Corporate Tax Avoidance and High Powered Incentives

This paper analyzes the links between corporate tax avoidance, the growth of highpowered incentives for managers, and the structure of corporate governance. We develop and test a simple model that highlights the role of complementarities between tax sheltering and managerial diversion in determining how high-powered incentives influence tax sheltering decisions. The model generates the testable hypothesis that firm governance characteristics determine how incentive compensation changes sheltering decisions. In order to test the model, we construct an empirical measure of corporate tax avoidance - the component of the book-tax gap not attributable to accounting accruals - and investigate the link between this measure of tax avoidance and incentive compensation. We find that, for the full sample of firms, increases in incentive compensation tend to reduce the level of tax sheltering, suggesting a complementary relationship between diversion and sheltering. As predicted by the model, the relationship between incentive compensation and tax sheltering is a function of a firm.s corporate governance. Our results may help explain the growing cross-sectional variation among firms in their levels of tax avoidance, the .undersheltering puzzle,. and why large book-tax gaps are associated with subsequent negative abnormal returns.

How health care delay and avoidance decisions are affected by finances and health insurance

Objective. To identify how an individual's finances and health insurance coverage affects their decision whether to avoid or delay medical care. Methods. Secondary data analysis of The Effects of Financial and Insurance Considerations on Health Care Utilization 2007 telephone survey data. Study inclusion criteria. 18 years old, Harris County resident, and had a need for medical care within the past year. Post weighing was done to correct for non-response bias. Results. Survey decision makers were predominately minorities (60%), Female (70%), and insured (71%). Ninety-two percent of participants sought care when needed, however, of this population 39% delayed medical care. Fifty-six percent of participants who delayed medical care sought care in the Doctor's office. For those who replied "Yes" to considering health insurance and finances in deciding to avoid medical care, 61% stated that they were confused about their insurance coverage as the explanation why. Fifty-five percent of Respondents indicated that delaying medical care was due to not knowing whether medical care was necessary. Conclusion. Additional research needs to be conducted to examine the relationship between onset of medical symptoms and final medical diagnosis to identify whether survey participants who delayed or avoided medical care actions were appropriate responses to their initial medical symptoms and final diagnosis. ^

El CIFOT como apoyo a la tarea empresarial : the CIFOT as support to the business task

Empresas como HEXA S.A. desean introducir SIG como elemento para incrementar el valor agregado de los proyectos de consultoría que realizan. Tal es el caso del trabajo encomendado por ellos al CIFOT titulado Esquema hidromorfológico de la cuenca del río Tunuyán Superior y clasificación de Usos del Suelo del Oasis Centro Oeste; Provincia de Mendoza. Se unifica en un único modelo digital una serie de cartas topográficas (IGM) digitalizan diversos niveles de información (curvas de nivel, hidrografía de la cuenca del río Tunuyán), red de caminos y su jerarquía. A partir de la base de datos lograda, se generan mapas temáticos de cada cuenca, apoyados con imágenes satelitales Landsat TM, que permiten identificar cobertura nivo- glacial y usos del suelo en el Oasis. Con esta información la empresa obtiene un modelo de simulación del Río Tunuyán para predecir el caudal que conduciría el río ante determinadas nevadas, por ejemplo. Se propone además un modelo de SIG para el monitoreo de la zona.

Contextual classification of full waveform lidar data in the Wadden Sea

The classification of airborne lidar data is a relevant task in different disciplines. The information about the geometry and the full waveform can be used in order to classify the 3D point cloud. In Wadden Sea areas the classification of lidar data is of main interest for the scientific monitoring of coastal morphology and habitats, but it becomes a challenging task due to flat areas with hardly any discriminative objects. For the classification we combine a Conditional Random Fields framework with a Random Forests approach. By classifying in this way, we benefit from the consideration of context on the one hand and from the opportunity to utilise a high number of classification features on the other hand. We investigate the relevance of different features for the lidar points in coastal areas as well as for the interaction of neighbouring points.

9-28 d of exposure to elevated pCO2 reduces avoidance of predator odour but had no effect on behavioural lateralization or swimming activity in a temperate wrasse(Ctenolabrus rupestris)

Most studies on the impact of near-future levels of carbon dioxide on fish behaviour report behavioural alterations, wherefore abnormal behaviour has been suggested to be a potential consequence of future ocean acidification and therefore a threat to ocean ecosystems. However, an increasing number of studies show tolerance of fish to increased levels of carbon dioxide. This variation among studies in susceptibility highlights the importance of continued investigation of the possible effects of elevated pCO2. Here, we investigated the impacts of increased levels of carbon dioxide on behaviour using the goldsinny wrasse (Ctenolabrus rupestris), which is a common species in European coastal waters and widely used as cleaner fish to control sea lice infestation in commercial fish farming in Europe. The wrasses were exposed to control water conditions (370 µatm) or elevated pCO2 (995 µatm) for 1 month, during which time behavioural trials were performed. We investigated the possible effects of CO2 on behavioural lateralization, swimming activity, and prey and predator olfactory preferences, all behaviours where disturbances have previously been reported in other fish species after exposure to elevated CO2. Interestingly, we failed to detect effects of carbon dioxide for most behaviours investigated, excluding predator olfactory cue avoidance, where control fish initially avoided predator cue while the high CO2 group was indifferent. The present study therefore shows behavioural tolerance to increased levels of carbon dioxide in the goldsinny wrasse. We also highlight that individual fish can show disturbance in specific behaviours while being apparently unaffected by elevated pCO2 in other behavioural tests. However, using experiments with exposure times measured in weeks to predict possible effects of long-term drivers, such as ocean acidification, has limitations, and the behavioural effects from elevated pCO2 in this experiment cannot be viewed as proof that these fish would show the same reaction after decades of evolution.

Perseveration in a guessing task by laying hens selected for high or low levels of feather pecking does not support classification of feather pecking as a stereotypy.

Feather pecking is a behaviour by which birds damage or destroy the feathers of themselves (self-pecking) or other birds (allo feather pecking), in some cases even plucking out feathers and eating these. The self-pecking is rarely seen in domestic laying hens but is not uncommon in parrots. Feather pecking in laying hens has been described as being stereotypic, i.e. a repetitive invariant motor pattern without an obvious function, and indeed the amount of self-pecking in parrots was found to correlate positively with the amount of recurrent perseveration (RP), the tendency to repeat responses inappropriately, which in humans and other animals was found to correlate with stereotypic behaviour. In the present experiment we set out to investigate the correlation between allo feather pecking and RP in laying hens. We used birds (N = 92) from the 10th and 11th generation (G10 and G11) of lines selectively bred for high feather pecking (HFP) and low feather pecking (LFP), and from an unselected control line (CON) with intermediate levels of feather pecking. We hypothesised that levels of RP would be higher, and the time taken (standardised latency) to repeat a response lower, in HFP compared to LFP hens, with CON hens in between. Using a two-choice guessing task, we found that lines differed significantly in their levels of RP, with HFP unexpectedly showing lower levels of RP than CON and LFP. Latency to make a repeat did not differ between lines. Latency to make a switch differed between lines with a shorter latency in HFP compared to LFP (in G10), or CON (in G11). Latency to peck for repeats vs. latency to peck for switches did not differ between lines. Total time to complete the test was significantly shorter in HFP compared to CON and LFP. Thus, our hypotheses were not supported by the data. In contrast, selection for feather pecking seems to induce the opposite effects than would be expected from stereotyping animals: pecking was less sequenced and reaction to make a switch and to complete the test was lower in HFP. This supports the hyperactivity-model of feather pecking, suggesting that feather pecking is related to a higher general activity, possibly due to changes in the dopaminergic system.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Cooperative Task Execution between Modular Robots Based on Tight-Loose Cooperation Strategies

The complexity in the execution of cooperative tasks is high due to the fact that a robot team requires movement coordination at the beginning of the mission and continuous coordination during the execution of the task. A variety of techniques have been proposed to give a solution to this problem assuming standard mobile robots. This work focuses on presenting the execution of a cooperative task by a modular robot team. The complexity of the task execution increases due to the fact that each robot is composed of modules which have to be coordinated in a proper way to successfully work. A combined tight and loose cooperation strategy is presented and a bar-pushing example is used as a cooperative task to show the performance of this type of system.

DAEDALUS at ImageCLEF 2011 Plant Identification Task: Using SIFT Keypoints for Object Detection

This paper describes the participation of DAEDALUS at ImageCLEF 2011 Plant Identification task. The task is evaluated as a supervised classification problem over 71 tree species from the French Mediterranean area used as class labels, based on visual content from scan, scan-like and natural photo images. Our approach to this task is to build a classifier based on the detection of keypoints from the images extracted using Lowe’s Scale Invariant Feature Transform (SIFT) algorithm. Although our overall classification score is very low as compared to other participant groups, the main conclusion that can be drawn is that SIFT keypoints seem to work significantly better for photos than for the other image types, so our approach may be a feasible strategy for the classification of this kind of visual content.

UPM system for the translation task

This paper describes the UPM system for translation task at the EMNLP 2011 workshop on statistical machine translation (http://www.statmt.org/wmt11/), and it has been used for both directions: Spanish-English and English-Spanish. This system is based on Moses with two new modules for pre and post processing the sentences. The main contribution is the method proposed (based on the similarity with the source language test set) for selecting the sentences for training the models and adjusting the weights. With system, we have obtained a 23.2 BLEU for Spanish-English and 21.7 BLEU for EnglishSpanish

Task granularity analysis in logic programs

While logic programming languages offer a great deal of scope for parallelism, there is usually some overhead associated with the execution of goals in parallel because of the work involved in task creation and scheduling. In practice, therefore, the "granularity" of a goal, i.e. an estimate of the work available under it, should be taken into account when deciding whether or not to execute a goal concurrently as a sepárate task. This paper describes a method for estimating the granularity of a goal at compile time. The runtime overhead associated with our approach is usually quite small, and the performance improvements resulting from the incorporation of grainsize control can be quite good. This is shown by means of experimental results.

Dysfunctional and compensatory duality in mild cognitive impairment during a continuous recognition memory task

One of the current issues of debate in the study of mild cognitive impairment (MCI) is deviations of oscillatory brain responses from normal brain states and its dynamics. This work aims to characterize the differences of power in brain oscillations during the execution of a recognition memory task in MCI subjects in comparison with elderly controls. Magnetoencephalographic (MEG) signals were recorded during a continuous recognition memory task performance. Oscillatory brain activity during the recognition phase of the task was analyzed by wavelet transform in the source space by means of minimum norm algorithm. Both groups obtained a 77% hit ratio. In comparison with healthy controls, MCI subjects showed increased theta (p < 0.001), lower beta reduction (p < 0.001) and decreased alpha and gamma power (p < 0.002 and p < 0.001 respectively) in frontal, temporal and parietal areas during early and late latencies. Our results point towards a dual pattern of activity (increase and decrease) which is indicative of MCI and specific to certain time windows, frequency bands and brain regions. These results could represent two neurophysiological sides of MCI. Characterizing these opposing processes may contribute to the understanding of the disorder.

Handmade Task Tracking Applied to Cognitive Rehabilitation

This article presents research focused on tracking manual tasks that are applied in cognitive rehabilitation so as to analyze the movements of patients who suffer from Apraxia and Action Disorganization Syndrome (AADS). This kind of patients find executing Activities of Daily Living (ADL) too difficult due to the loss of memory and capacity to carry out sequential tasks or the impossibility of associating different objects with their functions. This contribution is developed from the work of Universidad Politécnica de Madrid and Technical University of Munich in collaboration with The University of Birmingham. The KinectTM for Windows© device is used for this purpose. The data collected is compared to an ultrasonic motion capture system. The results indicate a moderate to strong correlation between signals. They also verify that KinectTM is very suitable and inexpensive. Moreover, it turns out to be a motion-capture system quite easy to implement for kinematics analysis in ADL.