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Preprint: Studies on the enzymatic reduction of levulinic acid using Chiralidon-R and Chiralidon-S
(2023)
The enzymatic reduction of levulinic acid by the chiral catalysts Chiralidon-R and Chiralidon-S which are commercially available superabsorbed alcohol dehydrogenases is described. The Chiralidon®-R/S reduces the levulinic acid to the (R,S)-4-hydroxy valeric acid and the (R)- or (S)- gamma-valerolactone.
Die Verfasser stellen in ihrem Beitrag die künftig in Kraft tretenden oder schon in Kraft getretenen Gesetzesvorhaben der europäischen Union vor. Vorab werde auf die abgelaufene Frist zur Anpassung von Standardvertragsklausel hingewiesen. Die Anpassung könne ggf. durch den Data Privacy Act der Kommission bewirkt werden, da dieser eine Angemessenheit suggeriere. Neben dem Digital Markets Act, der die Wahrung der Diskriminierungsfreiheit den Gatekeeper-Plattformen bezüglich der Bewerbung von Waren Dritter vorschreibt, sind ebenfalls der Digital Service Act und der Data Governance Act in Kraft getreten und werden künftig wirksam. Letzteres bezweckt den Datenaustausch von nicht-personenbezogenen Daten öffentlich-rechtlicher Datensätze, wobei anders als bei DSA, der die Verbraucherrechte durchsetzen möchte, mangels Verpflichtung die praktische Umsetzung ausbleiben werde. In der Entwurfsphase stecken der Artificial Intelligence Act, der Data Act, sowie der Cyber Resilience Act. Allen drei sei wegen dem weiten Anwendungsspielraum, der Bußgeldandrohung oder der Cyber-Bedrohungslage besondere praktische Relevanz beizumessen. Die Kommission weite durch diese Gesetzesvorhaben ihre Regelungsabsicht auch auf nicht-personenbezogene Daten und dem Datentransfer aus. Im Ergebnis werden die Unternehmen mit mehr Verpflichtungen konfrontiert, zu dessen Umsetzung ein funktionierendes Compliance-Management-System unabdingbar sei.
Datenschutz & Datenrecht – ein Ausblick auf 2023: Nationale Entwicklungen, EuGH-Vorlagen & Aufsicht
(2023)
Die Verfasser vermitteln einen Überblick über die nationalen Gesetzgebungsverfahren und wesentliche EuGH-Vorlagefragen betreffend den Datenschutz und das Datenrecht für das Jahr 2023. Zunächst folgen u.a. Hinweise in Bezug auf den Hinweisgeberschutz, die Verabschiedung der Einwilligungsverwaltungs-Verordnung zur Konkretisierung des § 26 TTDSG und das Mobilitätsdatengesetz. Anschließend werden Vorlagefragen deutscher Gerichte, die dem EuGH vorgelegt und bereits am 12.01.2023 beantwortet wurden, wie etwa C-154/21 und C-132/21 und die EuGH-Entscheidung vom 9.2.2023 (C-453/21), thematisiert. Überdies führen die Autoren wesentliche Entscheidungen des EuGH an, die im Jahr 2023 aus dem Bereich Datenrecht und Datenschutz zu erwarten seien. Auch Aktivitäten der Datenschutzaufsicht auf nationaler und europäischer Ebene finden Erwähnung. Die Verfasser machen abschließend auf besonders interessante Entscheidungen, die 2023 erwartet werden, wie etwa das EuGH-Urteil zum Auskunftsanspruch, sowie auf das Verhältnis des der Whistleblowing-RL umzusetzende Hinweisgeberschutzgesetz einerseits und Vorgaben des Datenschutzes andererseits, aufmerksam. Sie empfehlen, die künftige Rechtsprechung des EuGH im Blick zu behalten.
In dem vorliegenden Beitrag setzt sich der Verfasser mit dem Urteil des EuGH vom 4.5.2023 (Az.: C-60/22, DSB 2023, 178) zu den Auswirkungen eines formellen Verstoßes des Verantwortlichen gegen die Pflichten aus Artt. 26, 30 DSGVO (juris: EUV 2016/679) auf die Rechtmäßigkeit der Datenverarbeitung auseinander. Nachdem zunächst der zugrunde liegende Sachverhalt und der Hintergrund des Vorlageverfahrens skizziert wurden, gibt der Verfasser einen Überblick über die wesentlichen Entscheidungsgründe des EuGH. Insbesondere stelle der EuGH hier fest, dass die Rechtmäßigkeit der Verarbeitung in Art. 6 DSGVO geregelt sei und sich eine rechtswidrige Verarbeitung daher nur aus einem Verstoß gegen die Artt. 6 ff. DSGVO ergeben könne; die Pflichten aus Art. 26 und Art. 30 DSGVO würden nicht zu den Gründen für die Rechtmäßigkeit der Verarbeitung zählen. Mit Blick auf die Praxis lasse sich, so der Verfasser abschließend, festhalten, dass die Entscheidung insofern nicht überraschend sei; jedoch sei die Feststellung, dass sich aus Verstößen gegen Art. 26 und Art. 30 DSGVO kein Verstoß gegen das Grundrecht auf den Schutz personenbezogener Daten nachweisen lasse überraschend und bedenklich. Auch überrasche es, dass der EuGH eher in einem Nebensatz feststelle, dass der Verantwortliche im Prozess aufgrund seiner Rechenschaftspflicht gegenüber Betroffenen beweisbelastet ist; ob sich die Kammer hier der möglichen Auswirkungen ihrer Ausführungen bewusst gewesen sei, bleibe fraglich.
Umsatzbasierte Bußgelder – wie sonst nur aus dem Kartellrecht bekannt – waren einer der Gründe, warum die Datenschutz-Grundverordnung (DSGVO) vor ihrem Inkrafttreten für erhebliches Aufsehen sorgte. Die vielfach relevanteren Schadensersatzansprüche, die, wie bei „Dieselgate“, aufgrund der Vielzahl von betroffenen Personen und der aus Sicht von Rechtsdienstleistern bestehenden Skalierbarkeit mit weitaus höheren Einbußen für Unternehmen einhergehen können, blieben zunächst unbeachtet. Inzwischen ist der Schadensersatzanspruch gem. Art. 82 DSGVO die Vorschrift, die die meisten Vorlagen zum Europäischen Gerichtshof (EuGH) der letzten Jahre hervorgerufen hat. Am 4.5.2023 hat nun der EuGH (Urteil v. 4.5.2023 - Rs. C-300/21, NWB GAAAJ-41389) in einem Grundsatzurteil über zentrale Fragen rund um den Ersatz immaterieller Schäden als Folge von Datenschutzverstößen entschieden.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
Providing healthcare services frequently involves cognitively demanding tasks, including diagnoses and analyses as well as complex decisions about treatments and therapy. From a global perspective, ethically significant inequalities exist between regions where the expert knowledge required for these tasks is scarce or abundant. One possible strategy to diminish such inequalities and increase healthcare opportunities in expert-scarce settings is to provide healthcare solutions involving digital technologies that do not necessarily require the presence of a human expert, e.g., in the form of artificial intelligent decision-support systems (AI-DSS). Such algorithmic decision-making, however, is mostly developed in resource- and expert-abundant settings to support healthcare experts in their work. As a practical consequence, the normative standards and requirements for such algorithmic decision-making in healthcare require the technology to be at least as explainable as the decisions made by the experts themselves. The goal of providing healthcare in settings where resources and expertise are scarce might come with a normative pull to lower the normative standards of using digital technologies in order to provide at least some healthcare in the first place. We scrutinize this tendency to lower standards in particular settings from a normative perspective, distinguish between different types of absolute and relative, local and global standards of explainability, and conclude by defending an ambitious and practicable standard of local relative explainability.
The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.