Aggregation and errors in management accounting

Il est important pour les entreprises de compresser les informations détaillées dans des sets d'information plus compréhensibles. Au chapitre 1, je résume et structure la littérature sur le sujet « agrégation d'informations » en contrôle de gestion. Je récapitule l'analyse coûts-bénéfices que les comptables internes doivent considérer quand ils décident des niveaux optimaux d'agrégation d'informations. Au-delà de la perspective fondamentale du contenu d'information, les entreprises doivent aussi prendre en considération des perspectives cogni- tives et comportementales. Je développe ces aspects en faisant la part entre la comptabilité analytique, les budgets et plans, et la mesure de la performance. Au chapitre 2, je focalise sur un biais spécifique qui se crée lorsque les informations incertaines sont agrégées. Pour les budgets et plans, des entreprises doivent estimer les espérances des coûts et des durées des projets, car l'espérance est la seule mesure de tendance centrale qui est linéaire. A la différence de l'espérance, des mesures comme le mode ou la médiane ne peuvent pas être simplement additionnés. En considérant la forme spécifique de distributions des coûts et des durées, l'addition des modes ou des médianes résultera en une sous-estimation. Par le biais de deux expériences, je remarque que les participants tendent à estimer le mode au lieu de l'espérance résultant en une distorsion énorme de l'estimati¬on des coûts et des durées des projets. Je présente également une stratégie afin d'atténuer partiellement ce biais. Au chapitre 3, j'effectue une étude expérimentale pour comparer deux approches d'esti¬mation du temps qui sont utilisées en comptabilité analytique, spécifiquement « coûts basés sur les activités (ABC) traditionnelles » et « time driven ABC » (TD-ABC). Au contraire des affirmations soutenues par les défenseurs de l'approche TD-ABC, je constate que cette dernière n'est pas nécessairement appropriée pour les calculs de capacité. Par contre, je démontre que le TD-ABC est plus approprié pour les allocations de coûts que l'approche ABC traditionnelle. - It is essential for organizations to compress detailed sets of information into more comprehensi¬ve sets, thereby, establishing sharp data compression and good decision-making. In chapter 1, I review and structure the literature on information aggregation in management accounting research. I outline the cost-benefit trade-off that management accountants need to consider when they decide on the optimal levels of information aggregation. Beyond the fundamental information content perspective, organizations also have to account for cognitive and behavi¬oral perspectives. I elaborate on these aspects differentiating between research in cost accounti¬ng, budgeting and planning, and performance measurement. In chapter 2, I focus on a specific bias that arises when probabilistic information is aggregated. In budgeting and planning, for example, organizations need to estimate mean costs and durations of projects, as the mean is the only measure of central tendency that is linear. Different from the mean, measures such as the mode or median cannot simply be added up. Given the specific shape of cost and duration distributions, estimating mode or median values will result in underestimations of total project costs and durations. In two experiments, I find that participants tend to estimate mode values rather than mean values resulting in large distortions of estimates for total project costs and durations. I also provide a strategy that partly mitigates this bias. In the third chapter, I conduct an experimental study to compare two approaches to time estimation for cost accounting, i.e., traditional activity-based costing (ABC) and time-driven ABC (TD-ABC). Contrary to claims made by proponents of TD-ABC, I find that TD-ABC is not necessarily suitable for capacity computations. However, I also provide evidence that TD-ABC seems better suitable for cost allocations than traditional ABC.

Crescent and star shapes of members of the Chlamydiales order: impact of fixative methods.

Members of the Chlamydiales order all share a biphasic lifecycle alternating between small infectious particles, the elementary bodies (EBs) and larger intracellular forms able to replicate, the reticulate bodies. Whereas the classical Chlamydia usually harbours round-shaped EBs, some members of the Chlamydia-related families display crescent and star-shaped morphologies by electron microscopy. To determine the impact of fixative methods on the shape of the bacterial cells, different buffer and fixative combinations were tested on purified EBs of Criblamydia sequanensis, Estrella lausannensis, Parachlamydia acanthamoebae, and Waddlia chondrophila. A linear discriminant analysis was performed on particle metrics extracted from electron microscopy images to recognize crescent, round, star and intermediary forms. Depending on the buffer and fixatives used, a mixture of alternative shapes were observed in varying proportions with stars and crescents being more frequent in C. sequanensis and P. acanthamoebae, respectively. No tested buffer and chemical fixative preserved ideally the round shape of a majority of bacteria and other methods such as deep-freezing and cryofixation should be applied. Although crescent and star shapes could represent a fixation artifact, they certainly point towards a diverse composition and organization of membrane proteins or intracellular structures rather than being a distinct developmental stage.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Identifying the best body mass index metric to assess adiposity change in children.

OBJECTIVE: Although dual-energy X-ray absorptiometry (DEXA) is the preferred method to estimate adiposity, body mass index (BMI) is often used as a proxy. However, the ability of BMI to measure adiposity change among youth is poorly evidenced. This study explored which metrics of BMI change have the highest correlations with different metrics of DEXA change. METHODS: Data were from the Quebec Adipose and Lifestyle Investigation in Youth cohort, a prospective cohort of children (8-10 years at recruitment) from Québec, Canada (n=557). Height and weight were measured by trained nurses at baseline (2008) and follow-up (2010). Metrics of BMI change were raw (ΔBMIkg/m(2) ), adjusted for median BMI (ΔBMIpercentage) and age-sex-adjusted with the Centers for Disease Control and Prevention growth curves expressed as centiles (ΔBMIcentile) or z-scores (ΔBMIz-score). Metrics of DEXA change were raw (total fat mass; ΔFMkg), per cent (ΔFMpercentage), height-adjusted (fat mass index; ΔFMI) and age-sex-adjusted z-scores (ΔFMz-score). Spearman's rank correlations were derived. RESULTS: Correlations ranged from modest (0.60) to strong (0.86). ΔFMkg correlated most highly with ΔBMIkg/m(2) (r = 0.86), ΔFMI with ΔBMIkg/m(2) and ΔBMIpercentage (r = 0.83-0.84), ΔFMz-score with ΔBMIz-score (r = 0.78), and ΔFMpercentage with ΔBMIpercentage (r = 0.68). Correlations with ΔBMIcentile were consistently among the lowest. CONCLUSIONS: In 8-10-year-old children, absolute or per cent change in BMI is a good proxy for change in fat mass or FMI, and BMI z-score change is a good proxy for FM z-score change. However change in BMI centile and change in per cent fat mass perform less well and are not recommended.

Option valuation as an expectation in the complex domain: The Black-Scholes case

It is very well known that the first succesful valuation of a stock option was done by solving a deterministic partial differential equation (PDE) of the parabolic type with some complementary conditions specific for the option. In this approach, the randomness in the option value process is eliminated through a no-arbitrage argument. An alternative approach is to construct a replicating portfolio for the option. From this viewpoint the payoff function for the option is a random process which, under a new probabilistic measure, turns out to be of a special type, a martingale. Accordingly, the value of the replicating portfolio (equivalently, of the option) is calculated as an expectation, with respect to this new measure, of the discounted value of the payoff function. Since the expectation is, by definition, an integral, its calculation can be made simpler by resorting to powerful methods already available in the theory of analytic functions. In this paper we use precisely two of those techniques to find the well-known value of a European call

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.

Diffusion tensor imaging shows structural remodeling of stroke mirror region: results from a pilot study.

Background: The role of the non-injured hemisphere in stroke recovery is poorly understood. In this pilot study, we sought to explore the presence of structural changes detectable by diffusion tensor imaging (DTI) in the contralesional hemispheres of patients who recovered well from ischemic stroke. Methods: We analyzed serial DTI data from 16 stroke patients who had moderate initial neurological deficits (NIHSS scores 3-12) and good functional outcome at 3-6 months (NIHSS score 0 or modified Rankin Score ≤1). We segmented the brain tissue in gray and white matter (GM and WM) and measured the apparent diffusion coefficient (ADC) and fractional anisotropy in the infarct, in the contralesional infarct mirror region as well as in concentrically expanding regions around them. Results: We found that GM and WM ADC significantly increased in the infarct region (p < 0.01) from acute to chronic time points, whereas in the infarct mirror region, GM and WM ADC increased (p < 0.01) and WM fractional anisotropy decreased (p < 0.05). No significant changes were detected in other regions. Conclusion: DTI-based metrics are sensitive to regional structural changes in the contralesional hemisphere during stroke recovery. Prospective studies in larger cohorts with varying levels of recovery are needed to confirm our findings.

E-learning initiatives in forensic interpretation: report on experiences from current projects and outlook

This paper reports on the purpose, design, methodology and target audience of E-learning courses in forensic interpretation offered by the authors since 2010, including practical experiences made throughout the implementation period of this project. This initiative was motivated by the fact that reporting results of forensic examinations in a logically correct and scientifically rigorous way is a daily challenge for any forensic practitioner. Indeed, interpretation of raw data and communication of findings in both written and oral statements are topics where knowledge and applied skills are needed. Although most forensic scientists hold educational records in traditional sciences, only few actually followed full courses that focussed on interpretation issues. Such courses should include foundational principles and methodology - including elements of forensic statistics - for the evaluation of forensic data in a way that is tailored to meet the needs of the criminal justice system. In order to help bridge this gap, the authors' initiative seeks to offer educational opportunities that allow practitioners to acquire knowledge and competence in the current approaches to the evaluation and interpretation of forensic findings. These cover, among other aspects, probabilistic reasoning (including Bayesian networks and other methods of forensic statistics, tools and software), case pre-assessment, skills in the oral and written communication of uncertainty, and the development of independence and self-confidence to solve practical inference problems. E-learning was chosen as a general format because it helps to form a trans-institutional online-community of practitioners from varying forensic disciplines and workfield experience such as reporting officers, (chief) scientists, forensic coordinators, but also lawyers who all can interact directly from their personal workplaces without consideration of distances, travel expenses or time schedules. In the authors' experience, the proposed learning initiative supports participants in developing their expertise and skills in forensic interpretation, but also offers an opportunity for the associated institutions and the forensic community to reinforce the development of a harmonized view with regard to interpretation across forensic disciplines, laboratories and judicial systems.

Safety and Mobility Impacts of Winter Weather – Phase I, August 2011

Highway agencies spend millions of dollars to ensure safe and efficient winter travel. However, the effectiveness of winter weather maintenance practices on safety and mobility are somewhat difficult to quantify. Phase I of this project investigated opportunities for improving traffic safety on state-maintained roads in Iowa during winter weather conditions. The primary objective was to develop several preliminary means for the Iowa Department of Transportation (DOT) to identify locations of possible interest systematically with respect to winter weather-related safety performance based on crash history. Specifically, metrics were developed to assist in identifying possible habitual, winter weather-related crash sites on state-maintained rural highways in Iowa. In addition, the current state of practice, for both domestic and international highway agency practices, regarding integration of traffic safety- and mobility-related data in winter maintenance activities and performance measures were investigated. This investigation also included previous research efforts. Finally, a preliminary work plan, focusing on systematic use of safety-related data in support of winter maintenance activities and site evaluation, was prepared.

System-level Support for Mobile Ad hoc Communications: an Algorithmic & Practical Approach

A mobile ad hoc network (MANET) is a decentralized and infrastructure-less network. This thesis aims to provide support at the system-level for developers of applications or protocols in such networks. To do this, we propose contributions in both the algorithmic realm and in the practical realm. In the algorithmic realm, we contribute to the field by proposing different context-aware broadcast and multicast algorithms in MANETs, namely six-shot broadcast, six-shot multicast, PLAN-B and ageneric algorithmic approach to optimize the power consumption of existing algorithms. For each algorithm we propose, we compare it to existing algorithms that are either probabilistic or context-aware, and then we evaluate their performance based on simulations. We demonstrate that in some cases, context-aware information, such as location or signal-strength, can improve the effciency. In the practical realm, we propose a testbed framework, namely ManetLab, to implement and to deploy MANET-specific protocols, and to evaluate their performance. This testbed framework aims to increase the accuracy of performance evaluation compared to simulations, while keeping the ease of use offered by the simulators to reproduce a performance evaluation. By evaluating the performance of different probabilistic algorithms with ManetLab, we observe that both simulations and testbeds should be used in a complementary way. In addition to the above original contributions, we also provide two surveys about system-level support for ad hoc communications in order to establish a state of the art. The first is about existing broadcast algorithms and the second is about existing middleware solutions and the way they deal with privacy and especially with location privacy. - Un réseau mobile ad hoc (MANET) est un réseau avec une architecture décentralisée et sans infrastructure. Cette thèse vise à fournir un support adéquat, au niveau système, aux développeurs d'applications ou de protocoles dans de tels réseaux. Dans ce but, nous proposons des contributions à la fois dans le domaine de l'algorithmique et dans celui de la pratique. Nous contribuons au domaine algorithmique en proposant différents algorithmes de diffusion dans les MANETs, algorithmes qui sont sensibles au contexte, à savoir six-shot broadcast,six-shot multicast, PLAN-B ainsi qu'une approche générique permettant d'optimiser la consommation d'énergie de ces algorithmes. Pour chaque algorithme que nous proposons, nous le comparons à des algorithmes existants qui sont soit probabilistes, soit sensibles au contexte, puis nous évaluons leurs performances sur la base de simulations. Nous montrons que, dans certains cas, des informations liées au contexte, telles que la localisation ou l'intensité du signal, peuvent améliorer l'efficience de ces algorithmes. Sur le plan pratique, nous proposons une plateforme logicielle pour la création de bancs d'essai, intitulé ManetLab, permettant d'implémenter, et de déployer des protocoles spécifiques aux MANETs, de sorte à évaluer leur performance. Cet outil logiciel vise à accroître la précision desévaluations de performance comparativement à celles fournies par des simulations, tout en conservant la facilité d'utilisation offerte par les simulateurs pour reproduire uneévaluation de performance. En évaluant les performances de différents algorithmes probabilistes avec ManetLab, nous observons que simulateurs et bancs d'essai doivent être utilisés de manière complémentaire. En plus de ces contributions principales, nous fournissons également deux états de l'art au sujet du support nécessaire pour les communications ad hoc. Le premier porte sur les algorithmes de diffusion existants et le second sur les solutions de type middleware existantes et la façon dont elles traitent de la confidentialité, en particulier celle de la localisation.

Diffusion in spatially and temporarily inhomogeneous media

In this paper we consider diffusion of a passive substance C in a temporarily and spatially inhomogeneous two-dimensional medium. As a realization for the latter we choose a phase-separating medium consisting of two substances A and B, whose dynamics is determined by the Cahn-Hilliard equation. Assuming different diffusion coefficients of C in A and B, we find that the variance of the distribution function of the said substance grows less than linearly in time. We derive a simple identity for the variance using a probabilistic ansatz and are then able to identify the interface between A and B as the main cause for this nonlinear dependence. We argue that, finally, for very large times the here temporarily dependent diffusion "constant" goes like t-1/3 to a constant asymptotic value D¿. The latter is calculated approximately by employing the effective-medium approximation and by fitting the simulation data to the said time dependence.

New perspectives in the use of ink evidence in forensic science: Part III: Operational applications and evaluation

The research reported in this series of article aimed at (1) automating the search of questioned ink specimens in ink reference collections and (2) at evaluating the strength of ink evidence in a transparent and balanced manner. These aims require that ink samples are analysed in an accurate and reproducible way and that they are compared in an objective and automated way. This latter requirement is due to the large number of comparisons that are necessary in both scenarios. A research programme was designed to (a) develop a standard methodology for analysing ink samples in a reproducible way, (b) comparing automatically and objectively ink samples and (c) evaluate the proposed methodology in forensic contexts. This report focuses on the last of the three stages of the research programme. The calibration and acquisition process and the mathematical comparison algorithms were described in previous papers [C. Neumann, P. Margot, New perspectives in the use of ink evidence in forensic science-Part I: Development of a quality assurance process for forensic ink analysis by HPTLC, Forensic Sci. Int. 185 (2009) 29-37; C. Neumann, P. Margot, New perspectives in the use of ink evidence in forensic science- Part II: Development and testing of mathematical algorithms for the automatic comparison of ink samples analysed by HPTLC, Forensic Sci. Int. 185 (2009) 38-50]. In this paper, the benefits and challenges of the proposed concepts are tested in two forensic contexts: (1) ink identification and (2) ink evidential value assessment. The results show that different algorithms are better suited for different tasks. This research shows that it is possible to build digital ink libraries using the most commonly used ink analytical technique, i.e. high-performance thin layer chromatography, despite its reputation of lacking reproducibility. More importantly, it is possible to assign evidential value to ink evidence in a transparent way using a probabilistic model. It is therefore possible to move away from the traditional subjective approach, which is entirely based on experts' opinion, and which is usually not very informative. While there is room for the improvement, this report demonstrates the significant gains obtained over the traditional subjective approach for the search of ink specimens in ink databases, and the interpretation of their evidential value.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

Evaluating the Effectiveness of Red Light Running Camera Enforcement in Cedar Rapids and Developing Guidelines For Selection and use of Red Light Running Countermeasures, Midwest Transportation Consortium, Final Report November 2011.

Red light running (RLR) is a problem in the US that has resulted in 165,000 injuries and 907 fatalities annually. In Iowa, RLR-related crashes make up 24.5 percent of all crashes and account for 31.7 percent of fatal and major injury crashes at signalized intersections. RLR crashes are a safety concern due to the increased likelihood of injury compared to other types of crashes. One tool used to combat red light running is automated enforcement in the form of RLR cameras. Automated enforcement, while effective, is often controversial. Cedar Rapids, Iowa installed RLR and speeding cameras at seven intersections across the city. The intersections were chosen based on crash rates and whether cameras could feasibly be placed at the intersection approaches. The cameras were placed starting in February 2010 with the last one becoming operational in December 2010. An analysis of the effect of the cameras on safety at these intersections was determined prudent in helping to justify the installation and effectiveness of the cameras. The objective of this research was to assess the safety effectiveness of the RLR program that has been implemented in Cedar Rapids. This was accomplished by analyzing data to determine changes in the following metrics:  Reductions in red light violation rates based on overall changes, time of day changes, and changes by lane  Effectiveness of the cameras over time  Time in which those running the red light enter the intersection  Changes in the average headway between vehicles entering the intersection

Estimating the time since discharge of spent cartridges: a logical approach fro interpreting the evidence

Estimating the time since discharge of a spent cartridge or a firearm can be useful in criminal situa-tions involving firearms. The analysis of volatile gunshot residue remaining after shooting using solid-phase microextraction (SPME) followed by gas chromatography (GC) was proposed to meet this objective. However, current interpretative models suffer from several conceptual drawbacks which render them inadequate to assess the evidential value of a given measurement. This paper aims to fill this gap by proposing a logical approach based on the assessment of likelihood ratios. A probabilistic model was thus developed and applied to a hypothetical scenario where alternative hy-potheses about the discharge time of a spent cartridge found on a crime scene were forwarded. In order to estimate the parameters required to implement this solution, a non-linear regression model was proposed and applied to real published data. The proposed approach proved to be a valuable method for interpreting aging-related data.