From Models to Simulations

This book analyses the impact computerization has had on contemporary science and explains the origins, technical nature and epistemological consequences of the current decisive interplay between technology and science: an intertwining of formalism, computation, data acquisition, data and visualization and how these factors have led to the spread of simulation models since the 1950s. Using historical, comparative and interpretative case studies from a range of disciplines, with a particular emphasis on the case of plant studies, the author shows how and why computers, data treatment devices and programming languages have occasioned a gradual but irresistible and massive shift from mathematical models to computer simulations

Underwater archaeological surveys in Salento waters: results and methods

This paper is related to the survey carried out in several coastal sites of Puglia in 2020, within the project UnderwaterMuse. The core of the activities took place at the Torre S. Sabina site, on the Adriatic Sea, where an integrated topographic and photogrammetric survey has been conducted on the late-imperial Roman shipwreck. Another activity, always on the Adriatic Sea, are been conducted in the “Le Cesine” Natural Reserve how led to the identification of a big pier from the Augustan period. On the Ionian Sea, furthermore, in Porto Cesareo Marine Protected Area, new evidence has been added to the numerous ones already known, such as some spectacular formations composed uniquely by cemented sherds of Tripolitanian amphorae (2nd cent. AD). This evidence seems to be significant markers of sea-level changes and the evolution of the seascape

2nd AtlantOS progress report plus International Scientific and Technical Advisory Board minutes and AtlantOS Legacy document

Prior to the 3rd annual meeting in month 32 a project progress report for the external project boards will be prepared to enable them to as good as possible prepared for the meeting and to ensure consequently that AtlantOS receives as constructive as possible recommendations from the board. The report together with the external summary board meeting report will be part of D11.

Proceedings of IWAMISSE 2019 2nd International Workshop on Advanced Materials and Innovative Systems in Structural Engineering: Novel Researches

The Second International Workshop on Advanced Materials and Innovative Systems in Structural Engineering: Novel Researches, IWAMISSE 2019, is co-organised by The International Federation for Structural Concrete Turkey Branch, fib-Turkey, and Istanbul Technical University, ITU, on September 20, 2019 at ITU. The International Federation for Structural Concrete, fib, is a not-for-profit association formed by 42 national member groups and approximately 1000 corporate and individual members. The fib’s mission is to develop at an international level the study of scientific and practical matters capable of advancing the technical, economic, aesthetic and environmental performance of concrete construction. Istanbul Technical University (ITU) was established in 1773 and is a state university which defined and continues to update methods of engineering and architecture in Turkey. It provides its students with innovative educational facilities while retaining traditional values, as well as using its strong international contacts to mould young, talented individuals who can compete not only within their country borders but also in the global arena. With its educational facilities, social life and strong institutional contacts, ITU has always been preferred by Turkey’s most distinguished students since its foundation and has achieved a justified respect. The workshop covers the topics of advanced materials and innovative systems in structural engineering with a focus on seismic practices as well as other issues related with steel fiber reinforced concrete, anchors/fasteners, precast structures, and recent advances on different types of structural systems such as reinforced concrete, steel, and reinforced masonry structures. This event which is organized for the second time will provide a platform for exploring the potential national and international cooperation schemes in terms of research and application on the areas covered within the scope of the workshop. A part of this workshop is devoted to papers presenting the initial outcomes of the TUBITAK-RCUK Project “Rapid Earthquake Risk Assessment and Post-Earthquake Disaster Management Framework for Substandard Buildings in Turkey” coordinated jointly by Istanbul Technical University and Sheffield University. This proceeding book contains twelve papers from five countries worldwide. We have no doubt that the up-to-date subjects covered during the workshop will be highly beneficial for the academicians and practitioners in the field. We would like to thank all authors for their contributions to the workshop as well as the members of the International Scientific Committee for their rigorous work for reviewing the papers. We also gratefully acknowledge the support of the sponsoring companies and we express our sincere thanks to organization committee for their tireless efforts in the overall organization of the workshop. Many thanks go as well to undergraduate and graduate students from ITU for their assistance during all stages of the workshop.The International Federation for Structural Concrete Turkey Branch, fib-Turkey, and Istanbul Technical Universit

Exploiting multimodality and structure in world representations

An essential aim of artificial intelligence research is to design agents that will eventually cooperate with humans within the real world. To this end, embodied learning is emerging as one of the most important efforts contributed by the machine learning community towards this goal. Recently developing sub-fields concern various aspects of such systems---visual reasoning, language representations, causal mechanisms, robustness to out-of-distribution inputs, to name only a few. In particular, multimodal learning and language grounding are vital to achieving a strong understanding of the real world. Humans build internal representations via interacting with their environment, learning complex associations between visual, auditory and linguistic concepts. Since the world abounds with structure, graph-based encodings are also likely to be incorporated in reasoning and decision-making modules. Furthermore, these relational representations are rather symbolic in nature---providing advantages over other formats, such as raw pixels---and can encode various types of links (temporal, causal, spatial) which can be essential for understanding and acting in the real world. This thesis presents three research works that study and develop likely aspects of future intelligent agents. The first contribution centers on vision-and-language learning, introducing a challenging embodied task that shifts the focus of an existing one to the visual reasoning problem. By extending popular visual question answering (VQA) paradigms, I also designed several models that were evaluated on the novel dataset. This produced initial performance estimates for environment understanding, through the lens of a more challenging VQA downstream task. The second work presents two ways of obtaining hierarchical representations of graph-structured data. These methods either scaled to much larger graphs than the ones processed by the best-performing method at the time, or incorporated theoretical properties via the use of topological data analysis algorithms. Both approaches competed with contemporary state-of-the-art graph classification methods, even outside social domains in the second case, where the inductive bias was PageRank-driven. Finally, the third contribution delves further into relational learning, presenting a probabilistic treatment of graph representations in complex settings such as few-shot, multi-task learning and scarce-labelled data regimes. By adding relational inductive biases to neural processes, the resulting framework can model an entire distribution of functions which generate datasets with structure. This yielded significant performance gains, especially in the aforementioned complex scenarios, with semantically-accurate uncertainty estimates that drastically improved over the neural process baseline. This type of framework may eventually contribute to developing lifelong-learning systems, due to its ability to adapt to novel tasks and distributions. The benchmark, methods and frameworks that I have devised during my doctoral studies suggest important future directions for embodied and graph representation learning research. These areas have increasingly proved their relevance to designing intelligent and collaborative agents, which we may interact with in the near future. By addressing several challenges in this problem space, my contributions therefore take a few steps towards building machine learning systems to be deployed in real-life settings.DREAM CD

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Flight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector Machine models on a binary classification problem. The research revealed that the models achieved different accuracies of flight delay classifications. The Support Vector Machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector Machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher accuracy in a binary classification of flight delays. Finally, we recommended further studies on the applicability of the Deep Feedforward Neural Network in flight delays prediction with specific case studies of either airlines or airports to check the impact on the model's performance