Sustainability is increasingly requested by society due to rising global issues. However the majority of companies, particularly the large and hierarchical ones, face huge challenges in properly integrating sustainability in their business, and most importantly - in understanding the opportunities sustainability offers both for their competitiveness and shared value creation. The purpose of the thesis is to holistically define and explain the major aspects that are critical for win-win corporate sustainability (CS) embedding into large established companies. Firstly, a framework of CS embedding has been developed which is supported by a simple CS three-stage model to be used in assessing the CS integration stages and processes in a company. The framework in particular focuses on three major CS aspects: strategic and operational integration, innovation, and organisational culture. Secondly, the created model is applied in the case study of the large power company Vattenfall AB in order to assess its overall CS implementation situation, the challenges it faces and the stage of CS practices. The analysis resulted in structured findings and a list of major strategic recommendations to advice the company on CS advancement. The outcomes of the study can be applied as learning material in other large conservative companies of similar complexity that struggle with sustainability performance. The research has contributed in filling the knowledge gap of understanding how CS embedding works, its major aspects, challenges and opportunities it provides. The framework developed for embedding CS when used in conjunction with the CS three-stage model could be used for further empirical research or alternatively for practical application by companies themselves.

Sequences of event intervals occur in several application domains, while their inherent complexity hinders scalable solutions to tasks such as clustering and classification. In this thesis, we propose a novel spectral embedding representation of event interval sequences that relies on bipartite graphs. More concretely, each event interval sequence is represented by a bipartite graph by following three main steps: (1) creating a hash table that can quickly convert a collection of event interval sequences into a bipartite graph representation, (2) creating and regularizing a bi-adjacency matrix corresponding to the bipartite graph, (3) defining a spectral embedding mapping on the bi-adjacency matrix. In addition, we show that substantial improvements can be achieved with regard to classification performance through pruning parameters that capture the nature of the relations formed by the event intervals. We demonstrate through extensive experimental evaluation on five real-world datasets that our approach can obtain runtime speedups of up to two orders of magnitude compared to other state-of-the-art methods and similar or better clustering and classification performance. / Sekvenser av händelsesintervall förekommer i flera applikationsdomäner, medan deras inneboende komplexitet hindrar skalbara lösningar på uppgifter som kluster och klassificering. I den här avhandlingen föreslår vi en ny spektral inbäddningsrepresentation av händelsens intervallsekvenser som förlitar sig på bipartitgrafer. Mer konkret representeras varje händelsesintervalsekvens av en bipartitgraf genom att följa tre huvudsteg: (1) skapa en hashtabell som snabbt kan konvertera en samling händelsintervalsekvenser till en bipartig grafrepresentation, (2) skapa och reglera en bi-adjacency-matris som motsvarar bipartitgrafen, (3) definiera en spektral inbäddning på bi-adjacensmatrisen. Dessutom visar vi att väsentliga förbättringar kan uppnås med avseende på klassificeringsprestanda genom beskärningsparametrar som fångar arten av relationerna som bildas av händelsesintervallen. Vi demonstrerar genom omfattande experimentell utvärdering på fem verkliga datasätt att vår strategi kan erhålla runtime-hastigheter på upp till två storlekar jämfört med andra modernaste metoder och liknande eller bättre kluster- och klassificerings- prestanda.

Electronic Health Record (EHR) creates a huge number of databases which are being updated dynamically. Major goal of interoperability in healthcare is to facilitate the seamless exchange of healthcare related data and an environment to supports interoperability and secure transfer of data. The health care organisations face difficulties in exchanging patient’s health care information and laboratory reports etc. due to a lack of semantic interoperability. Hence, there is a need of semantic web technologies for addressing healthcare interoperability problems by enabling various healthcare standards from various healthcare entities (doctors, clinics, hospitals etc.) to exchange data and its semantics which can be understood by both machines and humans. Thus, a framework with a similarity analyser has been proposed in the thesis that dealt with semantic interoperability. While dealing with semantic interoperability, another consideration was the use of word embedding and ontology for knowledge discovery. In medical domain, the main challenge for medical information extraction system is to find the required information by considering explicit and implicit clinical context with high degree of precision and accuracy. For semantic similarity of medical text at different levels (conceptual, sentence and document level), different methods and techniques have been widely presented, but I made sure that the semantic content of a text that is presented includes the correct meaning of words and sentences. A comparative analysis of approaches included ontology followed by word embedding or vice-versa have been applied to explore the methodology to define which approach gives better results for gaining higher semantic similarity. Selecting the Kidney Cancer dataset as a use case, I concluded that both approaches work better in different circumstances. However, the approach in which ontology is followed by word embedding to enrich data first has shown better results. Apart from enriching the EHR, extracting relevant information is also challenging. To solve this challenge, the concept of analogy has been applied to explain similarities between two different contents as analogies play a significant role in understanding new concepts. The concept of analogy helps healthcare professionals to communicate with patients effectively and help them understand their disease and treatment. So, I utilised analogies in this thesis to support the extraction of relevant information from the medical text. Since accessing EHR has been challenging, tweets text is used as an alternative for EHR as social media has appeared as a relevant data source in recent years. An algorithm has been proposed to analyse medical tweets based on analogous words. The results have been used to validate the proposed methods. Two experts from medical domain have given their views on the proposed methods in comparison with the similar method named as SemDeep. The quantitative and qualitative results have shown that the proposed analogy-based method bring diversity and are helpful in analysing the specific disease or in text classification.

The rapidly increasing number of existing drugs with genomic, biomedical, and pharmacological data make computational analyses possible, which reduces the search space for drugs and facilitates drug repositioning (DR). Thus, artificial intelligence, machine learning, and data mining have been used to identify biological interactions such as drug-target interactions (DTI), drug-disease associations, and drug-response. The prediction of these biological interactions is seen as a critical phase needed to make drug development more sustainable. Furthermore, late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed. In this dissertation, we tried to address three crucial problems associated with the DR pipeline and presents several novel computational methods developed for DR. First, we developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. Second, because it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). We discussed how to develop more robust DTBA methods and subsequently developed Affinity2Vec, the first regression-based method that formulates the entire task as a graph-based method and combines several computational techniques from feature representation learning, graph mining, and machine learning with no 3D structural data of proteins. Affinity2Vec outperforms the state-of-the-art methods. Finally, since drug development failure is associated with sub-optimal target identification, we developed the first DL-based computational method (OncoRTT) to identify cancer-specific therapeutic targets for the ten most common cancers worldwide. Implementing our approach required creating a suitable dataset that could be used by the computational method to identify oncology-related DTIs. Thus, we created the OncologyTT datasets to build and evaluate our OncoRTT method. Our methods demonstrated their efficiency by achieving high prediction performance and identifying therapeutic targets for several cancer types. Overall, in this dissertation, we developed several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities.

For many tasks in market research it is important to model customers and products as comparable instances. Usually, the integration of customers and products into one model is not trivial. In this paper, we will detail an approach for a combined vector space of customers and products based on word embeddings learned from receipt data. To highlight the strengths of this approach we propose four different applications: recommender systems, customer and product segmentation and purchase prediction. Experimental results on a real-world dataset with 200M order receipts for 2M customers show that our word embedding approach is promising and helps to improve the quality in these applications scenarios.

Performance analysis of Digital Subscriber Line (DSL) technologies, which are implemented on existing telephone subscriber loops, is of vital importance to DSL service providers. This type of analysis requires accurate prediction of the local loop structure and precise identification of the cable parameters. These cables are the main components of the loop and are typically comprised of multi-conductor twisted pair type currently being used on existing telephone subscriber loops. This system identification problem was investigated through the application of single port measurements, with preference being placed on measurements taken from the service provider's end of the loop under investigation. Once the cabling system has been identified, the performance analysis of the loop was obtained through simulation. Accurate modeling is an important aspect of any system identification solution; therefore, the modeling of the twisted pair cables was thoroughly investigated in this research. Early modeling attempts of twisted pair cabling systems for use with (DSL) technology has not been vigorously investigated due to the difficulty in obtaining wideband physical data necessary for the task as well as the limitations of simulators to accurately model the skin effects of the conductors. Models are developed in this research that produce a wideband representation of the twisted pair cables through the use of the data measured in high frequency spectra. The twisted-pair cable models were then applied to the system identification problem through a de-embedding type approach. The identification process accurately characterizes the sections of the subscriber loop closest to the measurements node, and these identified sections were then modeled and de-embedded from the system measurement in a layer removing, or "peeling", type process. After each identified section was de-embedded from the system measurement, the process was repeated until the entire system was identified. Upon completion of the system identification process, the resulting system model was simulated between the central office (CO) and resulting identified customer nodes for the evaluation of performance analysis. The performance analysis allows the providers to identify points where the DSL technology is feasible, and where so, the rates of the data transfer to the nodes that can be expected. / Ph. D.

The work herein is, in part, motivated by the idea of creating optimized, actuating structures using additive manufacturing processes (AM). By developing a consistent, repeatable method for designing and manufacturing multi-material compliant mechanisms, significant performance improvements can be seen in application, such as increased mechanism deflection. There are three distinct categories of research that contribute to this overall motivating idea: 1) investigation of an appropriate multi-material topology optimization process for multi-material jetting, 2) understanding the role that manufacturing constraints play in the fabrication of complex, optimized structures, and 3) investigation of an appropriate process for embedding actuating elements within material jetted parts. PolyJet material jetting is the focus of this dissertation research as it is one of the only AM processes capable of utilizing multiple material phases (e.g., stiff and flexible) within a single build, making it uniquely qualified for manufacturing complex, multi-material compliant mechanisms. However, there are two limitations with the PolyJet process within this context: 1) there is currently a dearth of understanding regarding both single and multi-material manufacturing constraints in the PolyJet process and 2) there is no robust embedding methodology for the in-situ embedding of foreign actuating elements within the PolyJet process. These two gaps (and how they relate to the field of compliant mechanism design) will be discussed in detail in this dissertation. Specific manufacturing constraints investigated include 1) "design for embedding" considerations, 2) removal of support material from printed parts, 3) self-supporting angle of surfaces, 4) post-process survivability of fine features, 5) minimum manufacturable feature size, and 6) material properties of digital materials with relation to feature size. The key manufacturing process and geometric design factors that influence each of these constraints are experimentally determined, as well as the quantitative limitations that each constraint imposes on design. / Ph. D.

The analysis of signals stemming from a physical system is crucial for the experimental investigation of the underlying dynamics that drives the system itself. The field of time series analysis comprises a wide variety of techniques developed with the purpose of characterizing signals and, ultimately, of providing insights on the phenomena that govern the temporal evolution of the generating system. A renowned example in this field is given by spectral analysis: the use of Fourier or Laplace transforms to bring time-domain signals into the more convenient frequency space allows to disclose the key features of linear systems. A more complex scenario turns up when nonlinearity intervenes within a system's dynamics. Nonlinear coupling between a system's degrees of freedom brings about interesting dynamical regimes, such as self-sustained periodic (though anharmonic) oscillations ("limit cycles"), or quasi-periodic evolutions that exhibit sharp spectral lines while lacking strict periodicity ("limit tori"). Among the consequences of nonlinearity, the onset of chaos is definitely the most fascinating one. Chaos is a dynamical regime characterized by unpredictability and lack of periodicity, despite being generated by deterministic laws. Signals generated by chaotic dynamical systems appear as irregular: the corresponding spectra are broad and flat, prediction of future values is challenging, and evolutions within the systems' state spaces converge to strange attractor sets with noninteger dimensionality. Because of these properties, chaotic signals can be mistakenly classified as noise if linear techniques such as spectral analysis are used. The identification of chaos and its characterization require the assessment of dynamical invariants that quantify the complex features of a chaotic system's evolution. For example, Lyapunov exponents provide a marker of unpredictability; the estimation of attractor dimensions, on the other hand, highlights the unconventional geometry of a chaotic system's state space. Nonlinear time series analysis techniques act directly within the state space of the system under investigation. However, experimentally, full access to a system's state space is not always available. Often, only a scalar signal stemming from the dynamical system can be recorded, thus providing, upon sampling, a scalar sequence. Nevertheless, by virtue of a fundamental theorem by Takens, it is possible to reconstruct a proxy of the original state space evolution out of a single, scalar sequence. This reconstruction is carried out by means of the so-called embedding procedure: m-dimensional vectors are built by picking successive elements of the scalar sequence delayed by a lag L. On the other hand, besides posing some necessary conditions on the integer embedding parameters m and L, Takens' theorem does not provide any clue on how to choose them correctly. Although many optimal embedding criteria were proposed, a general answer to the problem is still lacking. As a matter of fact, conventional methods for optimal embedding are flawed by several drawbacks, the most relevant being the need for a subjective evaluation of the outcomes of applied algorithms. Tackling the issue of optimally selecting embedding parameters makes up the core topic of this thesis work. In particular, I will discuss a novel approach that was pursued by our research group and that led to the development of a new method for the identification of suitable embedding parameters. Rather than most conventional approaches, which seek a single optimal value for m and L to embed an input sequence, our approach provides a set of embedding choices that are equivalently suitable to reconstruct the dynamics. The suitability of each embedding choice m, L is assessed by relying on statistical testing, thus providing a criterion that does not require a subjective evaluation of outcomes. The starting point of our method are embedding-dependent correlation integrals, i.e. cumulative distributions of embedding vector distances, built out of an input scalar sequence. In the case of Gaussian white noise, an analytical expression for correlation integrals is available, and, by exploiting this expression, a gauge transformation of distances is introduced to provide a more convenient representation of correlation integrals. Under this new gauge, it is possible to test—in a computationally undemanding way—whether an input sequence is compatible with Gaussian white noise and, subsequently, whether the sequence is compatible with the hypothesis of an underlying chaotic system. These two statistical tests allow ruling out embedding choices that are unsuitable to reconstruct the dynamics. The estimation of correlation dimension, carried out by means of a newly devised estimator, makes up the third stage of the method: sets of embedding choices that provide uniform estimates of this dynamical invariant are deemed to be suitable to embed the sequence.The method was successfully applied to synthetic and experimental sequences, providing new insight into the longstanding issue of optimal embedding. For example, the relevance of the embedding window (m-1)L, i.e. the time span covered by each embedding vector, is naturally highlighted by our approach. In addition, our method provides some information on the adequacy of the sampling period used to record the input sequence.The method correctly distinguishes a chaotic sequence from surrogate ones generated out of it and having the same power spectrum. The technique of surrogate generation, which I also addressed during my Ph. D. work to develop new dedicated algorithms and to analyze brain signals, allows to estimate significance levels in situations where standard analytical algorithms are unapplicable. The novel embedding approach being able to tell apart an original sequence from surrogate ones shows its capability to distinguish signals beyond their spectral—or autocorrelation—similarities.One of the possible applications of the new approach concerns another longstanding issue, namely that of distinguishing noise from chaos. To this purpose, complementary information is provided by analyzing the asymptotic (long-time) behaviour of the so-called time-dependent divergence exponent. This embedding-dependent metric is commonly used to estimate—by processing its short-time linearly growing region—the maximum Lyapunov exponent out of a scalar sequence. However, insights on the kind of source generating the sequence can be extracted from the—usually overlooked—asymptotic behaviour of the divergence exponent. Moreover, in the case of chaotic sources, this analysis also provides a precise estimate of the system's correlation dimension. Besides describing the results concerning the discrimination of chaotic systems from noise sources, I will also discuss the possibility of using the related correlation dimension estimates to improve the third stage of the method introduced above for the identification of suitable embedding parameters. The discovery of chaos as a possible dynamical regime for nonlinear systems led to the search of chaotic behaviour in experimental recordings. In some fields, this search gave plenty of positive results: for example, chaotic dynamics was successfully identified and tamed in electronic circuits and laser-based optical setups. These two families of experimental chaotic systems eventually became versatile tools to study chaos and its possible applications. On the other hand, chaotic behaviour is also looked for in climate science, biology, neuroscience, and even economics. In these fields, nonlinearity is widespread: many smaller units interact nonlinearly, yielding a collective motion that can be described by means of few, nonlinearly coupled effective degrees of freedom. The corresponding recorded signals exhibit, in many cases, an irregular and complex evolution. A possible underlying chaotic evolution—as opposed to a stochastic one—would be of interest both to reveal the presence of determinism and to predict the system's future states. While some claims concerning the existence of chaos in these fields have been made, most results are debated or inconclusive. Nonstationarity, low signal-to-noise ratio, external perturbations and poor reproducibility are just few among the issues that hinder the search of chaos in natural systems. In the final part of this work, I will briefly discuss the problem of chasing chaos in experimental recordings by considering two example sequences, the first one generated by an electronic circuit and the second one corresponding to recordings of brain activity. The present thesis is organized as follows. The core concepts of time series analysis, including the key features of chaotic dynamics, are presented in Chapter 1. A brief review of the search for chaos in experimental systems is also provided; the difficulties concerning this quest in some research fields are also highlighted. Chapter 2 describes the embedding procedure and the issue of optimally choosing the related parameters. Thereupon, existing methods to carry out the embedding choice are reviewed and their limitations are pointed out. In addition, two embedding-dependent nonlinear techniques that are ordinarily used to characterize chaos, namely the estimation of correlation dimension by means of correlation integrals and the assessment of maximum Lyapunov exponent, are presented. The new approach for the identification of suitable embedding parameters, which makes up the core topic of the present thesis work, is the subject of Chapter 3 and 4. While Chapter 3 contains the theoretical outline of the approach, as well as its implementation details, Chapter 4 discusses the application of the approach to benchmark synthetic and experimental sequences, thus illustrating its perks and its limitations. The study of the asymptotic behaviour of the time-dependent divergent exponent is presented in Chapter 5. The alternative estimator of correlation dimension, which relies on this asymptotic metric, is discussed as a possible improvement to the approach described in Chapters 3, 4. The search for chaos out of experimental data is discussed in Chapter 6 by means of two examples of real-world recordings. Concluding remarks are finally drawn in Chapter 7.

Large language models (LLMs) are very large deep learning models which are retrained on a huge amount of data. Among the LLMs are sentence bidirectional encoder representations from transformers (SBERT) where advanced training methods such as transformer-based denoising autoEncoder (TSDAE), generative query network (GenQ) and an adaption of generative pseudo labelling (GPL) can be applied. This thesis project aims to develop a semantic search tool for Swedish legal judgments in order to overcome the limitations of traditional keyword searches in legal document retrieval. For this aim, a model adept at understanding the semantic nuances of legal language has been developed by leveraging natural language processing (NLP) and fine- tuning LLMs like SBERT, using advanced training methods such as TSDAE, GenQ, and an adaption of GPL. To generate labeled data out of unlabelled data, a GPT3.5 model was used after it was fine-tuned. The generation of labeled data with the use of a generative model was crucial for this project to train the SBERT efficiently. The search tool has been evaluated. The evaluation demonstrates that the search tool can accurately retrieve relevant documents based on semantic queries and simnifically improve the efficiency and accuracy of legal research. GenQ has been shown to be the most efficient training method for this use case.

Cette thèse porte sur les processus croissants pour l'ordre convexe que nous désignons sous le nom de peacocks. Un résultat remarquable dû à Kellerer stipule qu'un processus stochastique à valeurs réelles est un peacock si et seulement s'il possède les mêmes marginales unidimensionnelles qu'une martingale. Une telle martingale est dite associée à ce processus. Mais dans son article, Kellerer ne donne ni d'exemple de peacock, ni d'idée précise sur la construction d'une martingale associée pour un peacock donné. Ainsi, comme d'autres travaux sur les peacocks, notre étude vise deux objectifs. Il s'agit d'exhiber de nouvelles familles de peacocks et de construire des martingales associées pour certains peacocks. Dans les trois premiers chapitres, nous exhibons diverses classes de peacocks en utilisant successivement les notions de monotonie conditionnelle, de peacock très fort et de positivité totale d'ordre 2. En particulier, nous fournissons plusieurs extensions du résultat de Carr-Ewald-Xiao selon lequel la moyenne arithmétique du mouvement brownien géométrique, encore appelée "option asiatique" est un peacock. L'objet du dernier chapitre est de construire des martingales associées pour une classe de peacocks. Pour cela, nous utilisons les plongements d'Azéma-Yor et de Bertoin-Le Jan. L'originalité de ce chapitre est l'utilisation de la positivité totale d'ordre 2 dans l'étude du plongement d'Azéma-Yor / This thesis deals with real valued stochastic processes which increase in the convex order. We call them peacocks. A remarkable result due to Kellerer states that a real valued process is a peacock if and only if it has the same one-dimensional marginals as a martingale. Such a martingale is said to be associated to this process. But in his article, Kellerer provides neither an example of peacock nor a concrete idea to construct an associated martingale to a given peacock. Hence, as other investigations on peacocks, our study has two purposes. We first exhibit new families of peacocks and then, we contruct associated martingales to certain of them. In the first three chapters, we exhibit several classes of peacocks using successively the notions of conditional monotonicity, very strong peacock and total positivity of order 2. In particular, we provide many extensions of Carr-Ewald-Xiao result which states that the arithmetic mean of geometric Brownian motion, also called "Asian option" is a peacock. The purpose of the last chapter is to construct associated martingales to certain peacocks. To this end, we use Azéma-Yor and Bertoin-Le Jan embedding algorithms. The originality of this chapter is the use of total positivity of order 2 in the study of Azéma-Yor embedding algorithm