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Introduction The waterfront of Stockholm, one of Europe's fastest-growing cities, stands at the forefront of climate change challenges. As such, there is a pressing need for innovative solutions and resilient urban design. The SOS Climate Waterfront research project gathered international experts and local representatives, coming from different disciplines to work together in May-June 2022 to discuss, explore proposals and design Sustainable Open Solutions (SOS). This book explores three urban sites in Stockholm, holding significant implications for the city's waterfront— Lövholmen, Frihamnen, and Södra Värtan. During the workshop, SOS Climate Waterfront participants, mainly European researchers, analyzed future challenges, raised new questions, and depicted solutions, which can now contribute to cross-country comparisons in a larger EU-framework. The three sites are not only driven by the demand for more housing but also face crucial issues related to cultural heritage, climate change, landscape ecology, and social development. Achieving a delicate balance between these aspects and economic interests presents a significant task for the city. The waterfront of Stockholm holds substantial relevance in the context of climate change and its impact on coastal areas. Thus, analysis of the Swedish context, based on data collected and on-site knowledge sustains a deeper understanding of the challenges and opportunities that lie ahead. Stockholm is expected to be affected by the impacts of climate change, including temperature increases, changing precipitation patterns, and the potential for more frequent cloudbursts. While the rising sea level is a long-term challenge rather than an immediate concern, increasing risks of extreme weather events and flooding were taken in consideration. Stockholm rests on two different bodies of water, at a location where the Baltic Sea (Östersjön in Swedish) with brackish water meets Lake Mälaren, which is an important provider of freshwater for the larger Stockholm area. As the lyrics of a popular contemporary Swedish song (by Robert Broberg) describe it: “the city is full of water”. However, to ensure that the ecological and chemical status will be maintained, in facing future challenges in terms of urbanisation and climate change, much attention has been paid to ensure the preservation of the water quality of the Mälaren Lake, a vital water source for two million people. The city values its water and continuously invests in improving the situation (e.g. the new sluice at Slussen). The activities carried out in the SOS Climate Waterfront workshop in Stockholm integrated this relationship to water as well as the continuing land-rise, the balance of which adds complexity to the sea level modelling and therefore also to the anticipations and scenarios for the future. In this book, the authors explore innovative strategies and design proposals to tackle these challenges while preserving the cultural identity and heritage value of the sites. Researchers from various European cities, supported by experts and academic lectures, analyze extensive input materials and information, ranging from planning documents and historical records to consultation reports and city visions. By drawing upon multidisciplinary backgrounds and experiences, the researchers identify the socioeconomic and environmental qualities of each site, ultimately developing site design concepts and solutions that address climate change challenges, the maintenance of cultural identities, and the protection of biodiversity. Throughout the book, the proposed designs emphasize the importance of finding a balance between preserving cultural heritage, the values of local communities, the stimulating economic growth, and promotion of sustainable urban development. Key elements include the reuse of existing infrastructure, the integration of green-blue schemes, the improvement of biodiversity, and the creation of vibrant and multi-functional neighbourhoods that connect people to each other and their surroundings. While design solutions present promising approaches, their implementation and the institutional challenges that may arise in specific city contexts remain external to the results presented here. The book acknowledges the need for further research and highlights the shared recognition among the workshop participants regarding the gaps and blind spots in their findings. The following chapters of the book delve into climate change in Sweden, the role of culture and arts in the environmental movement, and specific case studies and design proposals for each site. By exploring these diverse perspectives, this book aims to contribute to the ongoing discourse on sustainable urban design and planning, to inspire innovative approaches in addressing complex challenges faced by Stockholm in the future. PART 1 of the book offers a comprehensive understanding of climate change in Sweden, street fishing in Stockholm, and the role of culture and arts in the environmental movement in the Nordic Region and internationally. Furthermore, the lessons from Stockholm and its surroundings in this report draw on presentations, by professionals and researchers from various fields, made during the workshop. Some of these lessons have been written into interesting articles, introduced below. The chapter “Climate change in Sweden” by Magnus Joelsson from the Swedish Meteorological and Hydrological Institute (SMHI) provides an updated analysis with data and the context for discussing climate change in Sweden. The text makes the distinction between weather and climate, referring to the expression “Climate is what you expect, weather is what you get” that Mark Twain is said to have coined. Moreover, calling for actions by emphasising that the trend of climate change is expected to continue, both globally and in Sweden. What will happen in the far future still depends on our actions, now and in the future. The contribution entitled “Urban nature does not stop at the waterfront, neither should urban planning, a case study of street fishing in Stockholm” raises questions about how planning and strategies for waterfront areas in cities should consider more perspectives from a wider group of interests. It discusses how urban dwellers live with water, with a focus on recreational fishing and what this use entails. The authors (Anja Moum Rieser, from KTH Royal Institute of Technology, Wieben Johannes Boonstra and Rikard Hedling, both from Uppsala University) go beyond the human-centric view and expand the gaze to other species’ needs and also incorporating the body of water in planning for the urban waterfront areas. The chapter “The role of culture and arts in the environmental movement in the Nordic Region and internationally” by Elisavet Papageorgiou and Iwona Preis from Intercult, discusses artistic perspectives on sustainability and climate change. This focuses on how art and culture can raise awareness, provide inspiring actions, and promote social cohesion around sustainable practices. Drawing on experiences from projects aiming to invite and engage community dialogues, they argue that artistic strategies can challenge dominant narratives and promote alternative visions for a sustainable future. The contribution “Sense the Marsh” by Thelma Dethelfsen from KTH The Royal Institute of Technology, emphasises the importance of architecture and landscape design in creating adaptive and resilient strategies to manage flooding and sea level rise. The study focuses on how designs can encourage interaction and awareness with the surroundings. Thereby highlighting the interfaces between humans and nature and raising questions about how flooding can be used as a quality and catalyst to attract more people to an area. The resulting design provides an opportunity to experience nature though the design and architectural solutions, situated on the border between human, non-human species and nature. In PART 2, readers will explore the detailed design proposals developed by different groups for the urban sites in focus. These proposals aim to intertwine sustainability, cultural identity, and economic interests, offering insights into the potential for resilient and vibrant urban spaces. By assessing existing conditions on three sites analysed in Stockholm, including Lövholmen, Frihamnen, and Södra Värtan, the teams participating in the workshop actively contributed to the analysis of the sites and development of design solutions for the areas, in the end forming strategies for better preparedness for future challenges and better lives for the inhabitants. Lövholmen is located in the north-western part of Liljeholmen, one of the major developmental centres in Stockholm. The area is currently a closed-off industrial site, but the municipality’s intention is to redevelop it into a mixed urban space with homes, workplaces, shops, schools, and more. It's expected that 1500 new homes will be built in the area. Many of the current industrial buildings are empty and in bad shape. While some of these will be replaced with housing, other industrial buildings have heritage value and should be protected during the development, after which a new use should be found for them. Frihamnen is, together with the Södra Värtan project, part of the larger development of ”Norra Djurgårdsstaden”, the Stockholm Royal Seaport. Frihamnen is located to the south of Värtahamnen and is in turn strongly connected to Loudden in the south. The municipality plans for the area to contain approximately 1700 homes, 4000 workplaces and 75,000 m2 of retail and office space. Some of the existing businesses in Frihamnen will remain, but much of the existing infrastructure is planned to be removed. The harbour no longer handles freight shipping, but passenger ships will continue to depart from the harbour (Frihamnspiren). Södra Värtan is planned to contain 1500 apartments, 20 preschool departments, 155,000 m2 of office and retail space, as well as 10,000 m2 of parks and a 600 m long waterfront walkway. The new development is intended to co-exist with the activities in the harbour, which creates challenges such as the blocking of noise stemming from the cruise ships. The walkways along the waterfront are planned to have shops and restaurants. The contributions of the articles, together with the SOS Climate Waterfront teams’ analysis of the three sites in Stockholm, provides relevant and timely interdisciplinary efforts to co-create novel solutions and future strategies to manage the climate challenges ahead. The solutions relate to the history of the urban territory, actors involved (or those excluded) and changes, over time, of planning ideals. A key theme is how to plan by creating inclusive strategies for the future by involving representatives of diverse interests, competences, and future visions for the sites. The consequences of climate change are affecting these different stakeholders and citizens in a wide range of ways, so including them in the process is crucial. This also includes the inclusion of future generations’ views on urban transformation. The largest challenge is to create new, novel solutions where these human interests, as well as those of local nature and non-human species, can be incorporated, in an effort to plan and design for a mitigation and management of the consequences of climate change. As we embark on this journey of exploration and innovation, we invite readers to delve into the pages of this book, where interdisciplinary research, creative design, and a shared commitment to sustainable urban development and decarbonisation strategies converge. Together, let us envision a future where cities thrive, harmoniously balancing their heritage, environment, and economic aspirations. QC 20231115 SOS Climate Waterfront https://cordis.europa.eu/project/id/823901

We present an online API to access a number of Natural Language Processing services developed at KTH. The services work on Swedish text. They include tokenization, part-of-speech tagging, shallow parsing, compound word analysis, word inflection, lemmatization, spelling error detection and correction, grammar checking, and more. The services can be accessed in several ways, including a RESTful interface, direct socket communication, and premade Web forms. The services are open to anyone. The source code is also freely available making it possible to set up another server or run the tools locally. We have also evaluated the performance of several of the services and compared them to other available systems. Both the precision and the recall for the Granska grammar checker are higher than for both Microsoft Word and Google Docs. The evaluation also shows that the recall is greatly improved when combining all the grammar checking services in the API, compared to any one method, and combining services is made easy by the API.

Companies do not exist in isolation. They are embedded in structural relationships with each other. Mapping a given company’s relationships with other companies in terms of competitors, subsidiaries, suppliers, and customers are key to understanding a company’s major risk factors and opportunities. Conventionally, obtaining and staying up to date with this key knowledge was achieved by reading financial news and reports by highly skilled manual labor like a financial analyst. However, with the development of Natural Language Processing (NLP) and graph databases, it is now possible to systematically extract and store structured information from unstructured data sources. The current go-to method to effectively extract information uses supervised machine learning models, which require a large amount of labeled training data. The data labeling process is usually time-consuming and hard to get in a domain-specific area. This project explores an approach to construct a company domain-specific Knowledge Graph (KG) that contains company-related entities and relationships from the U.S. Securities and Exchange Commission (SEC) 10-K filings by combining a pre-trained general NLP with rule-based patterns in Named Entity Recognition (NER) and Relation Extraction (RE). This approach eliminates the time-consuming data-labeling task in the statistical approach, and by evaluating ten 10-k filings, the model has the overall Recall of 53.6%, Precision of 75.7%, and the F1-score of 62.8%. The result shows it is possible to extract company information using the hybrid methods, which does not require a large amount of labeled training data. However, the project requires the time-consuming process of finding lexical patterns from sentences to extract company-related entities and relationships. Företag existerar inte som isolerade organisationer. De är inbäddade i strukturella relationer med varandra. Att kartlägga ett visst företags relationer med andra företag när det gäller konkurrenter, dotterbolag, leverantörer och kunder är nyckeln till att förstå företagets huvudsakliga riskfaktorer och möjligheter. Det konventionella sättet att hålla sig uppdaterad med denna viktiga kunskap var genom att läsa ekonomiska nyheter och rapporter från högkvalificerad manuell arbetskraft som till exempel en finansanalytiker. Men med utvecklingen av ”Natural Language Processing” (NLP) och grafdatabaser är det nu möjligt att systematiskt extrahera och lagra strukturerad information från ostrukturerade datakällor. Den nuvarande metoden för att effektivt extrahera information använder övervakade maskininlärningsmodeller som kräver en stor mängd märkta träningsdata. Datamärkningsprocessen är vanligtvis tidskrävande och svår att få i ett domänspecifikt område. Detta projekt utforskar ett tillvägagångssätt för att konstruera en företagsdomänspecifikt ”Knowledge Graph” (KG) som innehåller företagsrelaterade enheter och relationer från SEC 10-K-arkivering genom att kombinera en i förväg tränad allmän NLP med regelbaserade mönster i ”Named Entity Recognition” (NER) och ”Relation Extraction” (RE). Detta tillvägagångssätt eliminerar den tidskrävande datamärkningsuppgiften i det statistiska tillvägagångssättet och genom att utvärdera tio SEC 10-K arkiv har modellen den totala återkallelsen på 53,6 %, precision på 75,7 % och F1-poängen på 62,8 %. Resultatet visar att det är möjligt att extrahera företagsinformation med hybridmetoderna, vilket inte kräver en stor mängd märkta träningsdata. Projektet kräver dock en tidskrävande process för att hitta lexikala mönster från meningar för att extrahera företagsrelaterade enheter och relationer.

This paper describes the creation and evaluation of a synthetic dataset of Swedish multiple-choice questions (MCQs) for reading comprehension using GPT-3. Although GPT-3 is trained mostly on English data, with only 0.11% of Swedish texts in its training material, the model still managed to generate MCQs in Swedish. About 44% of the generated MCQs turned out to be of sufficient quality, i.e.\ they were grammatically correct and relevant, with exactly one answer alternative being correct and the others being plausible but wrong. We provide a detailed analysis of the errors and shortcomings of the rejected MCQs, as well an analysis of the level of difficulty of the accepted MCQs. In addition to giving insights into GPT-3, the synthetic dataset could be used for training and evaluation of special-purpose MCQ-generating models. QC 20230602

This Master's Thesis delves into the application of topological regularization techniques and relative latent representations within the realm of zero-shot model stitching. Building upon the prior work of Moschella et al. (2022) that introduces relative latent representations to enhance the similarities between latent spaces of different models, we incorporate the approach of Hofer et al. (2021), which combines Topological Data Analysis (TDA) and Machine Learning techniques for topological densification of class distributions in the latent space. The main research objective is to investigate the impact of topological regularization on zero-shot stitching performance when employing relative latent representations. Theoretical foundations for the relative transformation are established based on the intertwiner groups of activation functions. Empirical analyses are conducted to validate the assumptions underlying the construction of the relative transformation in the latent space. Moreover, experiments are performed on a Large Language Model trained on multilingual Amazon Reviews datasets to evaluate the effectiveness of zero-shot stitching while using the topological densification technique and the relative transformation. The findings indicate that the proposed methodologies can enhance the performance of multilingual model stitching. Specifically, enforcing the relative transformation to preserve the H0 homology death times distributions proves beneficial. Additionally, the presence of similar topological features plays a crucial role in achieving higher model compatibility. However, a more in-depth exploration of the geometric properties of the post-relative transformation latent space is necessary to further improve the topological densification technique. Overall, this work contributes to the emerging field of Topological Machine Learning and provides valuable insights for researchers in transfer learning and representation learning domains. Denna masteruppsats undersöker tillämpningen av topologiska regleringstekniker och relativa latenta representationer inom området för zero-shot model stitching. Genom att bygga vidare på tidigare arbete av Moschella et al. (2022), som introducerade relativa latenta representationer för att förbättra likheterna mellan latenta rummet hos olika modeller, inkorporerar vi tillvägagångssättet av Hofer et al. (2021), som kombinerar topologisk dataanalys (TDA) och maskininlärningstekniker för topologisk ``förtätning'' av klassfördelningar i det latenta utrymmet. Den huvudsakliga forskningsuppgiften är att undersöka effekten av topologisk reglering på zero-shot model stitching-prestanda när man använder relativa latenta representationer. Teoretiska grunder för den relativa transformationen etableras baserat på intertwinergrupperna för aktiveringsfunktioner. Empiriska analyser genomförs för att validera antagandena som ligger till grund för konstruktionen av den relativa transformationen i det latenta rummen. Dessutom utförs experiment på en stor språkmodell tränad på multilinguella Amazon Reviews-dataset för att utvärdera effektiviteten hos zero-shot model stitching med Hofer's topologiska reglering och relativa transformation. Resultaten visar att de föreslagna metoderna kan förbättra prestationen hos zero-shot model stitching för flerspråkiga modeller. Specifikt är det fördelaktigt att tvinga den relativa transformationen att bevara H0 homologins dödstidsfördelningar. Dessutom spelar närvaron av liknande topologiska egenskaper en avgörande roll för att uppnå högre modellkompatibilitet. Dock krävs en mer ingående utforskning av de geometriska egenskaperna hos det latenta utrymmet efter den relativa transformationen för att ytterligare förbättra Hofer's topologiska reglering. Sammanfattningsvis bidrar detta arbete till det framväxande området Topologisk Maskininlärning och ger värdefulla insikter för forskare inom ``transfer-inlärning'' och representationsinlärningsdomäner.

Uppgifter för behandling av naturliga språk (NLP) har under de senaste åren visat sig vara särskilt effektiva när man använder förtränade språkmodeller som BERT. Det enorma kravet på datorresurser som krävs för att träna sådana modeller gör det dock svårt att använda dem i verkligheten. För att lösa detta problem har komprimeringsmetoder utvecklats. I det här projektet studeras, genomförs och testas några av dessa metoder för komprimering av neurala nätverk för textbearbetning. I vårt fall var den mest effektiva metoden Knowledge Distillation, som består i att överföra kunskap från ett stort neuralt nätverk, som kallas läraren, till ett litet neuralt nätverk, som kallas eleven. Det finns flera varianter av detta tillvägagångssätt, som skiljer sig åt i komplexitet. Vi kommer att titta på två av dem i det här projektet. Den första gör det möjligt att överföra kunskap mellan ett neuralt nätverk och en mindre dubbelriktad LSTM, genom att endast använda resultatet från den större modellen. Och en andra, mer komplex metod som uppmuntrar elevmodellen att också lära sig av lärarmodellens mellanliggande lager för att utvinna kunskap. Det slutliga målet med detta projekt är att ge företagets datavetare färdiga komprimeringsmetoder för framtida projekt som kräver användning av djupa neurala nätverk för NLP. Natural language processing (NLP) tasks have proven to be particularly effective when using pre-trained language models such as BERT. However, the enormous demand on computational resources required to train such models makes their use in the real world difficult. To overcome this problem, compression methods have emerged in recent years. In this project, some of these neural network compression approaches for text processing are studied, implemented and tested. In our case, the most efficient method was Knowledge Distillation, which consists in transmitting knowledge from a large neural network, called teacher, to a small neural network, called student. There are several variants of this approach, which differ in their complexity. We will see two of them in this project, the first one which allows a knowledge transfer between any neural network and another smaller bidirectional LSTM, using only the output of the larger model. And a second, more complex approach that encourages the student model to also learn from the intermediate layers of the teacher model for incremental knowledge extraction. The ultimate goal of this project is to provide the company’s data scientists with ready-to-use compression methods for their future projects requiring the use of deep neural networks for NLP. 

Part of book: ISBN 978-1-009-10023-6QC 20221219

Found data - data used for something other than the purpose for which it was originally collected - holds great value in many regards. It typically reflects high ecological validity, a strong cultural worth, and there are significant quantities at hand. However, it is noisy, hard to search through, and its contents are often largely unknown. This thesis explores ways to gain insight into such data collections, specifically with regard to speech and audio data. In recent years, deep learning approaches have shown unrivaled performance in many speech and language technology tasks. However, in addition to large datasets, many of these methods require vast quantities of high-quality labels, which are costly to produce. Moreover, while there are exceptions, machine learning models are typically trained for solving well-defined, narrow problems and perform inadequately in tasks of more general nature - such as providing a high-level description of the contents in a large audio file. This observation reveals a methodological gap that this thesis aims to fill. An ideal system for tackling these matters would combine humans' flexibility and general intelligence with machines' processing power and pattern-finding capabilities. With this idea in mind, the thesis explores the value of including the human-in-the-loop, specifically in the context of gaining insight into collections of found speech. The aim is to combine techniques from speech technology, machine learning paradigms, and human-in-the-loop approaches, with the overall goal of developing and evaluating novel methods for efficiently exploring large quantities of found speech data. One of the main contributions is Edyson, a tool for fast browsing, exploring, and annotating audio. It uses temporally disassembled audio, a technique that decouples the audio from the temporal dimension, in combination with feature extraction methods, dimensionality reduction algorithms, and a flexible listening function, which allows a user to get an informative overview of the contents. Furthermore, crowdsourcing is explored in the context of large-scale perception studies and speech & language data collection. Prior reports on the usefulness of crowd workers for such tasks show promise and are here corroborated. The thesis contributions suggest that the explored approaches are promising options for utilizing large quantities of found audio data and deserve further consideration in research and applied settings. Funnet data - data som används för något annat än det syfte som det först samlades in för - är värdefullt i många avseenden. Det reflekterar vanligtvis hög ekologisk validitet, det har ett starkt kulturellt värde, och det finns stora mängder att ta del av. Det är dock fyllt av brus, svårt att få en överblick av, och ofta är innehållet inte tydligt. Denna avhandling utforskar metoder som ger insikt i dessa datasamlingar, specifikt vad gäller tal och ljud. På senare tid har djupinlärning producerat oöverträffade resultat inom tal och språkteknologi. Många av dessa metoder behöver dock väldiga mängder annoterat data, vilket är kostsamt att skapa. Dessutom är maskininlärningsmodeller vanligtvis tränade med väldefinierade problem i åtanke, och presterar sämre inom mer generella uppgifter - såsom att tillhandahålla en övergripande beskrivning av innehållet i en stor ljudfil. Denna observation visar på en brist inom existerande metodologier, således finns det ett behov av vidare tekniker vilket är något som denna avhandling syftar till att täcka. Ett idealt angreppsätt för dessa problem kombinerar flexibiliteten och den generella intelligensen hos en människa med beräkningskraften och mönsterigenkänningsförmågan hos en maskin. Utifrån dessa idéer utforskar avhandlingen värdet av att inkludera människan i loopen, specifikt utifrån hur insikter om stora insamlingar av funnet tal kan skapas. Huvudidén är således att kombinera tekniker från talteknologi, maskininlärningsparadigm, samt människa-i-loopen-metoder, med det övergripande målet att utveckla och utvärdera nya metoder för utforskandet av stora mängder funnet taldata. Ett primärt bidrag är Edyson, ett verktyg för snabb genomlyssning och annotering av ljud. Det bygger på tidsmässig isärtagning av ljud i kombination med särdragsextraktion, dimensionsreduceringsalgoritmer, samt en flexibel lyssningsfunktion, vilket ger en användare en informativ överblick av innehållet. Vidare undersöks crowdsourcing inom kontexten av storskaliga perceptionsstudier och datainsamling av tal och språkdata. Tidigare rapporter som visar på användbarheten av crowd workers är styrkta av avhandlingens bidrag. Avhandlingsbidragen visar att de undersökta metoderna är lovande alternativ för utforskandet av stora mängder funnet ljuddata och förtjänar vidare uppmärksamhet. QC 20220222

This article explores the life and works of Soviet ethnographer Andrei Grigor'evich Danilin (1896-1942). Drawing on the collections of his documents scattered across a number of archives, institutions and places, the authors aim to reconstruct Danilin's personal, intellectual and 'field' life histories. They further recreate the context in which the ethnographer's archives were formed, including his relationships with scholars, relatives, fieldwork partners, state bodies, and even material objects. The polyphony of the discovered documents and the personal ties that bind Danilin and the coauthor Lidiya A. Danilina (Danilin's daughter) allow us to consider our experiment as a historical ethnography with some elements of 'participant observation' and call the combination of these two dimensions - the archival and the personal - 'an ethnography of an ethnographer'. Настоящая статья посвящена жизни и деятельности советского этнографа Андрея Григорьевича Данилина (1896-1942). На основании документов из сохранившегося личного архива, а также частей «официального» архива, разбросанных по разным институциям, авторы ставят своей целью реконструировать его интеллектуальную, личную и полевую биографии. Второй целью исследования является реконструкция «архивного пространства» этнографа, а именно, как через отношения с разными людьми, бюрократическими инстанциями и порой вещами складывался его архив. Документальное многоголосье, а также персональная связь с главным героем (второй автор статьи - дочь этнографа) позволяет нам рассматривать наш эксперимент как историческую этнографию, которая содержит в себе некоторые элементы «включенного наблюдения». Соединение этих двух измерений мы предлагаем называть этнографией этнографа. QC 20250227