Community challenge towards consensus on characterization of biological tissue: C4Bio’s first findings

International audienceThis study investigates methodological variability across various expert laboratories worldwide, with regards to characterizing the mechanical properties of biological tissues. Two testing rounds were conducted on the specific use case of uniaxial tensile testing of porcine aorta. In the first round, 24 labs were invited to apply their established methods to assess inter-laboratory variability. This revealed significant methodological diversity and associated variability in the stress–stretch results, underscoring the necessity for a standardized approach. In the second round, a consensus protocol was collaboratively developed and adopted by 19 labs in an attempt to minimize variability. This involved standardized sample preparation and uniformity in testing protocol, including the use of a common cutting and thickness measurement tool. Despite protocol harmonization, significant variability persisted across labs, which could not be solely attributed to inherent biological differences in tissue samples. These results illustrate the challenges in unifying testing methods across different research settings, underlining the necessity for further refinement of testing practices. Enhancing consistency in biomechanical experiments is pivotal when comparing results across studies, as well as when using the resulting material properties for in silico simulations in medical research

Finite element modelling for the reproduction of dynamic OCE measurements in the cornea

International audienceRecent advances in dynamic elastography, particularly through optical coherence tomography combined with transient excitations have enabled rapid, localized, and non-invasive mechanical data acquisition of the cornea. This dataopens the path to early-detection of pathologies and more accurate treatment. However, the analysis of the wave propagation is a complex mechanical problem: the cornea is a structure under pressure, with non-linear material behavior. Thus, computational analysis are needed to extract mechanical parameters from the data. In this study, we present a time-dependent finite element model for the reproduction of transient shear wave elastographic measurements in the cornea. The mechanical problem consists in a smallamplitude wave propagating in the cornea, largely deformed by intraocular pressure in physiological conditions. The model accounts for anisotropic, hyperelastic, and incompressible behavior of the cornea, as well as its accurate geometry, and the preloaded condition. We have implemented two different numerical approaches to solve first the static non-linear inflation of the cornea and then the linear wave propagation problem to reproduce the measurements. We investigate the impact of material anisotropy and prestress on wave propagation and demonstrate that intraocular pressure critically influences shear wave velocity. Additionally, by introducing a localized mechanical defect to simulate a pathological defect, we show that simulated shear wave can detect and quantify mechanical weaknesses, suggesting potential as a diagnostic tool to assess corneal health

Fair and efficient multi-agent routing for cooperative and autonomous agricultural fleets with implements

International audienceThe growing use of autonomous tractor fleets with detachable implements presents complex logistical challenges in agriculture. Current systems often rely on simple heuristics and avoid implement swapping, limiting efficiency. A central challenge is to dynamically coordinate vehicle routing and implement exchanges to enable efficient, low-intervention task execution. Due to high costs, such fleets are owned mainly by large enterprises or cooperatives, where fair task allocation and profit sharing are critical. Addressing both coordination and fairness, in this paper, we introduce the Agricultural Fleet Vehicle Routing Problem with Implements (AFVRPI). We propose a distributed model derived from a centralized formulation also presented in this paper. This model is embedded within a Distributed Multi-Agent System Architecture (DIMASA), where autonomous vehicle agents manage routing and implement use under limited fuel autonomy, while implement agents ensure compatibility and sufficient capacity to meet task demands. Our solution applies systematic egalitarian social welfare optimization to iteratively maximize the profit of the worst-off vehicle, balancing fairness with system efficiency. To enhance scalability, we use column generation in the distributed model, achieving solution quality comparable to the centralized model while significantly reducing computational time. Simulation results on new benchmark instances demonstrate that our distributed multi-agent AFVRPI approach is scalable, efficient, and fair

On design, analysis, and hybrid manufacturing of microstructured blade-like geometries

International audienceWith the evolution of new manufacturing technologies such as multi-material 3D printing, one can think of new type of objects that consist of considerably less, yet heterogeneous, material, consequently being porous, lighter and cheaper, while having the very same functionality as the original object when manufactured from one single solid material. We aim at questioning five decades of traditional paradigms in geometric CAD and focus at new generation of CAD objects that are not solid, but contain heterogeneous free-form internal microstructures. We propose a unified manufacturing pipeline that involves all stages, namely design, optimization, manufacturing, and inspection of microstructured free-form geometries. We demonstrate our pipeline on an industrial test case of a blisk blade that sustains the desired pressure limits, yet requires significantly less material when compared to the solid counterpart

PyroBuildS: Speeding up the exploration of large configuration spaces with incremental build

International audienceSoftware developers are acutely aware that software build is an essential but resource-intensive step in any software development process, all the more when building large and/or highly configurable systems, whose vast number of configuration options leads to an explosion in the number of variants to build and evaluate. A potential approach to speed up the builds of multiple configurations is to do incremental build, i.e., to not clean the build environment and reuse previous builds when building a new configuration. Previous exploratory studies showed some benefits and limitations of incremental build, but mainly on small configurable software systems and on a limited set of close configurations. However, for large configuration spaces, little is known whether the large distance across configurations impacts the correctness and efficiency of incremental build.This paper presents PyroBuildS, a new approach to speed up incremental builds while keeping reproducibility, featuring a configuration variation operator parameterized by two deny lists of problematic options and a mutation size (diversity).We evaluate PyroBuildS through an empirical study on three large complex configurable systems, namely Linux, BusyBox, and ToyBox, with respectively 18637, 1078, 330 configuration options. We first show that for all configurations PyroBuildS produces the exact same binaries as a clean build, except for Linux with some non-reproducible random configurations. We identify the reasons why incremental build speeds up or slows down the build of large configuration spaces – a knowledge that can be integrated into PyroBuildS. Incremental build systematically pays off, since problematic options are avoided in the first place — something only PyroBuildS does. We also show that a naive use of incremental build on random Linux configurations backfires, taking more time than clean builds. Thus, PyroBuildS controls diversity to avoid too many differences across configurations to perform efficient incremental builds.Thanks to its ability to operate over non-problematic options and close enough configurations, PyroBuildS significantly speeds up the exploration of large configuration spaces, with a gain in build time from 16% to 22% in all three systems with mutated configurations. Finally, with random configurations, PyroBuildS also speeds up the build time from 15% to 20% for ToyBox and BusyBox

Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings

International audienceAs the demand for AI-driven workloads increases, the energy consumption of Graphics Processing Units (GPUs) devices has come under intense scrutiny, particularly in hyperscale data centers where large numbers of accelerators are centralized and leased to diverse clients.In the context of cloud hyperscalers, GPUs power monitoring presents several challenges that vary depending on the product offered. The monitoring capabilities of physical devices may be limited or even absent for some products. However, given the substantial energy demands of GPUs, power monitoring is essential for both cloud providers and clients. Operators require tools to manage power distribution effectively, such as balancing workloads across Power Distribution Units (PDUs), while clients need visibility into power usage to optimize their workloads for energy efficiency.To address these challenges, we propose methods for estimating the energy consumption of jobs running on GPU devices in cloud environments, spanning from shared and managed offerings like ML-as-a-Service (MLaaS) to less managed products (e.g., Infrastructure-as-a-Service (IaaS)). Our models demonstrate the benefits of sharing GPUs for small AI workloads, as well as the current sub-optimal utilization of GPUs in cloud hyperscalers, based on insights from an IaaS GPU cluster

Untangling Vascular Trees for Surgery and Interventional Radiology

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Decisiveness for Countable MDPs and Insights for NPLCSs and POMDPs

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Exact Minimum Cuts in Hypergraphs at Scale

International audienceThe hypergraph minimum cut problem aims to partition the vertices of a hypergraph into two non-empty parts while minimizing the total weight of hyperedges crossing the cut. This problem lies at the core of many tasks in network reliability, VLSI placement, and community detection. We introduce HeiCut, the first algorithm that makes exact minimum cut computation feasible for both weighted and unweighted instances at scales of hundreds of millions of vertices. HeiCut presents seven exact reduction rules that provably preserve the minimum cut, and an optional heuristic contraction based on label propagation that shrinks complex and persistent structures. When no further reductions are possible, the remaining in stance is solved exactly with a known algorithm. Our extensive evaluation on more than 500 real-world hypergraphs reveals that the exact reductions alone already expose the minimum cut (i.e., the residual collapses to a single vertex or has no hyperedges) in over 85% of instances. Across all instances, HeiCut solves over twice as many instances as the state-of-the-art within set computational limits, and is up to five orders of magnitude faster. Thus, HeiCut significantly advances hyper graph minimum cut computation in real-world, large-scale scenarios

Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude Values

International audienceIn this work, we challenge the dominant use of logarithmic scales to communicate values spanning multiple orders of magnitude—Orders of Magnitude Values (OMVs)—to the general public. Focusing on bar charts, we incorporate cognitive insights into visualization design to better align with how humans perceive OMVs. Studies in cognitive psychology suggest that, for large numerical ranges such as millions and billions, people do not think logarithmically. Instead, they perceive numbers in a piecewise linear manner, grouping values into scale words (e.g., millions) and applying linear reasoning within each group. We build upon a recently introduced piecewise linear scale, EplusM, and validate its use in bar charts, which we refer to as EplusM bar charts. We also introduce two novel variants of the EplusM bar chart informed by findings in numerical perception: Bricks, which builds on the concepts of round numbers and subitizing, and Multi-Magnitude, which leverages categorical perception of large numbers. In a crowdsourced experiment, we evaluate four bar chart designs: 1) Log, 2) EplusM, 3) Bricks, and 4) Multi-Magnitude, across value retrieval and quantitative comparison tasks. Our results show that EplusM bar charts are significantly preferred over logarithmic designs, increase user confidence, and reduce perceived mental demand, while maintaining task performance. These findings suggest that EplusM bar charts can serve as effective alternatives to logarithmic ones when visualizing OMVs for general audiences