494 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Fuzzy Natural Logic in IFSA-EUSFLAT 2021

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    The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications

    Eger Journal of English Studies 21.

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    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Deep Neural Networks and Tabular Data: Inference, Generation, and Explainability

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    Over the last decade, deep neural networks have enabled remarkable technological advancements, potentially transforming a wide range of aspects of our lives in the future. It is becoming increasingly common for deep-learning models to be used in a variety of situations in the modern life, ranging from search and recommendations to financial and healthcare solutions, and the number of applications utilizing deep neural networks is still on the rise. However, a lot of recent research efforts in deep learning have focused primarily on neural networks and domains in which they excel. This includes computer vision, audio processing, and natural language processing. It is a general tendency for data in these areas to be homogeneous, whereas heterogeneous tabular datasets have received relatively scant attention despite the fact that they are extremely prevalent. In fact, more than half of the datasets on the Google dataset platform are structured and can be represented in a tabular form. The first aim of this study is to provide a thoughtful and comprehensive analysis of deep neural networks' application to modeling and generating tabular data. Apart from that, an open-source performance benchmark on tabular data is presented, where we thoroughly compare over twenty machine and deep learning models on heterogeneous tabular datasets. The second contribution relates to synthetic tabular data generation. Inspired by their success in other homogeneous data modalities, deep generative models such as variational autoencoders and generative adversarial networks are also commonly applied for tabular data generation. However, the use of Transformer-based large language models (which are also generative) for tabular data generation have been received scant research attention. Our contribution to this literature consists of the development of a novel method for generating tabular data based on this family of autoregressive generative models that, on multiple challenging benchmarks, outperformed the current state-of-the-art methods for tabular data generation. Another crucial aspect for a deep-learning data system is that it needs to be reliable and trustworthy to gain broader acceptance in practice, especially in life-critical fields. One of the possible ways to bring trust into a data-driven system is to use explainable machine-learning methods. In spite of this, the current explanation methods often fail to provide robust explanations due to their high sensitivity to the hyperparameter selection or even changes of the random seed. Furthermore, most of these methods are based on feature-wise importance, ignoring the crucial relationship between variables in a sample. The third aim of this work is to address both of these issues by offering more robust and stable explanations, as well as taking into account the relationships between variables using a graph structure. In summary, this thesis made a significant contribution that touched many areas related to deep neural networks and heterogeneous tabular data as well as the usage of explainable machine learning methods

    Evolution from the ground up with Amee – From basic concepts to explorative modeling

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    Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes

    Political Jouissance and the Vicissitudes of Mistrust

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    Routines and Applications of Symbolic Algebra Software

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    Computing has become an essential resource in modern research and has found application across a wide range of scientific disciplines. Developments in symbolic algebra tools have been particularly valuable in physics where calculations in fields such as general relativity, quantum field theory and physics beyond the standard model are becoming increasing complex and unpractical to work with by hand. The computer algebra system Cadabra is a tensor-first approach to symbolic algebra based on the programming language Python which has been used extensively in research in these fields while also having a shallow learning curve making it an excellent way to introduce students to methods in computer algebra. The work in this thesis has been concentrated on developing Cadabra, which has involved looking at two different elements which make up a computer algebra program. Firstly, the implementation of algebraic routines is discussed. This has primarily been focused on the introduction of an algorithm for detecting the equivalence of tensorial expressions related by index permutation symmetries. The method employed differs considerably from traditional canonicalisation routines which are commonly used for this purpose by using Young projection operators to make such symmetries manifest. The other element of writing a computer algebra program which is covered is the infrastruc- ture and environment. The importance of this aspect of software design is often overlooked by funding committees and academic software users resulting in an anti-pattern of code not being shared and contributed to in the way in which research itself is published and promulgated. The focus in this area has been on implementing a packaging system for Cadabra which allows the writing of generic libraries which can be shared by the community, and interfacing with other scientific computing packages to increase the capabilities of Cadabra

    Central and Eastern European Literary Theory and the West

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    The twentieth century saw intensive intellectual exchange between Eastern and Central Europe and the West. Yet political and linguistic obstacles meant that many important trends in East and Central European thought and knowledge hardly registered in Western Europe and the US. This book uncovers the hidden westward movements of Eastern European literary theory and its influence on Western scholarship

    The Agency of Error in Post-digital Print

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    This research investigates the tensions between different interpretations of error: from binary and digital evaluations to the more abstract and human ways we approach and think about error. My interest comes from a meshing together of these tendencies and the slippages between various modes of interpretation. Consequently, in my artistic practice error exists as both activity and subject matter, and the projects expose relationality rather than define discrete types of error. This approach goes against common understandings of error in digital culture, where systems try to reduce, if not erase, error. Equally in relation to printmaking practices, error is often understood only in terms of visual anomalies to be avoided through improvements to the printing process – whereas the problem is that there is a concern with error in print practices and culture, and yet a failure to address it critically. In this respect, error is encapsulated by Gilles Deleuze’s phrase ‘misadventures of thought’ (1994, p.148), which distinguishes error as a form of wandering and implies a state of unknowing. This expanded sense of error has the potential to open up new lines of thinking, in that not knowing upholds new beginnings and artistic potential. My research is not just about error in the ontological sense of the word, but about error in the context of a particular set of creative practices and concepts including printmaking, ideas of the post-digital, and how these allow for an emphasis on what I refer to as the relational agency of error. In his elaboration of the post-digital, the theorist Florian Cramer suggests that artists favour the misbehaviour of failing analogue and digital technologies (2014, p.20). I apply this theory to the expanded field of printmaking where errors created using analogue and digital print equipment co-exist as creative tools in artistic practice. Indeed, how is error in printmaking understood differently as a consequence of post-digital practices, and cultures? Artistic practice in post- digital printmaking takes issue with the crude distinction between digital and analogue creative processes, and instead reveals how new and old technologies intertwine in ’a space of creative action’ (Geary and Catanese, 2012, p.8). This places emphasis on relationality rather than predetermined or unified processes. From a post-digital perspective, and departing from information theory (Shannon and Weaver, 1948), errors and technologies begin to develop their own voices. In my research, I have found that rational thought starts to break down when error occurs – a useful discovery in terms of undermining pre- determined logic and intentionality. Drawing additionally on actor–network theory (Latour), new materialism (Barad and Bennett) and the power of cognitive nonconscious (Hayles), I consider error to be an active agent in the printmaking process, where any notion of the artist’s intention is part of a wider network of relations. Hence, my contribution to knowledge is to propose that error cannot be autonomous and is only active as part of a larger relational web of agency, or a co-constituted agency, distinct from a commonplace understanding where things or matter can exist independently (Harman, 2011a, p. 177). In addition to my artistic practice that forms part of this research and engages with different forms of error, I use diagramming to explore these entangled relations, and to highlight the importance that is ascribed to relationality in understanding error. In this sense, my written thesis and artistic practice can be described as attempts to diagram the concept of error
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