Publications associées à Beyond worst-case analysis, with or without predictions

A ride time-oriented scheduling algorithm for dial-a-ride problems

Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi

This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

Pergamon-Elsevier Science Ltd2024

The Current State of the OBI DICT Project: A Bilingual e-Dictionary of Oracle-Bone Inscriptions with AI Image Recognition

This article reports on the current state of the OBI DICT project, a bilingual e-dictionary of oracle-bone inscriptions (OBI), incorporating artificial intelligence (AI) image recognition technology. It first provides a brief overview of the development of ...

Buro Van Die Wat2024

Course Design - A Visual and Modular Approach

Marc Laperrouza

This book has been designed for instructors in higher education or adult training who wish to develop a new course or revise an existing one. It adopts a practical, visual, and modular approach based on the principle of constructive alignment. At its heart ...

EPFL Press2024

Teaching Not To

Charlotte Malterre-Barthes

Two design studios were successively taught in two schools of architecture: one at the Harvard Graduate School of Design (GSD) and the other at the Swiss Federal Institute of Technology in Lausanne (EPFL) in Lausanne, Switzerland, based on "A Global Morato ...

Taylor & Francis Inc2024

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Julian Thomas Blackwell, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy, Isadora Alves de Salles

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Nataliya Lopanitsyna, Guillaume André Jean Fraux, Arslan Mazitov

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Technology-Aware Logic Synthesis for Superconducting Electronics

Giovanni De Micheli, Dewmini Sudara Marakkalage, Siang-Yun Lee, Alessandro Tempia Calvino, Mingfei Yu, Rassul Bairamkulov

Superconducting electronics provide us with cryogenic digital circuits that can rival established technologies in performance and energy consumption. Today, the lack of tools for the design of large-scale integrated superconducting circuits is a major obst ...

2024

Models implemented in the methodological approach to design the initial STEP first wall contour

Kevin Henricus Annemarie Verhaegh, Dario Vaccaro, Filippo Bagnato, Davide Galassi

The official Spherical Tokamak for Energy Production mission aims to demonstrate the ability to generate net electricity from fusion with the STEP Prototype Power plant. One of the key technological and engineering challenges in fusion power plants is mana ...

Elsevier Science Sa2024

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Thermal Design of a Medium Voltage Modular Multilevel Converter Cell

Drazen Dujic, Yanick Patrick Frei

This paper presents the cooling concept for a medium voltage modular multilevel converter cell. To optimize power density, the metallic enclosure is used to dissipate the heat generated by semiconductor losses, while maintaining maximum temperatures below ...

New York2023

Computational Conceptual Design – Typological Exploration Of Spatial Truss Systems Through Optimization

Corentin Jean Dominique Fivet, Jonas Warmuth, Pierluigi D'Acunto

Exploring a wide range of relevant design options from the outset is crucial for every sound conceptual design process. Optimization techniques are generally employed to generate well-performing structural design options. However, focusing only on performa ...

2023

Computational conceptual design — typological exploration of spatial truss systems through optimization

Corentin Jean Dominique Fivet, Jonas Warmuth, Pierluigi D'Acunto

Exploring a wide range of design options is crucial for every sound conceptual design process. In the conventional structural design workflow, optimization techniques are generally employed to generate well-performing design options. However, focusing only ...

International Association for Shell and Spatial Structures (IASS)2023

Interactive design with optimum matchmaking of reused structural elements – a software implementation

Corentin Jean Dominique Fivet, Jan Friedrich Georg Brütting, Jonas Warmuth

Structural systems are a significant contributor to buildings’ embodied carbon and reusing structural elements in new load-bearing applications is one strategy to reduce this impact. However, integrating this approach into existing design workflows without ...

International Association for Shell and Spatial Structures (IASS)2023

Fairness and Explainability in Clustering Problems

Xinrui Jia

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

EPFL2023

Collective data-based drawings A common ground for adaptive contributive design

The laboratory ALICE (Atelier de la Conception de l’Espace) at the École Polytechnique Fédérale de Lausanne (EPFL) advances a comprehensive approach to data-based drawing oriented towards architectural and urban co-design processes. This drawing methodolog ...

2023

TRACE: A Conceptual Model to Guide the Design of Educational Chatbots

Denis Gillet, Sandy Ingram, Adrian Christian Holzer, Maria Jesus Rodriguez Triana, Juan Carlos Farah, Fanny Kim-Lan Lasne

Driven by the rising popularity of chatbots such as ChatGPT, there is a budding line of research proposing guidelines for chatbot design, both in general and specifically for digital education. Nevertheless, few researchers have focused on providing concep ...

Springer2023

Leveraging the Snap Buckling of Bistable Magnetic Shells to Design a Refreshable Braille Dot

Pedro Miguel Nunes Pereira de Almeida Reis, Bastien Freddy Gustave Aymon, Arefeh Abbasi, Tian Chen

A design concept is proposed for the building block, a dot, of programmable braille readers utilizing bistable shell buckling, magnetic actuation, and pneumatic loading. The design process is guided by Finite Element simulations, which are initially valida ...

Wiley2023

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Qiang Liu, Feng Yu, Yichen Xu

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

Assoc Computing Machinery2023

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Christophe Ballif, Nicolas Würsch, Laure-Emmanuelle Perret Aebi, Marine Dominique Cauz

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

2023