Residential density classification for sustainable housing development using a machine learning approach

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Research at the Laboratory of Architecture and Sustainable Technologies (LAST) at EPFL aims to explore the interactions between architectural design and transitions towards sustainability in the built environment. This concerns as much the polycentric rest ...

Demographic fairness or equity is a crucial aspect of machine learning models, particularly in critical domains such as healthcare, where errors can have severe consequences. A fair model avoids making distinctions between groups with different sensitive o ...

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method t ...

The dynamic landscape of sustainable smart cities is witnessing a signi ficant transformation due to the integration of emerging computational technologies and innovative models. These advancements are reshaping data -driven planning strategies, practices, ...

In essence, the problem of orbital pollution is a tragedy of commons. Space does not belong to anyone, thus, there is a tendency to exploit it excessively without accountability for mitigating the resulting consequences. While sustainable space solutions h ...

This study addresses the critical need for sustainable architectural designs within the context of climate change and the significant role the built environment plays in greenhouse gas emissions. The focus of this paper is on understanding the influence of ...

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

Uncertainty of spillover effects – including property devaluation - from proposed land-use change elicits opposition to local development. This hinders cities’ ability to implement land-use policy aimed at housing affordability and environmental sustainabi ...

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

Our understanding of the generalization capabilities of neural networks (NNs) is still incomplete. Prevailing explanations are based on implicit biases of gradient descent (GD) but they cannot account for the capabilities of models from gradient-free metho ...

Urban proximity planning is foreseen as a solution to foster a “sustainable” city, including economic viability, environmental soundness and social inclusivity. This paper focuses on the inclusivity aspects by questioning the adoption of urban proximities: ...

Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these da ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

With global environmental change and quality of life issues at the forefront of international discussions today, urban development strategies and policies are often framed around various sustainability or liveability goals. Regardless of the sustainability ...

A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods typically used i ...

This thesis is inscribed in the recent efforts to reconnect urban planning and public health. It focused on the context of sub-Saharan African cities, for they offer invaluable lessons regarding environmental health determinants and potential solutions to ...

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we p ...

As the world’s population grows in cities, urban dwellers spend a large amount of time inside their home, making housing health ever more important. Critical for residents’ health, the interactions between indoor residential environments and outdoor enviro ...

Evaluating visual landscape quality provides valuable information for urban development and spatial planning. In practice however, obtaining high resolution view-metrics and outcome data with sufficient geographic coverage has remained challenging. To over ...

Residential density classification for sustainable housing development using a machine learning approach

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Regeneration of Urban Areas: From Neighbourhoods in Transition to Innovative Components

Optimizing fairness and utility in healthcare machine learning models

Theory and applications of the sum-of-squares technique

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

Sustainable Solutions in the Space Sector: Factors Determining the Perception of the Opportunity Landscape by Key Players in the Space Industry

Statistical mechanics of deep learning

Embodied Carbon Premium for Cantilevers

Socially Intelligent Networks: A framework for decision making over graphs

Using automated design appraisal to model building-specific devaluation risk due to land-use change

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Neural Redshift: Random Networks are not Random Functions

Planning urban proximities: An empirical analysis of how residential preferences conflict with the urban morphologies and residential practices

Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" -Part 2

Validating functional redundancy with mixed generative adversarial networks

Urban sustainability assessment through spatial lenses: turning the spotlight on the local neighbourhood unit

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

Relating health benefits of water, sanitation, and hygiene services with the context of urban informal settlements: lessons from Côte d'Ivoire and Kenya

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

Housing, street and health: a new systemic research framework

Visual Capital: Evaluating building-level visual landscape quality at scale