1,336 research outputs found

    Discovering Causal Relations and Equations from Data

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    Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

    Interoperability framework of virtual factory and business innovation

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    Interoperability framework of virtual factory and business innovationTask T51 Design a common schema and schema evolution framework for supporting interoperabilityTask T52 Design interoperability framework for supporting datainformation transformation service composition and business process cooperation among partnersA draft version is envisioned for month 44 which will be updated to reflect incremental changes driven by the other working packages for month 72 deliverable 7.

    The mad manifesto

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    The “mad manifesto” project is a multidisciplinary mediated investigation into the circumstances by which mad (mentally ill, neurodivergent) or disabled (disclosed, undisclosed) students faced far more precarious circumstances with inadequate support models while attending North American universities during the pandemic teaching era (2020-2023). Using a combination of “emergency remote teaching” archival materials such as national student datasets, universal design for learning (UDL) training models, digital classroom teaching experiments, university budgetary releases, educational technology coursewares, and lived experience expertise, this dissertation carefully retells the story of “accessibility” as it transpired in disabling classroom containers trapped within intentionally underprepared crisis superstructures. Using rhetorical models derived from critical disability studies, mad studies, social work practice, and health humanities, it then suggests radically collaborative UDL teaching practices that may better pre-empt the dynamic needs of dis/abled students whose needs remain direly underserviced. The manifesto leaves the reader with discrete calls to action that foster more critical performances of intersectionally inclusive UDL classrooms for North American mad students, which it calls “mad-positive” facilitation techniques: 1. Seek to untie the bond that regards the digital divide and access as synonyms. 2. UDL practice requires an environment shift that prioritizes change potential. 3. Advocate against the usage of UDL as a for-all keystone of accessibility. 4. Refuse or reduce the use of technologies whose primary mandate is dataveillance. 5. Remind students and allies that university space is a non-neutral affective container. 6. Operationalize the tracking of student suicides on your home campus. 7. Seek out physical & affectual ways that your campus is harming social capital potential. 8. Revise policies and practices that are ability-adjacent imaginings of access. 9. Eliminate sanist and neuroscientific languaging from how you speak about students. 10. Vigilantly interrogate how “normal” and “belong” are socially constructed. 11. Treat lived experience expertise as a gift, not a resource to mine and to spend. 12. Create non-psychiatric routes of receiving accommodation requests in your classroom. 13. Seek out uncomfortable stories of mad exclusion and consider carceral logic’s role in it. 14. Center madness in inclusive methodologies designed to explicitly resist carceral logics. 15. Create counteraffectual classrooms that anticipate and interrupt kairotic spatial power. 16. Strive to refuse comfort and immediate intelligibility as mandatory classroom presences. 17. Create pathways that empower cozy space understandings of classroom practice. 18. Vector students wherever possible as dynamic ability constellations in assessment

    Transmuting values in artificial intelligence: investigating the motivations and contextual constraints shaping the ethics of artificial intelligence practitioners

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    Advances in Artificial Intelligence (AI) research and development have seen AI applied in various high-stakes domains such as healthcare and welfare. Furthermore, portrayals of AI are often characterised by narratives of perpetual progress and sleek optimisation, obscuring the intricate interactions of materiality and socio-political decision-making inherently embedded within wider systems of design and development. The resulting ethical and social concerns have prompted proposal of numerous frameworks, tools and guidelines for the ethical design and development of AI. However, translating these proposals into practice has proven challenging, and there is a paucity of research into the practical contexts shaping the ethico-onto-epistemology of AI practice. In this thesis I illustrate these contexts via the accounts of 24 AI practitioners, complemented by ethnographic observations from an industry research lab, examining the values which motivate practitioners, the constraints which shape their practice, and their approaches to ethics. Weaving through these discussions of practice, values, and the nature of responsibility, I examine how ambiguities pervade practice and shape the realities of ethical reflection and engagement at all stages of development. My findings uncover practitioner motivations linked with interconnected intellectual and moral values, how these related to intellectual conduct and culture within the field, and how practitioner heuristics for ethical decision-making are often relational and character-based in nature. This realization of values in practice is tempered by numerous constraints including hardware limitations, epistemic cultures, and ethical knowledge. Drawing upon the Ethics of Ambiguity, I discuss how the uncertainty, ambiguity and unequal access to resources shaping AI practice necessitate a process-focused ethics which pivots away from solutions, towards critical contextual reflexivity and awareness of how contexts impact realisation of values. To this end, I demonstrate how The Ethics of Ambiguity can offer a path forward for ethical AI practice. This vision of AI practice embraces ambiguities rather than attempting to segment and sideline them, focusing on how practitioner decisions (and their eventual outputs) impact others’ freedoms while acknowledging the multiplicity of values across socio- and geo-political contexts

    Statistical analysis and modelling of proteomic and genetic network data illuminate hidden roles of proteins and their connections

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    While many stable protein complexes are known, the dynamic interactome is still underexplored. Experimental techniques such as single-tag affinity purification, aim to close the gap and identify transient interactions, but need better filtering tools to discriminate between true interactors and noise. This thesis develops and contrasts two complementary approaches to the analysis of protein-protein interaction (PPI) networks derived from noisy experiments. The majority of data used for the analysis come from in vitro experiments aggregated from known databases (IntAct, BioGRID, BioPlex), but is also complemented by experiments done by our collaborators from the Ueffing group in the Institute of Ophthalmic Research, Tübingen University (Germany). Chapter 3 presents the statistical approach to the data analysis. It focuses on the case of a single dataset with target and control data in order to determine the significant interactions for the target. The procedure follows an expected trajectory of preprocessing, quality control, statistical testing, correction and discussion of results. The approach is tailored to the specific dataset, experiment design and related assumptions. This is specifically relevant for the missing value imputation where multiple approaches are discussed and a new method, building upon a previous method, is proposed and validated. Chapter 4 presents a different approach for the filtering of experimental results for PPIs. It is a statistic, WeSA (weighted socio-affinity), which improves upon previous methods of scoring PPIs from affinity proteomics data. It uses network analysis techniques to analyse the full PPI network without the need for controls. WeSA is tested on protein-protein networks of varying accuracy, including the curated IntAct dataset, the unfiltered records in BioGRID, and the large BioPlex dataset. The model is also tested against the previous same-goal method. While the function itself proves superior, another major advantage is that it can efficiently combine and compare observations across studies and can therefore be used to aggregate and clean results from incoming experiments in the context of all already available data. In the final part, uses of WeSA beyond wild-type PPI networks are analysed. The framework is proposed as a novel way to effectively compare mechanistic differences between variants of the same protein (e.g. mutant vs wild type). I also explore the use of WeSA to study other biological and non-biological networks such as genome-wide association studies (GWAS) and gene-phenotype associations, with encouraging results. In conclusion, this work presents and compares a variety of mathematical, statistical and computational approaches adapted, combined and/or developed specifically for the task of obtaining a better overview of protein-protein interaction networks. The novel methods performance is validated and, specifically, WeSA, is extensively tested and analysed, including beyond the field of PPI networks

    Multi-scale Pedestrian Navigation and Movement in Urban Areas

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    Sustainable transport planning highlights the importance of walking to low-carbon and healthy urban transport systems. Studies have identified multiple ways in which vehicle traffic can negatively impact pedestrians and inhibit walking intentions. However, pedestrian-vehicle interactions are underrepresented in models of pedestrian mobility. This omission limits the ability of transport simulations to support pedestrian-centric street design. Pedestrian navigation decisions take place simultaneously at multiple spatial scales. Yet most models of pedestrian behaviour focus either on local physical interactions or optimisation of routes across a road network. This thesis presents a novel hierarchical pedestrian route choice framework that integrates dynamic, perceptual decisions at the street level with abstract, network based decisions at the neighbourhood level. The framework is based on Construal Level Theory which states that decision makers construe decisions based on their psychological distance from the object of the decision. The route choice framework is implemented in a spatial agent-based simulation in which pedestrian and vehicle agents complete trips in an urban environment. Global sensitivity analysis is used to explore the behaviour produced by the multi-scale pedestrian route choice model. Finally, simulation experiments are used to explore the impacts of restrictions to pedestrian movement. The results demonstrate the potential insights that can be gained by linking street scale movement and interactions with neighbourhood level mobility patterns
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