Seasonal and interannual variability of North American isoprene emissions as determined by formaldehyde column measurements from space

Formaldehyde (HCHO) columns measured from space by solar UV backscatter allow mapping of reactive hydrocarbon emissions. The principal contributor to these emissions during the growing season is the biogenic hydrocarbon isoprene, which is of great importance for driving regional and global tropospheric chemistry. We present seven years (1995-2001) of HCHO column data for North America from the Global Ozone Monitoring Experiment (GOME), and show that the general seasonal and interannual variability of these data is consistent with knowledge of isoprene emission. There are some significant regional discrepancies with the seasonal patterns predicted from current isoprene emission models, and we suggest that these may reflect flaws in the models. The interannual variability of HCHO columns observed by GOME appears to follow the interannual variability of surface temperature, as expected from current isoprene emission models

When Does It Hurt? The Exchange Rate "Pain Threshold" for German Exports

This paper deals with the impact of the $/Euro exchange rate on German exports in the period from 1995Q1 to 2008Q4. Our main aim is to identify "pain thresholds" for German exporters. We rely on a non-linear model according to which suddenly strong spurts of exports occur when changes of the EXR go beyond a kind of "play area (analogous to a mechanical play). We implement an algorithm describing play-hysteresis into a regression framework. A unique "pain threshold" of the$/Euro exchange rate does not exist, since the borders of the play area and, thus, also the "pain threshold" (as the upper border) depend on the historical path of the whole process. We come up with an estimate of a play area width of 24 US dollar cent per euro. At the end of our estimation period, the previous exchange rate movements had shifted the upper bound of the play area to about 1.55 US dollar per euro. In our interpretation, this is the current "pain threshold", where a strong spurt reaction of exports to a further appreciation of the euro is expected to start.Exchange rate movements, play hysteresis, modelling techniques, switching regression, export demand

When does it hurt? The exchange rate "pain threshold" for German exports

This paper deals with the impact of the $/€ exchange rate on German exports in the period from 1995Q1 to 2008Q4. Our main aim is to identify 'pain thresholds' for German exporters. We rely on a non-linear model according to which suddenly strong spurts of exports occur when changes of the EXR go beyond a kind of 'play' area (analogous to a mechanical play). We implement an algorithm describing play-hysteresis into a regression framework. A unique 'pain threshold' of the$/€ exchange rate does not exist, since the borders of the play area and, thus, also the 'pain threshold' (as the upper border) depend on the historical path of the whole process. We come up with an estimate of a play area width of 24 US dollar cent per euro. At the end of our estimation period, the previous exchange rate movements had shifted the upper bound of the play area to about 1.55 US dollar per euro. In our interpretation, this is the current 'pain threshold', where a strong spurt reaction of exports to a further appreciation of the euro is expected to start. --exchange rate movements,play hysteresis,modelling techniques,switching regression,export demand

On the Systemic Nature of Weather Risk

Systemic weather risk is a major obstacle for the formation of private (non- subsidized) crop insurance. This paper explores the possibility of spatial diversification of insurance by estimating the joint occurrence of unfavorable weather conditions in different locations. For that purpose copula methods are employed that allow an adequate description of stochastic dependencies between multivariate random variables. The estimation procedure is applied to weather data in Germany. Our results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a national scale. Thus the possibility to reduce risk exposure by increasing the trading area of the insurance is limited. Irrespective of their economic implications our results pinpoint the necessity of a proper statistical modeling of the dependence structure of multivariate random variables. The usual approach of measuring stochastic dependence with linear correlation coefficients turned out to be questionable in the context of weather insurance as it may overestimate diversification effects considerably.weather risk, crop insurance, copula

Adaptation of NLP Techniques to Cultural Heritage Research and Documentation

The WissKI system provides a framework for ontology based science communication and cultural heritage documentation. In many cases, the documentation consists of semi-structured data records with free text fields. Most references in the texts comprise of person and place names, as well as time specifications. We present the WissKI tools for semantic annotation using controlled vocabularies and formal ontologies derived from CIDOC Conceptual Reference Model (CRM). Current research deals with the annotations as building blocks for event recognition. Finally, we outline how the CRM helps to build bridges between documentation in different scientific disciplines

Online Context-based Object Recognition for Mobile Robots

This work proposes a robotic object recognition system that takes advantage of the contextual information latent in human-like environments in an online fashion. To fully leverage context, it is needed perceptual information from (at least) a portion of the scene containing the objects of interest, which could not be entirely covered by just an one-shot sensor observation. Information from a larger portion of the scenario could still be considered by progressively registering observations, but this approach experiences difficulties under some circumstances, e.g. limited and heavily demanded computational resources, dynamic environments, etc. Instead of this, the proposed recognition system relies on an anchoring process for the fast registration and propagation of objects’ features and locations beyond the current sensor frustum. In this way, the system builds a graphbased world model containing the objects in the scenario (both in the current and previously perceived shots), which is exploited by a Probabilistic Graphical Model (PGM) in order to leverage contextual information during recognition. We also propose a novel way to include the outcome of local object recognition methods in the PGM, which results in a decrease in the usually high CRF learning complexity. A demonstration of our proposal has been conducted employing a dataset captured by a mobile robot from restaurant-like settings, showing promising results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

What Works Better? A Study of Classifying Requirements

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.Comment: 7 pages, the 25th IEEE International Conference on Requirements Engineering (RE'17