Improving Zero-Shot Text Classification with Graph-based Knowledge Representations

Insufficient training data is a key challenge for text classification. In particular, long-tail class distributions and emerging, new classes do not provide any training data for specific classes. Therefore, such a zeroshot setting must incorporate additional, external knowledge to enable transfer learning by connecting the external knowledge of previously unseen classes to texts. Recent zero-shot text classifier utilize only distributional semantics defined by large language models and based on class names or natural language descriptions. This implicit knowledge contains ambiguities, is not able to capture logical relations nor is it an efficient representation of factual knowledge. These drawbacks can be avoided by introducing explicit, external knowledge. Especially, knowledge graphs provide such explicit, unambiguous, and complementary, domain specific knowledge. Hence, this thesis explores graph-based knowledge as additional modality for zero-shot text classification. Besides a general investigation of this modality, the influence on the capabilities of dealing with domain shifts by including domain-specific knowledge is explored

Improving Zero-Shot Text Classification with Graph-based Knowledge Representations

Insufficient training data is a key challenge for text classification. In particular, long-tail class distributions and emerging, new classes do not provide any training data for specific classes. Therefore, such a zeroshot setting must incorporate additional, external knowledge to enable transfer learning by connecting the external knowledge of previously unseen classes to texts. Recent zero-shot text classifier utilize only distributional semantics defined by large language models and based on class names or natural language descriptions. This implicit knowledge contains ambiguities, is not able to capture logical relations nor is it an efficient representation of factual knowledge. These drawbacks can be avoided by introducing explicit, external knowledge. Especially, knowledge graphs provide such explicit, unambiguous, and complementary, domain specific knowledge. Hence, this thesis explores graph-based knowledge as additional modality for zero-shot text classification. Besides a general investigation of this modality, the influence on the capabilities of dealing with domain shifts by including domain-specific knowledge is explored

Nachhaltiges und zeitgemässes Pendeln : Möglichkeiten für einen Wandel in der Mobilität

Das Projekt Smart and Mobile Work in Growth Regions (Smart Commuting) erforscht neue Wege, Arbeit und Leben mit neuen intelligenten Mobilitätskonzepten für nachhaltiges Pendeln zu verbinden. Das Thema hat eine hohe Relevanz, denn die Nachfrage nach Mobilität in der Schweiz nimmt mit der wirtschaftlichen Entwicklung und dem Siedlungswachstum stetig zu. Steigende Einkommen, ein aktiver Lebensstil und das Bevölkerungswachstum durch Migration tragen weiter zu diesen Entwicklungen bei. Dieser Trend hat Folgen für Gesellschaft und Wirtschaft: Langes und mühsames Pendeln kann die Arbeitsproduktivität verringern und die Zeit für andere Tätigkeiten wie Freizeit, Erholung oder Familie einschränken. Die steigende Zahl von Pendlern und immer größere Entfernungen bringen auch die bestehenden Verkehrssysteme an ihre Kapazitätsgrenzen, erhöhen den Energieverbrauch und die gesundheits- und umweltschädlichen Emissionen. Infolgedessen hat die Arbeitskräftemobilität zunehmend negative soziale, ökonomische und ökologische Auswirkungen. Vor allem in überlasteten Ballungsräumen und in Städten mit hohem Pendleranteil müssen die Mobilitätsstrategien angepasst werden. Dabei können die Möglichkeiten neuer Technologien genutzt werden, um die Nachhaltigkeit der Mobilitätssysteme zu verbessern. Entsprechend wurden das Potenzial intermodaler Planungs- und Buchungssysteme wie "Mobility-as-a-Service (MaaS)" aber auch neue Mobilitätskonzepte wie Car- oder Ridesharing in diesem Projekt untersucht. Das übergeordnete Ziel von "Smart Commuting" war es, 1. die Potenziale für CO2-Reduktion im Bereich der Pendlermobilität zu identifizieren, 2. Ansatzpunkte für einen Wandel hin zu einer nachhaltigen Mobilität zu identifizieren und 3. Strategien für eine nachhaltige Mobilität für das individuelle Mobilitätsverhalten sowie für Unternehmen, den städtischen Verkehr und die Mobilitätsplanung abzuleiten. Das Projekt verfolgt einen integrativen Ansatz zur Entwicklung von Pendlermobilitätslösungen. Entwicklungen in Gesellschaft und Arbeitswelt werden als Treiber für Mobilitätsnachfrage und als Ausgangspunkt für Maßnahmen berücksichtigt. Ziel war es auch, Entscheidungsträgern aus Politik und Planung bei der Gestaltung des Veränderungsprozesses hin zu einer nachhaltigen Mobilität Anhaltspunkte aufzuzeigen. Das Projekt ist Teil des ERA-NET Cofund Smart Cities and Communities (ENSCC), das von der Joint Programming Initiative (JPI) Urban Europe und der Smart Cities Member States Initiative (SC MSI) ins Leben gerufen wurde. In drei Fallstudien in Finnland, Österreich und der Schweiz wurden Daten erhoben, vergleichend analysiert und Empfehlungen für die Praxis erarbeitet. Der vorliegende Policy Brief stellt eine Zusammenfassung ausgewählter Ergebnisse und sich daraus ergebender Empfehlungen für Entscheidungsträger und Unternehmen im Transportbereich dar

Digitale Mobilitätsplattformen als Bestandteil einer nachhaltigen Pendlerstrategie im Raum Basel

Durch zunehmende Pendlerströme in den urbanen Regionen Europas resultieren Staus zu Spitzenzeiten, überlastete ÖV-Systeme und steigende Kosten. Gleichzeitig werden Lebensstile und Arbeitsweisen dynamischer und vielseitiger. Mobilitätsstrategien müssen dem angepasst werden. Das europäische Forschungsprojekt ENSCC Smart Commuting untersucht anhand einer Fallstudie in Basel unter Pendlern, inwieweit neue digitale Angebotskonzepte wie Mobility-as-a-Service (MaaS) Lösungen bieten können. Diese fassen die Planung, Buchung und Bezahlung von Mobilität in einem System zusammen.Es wird untersucht, wie gross das Potential solcher Systeme ist, eine Verlagerung von der individuellen Automobilität hin zu nachhaltigeren Verkehrsträgern, wie den öffentlichen- oder dem Aktivverkehr, zu begünstigen

New solutions in sustainable commuting : the attitudes and experience of European stakeholders and experts in Switzerland

New technologies and services can support sustainable mobility if they are successfully integrated into the given mobility system. Decision-makers play a decisive role as ‘enablers’ for such commodities. To find out how a transformation towards sustainable commuting can be forced by implementing innovative solutions like carsharing, Mobility as a Service, or autonomous vehicles, relevant stakeholders were identified for three European case studies. Their perspectives and openness towards trends and new solutions were researched in an online survey. In addition, five expert interviews and two workshops in Switzerland deepened the understanding of how new mobility services could be incorporated into companies through mobility management. Results reflect a strong distinction of stakeholders by their national borders and responsibilities. As new mobility technologies and solutions require collaboration, the acts of supporting strong cross-border and cross-disciplinary cooperation, as well as developing joint interests and work processes beyond traditional ones, are suggested as important starting points. The study reveals a high openness of important stakeholders towards new mobility services and discusses the experience of experts in company mobility management

Smart commuting? : a case study in Switzerland

Today, transportation is responsible for 32% of Switzerland’s CO2 emissions, making it the biggest CO2 emitter, even ahead of the industrial sector (20%). The motivation of the Smart Commuting project was to increase the share of public transport as well as active mobility and decrease private car usage in order to reduce CO2 emissions from transportation

Understanding Class Representations : An Intrinsic Evaluation of Zero-Shot Text Classification

Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance

Sparse optimal control of a quasilinear elliptic PDE in measure spaces

We considern a sparse optimal control problem governed by a quasilinear elliptic PDE in measure spaces. Under rather general assumptions we prove existence of optimal controls and derive first-order necessary optimality conditions. Under additional assumptions also second-order necessary and sufficient optimality conditions are obtained. The key technique of our analysis is the application of the so-called Kirchhoff transform, i.e. a nonlinear superposition operator that allows to transform the quasilinear state equation into a linear one

Axial T2* mapping in intervertebral discs: a new technique for assessment of intervertebral disc degeneration

Objectives: To demonstrate the potential benefits of biochemical axial T2* mapping of intervertebral discs (IVDs) regarding the detection and grading of early stages of degenerative disc disease using 1.5-Tesla magnetic resonance imaging (MRI) in a clinical setting. Methods: Ninety-three patients suffering from lumbar spine problems were examined using standard MRI protocols including an axial T2* mapping protocol. All discs were classified morphologically and grouped as "healthy” or "abnormal”. Differences between groups were analysed regarding to the specific T2* pattern at different regions of interest (ROIs). Results: Healthy intervertebral discs revealed a distinct cross-sectional T2* value profile: T2* values were significantly lower in the annulus fibrosus compared with the nucleus pulposus (P = 0.01). In abnormal IVDs, T2* values were significantly lower, especially towards the centre of the disc representing the expected decreased water content of the nucleus (P = 0.01). In herniated discs, ROIs within the nucleus pulposus and ROIs covering the annulus fibrosus showed decreased T2* values. Conclusions: Axial T2* mapping is effective to detect early stages of degenerative disc disease. There is a potential benefit of axial T2* mapping as a diagnostic tool, allowing the quantitative assessment of intervertebral disc degeneration. Key Points : • Axial T2* mapping effective in detecting early degenerative disc disease. • Healthy and abnormal intervertebral discs revealed distinct cross-sectional T2* value profiles. • T2* can be performed at 1.5T in a clinical settin

Implementing the Singular Value Decomposition in the Helmholtz Analytics Toolkit HeAT

Singular value decomposition (SVD) is a fundamental tool in data science and often used as, e.g., a preprocessing step. When dealing with very large data sets as they often arise at the DLR, performing the analysis in a scalable way on HPC systems can be necessary; this also includes the computation of the SVD. In this talk we present work in progress regarding our implementation of a parallel SVD within the PyTorch- and mpi4py-based HPC-data analytics software HEAT (Helmholtz Analytics Toolkit) developed at DLR, JSC, and KIT (Götz et al., 2020 IEEE International Conference on Big Data, pp. 276-287)