One of the specific characteristics of Austrian Foreign Direct Investment (FDI) abroad is that a large part is carried out by firms, which themselves are affiliates of foreign Multinational Enterprises (MNEs). Such investment is termed indirect FDI in order to distinguish it from direct FDI, made by Austrian-owned firms. The objective of this paper is to analyse, whether the relatively better domestic employment performance of domestic firms (direct FDI) compared to foreign-owned firms (indirect FDI) can be linked to FDI abroad. Based on an analysis of the sales and trade structure of a sample of Austrian investors in Central and East European Countries (CEECs), this paper tests the hypothesis that these two groups of investors have different motives to invest in CEECs and therefore their activities in CEECs differ by type (sales affiliate, production abroad) and consequently the employment effects at home. Regression results confirm that direct FDI are more strongly determined by labour costs and exhibit an employment pattern related to a deeper international division of labour (including production), while indirect FDI is based relatively more on market seeking investment. Empirical results also confirm that employment effects at home differ. The positive (negative) effect of one additional unit of parent (affiliate) sales on domestic employment for indirect FDI compared to direct FDI is larger (smaller). The - despite this empirical fact - relatively better domestic employment performance of direct FDI is explained by their superior sales performance, resulting from restructuring their international division of labour. / Series: Working Papers Series "Growth and Employment in Europe: Sustainability and Competitiveness"

abstract: The F1Fo ATP synthase is required for energy conversion in almost all living organisms. The F1 complex is a molecular motor that uses ATP hydrolysis to drive rotation of the γ–subunit. It has not been previously possible to resolve the speed and position of the γ–subunit of the F1–ATPase as it rotates during a power stroke. The single molecule experiments presented here measured light scattered from 45X91 nm gold nanorods attached to the γ–subunit that provide an unprecedented 5 μs resolution of rotational position as a function of time. The product of velocity and drag, which were both measured directly, resulted in an average torque of 63±8 pN nm for the Escherichia coli F1-ATPase that was determined to be independent of the load. The rotational velocity had an initial (I) acceleration phase 15° from the end of the catalytic dwell, a slow (S) acceleration phase during ATP binding/ADP release (15°–60°), and a fast (F) acceleration phase (60°–90°) containing an interim deceleration (ID) phase (75°–82°). High ADP concentrations decreased the velocity of the S phase proportional to 'ADP-release' dwells, and the F phase proportional to the free energy derived from the [ADP][Pi]/[ATP] chemical equilibrium. The decreased affinity for ITP increased ITP-binding dwells by 10%, but decreased velocity by 40% during the S phase. This is the first direct evidence that nucleotide binding contributes to F1–ATPase torque. Mutations that affect specific phases of rotation were identified, some in regions of F1 previously considered not to contribute to rotation. Mutations βD372V and γK9I increased the F phase velocity, and γK9I increased the depth of the ID phase. The conversion between S and F phases was specifically affected by γQ269L. While βT273D, βD305E, and αR283Q decreased the velocity of all phases, decreases in velocity due to βD302T, γR268L and γT82A were confined to the I and S phases. The correlations between the structural locations of these mutations and the phases of rotation they affect provide new insight into the molecular basis for F1–ATPase γ-subunit rotation. / Dissertation/Thesis / Ph.D. Molecular and Cellular Biology 2012

A common impairment after a traumatic brain injury (TBI) is a deficit in emotional recognition, such as inferences of others’ intentions. Some researchers have found these impairments in 39\% of the TBI population. Our research information needed to make inferences about emotions and mental states comes from visually presented, nonverbal cues (e.g., facial expressions or gestures). Theory of mind (ToM) deficits after TBI are partially explained by impaired visual attention and the processing of these important cues. This research found that patients with deficits in visual processing differ from healthy controls (HCs). Furthermore, we found visual processing problems can be determined by looking at the eye tracking data developed from industry standard eye tracking hardware and software. We predicted that the eye tracking data of the overall population is correlated to the TASIT test. The visual processing of impaired (who got at least one answer wrong from TASIT questions) and unimpaired (who got all answer correctly from TASIT questions) differs significantly. We have divided the eye-tracking data into 3 second time blocks of time series data to detect the most salient individual blocks to the TASIT score. Our preliminary results suggest that we can predict the whole population's impairment using eye-tracking data with an improved f1 score from 0.54 to 0.73. For this, we developed optimized support vector machine (SVM) and random forest (RF) classifier.

Das molekulare Drehlager der ATP-Synthase 1. Sechs verschiedene EF1-Deletionsmutanten mit verkürzten gamma-Untereinheiten wur­den mittels PCR hergestellt. Die Deletionen befinden sich jeweils am C-terminalen Ende der Untereinheit und umfassen 3 (MM16), 6 (MM20), 9 (MM17), 12 (MM8), 15 (MM18) und 18 (MM19) Aminosäurereste. 2. Durch einen Wachstumstest auf Succinat-Agarplatten wurde festgestellt, daß die von den Plasmiden pMM16, pMM20, pMM17 und pMM8 codierten ATP-Syntha­sen in E. coli DK8 funktionell exprimiert werden können, während pMM18 und pMM19 funktionsunfähige ATP-Synthasen liefern. Die Wachstumsgeschwindig­keit der DK8-Mutanten MM16, MM20 und MM17 auf Succinatmedium wird durch die Deletionen nicht beeinflußt. Hingegen besitzt E. coli DK8 pMM8 auf diesem Medium eine um etwa 33% geringere Wachstumsgeschwindigkeit. 3. Die DK8-Klone MM16, MM20, MM17 und MM8 wurden für die Expression und Isolierung von EF1-Komplexen verwendet. MM18 und MM19 lieferten keine mit Hilfe des S+G-Proteintests nachweisbaren Mengen an EF1. Durch die Verkürzung der Untereinheit gamma kam es bei der Aufreinigung der Enzymkomplexe nicht zu ei­nem verstärkten Verlust dieser Untereinheit. Aufgereinigtes KH7-EF1 (Kontroll-EF1 ohne Verkürzung des gamma-C-Terminus) und die EF1-Komplexe der Deletions­mutanten wiesen ein ähnliches alpha/beta:gamma-Verhältnis auf. 4. Die ATP-Hydrolyseaktivitäten der EF1-Deletionsmutanten zeigten eine starke Ab­hängigkeit von der Deletionslänge. So fielen die Aktivitäten bei 35ºC von 93 u/mg (KH7-EF1) um ca. 75% auf 22 u/mg (MM8-EF1) ab. Dagegen sanken die Aktivie­rungsenergien für die ATP-Hydrolyse durch die EF1-Deletionsmutanten mit zu­nehmender Deletionslänge erheblich schwächer ab. Hier konnte für KH7-EF1 eine Aktivierungsenergie von 54 kJ/mol und für MM8-EF1 35 kJ/mol ermittelt werden. Dies entspricht einer Abnahme um ca. 35%. 5. Das durch die Rotor-Untereinheit gamma erzeugte Drehmoment zeigte nur eine geringe Abhängigkeit von der Deletionslänge. So wiesen die EF1-Komplexe der Deleti­onsmutanten gegenüber KH7-EF1 nur ein um ca. 20% verringertes Drehmoment auf. Eine starke Abhängigkeit von der Deletionslänge wies im mikrovideographi­schen Rotationstest jedoch die Ausbeute an Rotatoren bezogen auf die Beobach­tungszeit der Küvetten auf. Dabei nahm die Ausbeute von KH7-EF1 zu MM8-EF1 erheblich ab. Keine Abhängigkeit von der Deletionslänge zeigte dagegen das Ro­tations/Rotationspausen-Verhältnis innerhalb der Laufzeiten der Rotatoren, die von etwa 10 – 190 s (durchschnittlich ca. 60 s) variierten. Aufgrund der verhältnismä­ßig kurzen Laufzeiten ist eine exakte Angabe des Rotations/Rotationspausen-Ver­hältnisses jedoch nicht möglich. 6. Rotationsexperimente mit EFOF1-Komplexen, die über die C-terminale gamma-Deletion deltaS281-V286 (gamma-6) verfügten, scheiterten möglicherweise aufgrund der Instabilität der Enzymkomplexe. 7. Da die Funktion aktiver EF1-Komplexe durch die gamma-Deletionen offenbar kaum beeinflußt wird, muß davon ausgegangen werden, daß die geringere ATP-Hydroly­seaktivität der Deletionsmutanten durch ein verändertes Verhältnis von aktiven und inaktiven Enzymkomplexen hervorgerufen wird. Die Deletionen beeinflußen damit weniger die mechanische Funktion des Enzyms sondern vielmehr die Stabilität ak­tiver Enzymkomplexe. 8. Mit der Mutante MM10 wurde ein EF1-Komplex erzeugt, der eine Verknüpfung der Rotoruntereinheit gamma mit einer Statoruntereinheit alpha über die Cysteine alphaC280 und gammaC285 ermöglichte. Eine Verknüpfung der beiden Untereinheiten konnte durch Oxidation mit 100 µM DTNB in etwa 30 min zu >90% erreicht werden. Eine Öff­nung der Disulfidbrücke durch Reduktion mit 20 mM DTT erforderte Inkubations­zeiten von bis zu etwa 600 min (Ausbeute >90%). Die Bildung einer Disulfidbrü­cke zwischen alphaC280 und gammaC285 hatte weder einen Einfluß auf die ATP-Hydroly­seaktivität des Enzyms noch auf die Aktivierungsenergie der ATP-Hydrolyse durch MM10-EF1. 9. MM10-EF1 ließ sich im ATP-Hydrolyse-Aktivitätstest über einen Zeitraum von 5 min durch Zugabe von 1 mM AMP-PNP nahezu vollständig hemmen (ca. 96%), während sich mit 1 mM AMP-PNP + 1 mM ADP, 1 mM NaN3 und 1 mM NaN3 + 1 mM ADP nur Inhibierungsgrade von 77-88% erreichen ließen. 10. Durch einen biochemischen Rotationstest konnte die freie Drehbarkeit des C-termi­nalen Bereichs von gamma im alpha3beta3-Hexagon des EF1-Komplexes nachgewiesen werden. Die Drehbarkeit ließ sich auch durch Zugabe von Inhibitoren des Enzym­komplexes im untersuchten Zeitbereich von Stunden nicht blockieren. Dadurch kann nicht bewiesen werden, daß die rotatorische Mobilität des C-terminalen gamma-Be­reichs ausschließlich auf eine katalytisch bedingte gamma-Rotation zurückzuführen ist. Eine weitere Ursache für die rotatorische Mobilität könnte in einer hohen struktu­rellen Flexibilität des gesamten Lagerbereichs von EF1 bestehen. Der Rotationstest zeigt jedoch, daß die durch molekulardynamische Berechnungen nahegelegte Fi­xierung des C-terminalen gamma-Bereichs bei der gamma-Rotation für die Funktion des EF1-Komplexes offenbar keine Rolle spielt. Ein weiterer biochemischer Rotationstest zum Nachweis der Inhibierung der gamma Ro­tationsbewegung durch kompetetive Inhibitoren über einen Zeitraum von etwa 18 h scheiterte offenbar aufgrund der experimentellen Auslegung des Versuchs.

Indiana University-Purdue University Indianapolis (IUPUI) / A common impairment after a traumatic brain injury (TBI) is a deficit in emotional recognition, such as inferences of others’ intentions. Some researchers have found these impairments in 39\% of the TBI population. Our research information needed to make inferences about emotions and mental states comes from visually presented, nonverbal cues (e.g., facial expressions or gestures). Theory of mind (ToM) deficits after TBI are partially explained by impaired visual attention and the processing of these important cues. This research found that patients with deficits in visual processing differ from healthy controls (HCs). Furthermore, we found visual processing problems can be determined by looking at the eye tracking data developed from industry standard eye tracking hardware and software. We predicted that the eye tracking data of the overall population is correlated to the TASIT test. The visual processing of impaired (who got at least one answer wrong from TASIT questions) and unimpaired (who got all answer correctly from TASIT questions) differs significantly. We have divided the eye-tracking data into 3 second time blocks of time series data to detect the most salient individual blocks to the TASIT score. Our preliminary results suggest that we can predict the whole population's impairment using eye-tracking data with an improved f1 score from 0.54 to 0.73. For this, we developed optimized support vector machine (SVM) and random forest (RF) classifier.

No description available.

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Différents types de risques existent dans le domaine financier, tels que le financement du terrorisme, le blanchiment d’argent, la fraude de cartes de crédit, la fraude d’assurance, les risques de crédit, etc. Tout type de fraude peut entraîner des conséquences catastrophiques pour des entités telles que les banques ou les compagnies d’assurances. Ces risques financiers sont généralement détectés à l'aide des algorithmes de classification. Dans les problèmes de classification, la distribution asymétrique des classes, également connue sous le nom de déséquilibre de classe (class imbalance), est un défi très commun pour la détection des fraudes. Des approches spéciales d'exploration de données sont utilisées avec les algorithmes de classification traditionnels pour résoudre ce problème. Le problème de classes déséquilibrées se produit lorsque l'une des classes dans les données a beaucoup plus d'observations que l’autre classe. Ce problème est plus vulnérable lorsque l'on considère dans le contexte des données massives (Big Data). Les données qui sont utilisées pour construire les modèles contiennent une très petite partie de groupe minoritaire qu’on considère positifs par rapport à la classe majoritaire connue sous le nom de négatifs. Dans la plupart des cas, il est plus délicat et crucial de classer correctement le groupe minoritaire plutôt que l'autre groupe, comme la détection de la fraude, le diagnostic d’une maladie, etc. Dans ces exemples, la fraude et la maladie sont les groupes minoritaires et il est plus délicat de détecter un cas de fraude en raison de ses conséquences dangereuses qu'une situation normale. Ces proportions de classes dans les données rendent très difficile à l'algorithme d'apprentissage automatique d'apprendre les caractéristiques et les modèles du groupe minoritaire. Ces algorithmes seront biaisés vers le groupe majoritaire en raison de leurs nombreux exemples dans l'ensemble de données et apprendront à les classer beaucoup plus rapidement que l'autre groupe. Dans ce travail, nous avons développé deux approches : Une première approche ou classifieur unique basée sur les k plus proches voisins et utilise le cosinus comme mesure de similarité (Cost Sensitive Cosine Similarity K-Nearest Neighbors : CoSKNN) et une deuxième approche ou approche hybride qui combine plusieurs classifieurs uniques et fondu sur l'algorithme k-modes (K-modes Imbalanced Classification Hybrid Approach : K-MICHA). Dans l'algorithme CoSKNN, notre objectif était de résoudre le problème du déséquilibre en utilisant la mesure de cosinus et en introduisant un score sensible au coût pour la classification basée sur l'algorithme de KNN. Nous avons mené une expérience de validation comparative au cours de laquelle nous avons prouvé l'efficacité de CoSKNN en termes de taux de classification correcte et de détection des fraudes. D’autre part, K-MICHA a pour objectif de regrouper des points de données similaires en termes des résultats de classifieurs. Ensuite, calculez les probabilités de fraude dans les groupes obtenus afin de les utiliser pour détecter les fraudes de nouvelles observations. Cette approche peut être utilisée pour détecter tout type de fraude financière, lorsque des données étiquetées sont disponibles. La méthode K-MICHA est appliquée dans 3 cas : données concernant la fraude par carte de crédit, paiement mobile et assurance automobile. Dans les trois études de cas, nous comparons K-MICHA au stacking en utilisant le vote, le vote pondéré, la régression logistique et l’algorithme CART. Nous avons également comparé avec Adaboost et la forêt aléatoire. Nous prouvons l'efficacité de K-MICHA sur la base de ces expériences. Nous avons également appliqué K-MICHA dans un cadre Big Data en utilisant H2O et R. Nous avons pu traiter et analyser des ensembles de données plus volumineux en très peu de temps / There are different types of risks in financial domain such as, terrorist financing, money laundering, credit card fraudulence and insurance fraudulence that may result in catastrophic consequences for entities such as banks or insurance companies. These financial risks are usually detected using classification algorithms. In classification problems, the skewed distribution of classes also known as class imbalance, is a very common challenge in financial fraud detection, where special data mining approaches are used along with the traditional classification algorithms to tackle this issue. Imbalance class problem occurs when one of the classes have more instances than another class. This problem is more vulnerable when we consider big data context. The datasets that are used to build and train the models contain an extremely small portion of minority group also known as positives in comparison to the majority class known as negatives. In most of the cases, it’s more delicate and crucial to correctly classify the minority group rather than the other group, like fraud detection, disease diagnosis, etc. In these examples, the fraud and the disease are the minority groups and it’s more delicate to detect a fraud record because of its dangerous consequences, than a normal one. These class data proportions make it very difficult to the machine learning classifier to learn the characteristics and patterns of the minority group. These classifiers will be biased towards the majority group because of their many examples in the dataset and will learn to classify them much faster than the other group. After conducting a thorough study to investigate the challenges faced in the class imbalance cases, we found that we still can’t reach an acceptable sensitivity (i.e. good classification of minority group) without a significant decrease of accuracy. This leads to another challenge which is the choice of performance measures used to evaluate models. In these cases, this choice is not straightforward, the accuracy or sensitivity alone are misleading. We use other measures like precision-recall curve or F1 - score to evaluate this trade-off between accuracy and sensitivity. Our objective is to build an imbalanced classification model that considers the extreme class imbalance and the false alarms, in a big data framework. We developed two approaches: A Cost-Sensitive Cosine Similarity K-Nearest Neighbor (CoSKNN) as a single classifier, and a K-modes Imbalance Classification Hybrid Approach (K-MICHA) as an ensemble learning methodology. In CoSKNN, our aim was to tackle the imbalance problem by using cosine similarity as a distance metric and by introducing a cost sensitive score for the classification using the KNN algorithm. We conducted a comparative validation experiment where we prove the effectiveness of CoSKNN in terms of accuracy and fraud detection. On the other hand, the aim of K-MICHA is to cluster similar data points in terms of the classifiers outputs. Then, calculating the fraud probabilities in the obtained clusters in order to use them for detecting frauds of new transactions. This approach can be used to the detection of any type of financial fraud, where labelled data are available. At the end, we applied K-MICHA to a credit card, mobile payment and auto insurance fraud data sets. In all three case studies, we compare K-MICHA with stacking using voting, weighted voting, logistic regression and CART. We also compared with Adaboost and random forest. We prove the efficiency of K-MICHA based on these experiments

This paper presents new empirical evidence on the determinants of corruption, focussing on the role of globalization and inequality. The estimates for a panel of 102 countries over the period 1995-2005 point to three main results: i) Detection technologies, reflected in a high level of development, human capital, and political rights reduce corruption, whereas natural resource rents increase corruption. ii) Globalization (in terms of both trade and financial openness) has a negative effect on corruption, which is more pronounced in developing countries. iii) Inequality increases corruption, and once the role of inequality is accounted for, the impact of globalization on corruption is halved. In line with recent theory, this suggests that globalization - besides reducing corruption through enhanced competition - affects corruption also by reducing inequality. / Series: Department of Economics Working Paper Series

<p>In mammals sex chromosomes direct and initiate the development of male and female gonads and subsequently secondary sex characteristics. In most vertebrates each individual is pre-destined to either become male or female. The process by which this genetic decision is carried out takes place during the embryonic development and involves a wide range of genes. The <i>fushi tarazu</i> factor-1 (FTZ-F1) is a nuclear receptor and transcription factor, which in mammals has proven to be essential for gonad development and directs the differentiation of testicular Sertoli cells. A mammalian FTZ-F1 homologue subtype, steroidogenic factor-1 (SF-1), is a member of the nuclear receptor 5A1 (NR5A1) group and regulate several enzymes involved in steroid hormone synthesis. It also regulates the expression of the gonadotropin releasing hormone receptor GnRHr and the β-subunit of the luteinizing hormone (LH), indicating that it functions at all levels of the reproductive axis. Another mammalian FTZ-F1 subtype, NR5A2, is in contrast to SF-1, not linked to steroidogenesis or sex determination. Rather, NR5A2 is involved in cholesterol metabolism and bile acid synthesis in liver. Hormones and environmental factors such as temperature and pH can influence teleost development and reproductive traits, rendering them vulnerable to pollutants and climate changes. Very little is known about teleost FTZ-F1 expression, regulation and function. In this thesis, expression patterns of four zebrafish FTZ-F1 genes (ff1a, b, c and d) and two Arctic char genes (acFF1α and β) were studied during development, displaying complex embryonic expression patterns. Ff1a expression was in part congruent with expression of both mammalian NR5A1 and NR5A2 genes but also displayed novel expression domains. The complexity of the expression pattern of ff1a led to the conclusion that the gene may be involved in several developmental processes, including gonad development, which also was indicated by its transcriptional regulation via Sox9a. Two ff1a homologues were also cloned in Arctic char and were shown to be involved in the reproductive cycle, as the expression displayed seasonal cyclicity and preceded that of the down stream steroidogenic genes StAR and CYP11A. High levels were correlated to elevated plasma levels of 11-ketotestosterone (11KT) in males and 17β-estradiol (E2) in females respectively. Treatment with 11KT did not affect FTZ-F1 expression directly but was indicated to alter expression of CYP11A and 3β-hydroxysteroid dehydrogenase. E2 treatment was indicated to down-regulate the expression of testicular FTZ-F1, which may contribute to the feminising effect previously observed in E2 treated salmonids. Ff1d is a novel FTZ-F1 gene, expressed in pituitary and interrenal cells during development, suggesting steroidogenic functions. In adult testis and ovary ff1d was co-expressed with anti-Mullerian hormone (AMH), a gene connected to sex determination in mammals and previously not characterised in teleost fish. The co-expression between ff1d and AMH was found in Sertoli and granulosa cells, which is congruent with the co-expression of mammalian SF-1 and AMH. This suggests that ff1d and AMH may have similar functions in teleost sex differentiation and reproduction, as their mammalian homologues. In conclusion, this study present data that connects members of the teleost FTZ-F1 family to reproduction, cholesterol metabolism and sex determination and differentiation.</p>