing, fix typos, add acknowledgemen

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding

segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection

In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection. We frame the problem as inference in a Markov Random Field, in which each detection hypothesis scores object appearance as well as contextual information using Convolutional Neural Networks, and allows the hypothesis to choose and score a segment out of a large pool of accurate object segmentation proposals. This enables the detector to incorporate additional evidence when it is available and thus results in more accurate detections. Our experiments show an improvement of 4.1% in mAP over the R-CNN baseline on PASCAL VOC 2010, and 3.4% over the current state-of-the-art, demonstrating the power of our approach