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We consider (random) walks in a multidimensional orthant. Using the idea of universality in probability theory, one can associate a unique polyhedral domain to any given walk model. We use this connection to prove two sets of new results. First, we are interested in a group of transformations naturally associated with any small step model; as it turns out, this group is central to the classification of walk models. We show a strong connection between this group and the reflection group through the walls of the polyhedral domain. As a consequence, we can derive various conditions for the combinatorial group to be infinite. Secondly, we consider the asymptotics of the number of excursions, whose critical exponent is known to be computable in terms of the eigenvalue of the above polyhedral domain. We prove new results from spectral theory on the eigenvalues of polyhedral nodal domains. We believe that these results are interesting in their own right; they can also be used to find new exact asymptotic results for walk models corresponding to these nodal polyhedral domains., Comment: 30 pages

Neutron time correlation measurements are used to characterize fissile systems. For this purpose, detailed information are obtained using list mode measurements where the neutron detection times are recorded in a file called the time list file. In this article, we present the simulation and analysis of list mode measurements performed on SILENE reactor. Analogue Monte Carlo simulations are performed using MCNPX code and the synthetic time list file is obtained by processing the MCNPX-PTRAC coincidence capture file. Both measured and computed time list files are processed using the same postprocessor that we have developed. This software is used to calculate counting statistics data like counting number probability distribution, Rossi alpha and Feynman functions for time gates of increasing duration. The parameter uncertainties are evaluated using a block bootstrap method.

We consider a collection of independent standard Brownian particles (or random walks), starting from a configuration where at least one particle is positive, and study the first time they all become negative. This is clearly equivalent to studying the first hitting time from the negative orthant or the first exit time from the complement of the negative orthant. While it turns out to be possible to compute the distribution of these hitting times for one and two particles, the distribution (and even its tail asymptotics) is not known in closed form for three or more particles. In this paper we study the tail asymptotics of the distribution as the number of particles tends to infinity. Our main techniques come from spectral geometry: we prove new asymptotic estimates for the principal eigenvalue of the complement of a high-dimensional orthant, which we believe are of independent interest., Comment: 8 pages

For a number of applications like low-source reactor start-up or neutron coincidence counting it is necessary to take into account the stochastic nature of neutron transport and go beyond the average neutron density, which is solution of a linear Boltzmann equation. In this work, we are particularly interested in calculating the moments and probabilities of the number of neutrons detected during a time window. It is known that in case of a single initial neutron these quantities are solution of a system of coupled adjoint transport equations and that a neutron source can be taken into account in a second time using summations with the source strength. The purpose of the present work is first to present the derivation of these equations in a form where they can be solved up an arbitrary order and where the fission terms are expanded according to the moments of the fission multiplicity. For simplicity, the derivation is first presented in a lumped also called point model approximation before considering the full phase-space case. Thereafter, we describe the implementation of the solution algorithm in the deterministic, discrete ordinates code PANDA and finally, we present some numerical results and inter-code comparisons for verification purposes and to illustrate the applicability of the method.

This paper presents SibylSat, a novel SAT-based method designed to efficiently solve totally-ordered HTN problems (TOHTN). In contrast to prevailing SAT-based HTN planners that employ a breadth-first search strategy, SibylSat adopts a greedy search approach, enabling it to identify promising decompositions for expansion. The selection process is facilitated by a heuristic derived from solving a relaxed problem, which is also expressed as a SAT problem. Our experimental evaluations demonstrate that SibylSat outperforms existing SAT-based TOHTN approaches in terms of both runtime and plan quality on most of the IPC benchmarks, while also solving a larger number of problems.

Characterizing the dimensionality of the superconducting state in the infinite-layer (IL) nickelates is crucial to understand its nature. Most studies have addressed the problem by studying the anisotropy of the upper critical fields. Yet, the dominance of Pauli-paramagnetism effects over orbital ones makes it challenging to interpret the experiments in terms of dimensionality. Here we address the question from a different perspective, by investigating the vortex phase diagram in the mixed-state. We demonstrate that superconducting Pr0.8Sr0.2NiO2 thin films present a vortex liquid-to-glass transition of a purely two-dimensional nature. The obtained results suggest that bidimensionality is an intrinsic property, and that superconductivity resides in fully-decoupled NiO2 planes. In this scenario, the coherence length along the c-axis must be shorter than the distance between those planes, while Josephson and magnetostatic coupling between them must be negligible. We believe that these conclusions are relevant for theories on the origin of superconductivity in the IL-nickelates.

Many modern software applications present numerous ethical concerns due to conflicts between users' values and companies' priorities. Intersectional communities, those with multiple marginalized identities, are disproportionately affected by these ethical issues, leading to legal, financial, and reputational issues for software companies, as well as real-world harm for intersectional users. Historically, the voices of intersectional communities have been systematically marginalized and excluded from contributing their unique perspectives to software design, perpetuating software-related ethical concerns. This work aims to fill the gap in research on intersectional users' software-related perspectives and provide software practitioners with a starting point to address their ethical concerns. We aggregated and analyzed the intersectional users' ethical concerns over time and developed a prioritization method to identify critical concerns. To achieve this, we collected posts from over 700 intersectional subreddits discussing software applications, utilized deep learning to identify ethical concerns in these posts, and employed state-of-the-art techniques to analyze their content in relation to time and priority. Our findings revealed that intersectional communities report \textit{critical} complaints related to cyberbullying, inappropriate content, and discrimination, highlighting significant flaws in modern software, particularly for intersectional users. Based on these findings, we discuss how to better address the ethical concerns of intersectional users in software development.

Neutron noise analysis is a predominant technique for fissile matter identification with passive methods. Quantifying the uncertainties associated with the estimated nuclear parameters is crucial for decision-making. A conservative uncertainty quantification procedure is possible by solving a Bayesian inverse problem with the help of statistical surrogate models but generally leads to large uncertainties due to the surrogate models' errors. In this work, we develop two methods for robust uncertainty quantification in neutron and gamma noise analysis based on the resolution of Bayesian inverse problems. We show that the uncertainties can be reduced by including information on gamma correlations. The investigation of a joint analysis of the neutron and gamma observations is also conducted with the help of active learning strategies to fine-tune surrogate models. We test our methods on a model of the SILENE reactor core, using simulated and real-world measurements.

We show that, given a real or complex hyperbolic metric $g_0$ on a closed manifold $M$ of dimension $n\geq 3$, there exists a neighborhood $\mathcal U$ of $g_0$ in the space of negatively curved metrics such that for any $g\in \mathcal U$, the topological entropy and Liouville entropy of $g$ coincide if and only if $g$ and $g_0$ are homothetic. This provides a partial answer to Katok's entropy rigidity conjecture. As a direct consequence of our theorem, we obtain a local rigidity result for the hyperbolic rank and for metrics with $C^2$ Anosov foliations near complex hyperbolic metrics., Comment: 40 pages, 1 figure, new corollary added on the rigidity of negatively curved metric with C^2 foliation

Methods used to infer nuclear parameters from neutron count statistics fall into two categories depending on whether they use moments or count number probabilities. As probabilities are in general more difficult to calculate, we are interested here in the reconstruction of distributions from their first moments. For this, we explore two approaches. The first one relies on a generalization of the 2-forked branching correlation (quadratic) approximation used in the PMZBB and Poisson radical distributions, the second one is based on the expansion of the distribution on a basis of Meixner discrete orthogonal polynomials., Comment: 25 pages, 9 figures

The 5.0 industry promotes collaborative robots (cobots). This research studies the impacts of cobot collaboration using an experimental setup. 120 participants realized a simple and a complex assembly task. 50% collaborated with another human (H/H) and 50% with a cobot (H/C). The workload and the acceptability of the cobotic collaboration were measured. Working with a cobot decreases the effect of the task complexity on the human workload and on the output quality. However, it increases the time completion and the number of gestures (while decreasing their frequency). The H/C couples have a higher chance of success but they take more time and more gestures to realize the task. The results of this research could help developers and stakeholders to understand the impacts of implementing a cobot in production chains.

We study conformal prediction in the one-shot federated learning setting. The main goal is to compute marginally and training-conditionally valid prediction sets, at the server-level, in only one round of communication between the agents and the server. Using the quantile-of-quantiles family of estimators and split conformal prediction, we introduce a collection of computationally-efficient and distribution-free algorithms that satisfy the aforementioned requirements. Our approaches come from theoretical results related to order statistics and the analysis of the Beta-Beta distribution. We also prove upper bounds on the coverage of all proposed algorithms when the nonconformity scores are almost surely distinct. For algorithms with training-conditional guarantees, these bounds are of the same order of magnitude as those of the centralized case. Remarkably, this implies that the one-shot federated learning setting entails no significant loss compared to the centralized case. Our experiments confirm that our algorithms return prediction sets with coverage and length similar to those obtained in a centralized setting.

A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, including the transductive setting and the inductive setting. In the transductive setting, the trained model can only predict the labels of nodes that were observed at the training time. In the inductive setting, the trained model can be generalized to new nodes/graphs. Due to its flexibility, the inductive setting is the most popular GNN setting at the moment. Previous work has shown that transductive GNNs are vulnerable to a series of privacy attacks. However, a comprehensive privacy analysis of inductive GNN models is still missing. This paper fills the gap by conducting a systematic privacy analysis of inductive GNNs through the lens of link stealing attacks, one of the most popular attacks that are specifically designed for GNNs. We propose two types of link stealing attacks, i.e., posterior-only attacks and combined attacks. We define threat models of the posterior-only attacks with respect to node topology and the combined attacks by considering combinations of posteriors, node attributes, and graph features. Extensive evaluation on six real-world datasets demonstrates that inductive GNNs leak rich information that enables link stealing attacks with advantageous properties. Even attacks with no knowledge about graph structures can be effective. We also show that our attacks are robust to different node similarities and different graph features. As a counterpart, we investigate two possible defenses and discover they are ineffective against our attacks, which calls for more effective defenses., Comment: To appear in the 24th Privacy Enhancing Technologies Symposium (PETS 2024), July 15-20, 2024

YouTube introduced the Shorts video format in 2021, allowing users to upload short videos that are prominently displayed on its website and app. Despite having such a large visual footprint, there are no studies to date that have looked at the impact Shorts introduction had on the production and consumption of content on YouTube. This paper presents the first comparative analysis of YouTube Shorts versus regular videos with respect to user engagement (i.e., views, likes, and comments), content creation frequency and video categories. We collected a dataset containing information about 70k channels that posted at least one Short, and we analyzed the metadata of all the videos (9.9M Shorts and 6.9M regular videos) they uploaded between January 2021 and December 2022, spanning a two-year period including the introduction of Shorts. Our longitudinal analysis shows that content creators consistently increased the frequency of Shorts production over this period, especially for newly-created channels, which surpassed that of regular videos. We also observe that Shorts target mostly entertainment categories, while regular videos cover a wide variety of categories. In general, Shorts attract more views and likes per view than regular videos, but attract less comments per view. However, Shorts do not outperform regular videos in the education and political categories as much as they do in other categories. Our study contributes to understanding social media dynamics, to quantifying the spread of short-form content, and to motivating future research on its impact on society., Comment: 11 pages, 9 figures, to be published in the proceedings of ACM Web Science Conference 2024 (WEBSCI24)

Sequential design is a highly active field of research in active learning which provides a general framework for designing computer experiments with limited computational budgets. It aims to create efficient surrogate models to replace complex computer codes. Some sequential design strategies can be understood within the Stepwise Uncertainty Reduction (SUR) framework. In the SUR framework, each new design point is chosen by minimizing the expectation of a metric of uncertainty with respect to the yet unknown new data point. These methods offer an accessible framework for sequential experiment design, including almost sure convergence for common uncertainty functionals. This paper introduces two strategies. The first one, entitled Constraint Set Query (CSQ) is adapted from D-optimal designs where the search space is constrained in a ball for the Mahalanobis distance around the maximum a posteriori. The second, known as the IP-SUR (Inverse Problem SUR) strategy, uses a weighted-integrated mean squared prediction error as the uncertainty metric and is derived from SUR methods. It is tractable for Gaussian process surrogates with continuous sample paths. It comes with a theoretical guarantee for the almost sure convergence of the uncertainty functional. The premises of this work are highlighted in various test cases, in which these two strategies are compared to other sequential designs., Comment: 22 pages, 12 figures, 2 appendices

Along with the increasing frequency and severity of cyber incidents, understanding their economic implications is paramount. In this context, listed firms' reactions to cyber incidents are compelling to study since they (i) are a good proxy to estimate the costs borne by other organizations, (ii) have a critical position in the economy, and (iii) have their financial information publicly available. We extract listed firms' cyber incident dates and characteristics from newswire headlines. We use an event study over 2012--2022, using a three-day window around events and standard benchmarks. We find that the magnitude of abnormal returns around cyber incidents is on par with previous studies using newswire or alternative data to identify cyber incidents. Conversely, as we adjust the standard errors accounting for event-induced variance and residual cross-correlation, we find that the previously claimed significance of abnormal returns vanishes. Given these results, we run a horse race of specifications, in which we test for the marginal effects of type of cyber incidents, target firm sector, periods, and their interactions. Data breaches are the most detrimental incident type with an average loss of -1.3\% or (USD -1.9 billion) over the last decade. The health sector is the most sensitive to cyber incidents, with an average loss of -5.21\% (or USD -1.2 billion), and even more so when these are data breaches. Instead, we cannot show any time-varying effect of cyber incidents or a specific effect of the type of news as had previously been advocated., Comment: This document results from a research project funded by the Cyber-Defence Campus, armasuisse Science and Technology. Our code will be available at https://github.com/technometrics-lab/17-The-determinants-of-cyberattack-costs-An-event-study. Corresponding author: Daniel Celeny e-mail: daniel.celeny@alumni.epfl.ch, Cyber-Defence Campus, Innovation Park, EPFL, 1015 Lausanne

Early-stage firms play a significant role in driving innovation and creating new products and services, especially for cybersecurity. Therefore, evaluating their performance is crucial for investors and policymakers. This work presents a financial evaluation of early-stage firms' performance in 19 cybersecurity sectors using a private-equity dataset from 2010 to 2022 retrieved from Crunchbase. We observe firms, their primary and secondary activities, funding rounds, and pre and post-money valuations. We compare cybersecurity sectors regarding the amount raised over funding rounds and post-money valuations while inferring missing observations. We observe significant investor interest variations across categories, periods, and locations. In particular, we find the average capital raised (valuations) to range from USD 7.24 mln (USD 32.39 mln) for spam filtering to USD 45.46 mln (USD 447.22 mln) for the private cloud sector. Next, we assume a log process for returns computed from post-money valuations and estimate the expected returns, systematic and specific risks, and risk-adjusted returns of investments in early-stage firms belonging to cybersecurity sectors. Again, we observe substantial performance variations with annualized expected returns ranging from 9.72\% for privacy to 177.27\% for the blockchain sector. Finally, we show that overall, the cybersecurity industry performance is on par with previous results found in private equity. Our results shed light on the performance of cybersecurity investments and, thus, on investors' expectations about cybersecurity., Comment: This document results from a research project funded by the Cyber-Defence Campus, armasuisse Science and Technology. We appreciate helpful comments from seminar participants at the Cyber Alp Retreat 2022 and WEIS 2023

We combine methods from microlocal analysis and dimension theory to study resonances with largest real part for an Anosov flow with smooth real valued potential. We show that the resonant states are closely related to special systems of measures supported on the stable manifolds introduced by Climenhaga. As a result, we relate the presence of the resonances on the critical axis to mixing properties of the flow with respect to certain equilibrium measures and show that these equilibrium measures can be reconstructed from the spectral theory of the Anosov flow., Comment: 41 pages, 1 figure, new corollary added on complex valued potentials

The Super Proton Synchrotron (SPS) is the last stage in the injector chain for CERN's Large Hadron Collider, and it also provides proton and ion beams for several fixed-target experiments. The SPS has been in operation since 1976, and it has been upgraded over the years. For the SPS to operate safely, its internal beam dump must be able to repeatedly absorb the energy of the circulating beams without sustaining damage that would affect its function. The latest upgrades of the SPS led to the requirement for its beam dump to absorb proton beams with a momentum spectrum from 14 to 450~GeV/$c$ and an average beam power up to $\sim$270~kW. This paper presents the technical details of a new design of SPS beam dump that was installed in one of the long straight sections of the SPS during the 2019--2020 shutdown of CERN's accelerator complex. This new beam dump has been in operation since May 2021, and it is foreseen that it will operate with a lifetime of 20~years. The key challenges in the design of the beam dump were linked to the high levels of thermal energy to be dissipated -- to avoid overheating and damage to the beam dump itself -- and high induced levels of radiation, which have implications for personnel access to monitor the beam dump and repair any problems occurring during operation. The design process therefore included extensive thermomechanical finite-element simulations of the beam-dump core and its cooling system's response to normal operation and worst-case scenarios for beam dumping. To ensure high thermal conductivity between the beam-dump core and its water-cooling system, hot isostatic pressing techniques were used in its manufacturing process. A comprehensive set of instrumentation was installed in the beam dump to monitor it during operation and to cross-check the numerical models with operational feedback., Comment: 20 pages, 36 figures, submitted to Phys. Rev. Accel. Beams

Large-scale pre-trained models are increasingly adapted to downstream tasks through a new paradigm called prompt learning. In contrast to fine-tuning, prompt learning does not update the pre-trained model's parameters. Instead, it only learns an input perturbation, namely prompt, to be added to the downstream task data for predictions. Given the fast development of prompt learning, a well-generalized prompt inevitably becomes a valuable asset as significant effort and proprietary data are used to create it. This naturally raises the question of whether a prompt may leak the proprietary information of its training data. In this paper, we perform the first comprehensive privacy assessment of prompts learned by visual prompt learning through the lens of property inference and membership inference attacks. Our empirical evaluation shows that the prompts are vulnerable to both attacks. We also demonstrate that the adversary can mount a successful property inference attack with limited cost. Moreover, we show that membership inference attacks against prompts can be successful with relaxed adversarial assumptions. We further make some initial investigations on the defenses and observe that our method can mitigate the membership inference attacks with a decent utility-defense trade-off but fails to defend against property inference attacks. We hope our results can shed light on the privacy risks of the popular prompt learning paradigm. To facilitate the research in this direction, we will share our code and models with the community., Comment: To appear in the 33rd USENIX Security Symposium, August 14-16, 2024

Purpose. NTCP modelling is rapidly embracing DL methods as the need to include spatial dose information is acknowledged. Finding the most appropriate way of combining radiation dose distribution images and clinical data involves technical challenges and requires domain knowledge. We propose different data fusion strategies that we hope will serve as a starting point for future DL NTCP studies. Methods. Early, joint and late DL multi-modality fusion strategies were compared using clinical variables and mandibular radiation dose distribution volumes. The discriminative performance of the multi-modality models was compared to that of single-modality models. All the experiments were conducted on a control-case matched cohort of 92 ORN cases and 92 controls from a single institution. Results. The highest ROC AUC score was obtained with the late fusion model (0.70), but no statistically significant differences in discrimination performance were observed between strategies. While late fusion was the least technically complex strategy, its design did not model the inter-modality interactions that are required for NTCP modelling. Joint fusion involved the most complex design but resulted in a single network training process which included intra- and inter-modality interactions in its model parameter optimisation. Conclusions. This is the first study that compares different strategies for including image data into DL NTCP models in combination with lower dimensional data such as clinical variables. The discrimination performance of such multi-modality NTCP models and the choice of fusion strategy will depend on the distribution and quality of both types of data. We encourage future DL NTCP studies to report on different fusion strategies to better justify their choice of DL pipeline., Comment: 10 pages, 4 figures, 3 tables

The normal-state conductivity and superconducting critical temperature of oxygen-deficient YBa2Cu3O7-x can be persistently enhanced by illumination. Strongly debated for years, the origin of those effects -- termed persistent photoconductivity (PPC) and photosuperconductivity (PPS) -- has remained an unsolved critical problem, whose comprehension may provide key insights to harness the origin of high-temperature superconductivity itself. Here we make essential steps toward understanding PPS. While the models proposed so far assume that it is caused by a carrier-density increase (photodoping) observed concomitantly, our experiments contradict such conventional belief: we demonstrate that it is instead linked to a photo-induced decrease of the electronic scattering rate. Furthermore, we find that the latter effect and photodoping are completely disconnected and originate from different microscopic mechanisms since they present different wavelength and oxygen-content dependencies as well as strikingly different relaxation dynamics. Besides helping disentangle photodoping, PPC, and PPS, our results provide new evidence for the intimate relation between critical temperature and scattering rate, a key ingredient in modern theories on high-temperature superconductivity.

We initiate the mathematical study of the Bistritzer-MacDonald Hamiltonian for twisted trilayer graphene in the chiral limit (and beyond). We develop a spectral theoretic approach to investigate the presence of flat bands under specific magic parameters. This allows us to derive trace formulae that show that the tunnelling parameters that lead to flat bands are nowhere continuous as functions of the twisting angles.

Recurrent neural network-based reinforcement learning systems are capable of complex motor control tasks such as locomotion and manipulation, however, much of their underlying mechanisms still remain difficult to interpret. Our aim is to leverage computational neuroscience methodologies to understanding the population-level activity of robust robot locomotion controllers. Our investigation begins by analyzing topological structure, discovering that fragile controllers have a higher number of fixed points with unstable directions, resulting in poorer balance when instructed to stand in place. Next, we analyze the forced response of the system by applying targeted neural perturbations along directions of dominant population-level activity. We find evidence that recurrent state dynamics are structured and low-dimensional during walking, which aligns with primate studies. Additionally, when recurrent states are perturbed to zero, fragile agents continue to walk, which is indicative of a stronger reliance on sensory input and weaker recurrence.

Cuprate superconductors are strongly sensitive materials to disorder and oxygen stoichiometry; even minute variations of those parameters drastically change their electronic properties. Here we exploit this characteristic to engineer a memristive device based on the high-T_C superconductor YBa2Cu3O7-x (YBCO), in which local changes of the oxygen content and induced disorder are exploited to produce memory effects. These effects are triggered electrically in a three-terminal device whose structure is reminiscent of a transistor, consisting of a YBCO channel and an Al gate. The Al/YBCO interface, which controls the gate conductance, displays a giant, bipolar, reversible switching across a continuum of non-volatile conductance states that span over two Decades. This phenomenon is controlled by the gate voltage magnitude and is caused by oxygen exchange between YBCO and Al. Concomitantly, the channel shows a gradual, irreversible superconductor-to-insulator transition that retains a memory of the power dissipated in the device, and can be explained by induced bulk disorder. The observed effects, and the understanding of the interplay between the underlying mechanisms, constitute interesting ingredients for the design and realization of novel memristors and switches for superconducting electronics.

The Hierarchical Task Network (HTN) formalism is used to express a wide variety of planning problems as task decompositions, and many techniques have been proposed to solve them. However, few works have been done on temporal HTN. This is partly due to the lack of a formal and consensual definition of what a temporal hierarchical planning problem is as well as the difficulty to develop heuristics in this context. In response to these inconveniences, we propose in this paper a new general POCL (Partial Order Causal Link) approach to represent and solve a temporal HTN problem by using existing heuristics developed to solve non temporal problems. We show experimentally that this approach is performant and can outperform the existing ones.

Real world applications as in industry and robotics need modelling rich and diverse automated planning problems. Their resolution usually requires coordinated and concurrent action execution. In several cases, these problems are naturally decomposed in a hierarchical way and expressed by a Hierarchical Task Network (HTN) formalism. HDDL, a hierarchical extension of the Planning Domain Definition Language (PDDL), unlike PDDL 2.1 does not allow to represent planning problems with numerical and temporal constraints, which are essential for real world applications. We propose to fill the gap between HDDL and these operational needs and to extend HDDL by taking inspiration from PDDL 2.1 in order to express numerical and temporal expressions. This paper opens discussions on the semantics and the syntax needed for a future HDDL 2.1 extension., Comment: 5 pages, International Workshop of Hierarchical Planning (ICAPS), 2023

We prove that in the chiral limit of the Bistritzer-MacDonald Hamiltonian, there exist magic angles at which the Hamiltonian exhibits flat bands of multiplicity four instead of two. We analyze the structure of the Bloch functions associated with the four bands, the corresponding Chern number, and show that there exist infinitely many degenerate magic angles for a generic choice of tunnelling potentials., Comment: New results on the existence of non-real magic angles

Since cobots (collaborative robots) are increasingly being introduced in industrial environments, being aware of their potential positive and negative impacts on human collaborators is essential. This study guides occupational health workers by identifying the potential gains (reduced perceived time demand, number of gestures and number of errors) and concerns (the cobot takes a long time to perceive its environment, which eads to an increased completion time) associated with working with cobots. In our study, the collaboration between human and cobot during an assembly task did not negatively impact perceived cognitive load, increased completion time (but decreased perceived time demand), and decreased the number of gestures performed by participants and the number of errors made. Thus, performing the task in collaboration with a cobot improved the user's experience and performance, except for completion time, which increased. This study opens up avenues to investigate how to improve cobots to ensure the usability of the human-machine system at work., Comment: 12 pages