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With the recent growth of sensors, cloud computing handles the data processing of many applications. Processing some of this data on the cloud raises, however, many concerns regarding, e.g., privacy, latency, or single points of failure. Alternatively, thanks to the development of embedded systems, smart wireless devices can share their computation capacity, creating a local wireless cloud for in-network processing. In this context, the processing of an application is divided into smaller jobs so that a device can run one or more jobs.
The contribution of this thesis to this scenario is divided into three parts. In part one, I focus on wireless aspects, such as power control and interference management, for deciding which jobs to run on which node and how to route data between nodes. Hence, I formulate optimization problems and develop heuristic and meta-heuristic algorithms to allocate wireless and computation resources. Additionally, to deal with multiple applications competing for these resources, I develop a reinforcement learning (RL) admission controller to decide which application should be admitted. Next, I look into acoustic applications to improve wireless throughput by using microphone clock synchronization to synchronize wireless transmissions.
In the second part, I jointly work with colleagues from the acoustic processing field to optimize both network and application (i.e., acoustic) qualities. My contribution focuses on the network part, where I study the relation between acoustic and network qualities when selecting a subset of microphones for collecting audio data or selecting a subset of optional jobs for processing these data; too many microphones or too many jobs can lessen quality by unnecessary delays. Hence, I develop RL solutions to select the subset of microphones under network constraints when the speaker is moving while still providing good acoustic quality. Furthermore, I show that autonomous vehicles carrying microphones improve the acoustic qualities of different applications. Accordingly, I develop RL solutions (single and multi-agent ones) for controlling these vehicles.
In the third part, I close the gap between theory and practice. I describe the features of my open-source framework used as a proof of concept for wireless in-network processing. Next, I demonstrate how to run some algorithms developed by colleagues from acoustic processing using my framework. I also use the framework for studying in-network delays (wireless and processing) using different distributions of jobs and network topologies.
Functional characterization of ROS-responsive genes, ANAC085 and ATR7, in Arabidopsis thaliana
(2023)
The CH2Cl2/MeOH (1:1) extract of Zanthoxylum holstzianum stem bark showed good antiplasmodial activity (IC50 2.5 +/- 0.3 and 2.6 +/- 0.3 mu g/mL against the W2 and D6 strains of Plasmodium falciparum, respectively). From the extract five benzophenanthridine alkaloids [8-acetonyldihydrochelerythrine (1), nitidine (2), dihydrochelerythine (3), norchelerythrine (5), arnottianamide (8)]; a 2-quinolone alkaloid [N-methylflindersine (4)]; a lignan [4,4 '-dihydroxy-3,3 '-dimethoxylignan-9,9 '-diyl diacetate (7)] and a dimer of a benzophenanthridine and 2-quinoline [holstzianoquinoline (6)] were isolated. The CH2Cl2/MeOH (1:1) extract of the root bark afforded 1, 3-6, 8, chelerythridimerine (9) and 9-demethyloxychelerythrine (10). Holstzianoquinoline (6) is new, and is the second dimer linked by a C-C bond of a benzophenanthridine and a 2-quinoline reported thus far. The compounds were identified based on spectroscopic evidence. Amongst five compounds (1-5) tested against two strains of P. falciparum, nitidine (IC50 0.11 +/- 0.01 mu g/mL against W2 and D6 strains) and norchelerythrine (IC50 value of 0.15 +/- 0.01 mu g/mL against D6 strain) were the most active.
Sich mit dem eigenen Denken und Handeln reflexiv auseinanderzusetzen ist ein zentrales Element im Selbstreifungsprozess von Lehrkräften im Studium, über die Ausbildung bis hin zur Lehrer:innentätigkeit im Alltag. Der Workshop ermöglichte Zugänge zur selbstreflexiven Auseinandersetzung mit den eigenen Handlungsmustern mithilfe vom Autor (weiter)entwickelter Coachingtools, die im Rahmen der seminaristischen Lehrer:innenbildung in der zweiten Phase konzipiert und erprobt wurden.
This thesis explores the variation in coreference patterns across language modes (i.e., spoken and written) and text genres. The significance of research on variation in language use has been emphasized in a number of linguistic studies. For instance, Biber and Conrad [2009] state that “register/genre variation is a fundamental aspect of human language” and “Given the ubiquity of register/genre variation, an understanding of how linguistic features are used in patterned ways across text varieties is of central importance for both the description of particular languages and the development of cross-linguistic theories of language use.”[p.23]
We examine the variation across genres with the primary goal of contributing to the body of knowledge on the description of language use in English. On the computational side, we believe that incorporating linguistic knowledge into learning-based systems can boost the performance of automatic natural language processing systems, particularly for non-standard texts. Therefore, in addition to their descriptive value, the linguistic findings we provide in this study may prove to be helpful for improving the performance of automatic coreference resolution, which is essential for a good text understanding and beneficial for several downstream NLP applications, including machine translation and text summarization.
In particular, we study a genre of texts that is formed of conversational interactions on the well-known social media platform Twitter. Two factors motivate us: First, Twitter conversations are realized in written form but resemble spoken communication [Scheffler, 2017], and therefore they form an atypical genre for the written mode. Second, while Twitter texts are a complicated genre for automatic coreference resolution, due to their widespread use in the digital sphere, at the same time they are highly relevant for applications that seek to extract information or sentiments from users’ messages. Thus, we are interested in discovering more about the linguistic and computational aspects of coreference in Twitter conversations. We first created a corpus of such conversations for this purpose and annotated it for coreference. We are interested in not only the coreference patterns but the overall discourse behavior of Twitter conversations. To address this, in addition to the coreference relations, we also annotated the coherence relations on the corpus we compiled. The corpus is available online in a newly developed form that allows for separating the tweets from their annotations.
This study consists of three empirical analyses where we independently apply corpus-based, psycholinguistic and computational approaches for the investigation of variation in coreference patterns in a complementary manner. (1) We first make a descriptive analysis of variation across genres through a corpus-based study. We investigate the linguistic aspects of nominal coreference in Twitter conversations and we determine how this genre relates to other text genres in spoken and written modes. In addition to the variation across genres, studying the differences in spoken-written modes is also in focus of linguistic research since from Woolbert [1922]. (2) In order to investigate whether the language mode alone has any effect on coreference patterns, we carry out a crowdsourced experiment and analyze the patterns in the same genre for both spoken and written modes. (3) Finally, we explore the potentials of domain adaptation of automatic coreference resolution (ACR) for the conversational Twitter data. In order to answer the question of how the genre of Twitter conversations relates to other genres in spoken and written modes with respect to coreference patterns, we employ a state-of-the-art neural ACR model [Lee et al., 2018] to examine whether ACR on Twitter conversations will benefit from mode-based separation in out-of-domain training data.
#Gesellschaftslehre 9/10
(2023)
Innovat MOOC
(2023)
The COVID-19 pandemic has revealed the importance for university teachers to have adequate pedagogical and technological competences to cope with the various possible educational scenarios (face-to-face, online, hybrid, etc.), making use of appropriate active learning methodologies and supporting technologies to foster a more effective learning environment. In this context, the InnovaT project has been an important initiative to support the development of pedagogical and technological competences of university teachers in Latin America through several trainings aiming to promote teacher innovation. These trainings combined synchronous online training through webinars and workshops with asynchronous online training through the MOOC “Innovative Teaching in Higher Education.” This MOOC was released twice. The first run took place right during the lockdown of 2020, when Latin American teachers needed urgent training to move to emergency remote teaching overnight. The second run took place in 2022 with the return to face-to-face teaching and the implementation of hybrid educational models. This article shares the results of the design of the MOOC considering the constraints derived from the lockdowns applied in each country, the lessons learned from the delivery of such a MOOC to Latin American university teachers, and the results of the two runs of the MOOC.
Das Spannungsverhältnis zwischen Meinungsfreiheit und allgemeinem Persönlichkeitsrecht hat das Bundesverfassungsgericht seit Jahrzehnten und wiederholt beschäftigt. Insbesondere in den letzten drei Jahren hatte sich das Gericht erneut intensiv damit zu befassen, einen angemessenen Ausgleich zwischen den sich gegenüberstehenden Grundrechtspositionen zu formulieren. Der Beitrag analysiert überblicksartig, inwieweit es in seinen neueren Entscheidungen seine bisherige Rechtsprechung fortgeführt, ergänzt oder gar nachjustiert hat.
The chapter describes the German procedure for obtaining compensation for wrongful convictions. Besides specific cases of compensation for convictions based on subsequently repealed legislation and claims arising from general official liability, German law provides a mechanism for compensation, insofar as a conviction is eliminated or mitigated in a retrial in criminal proceedings. Still within this retrial, the criminal court is called upon to determine whether a claim exists for compensation of material and/or non-material damages on the merits. The amount of compensation is assessed in a judicial administrative procedure at the request of the person concerned. The compensation for non-material harm is calculated on the basis of a lump sum of currently €75 per day of imprisonment; the extent of material harm must be submitted and proven by the person concerned. She has the right to take legal action in the civil courts against the decision on the amount, she has the right to take legal action in the civil courts.