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Hyperbaric exposure (1.3 atmospheres absolute with 20.9% O-2) for 1 h a day was shown to improve exercise capacity. The present study was designed to reveal whether the daily exposure time affects exercise performance and metabolism in skeletal and cardiac muscles. Male mice in the training group were housed in a cage with a wheel activity device for 7 weeks from 5 weeks old. Trained mice were then subjected to hybrid training (HT, endurance exercise for 30 min followed by sprint interval exercise for 30 min). Hyperbaric exposure was applied following daily HT for 15 min (15HT), 30 min (30HT), or 60 min (60HT) for 4 weeks. In the endurance capacity test, maximal work values were significantly increased by 30HT and 60HT. In the left ventricle (LV), activity levels of 3-hydroxyacyl-CoA-dehydrogenase, citrate synthase, and carnitine palmitoyl transferase (CPT) 2 were significantly increased by 60HT. CPT2 activity levels were markedly increased by hyperbaric exposure in red gastrocnemius (Gr) and plantaris muscle (PL). Pyruvate dehydrogenase complex activity values in PL were enhanced more by 30HT and 60HT than by HT. Protein levels of N-terminal isoform of PGC1 alpha (NT-PGC1 alpha) protein were significantly enhanced in three hyperbaric exposed groups in Gr, but not in LV. These results indicate that hyperbaric exposure for 30 min or longer has beneficial effects on endurance, and 60-min exposure has the potential to further increase performance by facilitating fatty acid metabolism in skeletal and cardiac muscles in highly trained mice. NT-PGC1 alpha may have important roles for these adaptations in skeletal muscle.

Hyperbaric exposure (1.3 atmospheres absolute with 20.9% O-2) for 1 h a day was shown to improve exercise capacity. The present study was designed to reveal whether the daily exposure time affects exercise performance and metabolism in skeletal and cardiac muscles. Male mice in the training group were housed in a cage with a wheel activity device for 7 weeks from 5 weeks old. Trained mice were then subjected to hybrid training (HT, endurance exercise for 30 min followed by sprint interval exercise for 30 min). Hyperbaric exposure was applied following daily HT for 15 min (15HT), 30 min (30HT), or 60 min (60HT) for 4 weeks. In the endurance capacity test, maximal work values were significantly increased by 30HT and 60HT. In the left ventricle (LV), activity levels of 3-hydroxyacyl-CoA-dehydrogenase, citrate synthase, and carnitine palmitoyl transferase (CPT) 2 were significantly increased by 60HT. CPT2 activity levels were markedly increased by hyperbaric exposure in red gastrocnemius (Gr) and plantaris muscle (PL). Pyruvate dehydrogenase complex activity values in PL were enhanced more by 30HT and 60HT than by HT. Protein levels of N-terminal isoform of PGC1 alpha (NT-PGC1 alpha) protein were significantly enhanced in three hyperbaric exposed groups in Gr, but not in LV. These results indicate that hyperbaric exposure for 30 min or longer has beneficial effects on endurance, and 60-min exposure has the potential to further increase performance by facilitating fatty acid metabolism in skeletal and cardiac muscles in highly trained mice. NT-PGC1 alpha may have important roles for these adaptations in skeletal muscle.

The planimetric accuracy of old maps can be calculated using cartometric methods like Helmert transformations. As the choice of control points for these transformations is somewhat arbitrary, this may cause stochastic variations in the results. This paper addresses these stochastic variations and proposes to apply Bayesian data analysis methods to quantify the uncertainty of the cartometric calculations. The paper shows that the Bayesian data analysis is consistent with the deterministic calculations, and it provides safe statistical bounds for the results.

The planimetric accuracy of old maps can be calculated using cartometric methods like Helmert transformations. As the choice of control points for these transformations is somewhat arbitrary, this may cause stochastic variations in the results. This paper addresses these stochastic variations and proposes to apply Bayesian data analysis methods to quantify the uncertainty of the cartometric calculations. The paper shows that the Bayesian data analysis is consistent with the deterministic calculations, and it provides safe statistical bounds for the results.

The planimetric accuracy of old maps can be calculated using cartometric methods like Helmert transformations. As the choice of control points for these transformations is somewhat arbitrary, this may cause stochastic variations in the results. This paper addresses these stochastic variations and proposes to apply Bayesian data analysis methods to quantify the uncertainty of the cartometric calculations. The paper shows that the Bayesian data analysis is consistent with the deterministic calculations, and it provides safe statistical bounds for the results.

Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects (N= 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect. © 2021, The Author(s)., open accessA correction to this paper has been published: https://doi.org/10.1007/s00766-021-00354-4

Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects (N= 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect. © 2021, The Author(s)., open accessA correction to this paper has been published: https://doi.org/10.1007/s00766-021-00354-4

Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects (N= 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect. © 2021, The Author(s)., open accessA correction to this paper has been published: https://doi.org/10.1007/s00766-021-00354-4

Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects (N= 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect. © 2021, The Author(s)., open accessA correction to this paper has been published: https://doi.org/10.1007/s00766-021-00354-4

Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects (N= 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect. © 2021, The Author(s)., open accessA correction to this paper has been published: https://doi.org/10.1007/s00766-021-00354-4

Expert judgement is a common method for software effort estimations in practice today. Estimators are often shown extra obsolete requirements together with the real ones to be implemented. Only one previous study has been conducted on if such practices bias the estimations. We conducted six experiments with both students and practitioners to study, and quantify, the effects of obsolete requirements on software estimation. By conducting a family of six experiments using both students and practitioners as research subjects (N= 461), and by using a Bayesian Data Analysis approach, we investigated different aspects of this effect. We also argue for, and show an example of, how we by using a Bayesian approach can be more confident in our results and enable further studies with small sample sizes. We found that the presence of obsolete requirements triggered an overestimation in effort across all experiments. The effect, however, was smaller in a field setting compared to using students as subjects. Still, the over-estimations triggered by the obsolete requirements were systematically around twice the percentage of the included obsolete ones, but with a large 95% credible interval. The results have implications for both research and practice in that the found systematic error should be accounted for in both studies on software estimation and, maybe more importantly, in estimation practices to avoid over-estimations due to this systematic error. We partly explain this error to be stemming from the cognitive bias of anchoring-and-adjustment, i.e. the obsolete requirements anchored a much larger software. However, further studies are needed in order to accurately predict this effect. © 2021, The Author(s)., open accessA correction to this paper has been published: https://doi.org/10.1007/s00766-021-00354-4

Because fish that enter the interior Delta have poorer survival than those emigrating via the Sacramento River, understanding the mechanisms that drive entrainment rates at side channel junctions is critically important for the management of imperiled juvenile salmon. Here, we implement a previously proposed process-based conceptual model to study entrainment rates based on three linked elements: the entrainment zone, critical streakline, and cross-sectional distribution of fish. The critical streakline is the location along a channel cross-section immediately upstream of a junction that forms the spatial divide between parcels of water that enter a side channel or remain in the main channel. The critical streakline therefore divides the main channel into entrainment zones within which fish would likely enter each channel. Combined with information about the cross-sectional distribution of fish upstream of a junction, this conceptual model provides a means to predict fish entrainment into each channel. To apply this conceptual model, we combined statistical models of the critical streakline, the cross-sectional distribution of acoustic tagged juvenile Chinook salmon, and their probability of entrainment into Georgiana Slough. We fit joint beta regression and logistic regression models to acoustic telemetry data gathered in 2011 and 2012 to estimate the cross-sectional distribution of fish upstream of the junction, and to estimate the probability of entrainment for fish on either side of the critical streakline. We show that entrainment rates can be predicted by understanding how the combination of critical streakline position and cross-sectional distribution of fish co-vary as a function of environmental covariates. By integrating over individual positions and entrainment fates to arrive at population-level entrain probability in relation to environmental covariates, our model offers managers a simple but powerful tool to evaluate how alternative actions affect migr

Characterizing seismic moment tensors of small‐ to moderate‐magnitude earthquakes (i.e., 2.5

Because fish that enter the interior Delta have poorer survival than those emigrating via the Sacramento River, understanding the mechanisms that drive entrainment rates at side channel junctions is critically important for the management of imperiled juvenile salmon. Here, we implement a previously proposed process-based conceptual model to study entrainment rates based on three linked elements: the entrainment zone, critical streakline, and cross-sectional distribution of fish. The critical streakline is the location along a channel cross-section immediately upstream of a junction that forms the spatial divide between parcels of water that enter a side channel or remain in the main channel. The critical streakline therefore divides the main channel into entrainment zones within which fish would likely enter each channel. Combined with information about the cross-sectional distribution of fish upstream of a junction, this conceptual model provides a means to predict fish entrainment into each channel. To apply this conceptual model, we combined statistical models of the critical streakline, the cross-sectional distribution of acoustic tagged juvenile Chinook salmon, and their probability of entrainment into Georgiana Slough. We fit joint beta regression and logistic regression models to acoustic telemetry data gathered in 2011 and 2012 to estimate the cross-sectional distribution of fish upstream of the junction, and to estimate the probability of entrainment for fish on either side of the critical streakline. We show that entrainment rates can be predicted by understanding how the combination of critical streakline position and cross-sectional distribution of fish co-vary as a function of environmental covariates. By integrating over individual positions and entrainment fates to arrive at population-level entrain probability in relation to environmental covariates, our model offers managers a simple but powerful tool to evaluate how alternative actions affect migr

[ES] En la presente investigación se estudiarán diversos post procesadores estadísticos, aplicados a los caudales mensuales producto de modelos hidrologicos con el objeto de cuantificar la incertidumbre predictiva y mejorar el pronóstico hidrológico. De acuerdo con el estado del arte, los post procesadores más utilizados son : Model Conditional Processor MCP (Todini, 2008), Model Conditional Processor with truncated Normal MCPt (Coccia et al, 2011), Model Output Statistics (MOS), Clúster con multi-gaussianas (Hernández-López et al, 2017), etc. En varios de los enfoques descritos anteriormente, se aplica la Transformada de cuantiles normales (NQT) para proyectar las observaciones y las predicciones en el espacio Normal. Con el ánimo de generalizar los resultados de la comparación, los datos a utilizar son los caudales de la base de datos del Segundo Taller sobre experimento de estimación de Parámetros de Modelo (MOPEX), de 12 cuencas del sureste de los EE. UU, correspondientes al período de 1960 a 1998, la proporción de escorrentía anual a precipitación es de 0.15 a 0.63, por lo que las condiciones hidroclimáticas son diferentes entre las 12 cuencas. Se utilizarán métricas de desempeño deterministas y probabilísticas como NSE, KGE, confiabilidad, precisión y otros. Posteriormente se compararán de forma ordenada y sistemática los resultados de los post procesadores aplicados en las 12 cuencas, siendo estas clasificadas por condiciones hidroclimáticas de acuerdo la Curva de Budyko. Por último se presentarán las discusiones y las conclusiones., [EN] There are different methods to reduce uncertainty and improve hydrological predictions, for example, evaluation of observed rainfall errors, analysis of rating curves errors, Bayesian calibration of hydrological model parameters and hydrological post-processing. In recent years, statistical methodologies have been developed that work directly on the results of the hydrological model to quantify and reduce uncertainty. This Master's Thesis compares the performance of five hydrological post-processors, using deterministic and probabilistic verification metrics. To this end, the 12 MOPEX catchments have been used with the observed and simulated flows of the GR4J hydrological model. The results show that in general, the post-processed hydrological predictions are better than the predictions of the GR4J model (without post-processing). The GMM post-processor produces the best performance in all catchments and most performance metrics, especially in dry catchments. The performance of the ABC post-processor is not the best, but this method uses less information (statistical summaries), being the post-processor more parsimonious and of possible use in ungauged basins. In general, post-processors present better performance in wet basins. However, no correlation was found between the aridity index and the performance of the post-processors.

[ES] En la presente investigación se estudiarán diversos post procesadores estadísticos, aplicados a los caudales mensuales producto de modelos hidrologicos con el objeto de cuantificar la incertidumbre predictiva y mejorar el pronóstico hidrológico. De acuerdo con el estado del arte, los post procesadores más utilizados son : Model Conditional Processor MCP (Todini, 2008), Model Conditional Processor with truncated Normal MCPt (Coccia et al, 2011), Model Output Statistics (MOS), Clúster con multi-gaussianas (Hernández-López et al, 2017), etc. En varios de los enfoques descritos anteriormente, se aplica la Transformada de cuantiles normales (NQT) para proyectar las observaciones y las predicciones en el espacio Normal. Con el ánimo de generalizar los resultados de la comparación, los datos a utilizar son los caudales de la base de datos del Segundo Taller sobre experimento de estimación de Parámetros de Modelo (MOPEX), de 12 cuencas del sureste de los EE. UU, correspondientes al período de 1960 a 1998, la proporción de escorrentía anual a precipitación es de 0.15 a 0.63, por lo que las condiciones hidroclimáticas son diferentes entre las 12 cuencas. Se utilizarán métricas de desempeño deterministas y probabilísticas como NSE, KGE, confiabilidad, precisión y otros. Posteriormente se compararán de forma ordenada y sistemática los resultados de los post procesadores aplicados en las 12 cuencas, siendo estas clasificadas por condiciones hidroclimáticas de acuerdo la Curva de Budyko. Por último se presentarán las discusiones y las conclusiones., [EN] There are different methods to reduce uncertainty and improve hydrological predictions, for example, evaluation of observed rainfall errors, analysis of rating curves errors, Bayesian calibration of hydrological model parameters and hydrological post-processing. In recent years, statistical methodologies have been developed that work directly on the results of the hydrological model to quantify and reduce uncertainty. This Master's Thesis compares the performance of five hydrological post-processors, using deterministic and probabilistic verification metrics. To this end, the 12 MOPEX catchments have been used with the observed and simulated flows of the GR4J hydrological model. The results show that in general, the post-processed hydrological predictions are better than the predictions of the GR4J model (without post-processing). The GMM post-processor produces the best performance in all catchments and most performance metrics, especially in dry catchments. The performance of the ABC post-processor is not the best, but this method uses less information (statistical summaries), being the post-processor more parsimonious and of possible use in ungauged basins. In general, post-processors present better performance in wet basins. However, no correlation was found between the aridity index and the performance of the post-processors.

[ES] En la presente investigación se estudiarán diversos post procesadores estadísticos, aplicados a los caudales mensuales producto de modelos hidrologicos con el objeto de cuantificar la incertidumbre predictiva y mejorar el pronóstico hidrológico. De acuerdo con el estado del arte, los post procesadores más utilizados son : Model Conditional Processor MCP (Todini, 2008), Model Conditional Processor with truncated Normal MCPt (Coccia et al, 2011), Model Output Statistics (MOS), Clúster con multi-gaussianas (Hernández-López et al, 2017), etc. En varios de los enfoques descritos anteriormente, se aplica la Transformada de cuantiles normales (NQT) para proyectar las observaciones y las predicciones en el espacio Normal. Con el ánimo de generalizar los resultados de la comparación, los datos a utilizar son los caudales de la base de datos del Segundo Taller sobre experimento de estimación de Parámetros de Modelo (MOPEX), de 12 cuencas del sureste de los EE. UU, correspondientes al período de 1960 a 1998, la proporción de escorrentía anual a precipitación es de 0.15 a 0.63, por lo que las condiciones hidroclimáticas son diferentes entre las 12 cuencas. Se utilizarán métricas de desempeño deterministas y probabilísticas como NSE, KGE, confiabilidad, precisión y otros. Posteriormente se compararán de forma ordenada y sistemática los resultados de los post procesadores aplicados en las 12 cuencas, siendo estas clasificadas por condiciones hidroclimáticas de acuerdo la Curva de Budyko. Por último se presentarán las discusiones y las conclusiones., [EN] There are different methods to reduce uncertainty and improve hydrological predictions, for example, evaluation of observed rainfall errors, analysis of rating curves errors, Bayesian calibration of hydrological model parameters and hydrological post-processing. In recent years, statistical methodologies have been developed that work directly on the results of the hydrological model to quantify and reduce uncertainty. This Master's Thesis compares the performance of five hydrological post-processors, using deterministic and probabilistic verification metrics. To this end, the 12 MOPEX catchments have been used with the observed and simulated flows of the GR4J hydrological model. The results show that in general, the post-processed hydrological predictions are better than the predictions of the GR4J model (without post-processing). The GMM post-processor produces the best performance in all catchments and most performance metrics, especially in dry catchments. The performance of the ABC post-processor is not the best, but this method uses less information (statistical summaries), being the post-processor more parsimonious and of possible use in ungauged basins. In general, post-processors present better performance in wet basins. However, no correlation was found between the aridity index and the performance of the post-processors.

[ES] En la presente investigación se estudiarán diversos post procesadores estadísticos, aplicados a los caudales mensuales producto de modelos hidrologicos con el objeto de cuantificar la incertidumbre predictiva y mejorar el pronóstico hidrológico. De acuerdo con el estado del arte, los post procesadores más utilizados son : Model Conditional Processor MCP (Todini, 2008), Model Conditional Processor with truncated Normal MCPt (Coccia et al, 2011), Model Output Statistics (MOS), Clúster con multi-gaussianas (Hernández-López et al, 2017), etc. En varios de los enfoques descritos anteriormente, se aplica la Transformada de cuantiles normales (NQT) para proyectar las observaciones y las predicciones en el espacio Normal. Con el ánimo de generalizar los resultados de la comparación, los datos a utilizar son los caudales de la base de datos del Segundo Taller sobre experimento de estimación de Parámetros de Modelo (MOPEX), de 12 cuencas del sureste de los EE. UU, correspondientes al período de 1960 a 1998, la proporción de escorrentía anual a precipitación es de 0.15 a 0.63, por lo que las condiciones hidroclimáticas son diferentes entre las 12 cuencas. Se utilizarán métricas de desempeño deterministas y probabilísticas como NSE, KGE, confiabilidad, precisión y otros. Posteriormente se compararán de forma ordenada y sistemática los resultados de los post procesadores aplicados en las 12 cuencas, siendo estas clasificadas por condiciones hidroclimáticas de acuerdo la Curva de Budyko. Por último se presentarán las discusiones y las conclusiones., [EN] There are different methods to reduce uncertainty and improve hydrological predictions, for example, evaluation of observed rainfall errors, analysis of rating curves errors, Bayesian calibration of hydrological model parameters and hydrological post-processing. In recent years, statistical methodologies have been developed that work directly on the results of the hydrological model to quantify and reduce uncertainty. This Master's Thesis compares the performance of five hydrological post-processors, using deterministic and probabilistic verification metrics. To this end, the 12 MOPEX catchments have been used with the observed and simulated flows of the GR4J hydrological model. The results show that in general, the post-processed hydrological predictions are better than the predictions of the GR4J model (without post-processing). The GMM post-processor produces the best performance in all catchments and most performance metrics, especially in dry catchments. The performance of the ABC post-processor is not the best, but this method uses less information (statistical summaries), being the post-processor more parsimonious and of possible use in ungauged basins. In general, post-processors present better performance in wet basins. However, no correlation was found between the aridity index and the performance of the post-processors.

Policy Analysis

Policy Analysis

Les ones gravitatòries són una predicció de la teoria de la Relativitat General d'Einstein, la detecció i anàlisi de les quals obrirà una nova àrea en el nostre coneixement de l'Univers. En efecte, aquestes ones porten informació de sistemes amb gravetat molt forta i que són molt difícils d'observar amb instruments convencionals, basats en la detecció de llum, per a qual cosa el nostre coneixement actual només en pot ser parcial. Avui en dia tenim evidències experimentals de l'existència de les ones gravitatòries, com la variació del període orbital del pulsar binari PSRB1913+16 (Premi Nobel 1993). Tot i això, encara no se n'ha pogut obtenir una detecció. La comunitat científica té doncs un gran interès en assolir aquesta detecció com es demostra per la quantitat d'observatoris repartits arreu del món: LIGO (Estats Units), VIRGO (Italia, França), GEO600 (Alemanya, Regne Unit), LCGT (Japó), etc. La missió LISA (Laser Interferomer Space Antenna) és una proposta per posar un d'aquests observatoris en òrbita heliocèntrica mitjançant tres satèl.lits que contindrien masses en caiguda lliure la distància entre les quals es mesuraria mitjançant interferometria laser. El link laser connectaria els diferents satèl.lits que es trobarien separats 1 mil.lió de quilòmetres, conseguint una configuració triangular que seguiria la Terra en la seva òrbita. Aquesta proposta ha estat acceptada per l'Agència Espacia Europea l'any 2013 en el seu pla científic, com el tema d'una missió que seria llançada en la decàda del 2030. Aquesta tesis s'emmarca dins la missió LISA Pathfinder, que és la missió precursora de LISA i que té data de llançament el 2015. Aquesta missió posarà a proba la tecnologia que requereix la futura missió LISA i per això conté els principals elements (laser, massa de test, etc.) però en una versió reduïda i en un únic satèl.lit. L'objectiu científic principal és aconseguir mesurar l'acceleració relativa entre dues masses en caiguda lliure fins a nivells de 10^(-14) m, Gravitational waves are a prediction of Einstein's General Relativity, the detection and analysis of which will open a new area in our understanding of the Universe. Indeed, these waves carry information from systems with strong gravity and are very difficult to observe with conventional instruments, which are based on the detection of light. Today, we have experimental evidence of the existence of gravitational waves, as the variation of the orbital period of the binary pulsar PSRB1913 + 16 (Nobel Prize 1993). However, there has been no detection. The scientific community is pursuing this detection with several observatories spread around the world: LIGO (USA), VIRGO (Italy, France), GEO600 (Germany, UK), LCGT (Japan ), etc. LISA (Laser Interferomer Space Antenna) is a proposal to put one of these observatories in heliocentric orbit using three satellites that contain masses in free fall. A laser interferometer is used to measure the distance between them. The satellites will be separated 1 Mkm, resulting in a triangular configuration that follows the Earth in its orbit. This proposal was accepted by the European Agency Espacia in 2013, as the subject of a mission to be launched in the early 2030's. This thesis is part of the LISA Pathfinder mission, which is the precursor mission of LISA and has a release date in 2015. This mission will test the technology required for the future LISA mission and therefore contains the main elements (laser, test mass, etc.) but in a smaller version and in a single satellite. The main scientific objective is to measure the relative acceleration between two masses in free fall to levels down to 10^(-14) m / s^2 / Hz ^(1/2) in the low frequency band, i.e. 1mHz. This thesis develops the methods known as Markov Chain Monte Carlo (MCMC), which are used, among others, for the estimation of parameters. These techniques have been put to the test in recent years through various campaigns with simulated data and are currently part of the LTP

Les ones gravitatòries són una predicció de la teoria de la Relativitat General d'Einstein, la detecció i anàlisi de les quals obrirà una nova àrea en el nostre coneixement de l'Univers. En efecte, aquestes ones porten informació de sistemes amb gravetat molt forta i que són molt difícils d'observar amb instruments convencionals, basats en la detecció de llum, per a qual cosa el nostre coneixement actual només en pot ser parcial. Avui en dia tenim evidències experimentals de l'existència de les ones gravitatòries, com la variació del període orbital del pulsar binari PSRB1913+16 (Premi Nobel 1993). Tot i això, encara no se n'ha pogut obtenir una detecció. La comunitat científica té doncs un gran interès en assolir aquesta detecció com es demostra per la quantitat d'observatoris repartits arreu del món: LIGO (Estats Units), VIRGO (Italia, França), GEO600 (Alemanya, Regne Unit), LCGT (Japó), etc. La missió LISA (Laser Interferomer Space Antenna) és una proposta per posar un d'aquests observatoris en òrbita heliocèntrica mitjançant tres satèl.lits que contindrien masses en caiguda lliure la distància entre les quals es mesuraria mitjançant interferometria laser. El link laser connectaria els diferents satèl.lits que es trobarien separats 1 mil.lió de quilòmetres, conseguint una configuració triangular que seguiria la Terra en la seva òrbita. Aquesta proposta ha estat acceptada per l'Agència Espacia Europea l'any 2013 en el seu pla científic, com el tema d'una missió que seria llançada en la decàda del 2030. Aquesta tesis s'emmarca dins la missió LISA Pathfinder, que és la missió precursora de LISA i que té data de llançament el 2015. Aquesta missió posarà a proba la tecnologia que requereix la futura missió LISA i per això conté els principals elements (laser, massa de test, etc.) però en una versió reduïda i en un únic satèl.lit. L'objectiu científic principal és aconseguir mesurar l'acceleració relativa entre dues masses en caiguda lliure fins a nivells de 10^(-14) m, Gravitational waves are a prediction of Einstein's General Relativity, the detection and analysis of which will open a new area in our understanding of the Universe. Indeed, these waves carry information from systems with strong gravity and are very difficult to observe with conventional instruments, which are based on the detection of light. Today, we have experimental evidence of the existence of gravitational waves, as the variation of the orbital period of the binary pulsar PSRB1913 + 16 (Nobel Prize 1993). However, there has been no detection. The scientific community is pursuing this detection with several observatories spread around the world: LIGO (USA), VIRGO (Italy, France), GEO600 (Germany, UK), LCGT (Japan ), etc. LISA (Laser Interferomer Space Antenna) is a proposal to put one of these observatories in heliocentric orbit using three satellites that contain masses in free fall. A laser interferometer is used to measure the distance between them. The satellites will be separated 1 Mkm, resulting in a triangular configuration that follows the Earth in its orbit. This proposal was accepted by the European Agency Espacia in 2013, as the subject of a mission to be launched in the early 2030's. This thesis is part of the LISA Pathfinder mission, which is the precursor mission of LISA and has a release date in 2015. This mission will test the technology required for the future LISA mission and therefore contains the main elements (laser, test mass, etc.) but in a smaller version and in a single satellite. The main scientific objective is to measure the relative acceleration between two masses in free fall to levels down to 10^(-14) m / s^2 / Hz ^(1/2) in the low frequency band, i.e. 1mHz. This thesis develops the methods known as Markov Chain Monte Carlo (MCMC), which are used, among others, for the estimation of parameters. These techniques have been put to the test in recent years through various campaigns with simulated data and are currently part of the LTP

Substance dependent individuals (SDI) often exhibit decision-making deficits; however, it remains unclear whether the nature of the underlying decision-making processes is the same in users of different classes of drugs and whether these deficits persist after discontinuation of drug use. We used computational modeling to address these questions in a unique sample of relatively “pure” amphetamine-dependent (N=38) and heroin-dependent individuals (N=43) who were currently in protracted abstinence, and in 48 healthy controls. A Bayesian model comparison technique, a simulation method, and parameter recovery tests were used to compare three cognitive models: (1) Prospect Valence Learning with decay reinforcement learning rule (PVL-DecayRI), (2) PVL with delta learning rule (PVL-Delta), and (3) Value-Plus-Perseverance (VPP) models based on Win-Stay-Lose-Switch (WSLS) strategy. The model comparison results indicated that the VPP model, a hybrid model of reinforcement learning (RL) and a heuristic strategy of perseverance had the best post hoc model fit, but the two PVL models showed better simulation performance. Computational modeling results suggested that overall all three groups relied more on RL than on a WSLS strategy. Heroin users displayed reduced loss aversion relative to healthy controls across all three models, which suggests that their decision-making deficits are longstanding (or pre-existing) and may be driven by reduced sensitivity to loss. In contrast, amphetamine users showed comparable cognitive functions to healthy controls with the VPP model, whereas the second best-fitting model with relatively good simulation performance (PVL-DecayRI) revealed increased reward sensitivity relative to healthy controls. These results suggest that some decision-making deficits persist in protracted abstinence and may be mediated by different mechanisms in opiate and stimulant users.

Substance dependent individuals (SDI) often exhibit decision-making deficits; however, it remains unclear whether the nature of the underlying decision-making processes is the same in users of different classes of drugs and whether these deficits persist after discontinuation of drug use. We used computational modeling to address these questions in a unique sample of relatively “pure” amphetamine-dependent (N=38) and heroin-dependent individuals (N=43) who were currently in protracted abstinence, and in 48 healthy controls. A Bayesian model comparison technique, a simulation method, and parameter recovery tests were used to compare three cognitive models: (1) Prospect Valence Learning with decay reinforcement learning rule (PVL-DecayRI), (2) PVL with delta learning rule (PVL-Delta), and (3) Value-Plus-Perseverance (VPP) models based on Win-Stay-Lose-Switch (WSLS) strategy. The model comparison results indicated that the VPP model, a hybrid model of reinforcement learning (RL) and a heuristic strategy of perseverance had the best post hoc model fit, but the two PVL models showed better simulation performance. Computational modeling results suggested that overall all three groups relied more on RL than on a WSLS strategy. Heroin users displayed reduced loss aversion relative to healthy controls across all three models, which suggests that their decision-making deficits are longstanding (or pre-existing) and may be driven by reduced sensitivity to loss. In contrast, amphetamine users showed comparable cognitive functions to healthy controls with the VPP model, whereas the second best-fitting model with relatively good simulation performance (PVL-DecayRI) revealed increased reward sensitivity relative to healthy controls. These results suggest that some decision-making deficits persist in protracted abstinence and may be mediated by different mechanisms in opiate and stimulant users.

Substance dependent individuals (SDI) often exhibit decision-making deficits; however, it remains unclear whether the nature of the underlying decision-making processes is the same in users of different classes of drugs and whether these deficits persist after discontinuation of drug use. We used computational modeling to address these questions in a unique sample of relatively “pure” amphetamine-dependent (N=38) and heroin-dependent individuals (N=43) who were currently in protracted abstinence, and in 48 healthy controls. A Bayesian model comparison technique, a simulation method, and parameter recovery tests were used to compare three cognitive models: (1) Prospect Valence Learning with decay reinforcement learning rule (PVL-DecayRI), (2) PVL with delta learning rule (PVL-Delta), and (3) Value-Plus-Perseverance (VPP) models based on Win-Stay-Lose-Switch (WSLS) strategy. The model comparison results indicated that the VPP model, a hybrid model of reinforcement learning (RL) and a heuristic strategy of perseverance had the best post hoc model fit, but the two PVL models showed better simulation performance. Computational modeling results suggested that overall all three groups relied more on RL than on a WSLS strategy. Heroin users displayed reduced loss aversion relative to healthy controls across all three models, which suggests that their decision-making deficits are longstanding (or pre-existing) and may be driven by reduced sensitivity to loss. In contrast, amphetamine users showed comparable cognitive functions to healthy controls with the VPP model, whereas the second best-fitting model with relatively good simulation performance (PVL-DecayRI) revealed increased reward sensitivity relative to healthy controls. These results suggest that some decision-making deficits persist in protracted abstinence and may be mediated by different mechanisms in opiate and stimulant users.

Substance dependent individuals (SDI) often exhibit decision-making deficits; however, it remains unclear whether the nature of the underlying decision-making processes is the same in users of different classes of drugs and whether these deficits persist after discontinuation of drug use. We used computational modeling to address these questions in a unique sample of relatively “pure” amphetamine-dependent (N=38) and heroin-dependent individuals (N=43) who were currently in protracted abstinence, and in 48 healthy controls. A Bayesian model comparison technique, a simulation method, and parameter recovery tests were used to compare three cognitive models: (1) Prospect Valence Learning with decay reinforcement learning rule (PVL-DecayRI), (2) PVL with delta learning rule (PVL-Delta), and (3) Value-Plus-Perseverance (VPP) models based on Win-Stay-Lose-Switch (WSLS) strategy. The model comparison results indicated that the VPP model, a hybrid model of reinforcement learning (RL) and a heuristic strategy of perseverance had the best post hoc model fit, but the two PVL models showed better simulation performance. Computational modeling results suggested that overall all three groups relied more on RL than on a WSLS strategy. Heroin users displayed reduced loss aversion relative to healthy controls across all three models, which suggests that their decision-making deficits are longstanding (or pre-existing) and may be driven by reduced sensitivity to loss. In contrast, amphetamine users showed comparable cognitive functions to healthy controls with the VPP model, whereas the second best-fitting model with relatively good simulation performance (PVL-DecayRI) revealed increased reward sensitivity relative to healthy controls. These results suggest that some decision-making deficits persist in protracted abstinence and may be mediated by different mechanisms in opiate and stimulant users.

[Abstract] In V1, local circuitry depends on the position in the orientation map: close to pinwheel centers, recurrent inputs show variable orientation preferences; within iso-orientation domains, inputs are relatively uniformly tuned. Physiological properties such as cell's membrane potentials, spike outputs, and temporal characteristics change systematically with map location. We investigate in a firing rate and a Hodgkin–Huxley network model what constraints these tuning characteristics of V1 neurons impose on the cortical operating regime. Systematically varying the strength of both recurrent excitation and inhibition, we test a wide range of model classes and find the likely models to account for the experimental observations. We show that recent intracellular and extracellular recordings from cat V1 provide the strongest evidence for a regime where excitatory and inhibitory recurrent inputs are balanced and dominate the feed-forward input. Our results are robust against changes in model assumptions such as spatial extent and strength of lateral inhibition. Intriguingly, the most likely recurrent regime is in a region of parameter space where small changes have large effects on the network dynamics, and it is close to a regime of “runaway excitation,” where the network shows strong self-sustained activity. This could make the cortical response particularly sensitive to modulation.

Context: Manual graphical user interface (GUI) software testing presents a substantial part of the overall practiced testing efforts, despite various research efforts to further increase test automation. Augmented Testing (AT), a novel approach for GUI testing, aims to aid manual GUI-based testing through a tool-supported approach where an intermediary visual layer is rendered between the system under test (SUT) and the tester, superimposing relevant test information. Objective: The primary objective of this study is to gather empirical evidence regarding AT's efficiency compared to manual GUI-based regression testing. Existing studies involving testing approaches under the AT definition primarily focus on exploratory GUI testing, leaving a gap in the context of regression testing. As a secondary objective, we investigate AT's benefits, drawbacks, and usability issues when deployed with the demonstrator tool, Scout. Method: We conducted an experiment involving 13 industry professionals, from six companies, comparing AT to manual GUI-based regression testing. These results were complemented by interviews and Bayesian data analysis (BDA) of the study's quantitative results. Results: The results of the Bayesian data analysis revealed that the use of AT shortens test durations in 70% of the cases on average, concluding that AT is more efficient.When comparing the means of the total duration to perform all tests, AT reduced the test duration by 36% in total. Participant interviews highlighted nine benefits and eleven drawbacks of AT, while observations revealed four usability issues. Conclusion: This study makes an empirical contribution to understanding Augmented Testing, a promising approach to improve the efficiency of GUI-based regression testing in practice. Furthermore, it underscores the importance of continual refinements of AT.

Previous research in statistical post-processing has found systematic deficiencies in deterministic forecast guidance. As a result, ensemble forecasts of sensible weather variables often manifest biased central tendencies and anomalous dispersion. In this way, the numerical weather prediction community has largely focused on upgrades to upstream model components to improve forecast performance--that is, innovations in data assimilation, governing dynamics, numerical techniques, and various parameterizations of subgrid-scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to ensemble model output statistics. This technique directly parameterizes meteorological phenomena with probability distributions that describe the intrinsic structure of observable data. Bayesian posterior beliefs in model parameter were conditioned on previous observations and dynamical predictors available outside of the parent ensemble. An adaptive variant of the random-walk Metropolis algorithm was used to complete the inference scheme with block-wise multiparameter updates. This produced calibrated multivariate posterior predictive distributions (PPD) for 24-hour forecasts of diurnal extrema in surface temperature and wind speed. These Bayesian PPDs reliably characterized forecast uncertainty and outperformed the parent ensemble and a classical least-squares approach to multivariate multiple linear regression using both measures-oriented and distributions-oriented scoring rules., http://archive.org/details/ahierarchicalmul1094556188, Lieutenant Commander, United States Navy, Approved for public release; distribution is unlimited.