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See detailWhy is the Market Skewness-Return Relationship Negative?
Lehnert, Thorsten UL

Scientific Conference (2019, December 13)

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we ... [more ▼]

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we consider the usual interpretation of e.g. option-implied skewness as an indicator of jump risk or downside risk. One possible explanation for this inconsistency is that there are factors affecting option-implied market skewness other than jump risk in the stock market. In this paper, I find that price pressure associated with “crowded trades” of mutual funds is an important endogenous factor. Given that retail investors are prone to herding, the directional trading of mutual funds is correlated, and their collective actions can generate short-term price pressure on aggregate stock prices. Short sellers systematically exploit these patterns not only in the equity lending market, but also in the options market. In line with this economic channel, I find that firstly, the significant negative relationship between market skewness and returns becomes insignificant, once I control for price pressure. Secondly, the negative relationship is only present for the “bad” downside component of risk-neutral skewness, associated with out-of-the-money put options. For the “good” upside component of risk-neutral skewness, associated with out-of-the-money call options, the relationship is always positive. Thirdly, price pressure affects the skewness-return relationship, which can be clearly distinguished from the impact of flows on the volatility-return relationship in terms of the leverage effect. [less ▲]

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See detailWhy is the Market Skewness-Return Relationship Negative?
Lehnert, Thorsten UL

Scientific Conference (2019, December 13)

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we ... [more ▼]

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we consider the usual interpretation of e.g. option-implied skewness as an indicator of jump risk or downside risk. One possible explanation for this inconsistency is that there are factors affecting option-implied market skewness other than jump risk in the stock market. In this paper, I find that price pressure associated with “crowded trades” of mutual funds is an important endogenous factor. Given that retail investors are prone to herding, the directional trading of mutual funds is correlated, and their collective actions can generate short-term price pressure on aggregate stock prices. Short sellers systematically exploit these patterns not only in the equity lending market, but also in the options market. In line with this economic channel, I find that firstly, the significant negative relationship between market skewness and returns becomes insignificant, once I control for price pressure. Secondly, the negative relationship is only present for the “bad” downside component of risk-neutral skewness, associated with out-of-the-money put options. For the “good” upside component of risk-neutral skewness, associated with out-of-the-money call options, the relationship is always positive. Thirdly, price pressure affects the skewness-return relationship, which can be clearly distinguished from the impact of flows on the volatility-return relationship in terms of the leverage effect. [less ▲]

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See detailDEVELOPING INDIVIDUAL-BASED GUT MICROBIOME METABOLIC MODELS FOR THE INVESTIGATION OF PARKINSON’S DISEASE-ASSOCIATED INTESTINAL MICROBIAL COMMUNITIES
Baldini, Federico UL

Doctoral thesis (2019)

The human phenotype is a result of the interactions of environmental factors with genetic ones. Some environmental factors such as the human gut microbiota composition and the related metabolic functions ... [more ▼]

The human phenotype is a result of the interactions of environmental factors with genetic ones. Some environmental factors such as the human gut microbiota composition and the related metabolic functions are known to impact human health and were put in correlation with the development of different diseases. Most importantly, disentangling the metabolic role played by these factors is crucial to understanding the pathogenesis of complex and multifactorial diseases, such as Parkinson’s Disease. Microbial community sequencing became the standard investigation technique to highlight emerging microbial patterns associated with different health states. However, even if highly informative, such technique alone is only able to provide limited information on possible functions associated with specific microbial communities composition. The integration of a systems biology computational modeling approach termed constraint-based modeling with sequencing data (whole genome sequencing, and 16S rRNA gene sequencing), together with the deployment of advanced statistical techniques (machine learning), helps to elucidate the metabolic role played by these environmental factors and the underlying mechanisms. The first goal of this PhD thesis was the development and deployment of specific methods for the integration of microbial abundance data (coming from microbial community sequencing) into constraint-based modeling, and the analysis of the consequent produced data. The result was the implementation of a new automated pipeline, connecting all these different methods, through which the study of the metabolism of different gut microbial communities was enabled. Second, I investigated possible microbial differences between a cohort a Parkinson’s disease patients and controls. I discovered microbial and metabolic changes in Parkinson’s disease patients and their relative dependence on several physiological covariates, therefore exposing possible mechanisms of pathogenesis of the disease.Overall, the work presented in this thesis represents method development for the investigation of before unexplored functional metabolic consequences associated with microbial changes of the human gut microbiota with a focus on specific complex diseases such as Parkinson’s disease. The consequently formulated hypothesis could be experimentally validated and could represent a starting point to envision possible clinical interventions. [less ▲]

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See detailJoint Scheduling and Precoding for Frame-Based Multigroup Multicasting in Satellite Communications
Bandi, Ashok UL; Shankar, Bhavani UL; Chatzinotas, Symeon UL et al

Scientific Conference (2019, December 09)

Recent satellite standards enforce the coding of multiple users’ data in a frame. This transmission strategy mimics the well-known physical layer multigroup multicasting (MGMC). However, typical beam ... [more ▼]

Recent satellite standards enforce the coding of multiple users’ data in a frame. This transmission strategy mimics the well-known physical layer multigroup multicasting (MGMC). However, typical beam coverage with a large number of users and limited frame length lead to the scheduling of only a few users. Moreover, in emerging aggressive frequency reuse systems, scheduling is coupled with precoding. This is addressed in this work, through the joint design of scheduling and precoding for frame-based MGMC satellite systems. This aim is formulated as the maximization of the sum-rate under per beam power constraint and minimum SINR requirement of scheduled users. Further, a framework is proposed to transform the non-smooth SR objective with integer scheduling and nonconvex SINR constraints as a difference-of-convex problem that facilitates the joint update of scheduling and precoding. Therein, an efficient convex-concave procedure based algorithm is proposed. Finally, the gains (up to 50%) obtained by the jointed design over state-of-the-art methods is shown through Monte-Carlo simulations. [less ▲]

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See detailLa grande-duchesse Marie-Adélaïde en version non falsifiée
Scuto, Denis UL

Article for general public (2019)

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See detailDependability Assessment of the Android OS Through Fault Injection
Iannillo, Antonio Ken UL; Cotroneo, Domenico; Natella, Roberto et al

in IEEE Transaction on Reliability (2019)

The reliability of mobile devices is a challenge for vendors since the mobile software stack has significantly grown in complexity. In this article, we study how to assess the impact of faults on the ... [more ▼]

The reliability of mobile devices is a challenge for vendors since the mobile software stack has significantly grown in complexity. In this article, we study how to assess the impact of faults on the quality of user experience in the Android mobile OS through fault injection. We first address the problem of identifying a realistic fault model for the Android OS, by providing developers a set of lightweight and systematic guidelines for fault modeling. Then, we present an extensible fault injection tool (AndroFIT) to apply such fault model on actual, commercial Android devices. Finally, we present a large fault injection experimentation on three Android products from major vendors and point out several reliability issues and opportunities for improving the Android OS. [less ▲]

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See detailChallenges in completing EMU: asymmetric competition vs fiscal harmonisation. A case study of the Benelux countries
Danescu, Elena UL

in Journal of Contemporary European Research [=JCER] (2019)

This paper aims to investigate the concept, context and socio-economic consequences of fiscal competition in the integrated economic space of EMU in completion, to pinpoint the positive and negative ... [more ▼]

This paper aims to investigate the concept, context and socio-economic consequences of fiscal competition in the integrated economic space of EMU in completion, to pinpoint the positive and negative factors at work via a case study of the Benelux countries – both founder members of the EU and pioneers of EMU – and to examine the impact on European and international regulations in the field. In particular, it will endeavour to provide a comprehensive interpretation of fiscal policy in the Benelux countries via a comparative approach and from a historical perspective. It will look at the development of respective domestic fiscal policies, driven by national interests and by membership of a Community that is subject to requirements in terms of harmonisation and taxation, but also by constant contact (and frequent clashes) with the multilateral international environment. [less ▲]

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See detailThe DAta Protection REgulation COmpliance Model
Bartolini, Cesare UL; Lenzini, Gabriele UL; Robaldo, Livio UL

in IEEE Security and Privacy (2019), 17(6), 37-45

Understanding whether certain technical measures comply with the General Data Protection Regulation’s (GDPR’s) principles is complex legal work. This article describes a model of the GDPR that allows for ... [more ▼]

Understanding whether certain technical measures comply with the General Data Protection Regulation’s (GDPR’s) principles is complex legal work. This article describes a model of the GDPR that allows for semiautomatic processing of legal text and the leveraging of state-of-the-art legal informatics approaches, which are useful for legal reasoning, software design, information retrieval, or compliance checking. [less ▲]

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See detailEvery quasitrivial n-ary semigroup is reducible to a semigroup
Couceiro, Miguel; Devillet, Jimmy UL

in Algebra Universalis (2019), 80(4),

We show that every quasitrivial n-ary semigroup is reducible to a binary semigroup, and we provide necessary and sufficient conditions for such a reduction to be unique. These results are then refined in ... [more ▼]

We show that every quasitrivial n-ary semigroup is reducible to a binary semigroup, and we provide necessary and sufficient conditions for such a reduction to be unique. These results are then refined in the case of symmetric n-ary semigroups. We also explicitly determine the sizes of these classes when the semigroups are defined on finite sets. As a byproduct of these enumerations, we obtain several new integer sequences. [less ▲]

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See detailDistance-based vertex identification in graphs: The outer multiset dimension
Gil-Pons, Reynaldo; Ramirez Cruz, Yunior UL; Trujillo-Rasua, Rolando et al

in Applied Mathematics and Computation (2019), 363

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See detailTemporal 3D Human Pose Estimation for Action Recognition from Arbitrary Viewpoints
Adel Musallam, Mohamed; Baptista, Renato UL; Al Ismaeil, Kassem UL et al

in 6th Annual Conf. on Computational Science & Computational Intelligence, Las Vegas 5-7 December 2019 (2019, December)

This work presents a new view-invariant action recognition system that is able to classify human actions by using a single RGB camera, including challenging camera viewpoints. Understanding actions from ... [more ▼]

This work presents a new view-invariant action recognition system that is able to classify human actions by using a single RGB camera, including challenging camera viewpoints. Understanding actions from different viewpoints remains an extremely challenging problem, due to depth ambiguities, occlusion, and a large variety of appearances and scenes. Moreover, using only the information from the 2D perspective gives different interpretations for the same action seen from different viewpoints. Our system operates in two subsequent stages. The first stage estimates the 2D human pose using a convolution neural network. In the next stage, the 2D human poses are lifted to 3D human poses, using a temporal convolution neural network that enforces the temporal coherence over the estimated 3D poses. The estimated 3D poses from different viewpoints are then aligned to the same camera reference frame. Finally, we propose to use a temporal convolution network-based classifier for cross-view action recognition. Our results show that we can achieve state of art view-invariant action recognition accuracy even for the challenging viewpoints by only using RGB videos, without pre-training on synthetic or motion capture data. [less ▲]

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See detailThe Impact of Human Mobility on Edge Data Center Deployment in Urban Environments
Vitello, Piergiorgio UL; Capponi, Andrea UL; Fiandrino, Claudio UL et al

in IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019 (2019, December)

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end ... [more ▼]

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end, effective placement of EDCs in urban environments is key for proper load balance and to minimize outages. In this paper, we specifically tackle this problem. To fully understand how the computational demand of EDCs varies, it is fundamental to analyze the complex dynamics of cities. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments in order to minimize outages. To this end, we propose and compare two heuristics. In particular, we present the mobility-aware deployment algorithm (MDA) that outperforms approaches that do not consider citizens mobility. Simulations are conducted in Luxembourg City by extending the CrowdSenSim simulator and show that efficient EDCs placement significantly reduces outages. [less ▲]

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See detailTowards a complexity turn in border studies? Rückblicke und Ausblicke
Wille, Christian UL

Presentation (2019, December)

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See detailFrom tech to bench: Deep Learning pipeline for image segmentation of high-throughput high-content microscopy data
Garcia Santa Cruz, Beatriz UL; Jarazo, Javier UL; Saraiva, Claudia UL et al

Poster (2019, November 29)

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards ... [more ▼]

Automation of biological image analysis is essential to boost biomedical research. The study of complex diseases such as neurodegenerative diseases calls for big amounts of data to build models towards precision medicine. Such data acquisition is feasible in the context of high-throughput screening in which the quality of the results relays on the accuracy of image analysis. Although the state-of-the-art solutions for image segmentation employ deep learning approaches, the high cost of manual data curation is hampering the real use in current biomedical research laboratories. Here, we propose a pipeline that employs deep learning not only to conduct accurate segmentation but also to assist with the creation of high-quality datasets in a less time-consuming solution for the experts. Weakly-labelled datasets are becoming a common alternative as a starting point to develop real-world solutions. Traditional approaches based on classical multimedia signal processing were employed to generate a pipeline specifically optimized for the high-throughput screening images of iPSC fused with rosella biosensor. Such pipeline produced good segmentation results but with several inaccuracies. We employed the weakly-labelled masks produced in this pipeline to train a multiclass semantic segmentation CNN solution based on U-net architecture. Since a strong class imbalance was detected between the classes, we employed a class sensitive cost function: Dice coe!cient. Next, we evaluated the accuracy between the weakly-labelled data and the trained network segmentation using double-blind tests conducted by experts in cell biology with experience in this type of images; as well as traditional metrics to evaluate the quality of the segmentation using manually curated segmentations by cell biology experts. In all the evaluations the prediction of the neural network overcomes the weakly-labelled data quality segmentation. Another big handicap that complicates the use of deep learning solutions in wet lab environments is the lack of user-friendly tools for non-computational experts such as biologists. To complete our solution, we integrated the trained network on a GUI built on MATLAB environment with non-programming requirements for the user. This integration allows conducting semantic segmentation of microscopy images in a few seconds. In addition, thanks to the patch-based approach it can be employed in images with different sizes. Finally, the human-experts can correct the potential inaccuracies of the prediction in a simple interactive way which can be easily stored and employed to re-train the network to improve its accuracy. In conclusion, our solution focuses on two important bottlenecks to translate leading-edge technologies in computer vision to biomedical research: On one hand, the effortless obtention of high-quality datasets with expertise supervision taking advantage of the proven ability of our CNN solution to generalize from weakly-labelled inaccuracies. On the other hand, the ease of use provided by the GUI integration of our solution to both segment images and interact with the predicted output. Overall this approach looks promising for fast adaptability to new scenarios. [less ▲]

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See detailDeep Neural Networks for Personalized Sentiment Analysis with Information Decay
Guo, Siwen UL

Doctoral thesis (2019)

People have different lexical choices when expressing their opinions. Sentiment analysis, as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity. In ... [more ▼]

People have different lexical choices when expressing their opinions. Sentiment analysis, as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity. In this research, I look beyond the traditional population-level sentiment modeling and leverage socio-psychological theories to incorporate the concept of personalized modeling. In particular, a hierarchical neural network is constructed, which takes related information from a person’s past expressions to provide a better understanding of the sentiment from the expresser’s perspective. Such personalized models can suffer from the data sparsity issue, therefore they are difficult to develop. In this work, this issue is addressed by introducing the user information at the input such that the individuality from each user can be captured without building a model for each user and the network is trained in one process. The evolution of a person’s sentiment over time is another aspect to investigate in personalization. It can be suggested that recent incidents or opinions may have more effect on the person’s current sentiment than the older ones, and the relativeness between the targets of the incidents or opinions plays a role on the effect. Moreover, psychological studies have argued that individual variation exists in how frequently people change their sentiments. In order to study these phenomena in sentiment analysis, an attention mechanism which is reshaped with the Hawkes process is applied on top of a recurrent network for a user-specific design. Furthermore, the modified attention mechanism delivers a functionality in addition to the conventional neural networks, which offers flexibility in modeling information decay for temporal sequences with various time intervals. The developed model targets data from social platforms and Twitter is used as an example. After experimenting with manually and automatically labeled datasets, it can be found that the input formulation for representing the concerned information and the network design are the two major impact factors of the performance. With the proposed model, positive results have been observed which confirm the effectiveness of including user-specific information. The results reciprocally support the psychological theories through the real-world actions observed. The research carried out in this dissertation demonstrates a comprehensive study of the significance of considering individuality in sentiment analysis, which opens up new perspectives for future research in the area and brings opportunities for various applications. [less ▲]

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See detailDreaming of Constructivist Technology Integration Strategies in Future Teacher Students
Reuter, Bob UL; Busana, Gilbert UL

Scientific Conference (2019, November 27)

Based on previous experiences in preparing future teachers for technology integration (Reuter & Busana, 2017), and based on the recommendations from Kolb’s (2017) Triple E framework about effective uses ... [more ▼]

Based on previous experiences in preparing future teachers for technology integration (Reuter & Busana, 2017), and based on the recommendations from Kolb’s (2017) Triple E framework about effective uses of ICT in education, we have adapted the Educational Technology course in our Initial Teacher Training. Over the years, we have indeed observed that, when given the choice of the type of technology integration strategies, many students designed ICT-based learning and teaching scenarios that implemented a rather teacher-centred teaching model (Roblyer & Doering, 2013). These scenarios were often far from innovative nor did they implement the disruptive potential of ICT in education (Christensen, Horn & Johnson, 2008). In the winter semester 2018-2019 we thus decided to ask our students to design and develop constructivist technology integration scenarios. We assessed the success of this adaptation with the help of our own observations, the semester reports produced by our students and their answers to an end-of-semester course evaluation. In general, we saw that students were able to design rather attractive constructivist learning activities. We also observed that our students were quite surprised that such activities do not require complicated and expensive tools, but that they can be implemented with standard productivity tools. [less ▲]

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See detailA professional development course in translanguaging: Challenges and opportunities
Aleksic, Gabrijela UL; Dzoen, Bebic-Crestany

Scientific Conference (2019, November 27)

In Luxembourg, the new law in 2017 has declared multilingual early education mandatory. Not only that teachers need to help children develop their Luxembourgish, but also they need to familiarize them ... [more ▼]

In Luxembourg, the new law in 2017 has declared multilingual early education mandatory. Not only that teachers need to help children develop their Luxembourgish, but also they need to familiarize them with French and value their home languages. In order to support preschool teachers in this endeavour, our project aims to: (1) offer a professional development (PD) course in translanguaging, (2) involve children’s families to reinforce home-school collaboration, and (3) foster children’s cognitive, linguistic, and socio-emotional engagement in the classroom. We use a panoply of measures to reach our aims: focus groups and teacher questionnaires (aim 1), parent questionnaires and interviews (aim 2), a test in early literacy and numeracy in school and home languages, teacher assessment of children’s socio-emotional development and video observations with children (aim 3). Translanguaging, the main topic of our 22 hour PD course (June – December 2019), is the use of a full linguistic repertoire to make meaning (Otheguy, García, & Reid, 2015). In eight sessions, we explore multilingual ecology, parental involvement, and oracy and early literacy. We will present preliminary findings of the focus groups with teachers and tests in early literacy and numeracy in children’s home and school languages. Challenges and opportunities that emerged during the course will be explored as well. [less ▲]

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See detailTeacher Attitudes towards Ethnic Minority Students: Effects of Schools´ Cultural Diversity
Glock, Sabine UL; Kovacs, Carrie UL; Pit-Ten Cate, Ineke UL

in British Journal of Educational Psychology (2019), 89

Background: Research exploring mechanisms driving inequalities in school systems, has found that biased teacher judgments contribute to observed disadvantages for ethnic minority students. Teacher ... [more ▼]

Background: Research exploring mechanisms driving inequalities in school systems, has found that biased teacher judgments contribute to observed disadvantages for ethnic minority students. Teacher judgments may be driven by explicit and implicit attitudes. Aims: The current research explored the effect of cultural diversity at schools (actual or imagined) on teachers’ attitudes toward ethnic minority students. Samples: One hundred and-five preservice teachers (90 female) with a mean age 26.20 of years (teaching experience: 57.55 weeks) participated in Study 1. Two hundred and thirty-one teachers (159 female) with a mean age of 41.00 years (teaching experience: 12.92 years) participated Study 2. Method: Cultural diversity was operationalized via a fictive description of a school (Study 1) or via the actual proportion of ethnic minority students at the school (Study 2). An Implicit Association Test assessed implicit attitudes toward ethnic minority students. Explicit attitudes were assessed via questionnaire. Results: Preservice teachers imagining a more culturally diverse school held more negative implicit attitudes toward ethnic minority students than those imagining a less diverse school. In contrast, in-service teachers actually working in more diverse schools held less negative implicit attitudes toward minority students. Preservice teachers associated teaching in culturally diverse schools with increased effort, whereas in-service teachers actually working in culturally diverse schools reported more enthusiasm toward teaching ethnic minority students. Conclusions: This research shows the challenge and the negative stereotypes preservice teachers associate with culturally diverse schools, while inservice teachers’ negative associations may be buffered by the actual experience of working with ethnic minority students. [less ▲]

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