SeamCAD: a hierarchy-oriented modeling language and a computer-aided tool for entreprise architecture

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Metal Forming is a basic and essential industrial process to provide materials for constructing complex products. To design an efficient metal forming process, the functional requirements and operational performance are two important aspects to be consider ...

Modern power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). Their presence complicates the analysis and mitigation of harmonics, whose creation and propagation may be amplified ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

IntroductionThe current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing ...

The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

In a collaboration between Ecole Polytechnique Fédérale de Lausanne (EPFL) and CEA, in the fall of 2020, the experimental Programme d’Étude en Transmission de l’Acier Lourd et ses Eléments (PETALE) was successfully carried out in the CROCUS reactor of EPFL ...

This article outlines the advancements made in broadening the application scope of the OpenMC neutron transport code to include thermohydraulic coupling and nuclear data uncertainty propagation. These developments primarily involve the incorporation of the ...

We present LISA, a proof system and proof assistant for constructing proofs in schematic first-order logic and axiomatic set theory. The logical kernel of the system is a proof checker for first-order logic with equality and schematic predicate and fun ...

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

The Dependent Object Type (DOT) calculus was designed to put Scala on a sound basis, but while DOT relies on structural subtyping, Scala is a fundamentally class-based language. This impedance mismatch means that a proof of DOT soundness by itself is ...

The thesis develops a planning framework for ADNs to achieve their dispatchability by means of ESS allocation while ensuring a reliable and secure operation of ADNs. Second, the framework is extended to include grid reinforcements and ESSs planning. Finall ...

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

The presence of conversational agents (or chatbots) in educational contexts has been steadily increasing over the past few years. Recent surveys have shown widespread interest in the use of chatbots in education, both for research and practice. Although th ...

The latest European polices highlight the urgent need to rehabilitate the existing building stock, responsible for 40 % of the EU's total energy consumption. In this process, a key role is played by thermal simulations, assessing the effective energy perfo ...

Moving from association to causal analysis of neuroimaging data is crucial to advance our understanding of brain function. The arrow-of-time (AoT), that is, the known asymmetric nature of the passage of time, is the bedrock of causal structures shaping phy ...

Natural language processing and other artificial intelligence fields have witnessed impressive progress over the past decade. Although some of this progress is due to algorithmic advances in deep learning, the majority has arguably been enabled by scaling ...

The recent developments of deep learning cover a wide variety of tasks such as image classification, text translation, playing go, and folding proteins. All these successful methods depend on a gradient-based learning algorithm to train a model on massive ...

Censuses are structured documents of great value for social and demographic history, which became widespread from the nineteenth century on. However, the plurality of formats and the natural variability of historical data make their extraction arduous and ...

SeamCAD: a hierarchy-oriented modeling language and a computer-aided tool for entreprise architecture

Chat with Graph Search

A semantic model-based systems engineering approach for assessing the operational performance of metal forming process

Unified Harmonic Power-Flow and Stability Analysis of Power Grids with Converter-Interfaced Distributed Energy Resources

Driving and suppressing the human language network using large language models

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

High-Resolution Modeling Without Computation Slowdown for PETALE in CROCUS

Tool Developments in the OpenMC Code: Correlated Sampling and Transient Fission Matrix Approach Coupled to OpenFOAM

LISA – A Modern Proof System

Robustness of Local Predictions in Atomistic Machine Learning Models

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Type-Preserving Compilation of Class-Based Languages

Dispatch-aware Optimal Planning of Active Distribution Networks including Energy Storage Systems

Equivariant Neural Architectures for Representing and Generating Graphs

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

A Conceptual Framework for Integrating Conversational Agents in Digital Education

A modelling framework for modern heritage buildings energy simulation

The arrow-of-time in neuroimaging time series identifies causal triggers of brain function

Text Representation Learning for Low Cost Natural Language Understanding

Transformer Models for Vision

An end-to-end pipeline for historical censuses processing