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About 10,000 papers for “deep structure”

Deep structure

Michael N. Pearson · 2010

In this stimulating and authoritative overview, Michael Pearson reverses the traditional angle of maritime history and looks from the sea to its shores - its impact on the land through trade, naval power, travel and scientific exploration. This vast ocean, both connecting and separating nations, has shaped many countries' cultures and ideologies through the movement of goods, people, ideas and religions across the sea. The Indian Ocean moves from a discussion of physical elements, its shape, winds, currents and boundaries, to a history from pre-Islamic times to the modern period of European dominance. Going far beyond pure maritime history, this compelling survey is an invaluable addition to political, cultural and economic world history.

Cited by 1

Deep Structure

Sagar, Ilona · Goldsmiths (University of London) · 2019

‘Deep Structure’, explores the links between architecture, health and community wellbeing through the lens of Sheffield’s Park Hill estate. Troubling the links between buildings, bodies and post-industrial landscapes, the film draws parallels between the unique sprawling structure of the building and the scientifically measured body. Designed in 1961, the estate is one of the UK’s most radical and significant post-war housing projects and a testimonial to an era that revolutionised social and residential housing.
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\nFilmed at Hope Cement Works in the Peak District, The Materials Science and Engineering Department at the University of Sheffield and the Park Hill estate, ‘Deep Structure’ focuses on material structures, considering the ways in which they are measured and analysed. Hope Cement Works, which opened in 1929 and is now the largest material factory in the UK, becomes a complex monolithic space within the film, representing something in-between industrialised networks and natural systems. Entangling these connections, ‘Deep Structure’ thinks about the factory and the estate as living bodies – machines for health, good and bad – considering the ways bodies and buildings are mapped, archived and translated into data.
\n
\nInformed by research in Sheffield Archives & Local Studies Library and ongoing conversations with former residents of the estate, ‘Deep Structure’ complicates official narratives around its history with the lived experiences of its inhabitants. The film experiments with the scale of speech, from the bureaucratic and instructional to the emotionally intimate and physical, where voice and sound acts as both a dislocation and a connecting element within the work.
\n
\nDeveloped in collaboration with Human Computer Interaction Design at City University, London, The Space Syntax Laboratory at The Bartlett, and the Department of Materials Science and Engineering, University of Sheffield, ‘Deep Structure’ uses scanning, spatial analysis techniques and archival data sets on Park Hill to unpick the ways in which buildings and bodies are surveilled, both through official-bureaucratic structures and shared social experiences. Built on mathematical algorithms, space syntax is a method of analysing spatial configurations. It measures the difficult to define space between buildings and the social or anti-social encounters that activate buildings, cities and landscapes. The application of algorithmic and data visualisation processes becomes a mode of navigation within the film, and reflects how scientific reasoning is authored and inscribed into both the body and civic systems.
\n
\nOne of the most unusual aspects of Park Hill’s design process was the emphasis placed on the residents themselves. The design encouraged social interaction, and uniquely involved tenants within a pioneering consultation throughout the design process and the building’s initial use. The Housing Act 1980 marked the start of a seismic shift towards home ownership, introducing caps on local authorities to limit their ability to borrow money to build council houses. Architects, town planners and designers have a powerful influence on our health and self-esteem, as well as ideas around community and society. Interrogating the contested civic ambitions of architecture, ‘Deep Structure’ is a darkly speculative work that examines our uneasy and increasingly precarious relationship to public welfare, housing and wellbeing.
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\nThis project was supported using public funding by the National Lottery through Arts Council England. Additional support has been generously provided by Sheffield Town Trust, The Freshgate Trust Foundation, AHRC Research in Film Awards (RIFA), technē AHRC Doctoral Training Partnership and Tenderpixel.

Cited by 0

Deep structure

Patrick Valiquet · 2021

One of the central innovations of actor-network theory (ANT) is its claim to include both objects and subjects in social analysis. The implications of this step are so radical, and so forcefully argued, that many assume that ANT has turned to objects at the expense of subjects. Eliot Bates, for instance, remarks that “ANT is not especially attuned to phenomenological questions and to the nuances of human feeling” and must therefore be supplemented with more inspiring “vital” materialisms. The overarching goal is thus to sketch out a level of analysis upon which musicological and ANT discourse are themselves aggregations of “epistemic objects”. The concept of generative grammar transformed scientific practice far beyond linguistics by casting human subjectivity in a radically new light. Importantly, generative grammar was not an empirical discovery, drawn from a collection of data, but rather an abstract hypothesis about the form of human language.

Cited by 0

Deep Structure

Sagar, Ilona · Goldsmiths (University of London) · 2019

‘Deep Structure’ by artist Ilona Sagar, explores the links between architecture, health and community wellbeing through the lens of Sheffield’s Park Hill estate. Troubling the links between buildings, bodies and post-industrial landscapes, the film draws parallels between the unique sprawling structure of the building and the scientifically measured body. Designed in 1961, the estate is one of the UK’s most radical and significant post-war housing projects and a testimonial to an era that revolutionised social and residential housing.
\n
\nFilmed at Hope Cement Works in the Peak District, The Materials Science and Engineering Department at the University of Sheffield and the Park Hill estate, ‘Deep Structure’ focuses on material structures, considering the ways in which they are measured and analysed. Hope Cement Works, which opened in 1929 and is now the largest material factory in the UK, becomes a complex monolithic space within the film, representing something in-between industrialised networks and natural systems. Entangling these connections, ‘Deep Structure’ thinks about the factory and the estate as living bodies – machines for health, good and bad – considering the ways bodies and buildings are mapped, archived and translated into data.
\n
\nInformed by research in Sheffield Archives & Local Studies Library and ongoing conversations with former residents of the estate, ‘Deep Structure’ complicates official narratives around its history with the lived experiences of its inhabitants. The film experiments with the scale of speech, from the bureaucratic and instructional to the emotionally intimate and physical, where voice and sound acts as both a dislocation and a connecting element within the work.
\n
\nDeveloped in collaboration with Human Computer Interaction Design at City University, London, The Space Syntax Laboratory at The Bartlett, and the Department of Materials Science and Engineering, University of Sheffield, ‘Deep Structure’ uses scanning, spatial analysis techniques and archival data sets on Park Hill to unpick the ways in which buildings and bodies are surveilled, both through official-bureaucratic structures and shared social experiences. Built on mathematical algorithms, space syntax is a method of analysing spatial configurations. It measures the difficult to define space between buildings and the social or anti-social encounters that activate buildings, cities and landscapes. The application of algorithmic and data visualisation processes becomes a mode of navigation within the film, and reflects how scientific reasoning is authored and inscribed into both the body and civic systems.
\n
\nOne of the most unusual aspects of Park Hill’s design process was the emphasis placed on the residents themselves. The design encouraged social interaction, and uniquely involved tenants within a pioneering consultation throughout the design process and the building’s initial use. The Housing Act 1980 marked the start of a seismic shift towards home ownership, introducing caps on local authorities to limit their ability to borrow money to build council houses. Architects, town planners and designers have a powerful influence on our health and self-esteem, as well as ideas around community and society. Interrogating the contested civic ambitions of architecture, ‘Deep Structure’ is a darkly speculative work that examines our uneasy and increasingly precarious relationship to public welfare, housing and wellbeing.

Cited by 0

Deep Structure

Sagar, Ilona · Goldsmiths (University of London) · 2019

‘Deep Structure’, explores the links between architecture, health and community wellbeing through the lens of Sheffield’s Park Hill estate. Troubling the links between buildings, bodies and post-industrial landscapes, the film draws parallels between the unique sprawling structure of the building and the scientifically measured body. Designed in 1961, the estate is one of the UK’s most radical and significant post-war housing projects and a testimonial to an era that revolutionised social and residential housing. Filmed at Hope Cement Works in the Peak District, The Materials Science and Engineering Department at the University of Sheffield and the Park Hill estate, ‘Deep Structure’ focuses on material structures, considering the ways in which they are measured and analysed. Hope Cement Works, which opened in 1929 and is now the largest material factory in the UK, becomes a complex monolithic space within the film, representing something in-between industrialised networks and natural systems. Entangling these connections, ‘Deep Structure’ thinks about the factory and the estate as living bodies – machines for health, good and bad – considering the ways bodies and buildings are mapped, archived and translated into data. Informed by research in Sheffield Archives & Local Studies Library and ongoing conversations with former residents of the estate, ‘Deep Structure’ complicates official narratives around its history with the lived experiences of its inhabitants. The film experiments with the scale of speech, from the bureaucratic and instructional to the emotionally intimate and physical, where voice and sound acts as both a dislocation and a connecting element within the work. Developed in collaboration with Human Computer Interaction Design at City University, London, The Space Syntax Laboratory at The Bartlett, and the Department of Materials Science and Engineering, University of Sheffield, ‘Deep Structure’ uses scanning, spatial analysis techniques and archival data sets on Park Hill to unpick the ways in which buildings and bodies are surveilled, both through official-bureaucratic structures and shared social experiences. Built on mathematical algorithms, space syntax is a method of analysing spatial configurations. It measures the difficult to define space between buildings and the social or anti-social encounters that activate buildings, cities and landscapes. The application of algorithmic and data visualisation processes becomes a mode of navigation within the film, and reflects how scientific reasoning is authored and inscribed into both the body and civic systems. One of the most unusual aspects of Park Hill’s design process was the emphasis placed on the residents themselves. The design encouraged social interaction, and uniquely involved tenants within a pioneering consultation throughout the design process and the building’s initial use. The Housing Act 1980 marked the start of a seismic shift towards home ownership, introducing caps on local authorities to limit their ability to borrow money to build council houses. Architects, town planners and designers have a powerful influence on our health and self-esteem, as well as ideas around community and society. Interrogating the contested civic ambitions of architecture, ‘Deep Structure’ is a darkly speculative work that examines our uneasy and increasingly precarious relationship to public welfare, housing and wellbeing. This project was supported using public funding by the National Lottery through Arts Council England. Additional support has been generously provided by Sheffield Town Trust, The Freshgate Trust Foundation, AHRC Research in Film Awards (RIFA), technē AHRC Doctoral Training Partnership and Tenderpixel.

Cited by 0

Surface Structure (and Deep Structure)

James Stanlaw · The International Encyclopedia of Linguistic Anthropology · 2020

The terms “surface structure” and “deep structure” are part key parts of the transformational‐generative grammar program proposed by Noam Chomsky in the 1960s. The surface structure is a representation of the syntax of a sentence – not an actual spoken sentence – derived from a presumed underlying deep structure (a syntactic representation at an even more abstract level).

Cited by 1

7. Deep structure

Linguistic & literary studies in Eastern Europe, LLSEE · 1987

Cited by 0

Deep Structure Model

Sergey Kashubin, O. V. Petrov, E. D. Milshtein et al. · Springer geology · 2021

Cited by 0

Structure-aware Deep Learning

Lorenz Wendlinger · OPUS (University of Passau) · 2026

Graph structures permeate the digital landscape in explicit and implicit forms. They connect or construct artifacts by combining semantic and structural information. We also observe them in the systems designed to process this data, in their learning algorithms and the very nature of the tasks they solve. At the same time, machine learning methods are extremely data-hungry, requiring petabytes of data for training. Due to their complexity, graphs remain an under-utilized resource in this regard. Many approaches cannot incorporate them due to being fully structurally unaware or not suited to the specific flavour of graphs encountered in some domains. This disconnect is sub-optimal from an effectiveness and efficiency perspective. We present methods that extend the scope of structure-aware deep learning through structural knowledge integration and enrichment, structural performance prediction, and synergistic transfer learning. Knowledge graphs organize information and make it directly available for querying. They provide a structured inference interface for manual and automated inspection, though they can suffer from data quality issues and require careful schema design. We rephrase the reconciliation of knowledge in knowledge graphs as a link prediction task, making it tractable with adapted graph neural networks, while also benefiting conventional link prediction tasks. We further combine textual semantics and structural expression for legal reference prediction via adapted heterogeneous graph neural networks operating on complex meta-information enriched graphs. Additionally, we explore methods for the integration of intermediary expressions in strongly typed heterogeneous graphs, improving prediction via meta-path-based processing. We also develop methods for automated machine learning workflow analysis and performance prediction. This includes the learning of salient representations for management as well as improvement of workflows through automatic suggestion and refinement of components. These are then extended to the prediction of Neural Architecture Search performance prediction, including adaptation to operation-on-edge spaces. Finally, we investigate the transfer capability of pre-trained attention structures for text-based prediction tasks and find it to be both inferior to directly optimized attention masks as well as highly dependent on inherent domain knowledge. We also show that the exploitation of hierarchical task formulation can improve prediction performance through joint learning in diverse learning domains, including link prediction, performance prediction, and specialized and general argumentation mining. The dissertation contains previously published or submitted texts: Wendlinger, L., Hübscher, G., Ekelhart, A., Granitzer, M. (2022). Reconciliation of Mental Concepts with Graph Neural Networks. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds): Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13427, p 133-146. Springer, Cham. https://doi.org/10.1007/978-3-031-12426-6_11; Wendlinger, L., Granitzer M. (2024). Informed Heterogeneous Attention Networks for Metapath Based Learning. In: SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, p 458-465, ACM, New York. https://doi.org/10.1145/3605098.3635890; Wendlinger, L., Nonn, S.A., Al Zubaer, A., Granitzer, M. (2026). The Missing Link: Joint Legal Citation Prediction Using Heterogeneous Graph Enrichment. In: Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2025. Lecture Notes in Computer Science, vol 16047, p 197-211. Springer, Cham. https://doi.org/10.1007/978-3-032-02088-8_14; Wendlinger, L., Stier, J., Granitzer, M. (2021). Evofficient: Reproducing a Cartesian Genetic Programming Method. In: Hu, T., Lourenço, N., Medvet, E. (eds) Genetic Programming. EuroGP 2021. Lecture Notes in Computer Science, vol 12691, p 162-178. Springer, Cham. https://doi.org/10.1007/978-3-030-72812-0_11; Wendlinger, L., Berndl, E., Granitzer, M. (2021). Methods for Automatic Machine-Learning Workflow Analysis. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science, vol 12979, p 52-67. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_4; Wendlinger, L., Granitzer, M., Fellicious, C. (2023). Pooling Graph Convolutional Networks for Structural Performance Prediction. In: Nicosia, G., et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811, p 1-16. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_1; Wendlinger, L., Braun, C., Zubaer, A., Nonn, S., Großkopf, S., Fellicious, C., Granitzer, M.: On the Suitability of pre-trained foundational LLMs for Analysis in German Legal Education, submitted to the proceedings of the International Conference on Machine Learning, Optimization, and Data Science 2025, preprint published: https://doi.org/10.48550/arXiv.2412.15902; Wendlinger, L., Kuhn, R., Mitrovic, J., Granitzer, M. (2025). Joint Learning for Efficient German Argument Mining. In: 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI), Athens, Greece, 2025, p 770-777. IEEE, Los Alamitos. https://doi.org/10.1109/ICTAI66417.2025.00111.

Cited by 0Open access

Case in Deep Structure

Ishiguro, Teruhiro, イシグロ, テルヒロ, 石黒, 昭博 · 1989

Cited by 0Open access

Structure-Preserving Deep Learning

Hernández Laín, Quercus Manuel, Cueto Prendes, Elías, Badías Herbera, Alberto · Zaguan (University of Zaragoza Repository) · 2023

Las tecnologías de simulación se han convertido en una herramienta útil para modelizar muchos sistemas en una amplia variedad de disciplinas, desde las ciencias sociales a las naturales. En el contexto de la ingeniería, la descripción matemática de los sistemas físicos es crucial para comprender mejor su comportamiento futuro y tomar las decisiones más apropiadas en términos de optimización de su diseño. En las últimas décadas, con la irrupción del llamado cuarto paradigma de la ciencia, se ha detectado un creciente interés por el aprendizaje automático de estas leyes científicas. Muchos de estos enfoques descartan por completo siglos de conocimiento científico en favor de modelos basados puramente en datos, cuya aplicación en la industria real sigue sin estar clara frente a los métodos tradicionales con amplio fundamento matemático.<br />El objetivo de esta tesis es desarrollar metodologías de aprendizaje profundo para aprender sistemas dinámicos a partir de datos. En este trabajo, el conocimiento científico se incorpora al modelo con la imposición de la correcta estructura termodinámica del problema. Así, se trata de un enfoque híbrido entre los métodos de caja negra basados en datos y las formulaciones analíticas tradicionales. <br />La primera parte de la tesis explora métodos de aprendizaje profundo que preservan la estructura para simular sistemas dinámicos a partir de datos. Esto se consigue utilizando varios sesgos inductivos, lo cual fuerza la estructura metripléctica de la dinámica por construcción y explota la estructura del dominio utilizando los principios del aprendizaje profundo geométrico. Estas arquitecturas de redes neuronales de última generación permiten identificar la dinámica de sistemas desconocidos incluso con un comportamiento altamente no lineal. Además, el esquema de integración resultante consigue predicciones rápidas y precisas con bajos requisitos de potencia computacional y almacenamiento en memoria. <br />La segunda parte describe un método para identificar la dimensionalidad intrínseca de un sistema dinámico con el fin de obtener un modelo de orden reducido, que puede integrarse en el tiempo utilizando una arquitectura que preserva la estructura. Este método es conveniente en datos de alta dimensionalidad cuya compleja estructura resulta impráctica para las técnicas estándar. <br />La tercera y última parte explora dos posibles aplicaciones de los métodos expuestos en la tesis. Se describe un procedimiento de aprendizaje para sistemas abiertos en los que la interacción con fuentes externas es relevante, basado en el formalismo port-Hamiltoniano y su extensión a sistemas metriplécticos. La segunda aplicación es una demo de realidad aumentada para la interacción de sólidos deformables en tiempo real, posibilitada por las rápidas predicciones de los simuladores desarrollados en esta tesis.<br />

Cited by 0Open access

Structure-preserving deep learning

Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann et al. · European Journal of Applied Mathematics · 2021

Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks. There are multiple challenging mathematical problems involved in applying deep learning: most deep learning methods require the solution of hard optimisation problems, and a good understanding of the trade-off between computational effort, amount of data and model complexity is required to successfully design a deep learning approach for a given problem.. A large amount of progress made in deep learning has been based on heuristic explorations, but there is a growing effort to mathematically understand the structure in existing deep learning methods and to systematically design new deep learning methods to preserve certain types of structure in deep learning. In this article, we review a number of these directions: some deep neural networks can be understood as discretisations of dynamical systems, neural networks can be designed to have desirable properties such as invertibility or group equivariance and new algorithmic frameworks based on conformal Hamiltonian systems and Riemannian manifolds to solve the optimisation problems have been proposed. We conclude our review of each of these topics by discussing some open problems that we consider to be interesting directions for future research.

Cited by 4Open access