Towards Deep Learning with Competing Generalisation Objectives

The unreasonable effectiveness of Deep Learning continues to deliver unprecedented Artificial Intelligence capabilities to billions of people. Growing datasets and technological advances keep extending the reach of expressive model architectures trained through efficient optimisations. Thus, deep learning approaches continue to provide increasingly proficient subroutines for, among others, computer vision and natural interaction through speech and text. Due to their scalable learning and inference priors, higher performance is often gained cost-effectively through largely automatic training. As a result, new and improved capabilities empower more people while the costs of access drop. The arising opportunities and challenges have profoundly influenced research. Quality attributes of scalable software became central desiderata of deep learning paradigms, including reusability, efficiency, robustness and safety. Ongoing research into continual, meta- and robust learning aims to maximise such scalability metrics in addition to multiple generalisation criteria, despite possible conflicts. A significant challenge is to satisfy competing criteria automatically and cost-effectively. In this thesis, we introduce a unifying perspective on learning with competing generalisation objectives and make three additional contributions. When autonomous learning through multi-criteria optimisation is impractical, it is reasonable to ask whether knowledge of appropriate trade-offs could make it simultaneously effective and efficient. Informed by explicit trade-offs of interest to particular applications, we developed and evaluated bespoke model architecture priors. We introduced a novel architecture for sim-to-real transfer of robotic control policies by learning progressively to generalise anew. Competing desiderata of continual learning were balanced through disjoint capacity and hierarchical reuse of previously learnt representations. A new state-of-the-art meta-learning approach is then proposed. We showed that meta-trained hypernetworks efficiently store and flexibly reuse knowledge for new generalisation criteria through few-shot gradient-based optimisation. Finally, we characterised empirical trade-offs between the many desiderata of adversarial robustness and demonstrated a novel defensive capability of implicit neural networks to hinder many attacks simultaneously

Prospektive Risikostratifizierung von COVID-19-Patienten auf der Basis eines KI-basierten CT-Algorithmus

Ziel Individuelle Risikoevaluierung von COVID-19-Patienten anhand einer bei stationärer Aufnahme initial angefertigten CT-Untersuchung des Thorax zur Prognose einer notwendigen intensivmedizinischen Versorgung im weiteren klinischen Verlauf. Material und Methoden Das CT-Thorax von 34 symptomatischen SARS-CoV-2-positiven Patienten (58,2 ± 11,8 Jahre) wurde mittels Künstlicher-Intelligenz-Algorithmus (KI-Algorithmus) [CT Pneumonia Analysis, Siemens Healthineers] analysiert. Neben der vollautomatischen Quantifizierung des pneumonischen Lungenbefalls (Opacity) wurden die Vitalparameter SpO2 und Atemfrequenz erfasst und deren Einfluss auf eine potenzielle intensivmedizinische Behandlung analysiert. Die anschließend erfolgte intensivmedizinische bzw. normalstationäre Versorgung sowie eine Lungenverdichtung < 10 % (Opacitylow) bzw. ≥ 10 % (Opacityhigh) wurden als Subkollektive definiert. Ergebnisse Patienten, welche drei Tage nach der CT intensivstationär versorgt werden mussten, wiesen eine höhere initiale Infiltration von im Mdn = 34,57 % (IQR = 59,76 %) im Vergleich zu normalstationär versorgten Patienten mit im Mdn = 5,53 % (IQR = 4,79%) (z = 3,599, p < 0,001, r = 0,617) auf. Für den 7. und 14. Tag post-CT ergaben sich vergleichbare Ergebnisse mit 28,45 % gegenüber 5,62 % initialer Infiltration (z = -3,289 p = 0,001, r = 0,564). Bei Opacityhigh- (7/13) war die Notwendigkeit einer intensivmedizinischen Behandlung häufiger als bei Opacitylow-Patienten (0/21) (p < 0,001). Die Kombination aus klinischen und computertomographischen Parametern mit Schwellenwerten von SpO2 ≤ 95 %, Atemfrequenz ≥ 20 Atemzüge/min und erhöhter Lungeninfiltration (≥ 10 %) ergab ein kombiniertes relatives Risiko für eine intensivmedizinische Versorgung von 9,75 95 % CI [2,43, 39,16] 7 und 14 Tage nach der initialen CT-Untersuchung. Schlussfolgerung Die KI-Algorithmus-basierte Auswertung des CT-Thorax ermöglicht eine automatisierte und untersucherunabhängige Quantifizierung der pulmonalen Infiltration bei COVID-19-Patienten. Mit der CT-Quantifizierung allein und in Kombination mit den klinischen Vitalparametern SpO2 und Atemfrequenz wird eine frühzeitige und individuelle prognostische Risikostratifizierung erreicht, welche für eine frühzeitige Therapieeskalation oder als Grundlage für die Planung intensivmedizinischer Kapazitäten im Rahmen der COVID-19-Pandemie herangezogen werden kann

DELINEATING CATCHMENT AREAS FOR THE EASTERN EUROPEAN AIRPORTS IN 2010

This paper investigates the relation between the LAU2 centroids of selected countries in Eastern Europe and the closest airports, in terms of contribution to the construction of the theoretical catchment areas for these air nodes. Using time distances calculated in the road network and demographic mass of 2010, the methodology we propose is a reproductible guideline for the estimation of the relation between different geographical objects that match on an administrative geometry, at local level. Taking a conceptual distance to notions such as spatial accessibility or potential of interaction, we delineated the catchment areas based on the relative demographic contribution of the LAU2 to the construction of the airports' territorial service areas. The main challenge we faced is not the complexity of the model, but the proper estimation of the time distances in the road network and the implementation of cumulated population functions that can be mapped, in order to decline our objective - the territorial catchment areas of the airports

ESTIMATING THE TERRITORIAL AUTOCORRELATION OF AIR PASSENGERS FLOWS IN EUROPE USING A MULTIVARIATE GRAVITY MODEL

Using a gravity model that includes the cost distances and the airports' mass at origin and destination, we have obtained a matrix of residuals for 21325 air links in Europe. These residuals served as the basis for calculating the coefficients of territorial autocorrelation in a multiple regional context. The coefficients we obtained were classified using an hierarchical clustering, at country level. This final typology shows that the national frontiers are more and more permeable to air traffic, having a limitated impact on the intensity of air links between the airports. Some spatial discontinuities are still at work in the European air space and they still have an influence on the amount of traffic, negatively (West-East opposition) or positively (the Scandinavian countries vs. the mainland). Mapping the residuals of the air flows gravity model indicate that these spatial discontinuities can occur also at regional scales of analysis, but they are fuelled by other logics: economic performance, tourism or workforce migration

Scenarios de localisations alternatives des services bancaires dans la Region de Developpement Nord-Est

Scenarii de localizare alternativă a serviciilor bancare din regiunea de dezvoltare Nord-Est. Acest articol îşi propune să construiască, la nivelul Regiunii de Dezvoltare Nord-Est, un model de localizare alternativă a serviciilor bancare de bază, plecând de la premisa că repartiţia actuală a acestora este corectabilă, în sensul unei ameliorări a accesibilităţii teoretice. Metodologia utilizată este pur cantitativă, bazată pe folosirea matricelor de distanţă euclidiană şi a regresiei liniare. Localizările alternative rezultate coincid într-o oarecare măsură cu foste nuclee urbane, active în ultimele două secole

ENTHALPY EU PROJECT: ENABLING THE DRYING PROCESS TO SAVE ENERGY AND WATER, REALISING PROCESS EFFICIENCY IN THE DAIRY CHAIN

The EU funded ENTHALPY project aims to significantly reduce the consumption of water and energy in milk powder production to increase efficiency in the dairy production chain. Using a systematic approach, ENTHALPY project focusses on innovations within the post-harvest chain representing the highest energy and water consumption such as RF heating, solar thermal energy, mono-disperse atomising, dryer modelling, inline monitoring, enzymatic cleaning and membrane technology