Taxing Sweets: Sweetener Input Tax or Final Consumption Tax?

In order to reduce obesity and associated costs, policymakers are considering various policies, including taxes, to change consumers high-calorie consumption habits. We investigate two tax policies aimed at reducing added sweetener consumption. Both a consumption tax on sweet goods and a sweetener input tax can reach the same policy target of reducing added sweetener consumption. Both tax instruments are regressive, but the associated surplus losses are limited. The tax on sweetener inputs targets sweeteners directly and causes about five times less surplus loss than the final consumption tax. Previous analyses have overlooked this important point.

In vivo reprogramming of circuit connectivity in postmitotic neocortical neurons.

The molecular mechanisms that control how progenitors generate distinct subtypes of neurons, and how undifferentiated neurons acquire their specific identity during corticogenesis, are increasingly understood. However, whether postmitotic neurons can change their identity at late stages of differentiation remains unknown. To study this question, we developed an electrochemical in vivo gene delivery method to rapidly manipulate gene expression specifically in postmitotic neurons. Using this approach, we found that the molecular identity, morphology, physiology and functional input-output connectivity of layer 4 mouse spiny neurons could be specifically reprogrammed during the first postnatal week by ectopic expression of the layer 5B output neuron-specific transcription factor Fezf2. These findings reveal a high degree of plasticity in the identity of postmitotic neocortical neurons and provide a proof of principle for postnatal re-engineering of specific neural microcircuits in vivo.

Grade and Cross Slope Estimation from LIDAR-based Surface Models, 2003

Many transportation agencies maintain grade as an attribute in roadway inventory databases; however, the information is often in an aggregated format. Cross slope is rarely included in large roadway inventories. Accurate methods available to collect grade and cross slope include global positioning systems, traditional surveying, and mobile mapping systems. However, most agencies do not have the resources to utilize these methods to collect grade and cross slope on a large scale. This report discusses the use of LIDAR to extract roadway grade and cross slope for large-scale inventories. Current data collection methods and their advantages and disadvantages are discussed. A pilot study to extract grade and cross slope from a LIDAR data set, including methodology, results, and conclusions, is presented. This report describes the regression methodology used to extract and evaluate the accuracy of grade and cross slope from three dimensional surfaces created from LIDAR data. The use of LIDAR data to extract grade and cross slope on tangent highway segments was evaluated and compared against grade and cross slope collected using an automatic level for 10 test segments along Iowa Highway 1. Grade and cross slope were measured from a surface model created from LIDAR data points collected for the study area. While grade could be estimated to within 1%, study results indicate that cross slope cannot practically be estimated using a LIDAR derived surface model.

Auguste Ott on Commercial Crises and Distributive Justice: An Early Input-Output Scheme

In 1851 the French Social economist Auguste Ott discussed the problem of gluts and commercial crises, together with the issue of distributive justice between workers in co-operative societies. He did so by means of a 'simple reproduction scheme' sharing some features with modern intersectoral transactions tables, in particular in terms of their graphical representation. This paper presents Ott's theory of crises (which was based on the disappointment of expectations) and the context of his model, and discusses its peculiarities, supplying a new piece for the reconstruction of the prehistory of input-output analysis.

Análisis de un input de las clases de economía pública: El nivel de cultura fiscal de los alumnos

El objetivo de este artículo es doble. Por un lado, cuantificar el nivel de cultura fiscal de los alumnos de Administración y Dirección de Empresas y de Economía antes de empezar a cursar asignaturas específicas de economía del sector público y de fiscalidad. Y, por otro lado, analizar los posibles factores determinantes de dicho nivel de cultura fiscal. Al tratarse de alumnos de segundo ciclo, éstos ya deberían conocer el funcionamiento de una economía de mercado y el papel que juega el sector público, lo que les debería comportar un mayor interés y una mayor motivación. La idea surgió del convencimiento de los profesores de que saber cuál es el nivel de conocimiento previo sobre cuestiones fiscales que tienen los estudiantes que van a cursar asignaturas de contenido fiscal es un input importante a considerar en el planteamiento de la docencia, puesto que permite mejorar el funcionamiento del curso, motivar el estudio de las asignaturas y mejorar los resultados.

Análisis de un input de las clases de economía pública: El nivel de cultura fiscal de los alumnos

El objetivo de este artículo es doble. Por un lado, cuantificar el nivel de cultura fiscal de los alumnos de Administración y Dirección de Empresas y de Economía antes de empezar a cursar asignaturas específicas de economía del sector público y de fiscalidad. Y, por otro lado, analizar los posibles factores determinantes de dicho nivel de cultura fiscal. Al tratarse de alumnos de segundo ciclo, éstos ya deberían conocer el funcionamiento de una economía de mercado y el papel que juega el sector público, lo que les debería comportar un mayor interés y una mayor motivación. La idea surgió del convencimiento de los profesores de que saber cuál es el nivel de conocimiento previo sobre cuestiones fiscales que tienen los estudiantes que van a cursar asignaturas de contenido fiscal es un input importante a considerar en el planteamiento de la docencia, puesto que permite mejorar el funcionamiento del curso, motivar el estudio de las asignaturas y mejorar los resultados.

Estimation of forest canopy cover : a comparison of field measurement techniques

Abstract

Effects of computing parameters and measurement locations on the estimation of 3D NPS in non-stationary MDCT images.

The goal of this study was to investigate the impact of computing parameters and the location of volumes of interest (VOI) on the calculation of 3D noise power spectrum (NPS) in order to determine an optimal set of computing parameters and propose a robust method for evaluating the noise properties of imaging systems. Noise stationarity in noise volumes acquired with a water phantom on a 128-MDCT and a 320-MDCT scanner were analyzed in the spatial domain in order to define locally stationary VOIs. The influence of the computing parameters in the 3D NPS measurement: the sampling distances bx,y,z and the VOI lengths Lx,y,z, the number of VOIs NVOI and the structured noise were investigated to minimize measurement errors. The effect of the VOI locations on the NPS was also investigated. Results showed that the noise (standard deviation) varies more in the r-direction (phantom radius) than z-direction plane. A 25 × 25 × 40 mm(3) VOI associated with DFOV = 200 mm (Lx,y,z = 64, bx,y = 0.391 mm with 512 × 512 matrix) and a first-order detrending method to reduce structured noise led to an accurate NPS estimation. NPS estimated from off centered small VOIs had a directional dependency contrary to NPS obtained from large VOIs located in the center of the volume or from small VOIs located on a concentric circle. This showed that the VOI size and location play a major role in the determination of NPS when images are not stationary. This study emphasizes the need for consistent measurement methods to assess and compare image quality in CT.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Correlations between Vocal Input and Visual Response Apparently Enhance Presence in a Virtual Environment

This work investigates novel alternative means of interaction in a virtual environment (VE).We analyze whether humans can remap established body functions to learn to interact with digital information in an environment that is cross-sensory by nature and uses vocal utterances in order to influence (abstract) virtual objects. We thus establish a correlation among learning, control of the interface, and the perceived sense of presence in the VE. The application enables intuitive interaction by mapping actions (the prosodic aspects of the human voice) to a certain response (i.e., visualization). A series of single-user and multiuser studies shows that users can gain control of the intuitive interface and learn to adapt to new and previously unseen tasks in VEs. Despite the abstract nature of the presented environment, presence scores were generally very high.

Soybean crop area estimation by Modis/Evi data

The objective of this work was to develop a procedure to estimate soybean crop areas in Rio Grande do Sul state, Brazil. Estimations were made based on the temporal profiles of the enhanced vegetation index (Evi) calculated from moderate resolution imaging spectroradiometer (Modis) images. The methodology developed for soybean classification was named Modis crop detection algorithm (MCDA). The MCDA provides soybean area estimates in December (first forecast), using images from the sowing period, and March (second forecast), using images from the sowing and maximum crop development periods. The results obtained by the MCDA were compared with the official estimates on soybean area of the Instituto Brasileiro de Geografia e Estatística. The coefficients of determination ranged from 0.91 to 0.95, indicating good agreement between the estimates. For the 2000/2001 crop year, the MCDA soybean crop map was evaluated using a soybean crop map derived from Landsat images, and the overall map accuracy was approximately 82%, with similar commission and omission errors. The MCDA was able to estimate soybean crop areas in Rio Grande do Sul State and to generate an annual thematic map with the geographic position of the soybean fields. The soybean crop area estimates by the MCDA are in good agreement with the official agricultural statistics.

Stratified estimation of forest inventory variables using spatially summarized stratifications

Abstract