NEGOSEIO: framework for the sustainability of model-oriented enterprise interoperability

Dissertation to obtain the degree of Doctor of Philosophy in Electrical and Computer Engineering(Industrial Information Systems)

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

Fear of model misspecifcation and the robustness premium

Robust decision making implies welfare costs or robustness premia when the approximating model is the true data generating process. To examine the importance of these premia at the aggregate level we employ a simple two-sector dynamic general equilibrium model with human capital and introduce an additional form of precautionary behavior. The latter arises from the robust decision maker s ability to reduce the effects of model misspecification through allocating time and existing human capital to this end. We find that the extent of the robustness premia critically depends on the productivity of time relative to that of human capital. When the relative efficiency of time is low, despite transitory welfare costs, there are gains from following robust policies in the long-run. In contrast, high relative productivity of time implies misallocation costs that remain even in the long-run. Finally, depending on the technology used to reduce model uncertainty, we fi nd that while increasing the fear of model misspecfi cation leads to a net increase in precautionary behavior, investment and output can fall.

Imperfect Credibility and Robust Monetary Policy

This paper studies the behavior of a central bank that seeks to conduct policy optimally while having imperfect credibility and harboring doubts about its model. Taking the Smets-Wouters model as the central bank.s approximating model, the paper's main findings are as follows. First, a central bank.s credibility can have large consequences for how policy responds to shocks. Second, central banks that have low credibility can bene.t from a desire for robustness because this desire motivates the central bank to follow through on policy announcements that would otherwise not be time-consistent. Third, even relatively small departures from perfect credibility can produce important declines in policy performance. Finally, as a technical contribution, the paper develops a numerical procedure to solve the decision-problem facing an imperfectly credible policymaker that seeks robustness.

Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty

This paper extends the Nelson-Siegel linear factor model by developing a flexible macro-finance framework for modeling and forecasting the term structure of US interest rates. Our approach is robust to parameter uncertainty and structural change, as we consider instabilities in parameters and volatilities, and our model averaging method allows for investors' model uncertainty over time. Our time-varying parameter Nelson-Siegel Dynamic Model Averaging (NS-DMA) predicts yields better than standard benchmarks and successfully captures plausible time-varying term premia in real time. The proposed model has significant in-sample and out-of-sample predictability for excess bond returns, and the predictability is of economic value.

Robust discrimination between EEG responses to categories of environmental sounds in early coma.

Humans can recognize categories of environmental sounds, including vocalizations produced by humans and animals and the sounds of man-made objects. Most neuroimaging investigations of environmental sound discrimination have studied subjects while consciously perceiving and often explicitly recognizing the stimuli. Consequently, it remains unclear to what extent auditory object processing occurs independently of task demands and consciousness. Studies in animal models have shown that environmental sound discrimination at a neural level persists even in anesthetized preparations, whereas data from anesthetized humans has thus far provided null results. Here, we studied comatose patients as a model of environmental sound discrimination capacities during unconsciousness. We included 19 comatose patients treated with therapeutic hypothermia (TH) during the first 2 days of coma, while recording nineteen-channel electroencephalography (EEG). At the level of each individual patient, we applied a decoding algorithm to quantify the differential EEG responses to human vs. animal vocalizations as well as to sounds of living vocalizations vs. man-made objects. Discrimination between vocalization types was accurate in 11 patients and discrimination between sounds from living and man-made sources in 10 patients. At the group level, the results were significant only for the comparison between vocalization types. These results lay the groundwork for disentangling truly preferential activations in response to auditory categories, and the contribution of awareness to auditory category discrimination.

A peripheral dose calculation model for estimating second cancer risk after breast radiotherapy

AbstractBreast cancer is one of the most common cancers affecting one in eight women during their lives. Survival rates have increased steadily thanks to early diagnosis with mammography screening and more efficient treatment strategies. Post-operative radiation therapy is a standard of care in the management of breast cancer and has been shown to reduce efficiently both local recurrence rate and breast cancer mortality. Radiation therapy is however associated with some late effects for long-term survivors. Radiation-induced secondary cancer is a relatively rare but severe late effect of radiation therapy. Currently, radiotherapy plans are essentially optimized to maximize tumor control and minimize late deterministic effects (tissue reactions) that are mainly associated with high doses (» 1 Gy). With improved cure rates and new radiation therapy technologies, it is also important to evaluate and minimize secondary cancer risks for different treatment techniques. This is a particularly challenging task due to the large uncertainties in the dose-response relationship.In contrast with late deterministic effects, secondary cancers may be associated with much lower doses and therefore out-of-field doses (also called peripheral doses) that are typically inferior to 1 Gy need to be determined accurately. Out-of-field doses result from patient scatter and head scatter from the treatment unit. These doses are particularly challenging to compute and we characterized it by Monte Carlo (MC) calculation. A detailed MC model of the Siemens Primus linear accelerator has been thoroughly validated with measurements. We investigated the accuracy of such a model for retrospective dosimetry in epidemiological studies on secondary cancers. Considering that patients in such large studies could be treated on a variety of machines, we assessed the uncertainty in reconstructed peripheral dose due to the variability of peripheral dose among various linac geometries. For large open fields (> 10x10 cm2), the uncertainty would be less than 50%, but for small fields and wedged fields the uncertainty in reconstructed dose could rise up to a factor of 10. It was concluded that such a model could be used for conventional treatments using large open fields only.The MC model of the Siemens Primus linac was then used to compare out-of-field doses for different treatment techniques in a female whole-body CT-based phantom. Current techniques such as conformai wedged-based radiotherapy and hybrid IMRT were investigated and compared to older two-dimensional radiotherapy techniques. MC doses were also compared to those of a commercial Treatment Planning System (TPS). While the TPS is routinely used to determine the dose to the contralateral breast and the ipsilateral lung which are mostly out of the treatment fields, we have shown that these doses may be highly inaccurate depending on the treatment technique investigated. MC shows that hybrid IMRT is dosimetrically similar to three-dimensional wedge-based radiotherapy within the field, but offers substantially reduced doses to out-of-field healthy organs.Finally, many different approaches to risk estimations extracted from the literature were applied to the calculated MC dose distribution. Absolute risks varied substantially as did the ratio of risk between two treatment techniques, reflecting the large uncertainties involved with current risk models. Despite all these uncertainties, the hybrid IMRT investigated resulted in systematically lower cancer risks than any of the other treatment techniques. More epidemiological studies with accurate dosimetry are required in the future to construct robust risk models. In the meantime, any treatment strategy that reduces out-of-field doses to healthy organs should be investigated. Electron radiotherapy might offer interesting possibilities with this regard.RésuméLe cancer du sein affecte une femme sur huit au cours de sa vie. Grâce au dépistage précoce et à des thérapies de plus en plus efficaces, le taux de guérison a augmenté au cours du temps. La radiothérapie postopératoire joue un rôle important dans le traitement du cancer du sein en réduisant le taux de récidive et la mortalité. Malheureusement, la radiothérapie peut aussi induire des toxicités tardives chez les patients guéris. En particulier, les cancers secondaires radio-induits sont une complication rare mais sévère de la radiothérapie. En routine clinique, les plans de radiothérapie sont essentiellement optimisées pour un contrôle local le plus élevé possible tout en minimisant les réactions tissulaires tardives qui sont essentiellement associées avec des hautes doses (» 1 Gy). Toutefois, avec l'introduction de différentes nouvelles techniques et avec l'augmentation des taux de survie, il devient impératif d'évaluer et de minimiser les risques de cancer secondaire pour différentes techniques de traitement. Une telle évaluation du risque est une tâche ardue étant donné les nombreuses incertitudes liées à la relation dose-risque.Contrairement aux effets tissulaires, les cancers secondaires peuvent aussi être induits par des basses doses dans des organes qui se trouvent hors des champs d'irradiation. Ces organes reçoivent des doses périphériques typiquement inférieures à 1 Gy qui résultent du diffusé du patient et du diffusé de l'accélérateur. Ces doses sont difficiles à calculer précisément, mais les algorithmes Monte Carlo (MC) permettent de les estimer avec une bonne précision. Un modèle MC détaillé de l'accélérateur Primus de Siemens a été élaboré et validé avec des mesures. La précision de ce modèle a également été déterminée pour la reconstruction de dose en épidémiologie. Si on considère que les patients inclus dans de larges cohortes sont traités sur une variété de machines, l'incertitude dans la reconstruction de dose périphérique a été étudiée en fonction de la variabilité de la dose périphérique pour différents types d'accélérateurs. Pour de grands champs (> 10x10 cm ), l'incertitude est inférieure à 50%, mais pour de petits champs et des champs filtrés, l'incertitude de la dose peut monter jusqu'à un facteur 10. En conclusion, un tel modèle ne peut être utilisé que pour les traitements conventionnels utilisant des grands champs.Le modèle MC de l'accélérateur Primus a été utilisé ensuite pour déterminer la dose périphérique pour différentes techniques dans un fantôme corps entier basé sur des coupes CT d'une patiente. Les techniques actuelles utilisant des champs filtrés ou encore l'IMRT hybride ont été étudiées et comparées par rapport aux techniques plus anciennes. Les doses calculées par MC ont été comparées à celles obtenues d'un logiciel de planification commercial (TPS). Alors que le TPS est utilisé en routine pour déterminer la dose au sein contralatéral et au poumon ipsilatéral qui sont principalement hors des faisceaux, nous avons montré que ces doses peuvent être plus ou moins précises selon la technTque étudiée. Les calculs MC montrent que la technique IMRT est dosimétriquement équivalente à celle basée sur des champs filtrés à l'intérieur des champs de traitement, mais offre une réduction importante de la dose aux organes périphériques.Finalement différents modèles de risque ont été étudiés sur la base des distributions de dose calculées par MC. Les risques absolus et le rapport des risques entre deux techniques de traitement varient grandement, ce qui reflète les grandes incertitudes liées aux différents modèles de risque. Malgré ces incertitudes, on a pu montrer que la technique IMRT offrait une réduction du risque systématique par rapport aux autres techniques. En attendant des données épidémiologiques supplémentaires sur la relation dose-risque, toute technique offrant une réduction des doses périphériques aux organes sains mérite d'être étudiée. La radiothérapie avec des électrons offre à ce titre des possibilités intéressantes.

The Role of model uncertainty and learning in the U.S. postwar policy response to oil prices

This paper studies optimal monetary policy in a framework that explicitly accounts for policymakers' uncertainty about the channels of transmission of oil prices into the economy. More specfically, I examine the robust response to the real price of oil that US monetary authorities would have been recommended to implement in the period 1970 2009; had they used the approach proposed by Cogley and Sargent (2005b) to incorporate model uncertainty and learning into policy decisions. In this context, I investigate the extent to which regulator' changing beliefs over different models of the economy play a role in the policy selection process. The main conclusion of this work is that, in the specific environment under analysis, one of the underlying models dominates the optimal interest rate response to oil prices. This result persists even when alternative assumptions on the model's priors change the pattern of the relative posterior probabilities, and can thus be attributed to the presence of model uncertainty itself.

The cost-effectiveness and public health benefit of nalmefene added to psychosocial support for the reduction of alcohol consumption in alcohol-dependent patients with high/very high drinking risk levels: a Markov model.

OBJECTIVES: To determine whether nalmefene combined with psychosocial support is cost-effective compared with psychosocial support alone for reducing alcohol consumption in alcohol-dependent patients with high/very high drinking risk levels (DRLs) as defined by the WHO, and to evaluate the public health benefit of reducing harmful alcohol-attributable diseases, injuries and deaths. DESIGN: Decision modelling using Markov chains compared costs and effects over 5 years. SETTING: The analysis was from the perspective of the National Health Service (NHS) in England and Wales. PARTICIPANTS: The model considered the licensed population for nalmefene, specifically adults with both alcohol dependence and high/very high DRLs, who do not require immediate detoxification and who continue to have high/very high DRLs after initial assessment. DATA SOURCES: We modelled treatment effect using data from three clinical trials for nalmefene (ESENSE 1 (NCT00811720), ESENSE 2 (NCT00812461) and SENSE (NCT00811941)). Baseline characteristics of the model population, treatment resource utilisation and utilities were from these trials. We estimated the number of alcohol-attributable events occurring at different levels of alcohol consumption based on published epidemiological risk-relation studies. Health-related costs were from UK sources. MAIN OUTCOME MEASURES: We measured incremental cost per quality-adjusted life year (QALY) gained and number of alcohol-attributable harmful events avoided. RESULTS: Nalmefene in combination with psychosocial support had an incremental cost-effectiveness ratio (ICER) of £5204 per QALY gained, and was therefore cost-effective at the £20,000 per QALY gained decision threshold. Sensitivity analyses showed that the conclusion was robust. Nalmefene plus psychosocial support led to the avoidance of 7179 alcohol-attributable diseases/injuries and 309 deaths per 100,000 patients compared to psychosocial support alone over the course of 5 years. CONCLUSIONS: Nalmefene can be seen as a cost-effective treatment for alcohol dependence, with substantial public health benefits. TRIAL REGISTRATION NUMBERS: This cost-effectiveness analysis was developed based on data from three randomised clinical trials: ESENSE 1 (NCT00811720), ESENSE 2 (NCT00812461) and SENSE (NCT00811941).

Robust Factor Analysis for Compositional Data

Factor analysis as frequent technique for multivariate data inspection is widely used also for compositional data analysis. The usual way is to use a centered logratio (clr)transformation to obtain the random vector y of dimension D. The factor model istheny = Λf + e (1)with the factors f of dimension k & D, the error term e, and the loadings matrix Λ.Using the usual model assumptions (see, e.g., Basilevsky, 1994), the factor analysismodel (1) can be written asCov(y) = ΛΛT + ψ (2)where ψ = Cov(e) has a diagonal form. The diagonal elements of ψ as well as theloadings matrix Λ are estimated from an estimation of Cov(y).Given observed clr transformed data Y as realizations of the random vectory. Outliers or deviations from the idealized model assumptions of factor analysiscan severely effect the parameter estimation. As a way out, robust estimation ofthe covariance matrix of Y will lead to robust estimates of Λ and ψ in (2), seePison et al. (2003). Well known robust covariance estimators with good statisticalproperties, like the MCD or the S-estimators (see, e.g. Maronna et al., 2006), relyon a full-rank data matrix Y which is not the case for clr transformed data (see,e.g., Aitchison, 1986).The isometric logratio (ilr) transformation (Egozcue et al., 2003) solves thissingularity problem. The data matrix Y is transformed to a matrix Z by usingan orthonormal basis of lower dimension. Using the ilr transformed data, a robustcovariance matrix C(Z) can be estimated. The result can be back-transformed tothe clr space byC(Y ) = V C(Z)V Twhere the matrix V with orthonormal columns comes from the relation betweenthe clr and the ilr transformation. Now the parameters in the model (2) can beestimated (Basilevsky, 1994) and the results have a direct interpretation since thelinks to the original variables are still preserved.The above procedure will be applied to data from geochemistry. Our specialinterest is on comparing the results with those of Reimann et al. (2002) for the Kolaproject data

Online Robust 3D Mapping Using Structure from Motion Cues

This paper presents a complete solution for creating accurate 3D textured models from monocular video sequences. The methods are developed within the framework of sequential structure from motion, where a 3D model of the environment is maintained and updated as new visual information becomes available. The camera position is recovered by directly associating the 3D scene model with local image observations. Compared to standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures. The obtained 3D information is used to generate high quality, composite visual maps of the scene (mosaics). The visual maps are used to create texture-mapped, realistic views of the scene

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation

Optimal robust estimates using the Hellinger distance

Optimal robust M-estimates of a multidimensional parameter are described using Hampel's infinitesimal approach. The optimal estimates are derived by minimizing a measure of efficiency under the model, subject to a bounded measure of infinitesimal robustness. To this purpose we define measures of efficiency and infinitesimal sensitivity based on the Hellinger distance.We show that these two measures coincide with similar ones defined by Yohai using the Kullback-Leibler divergence, and therefore the corresponding optimal estimates coincide too.We also give an example where we fit a negative binomial distribution to a real dataset of "days of stay in hospital" using the optimal robust estimates.

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

Developing methodologies to allow the robust prediction dynamic context dominated by perturbations

El principal objectiu del projecte era desenvolupar millores conceptuals i metodològiques que permetessin una millor predicció dels canvis en la distribució de les espècies (a una escala de paisatge) derivats de canvis ambientals en un context dominat per pertorbacions. En un primer estudi, vàrem comparar l'eficàcia de diferents models dinàmics per a predir la distribució de l'hortolà (Emberiza hortulana). Els nostres resultats indiquen que un model híbrid que combini canvis en la qualitat de l'hàbitat, derivats de canvis en el paisatge, amb un model poblacional espacialment explícit és una aproximació adequada per abordar canvis en la distribució d'espècies en contextos de dinàmica ambiental elevada i una capacitat de dispersió limitada de l'espècie objectiu. En un segon estudi abordarem la calibració mitjançant dades de seguiment de models de distribució dinàmics per a 12 espècies amb preferència per hàbitats oberts. Entre les conclusions extretes destaquem: (1) la necessitat de que les dades de seguiment abarquin aquelles àrees on es produeixen els canvis de qualitat; (2) el biaix que es produeix en la estimació dels paràmetres del model d'ocupació quan la hipòtesi de canvi de paisatge o el model de qualitat d'hàbitat són incorrectes. En el darrer treball estudiarem el possible impacte en 67 espècies d’ocells de diferents règims d’incendis, definits a partir de combinacions de nivells de canvi climàtic (portant a un augment esperat de la mida i freqüència d’incendis forestals), i eficiència d’extinció per part dels bombers. Segons els resultats dels nostres models, la combinació de factors antropogènics del regim d’incendis, tals com l’abandonament rural i l’extinció, poden ser més determinants per als canvis de distribució que els efectes derivats del canvi climàtic. Els productes generats inclouen tres publicacions científiques, una pàgina web amb resultats del projecte i una llibreria per a l'entorn estadístic R.