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We prove that $(a)$ any finitely smooth axially symmetric strictly convex domain, with everywhere positive curvature and sufficiently close to an ellipse, and $(b)$ any finitely smooth centrally symmetric strictly convex domain, even-rationally integrable, with everywhere positive curvature and sufficiently close to an ellipse area spectrally rigid. This means that any area-isospectral family of domains in these classes is necessarily equi-affine. We use the same technique -- adapted to symplectic billiards -- of the paper by De Simoi, Kaloshin, and Wei (2017). The novelty is that the result holds for axially symmetric domains, as in the cited paper, as well as for a subset of centrally symmetric ones.

We comprehensively study weighted projective Reed-Muller (WPRM) codes on weighted projective planes $\mathbb{P}(1,a,b)$. We provide the universal Gr\"obner basis for the vanishing ideal of the set $Y$ of $\mathbb{F}_q$--rational points of $\mathbb{P}(1,a,b)$ to get the dimension of the code. We determine the regularity set of $Y$ using a novel combinatorial approach. We employ footprint techniques to compute the minimum distance.

Nucleophobic axion models, wherein axion couplings to both protons and neutrons are simultaneously suppressed, can relax the stringent constraints from SN 1987A. However, it remains uncertain whether these models maintain their nucleophobic property under the influence of finite baryon density effects. These are especially relevant in astrophysical environments near saturation density, such as Supernovae (SNe). In this study, we demonstrate that the nucleophobic solution remains viable also at finite density. Furthermore, we show that the SN axion bound relaxes significantly in nucleophobic models, even when accounting for the integration over the non-homogeneous environment of the SN core., Comment: 9 pages, 6 figures

The introduction of artificial intelligence and robotics in telehealth is enabling personalised treatment and supporting teleoperated procedures such as lung ultrasound, which has gained attention during the COVID-19 pandemic. Although fully autonomous systems face challenges due to anatomical variability, teleoperated systems appear to be more practical in current healthcare settings. This paper presents an anatomy-aware control framework for teleoperated lung ultrasound. Using biomechanically accurate 3D models such as SMPL and SKEL, the system provides a real-time visual feedback and applies virtual constraints to assist in precise probe placement tasks. Evaluations on five subjects show the accuracy of the biomechanical models and the efficiency of the system in improving probe placement and reducing procedure time compared to traditional teleoperation. The results demonstrate that the proposed framework enhances the physician's capabilities in executing remote lung ultrasound examinations, towards more objective and repeatable acquisitions.

Nonlinear disordered media uniquely combine multiple scattering and second-harmonic generation. Here, we investigate the statistical properties of the nonlinear light generated within such media. We report super-Rayleigh statistics of the second-harmonic speckle intensity, and demonstrate that it is caused by the mesoscopic correlations arising in extreme scattering conditions. The measured conductance is the lowest ever observed in an isotropically scattering 3D medium, with applications in broadband second-harmonic generation, wavefront shaping in nonlinear disordered media, and photonic computing., Comment: 13 pages, 10 figures

Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of steps to achieve a goal; the lack of a standardized mapping between high-level actions and low-level commands; and the challenge of maintaining low computational overhead given the limited resources of robotic hardware. We introduce EMPOWER, a framework designed for open-vocabulary online grounding and planning for embodied agents aimed at addressing these issues. By leveraging efficient pre-trained foundation models and a multi-role mechanism, EMPOWER demonstrates notable improvements in grounded planning and execution. Quantitative results highlight the effectiveness of our approach, achieving an average success rate of 0.73 across six different real-life scenarios using a TIAGo robot., Comment: Accepted at IROS 2024

This paper presents the hardware implementation of two variants of projection-aggregation-based decoding of Reed-Muller (RM) codes, namely unique projection aggregation (UPA) and collapsed projection aggregation (CPA). Our study focuses on introducing hardware architectures for both UPA and CPA. Through thorough analysis and experimentation, we observe that the hardware implementation of UPA exhibits superior resource usage and reduced energy consumption compared to CPA for the vanilla IPA decoder. This finding underscores a critical insight: software optimizations, in isolation, may not necessarily translate into hardware cost-effectiveness.

Federated Learning (FL) is a pivotal approach in decentralized machine learning, especially when data privacy is crucial and direct data sharing is impractical. While FL is typically associated with supervised learning, its potential in unsupervised scenarios is underexplored. This paper introduces a novel unsupervised federated learning methodology designed to identify the complete set of categories (global K) across multiple clients within label-free, non-uniform data distributions, a process known as Federated Clustering. Our approach, Federated Cluster-Wise Refinement (FedCRef), involves clients that collaboratively train models on clusters with similar data distributions. Initially, clients with diverse local data distributions (local K) train models on their clusters to generate compressed data representations. These local models are then shared across the network, enabling clients to compare them through reconstruction error analysis, leading to the formation of federated groups.In these groups, clients collaboratively train a shared model representing each data distribution, while continuously refining their local clusters to enhance data association accuracy. This iterative process allows our system to identify all potential data distributions across the network and develop robust representation models for each. To validate our approach, we compare it with traditional centralized methods, establishing a performance baseline and showcasing the advantages of our distributed solution. We also conduct experiments on the EMNIST and KMNIST datasets, demonstrating FedCRef's ability to refine and align cluster models with actual data distributions, significantly improving data representation precision in unsupervised federated settings.

Large Language Models (LLMs) and Visual Language Models (VLMs) are attracting increasing interest due to their improving performance and applications across various domains and tasks. However, LLMs and VLMs can produce erroneous results, especially when a deep understanding of the problem domain is required. For instance, when planning and perception are needed simultaneously, these models often struggle because of difficulties in merging multi-modal information. To address this issue, fine-tuned models are typically employed and trained on specialized data structures representing the environment. This approach has limited effectiveness, as it can overly complicate the context for processing. In this paper, we propose a multi-agent architecture for embodied task planning that operates without the need for specific data structures as input. Instead, it uses a single image of the environment, handling free-form domains by leveraging commonsense knowledge. We also introduce a novel, fully automatic evaluation procedure, PG2S, designed to better assess the quality of a plan. We validated our approach using the widely recognized ALFRED dataset, comparing PG2S to the existing KAS metric to further evaluate the quality of the generated plans.

We study tetraquarks in large $N$ QCD with heavy quarks, in the domain where non-relativistic quantum mechanics offers an adequate approximation. Within the regime of validity of the Born-Oppenheimer approximation, we systematically study and explicitly construct tetraquark states. At leading order in the $1/N$ expansion, the bound spectrum consists of free mesons, while the $1/N$ corrections give rise to a Born-Oppenheimer potential that can bind the mesons into tetraquarks. We find two different types of tetraquarks, each endowed with distinct color-spatial wavefunctions. These states arise in the presence of an $\mathcal{O}(N)$ mass hierarchy between the quarks and the antiquarks. We provide a quantitative argument indicating that only for such a hierarchy is the ground state of the system a tetraquark. We discuss what the extrapolation of our results to realistic values of the parameters may imply for the QCD tetraquark states., Comment: 42 pages, 9 figures

The hadronic vacuum polarization contribution to $(g-2)_\mu$ can be determined via dispersive methods from $e^+e^-\to\;$hadrons data. We propose a novel approach to measure the hadronic cross section $\sigma_{\mathrm{had}}$ as an alternative to the initial-state radiation and energy scan techniques, which relies on positron annihilation off atomic electrons of a high $Z$ target ($^{238}$U, $Z=92$). We show that by leveraging the relativistic electron velocities of the inner atomic shells, a high-intensity $12\,$GeV positron beam, such as the one foreseen at JLab, can allow to measure $\sigma_{\mathrm{had}}$ with high statistical accuracy from the two-pion threshold up to above $\sqrt{s} \sim 1\,$GeV., Comment: 6 pages, 2 figures. One numerical error corrected. Conclusions unchanged

The use of deep learning methods for precision farming is gaining increasing interest. However, collecting training data in this application field is particularly challenging and costly due to the need of acquiring information during the different growing stages of the cultivation of interest. In this paper, we present a method for data augmentation that uses two GANs to create artificial images to augment the training data. To obtain a higher image quality, instead of re-creating the entire scene, we take original images and replace only the patches containing objects of interest with artificial ones containing new objects with different shapes and styles. In doing this, we take into account both the foreground (i.e., crop samples) and the background (i.e., the soil) of the patches. Quantitative experiments, conducted on publicly available datasets, demonstrate the effectiveness of the proposed approach. The source code and data discussed in this work are available as open source.

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider., Comment: This document summarises the International Muon Collider Collaboration (IMCC) progress and status of the Muon Collider R&D programme

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation,carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code., Comment: Accepted at 27th RoboCup International Symposium will be held on 22 July 2024, in Eindhoven, The Netherlands

Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of standardized benchmarks has limited the study of Bayesian optimization within the domain. To address this, we present CATBench, a comprehensive benchmarking suite that captures the complexities of compiler autotuning, ranging from discrete, conditional, and permutation parameter types to known and unknown binary constraints, as well as both multi-fidelity and multi-objective evaluations. The benchmarks in CATBench span a range of machine learning-oriented computations, from tensor algebra to image processing and clustering, and uses state-of-the-art compilers, such as TACO and RISE/ELEVATE. CATBench offers a unified interface for evaluating Bayesian optimization algorithms, promoting reproducibility and innovation through an easy-to-use, fully containerized setup of both surrogate and real-world compiler optimization tasks. We validate CATBench on several state-of-the-art algorithms, revealing their strengths and weaknesses and demonstrating the suite's potential for advancing both Bayesian optimization and compiler autotuning research.

We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multiple multimodal egocentric devices. The dataset comes with a) full-body ground-truth motion; b) multiple multimodal egocentric data from Project Aria devices with videos, eye tracking, IMUs and etc; and c) a third-person perspective by an additional observer. All devices are precisely synchronized and localized in on metric 3D world. We derive hierarchical protocol to add in-context language descriptions of human motion, from fine-grain motion narration, to simplified atomic action and high-level activity summarization. To the best of our knowledge, Nymeria dataset is the world's largest collection of human motion in the wild; first of its kind to provide synchronized and localized multi-device multimodal egocentric data; and the world's largest motion-language dataset. It provides 300 hours of daily activities from 264 participants across 50 locations, total travelling distance over 399Km. The language descriptions contain 301.5K sentences in 8.64M words from a vocabulary size of 6545. To demonstrate the potential of the dataset, we evaluate several SOTA algorithms for egocentric body tracking, motion synthesis, and action recognition. Data and code are open-sourced for research (c.f. https://www.projectaria.com/datasets/nymeria).

This is the third in a series of three papers in which we study a lattice gas subject to Kawasaki dynamics at inverse temperature $\beta>0$ in a large finite box $\Lambda_\beta \subset \mathbb{Z}^2$ whose volume depends on $\beta$. Each pair of neighbouring particles has a negative binding energy $-U<0$, while each particle has a positive activation energy $\Delta>0$. The initial configuration is drawn from the grand-canonical ensemble restricted to the set of configurations where all the droplets are subcritical. Our goal is to describe, in the metastable regime $\Delta \in (U,2U)$ and in the limit as $\beta\to\infty$, how and when the system nucleates, i.e., creates a critical droplet somewhere in $\Lambda_\beta$ that subsequently grows by absorbing particles from the surrounding gas. In the first paper we showed that subcritical droplets behave as quasi-random walks. In the second paper we used the results in the first paper to analyse how subcritical droplets form and dissolve on multiple space-time scales when the volume is moderately large, namely, $|\Lambda_\beta| = \mathrm{e}^{\theta\beta}$ with $\Delta < \theta < 2\Delta-U$. In the present paper we consider the setting where the volume is very large, namely, $|\Lambda_\beta| = \mathrm{e}^{\Theta\beta}$ with $\Delta < \Theta < \Gamma-(2\Delta-U)$, where $\Gamma$ is the energy of the critical droplet in the local model with fixed volume, and use the results in the first two papers to identify the nucleation time and the tube of typical trajectories towards nucleation. We will see that in a very large volume critical droplets appear more or less independently in boxes of moderate volume, a phenomenon referred to as homogeneous nucleation. One of the key ingredients in the proof is an estimate showing that no information can travel between these boxes on relevant time scales., Comment: 21 pages, 5 figures. arXiv admin note: text overlap with arXiv:2304.14099

Robots playing soccer often rely on hard-coded behaviors that struggle to generalize when the game environment change. In this paper, we propose a temporal logic based approach that allows robots' behaviors and goals to adapt to the semantics of the environment. In particular, we present a hierarchical representation of soccer in which the robot selects the level of operation based on the perceived semantic characteristics of the environment, thus modifying dynamically the set of rules and goals to apply. The proposed approach enables the robot to operate in unstructured environments, just as it happens when humans go from soccer played on an official field to soccer played on a street. Three different use cases set in different scenarios are presented to demonstrate the effectiveness of the proposed approach., Comment: RoboCup 2023: Robot World Cup XXVI Best Paper

When the available collision energy is much above the mass of the particles involved, scattering amplitudes feature kinematic configurations that are enhanced by the much lower virtuality of some intermediate particle. Such configurations generally factorise in terms of a hard scattering amplitude with exactly on-shell intermediate particle, times universal factors. In the case of real radiation emission, such factors are splitting amplitudes that describe the creation or the annihilation -- for initial or final state splittings -- of the low-virtuality particle and the creation of the real radiation particles. We compute at tree-level the amplitudes describing all the splittings that take place in the Standard Model when the collision energy is much above the electroweak scale. Unlike previous results, our splitting amplitudes fully describe the low-virtuality kinematic regime, which includes the region of collinear splitting, of soft emission, and combinations thereof. The splitting amplitudes are compactly represented as little-group tensors in an improved bi-spinor formalism for massive spin-1 particles that automatically incorporates the Goldstone Boson Equivalence Theorem. Simple explicit expressions are obtained using a suitably defined infinite-momentum helicity basis representation of the spinor variables. Our results, combined with the known virtual contributions, could enable systematic predictions of the leading electroweak radiation effects in high-energy scattering processes, with particularly promising phenomenological applications to the physics of future colliders with very high energy such as a muon collider., Comment: 45 pages, 4 figures

The rotation of trapped molecules offers a promising platform for quantum technologies and quantum information processing. In parallel, quantum error correction codes that can protect quantum information encoded in rotational states of a single molecule have been developed. These codes are currently an abstract concept, as no implementation strategy is yet known. Here, we present a step towards experimental implementation of these codes by introducing architecture-agnostic check and correction operators. These operators can be decomposed into elements of the quantum logic spectroscopy toolbox that is available for molecular ions. We then describe and analyze a measurement-based sequential as well as an autonomous implementation strategy in the presence of thermal background radiation, a major noise source for rotation in polar molecules. The presented strategies and methods might enable robust sensing or even fault-tolerant quantum computing using the rotation of individual molecules.

A pseudo Nambu-Goldstone boson (PNGB) coupled to a confining gauge group via an anomalous term is characterised, during the confining phase transition, by a temperature dependent mass $m^2(T) \propto T^{-n}$. For $n>2$, a non-relativistic population of such particles dominating the cosmological energy density would act as dark energy (DE), accelerating the expansion. We study the possibility that a PNGB $\varphi_b$ coupled to a hidden gauge group that is presently undergoing confinement could realise this scenario. To obtain the observed amount of DE, the number density of $\varphi_b$ must be boosted by some mechanism. Assuming that the QCD axion $\varphi_a$ constitutes the dark matter (DM), a non-adiabatic level crossing between $\varphi_a$ and $\varphi_b$ shortly before matter-DE equality can convert a small fraction of DM into DE, providing such mechanism and explaining the coincidence puzzle., Comment: 6 pages, 2 figures

Optical amplification, crucial for modern communication, primarily relies on erbium-doped fiber amplifiers (EDFAs) to enhance signals without distortion. However, EDFAs only cover a portion of the low-loss spectrum of optical fibers. Optical parametric amplification, utilizing material nonlinearities, offers expanded capacity, but has faced limitations in gain and bandwidth. Addressing this, we present a high-gain continuous-traveling-wave parametric amplifier, surpassing the erbium gain window. Leveraging low-loss gallium phosphide-on-insulator photonic integrated waveguides, we achieve up to 35 dB of parametric gain, with a fiber-to-fiber net gain exceeding 10 dB across a wide wavelength range of approximately 140 nm centered at 1550 nm. We demonstrate the capability to amplify ultra-weak signals, extending over 6 orders of magnitude of input powers, optical frequency combs, and communication signals, highlighting the practical potential of our photonic-chip-based amplifier., Comment: 17 pages, 11 figures

Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited availability of labeled data, class imbalance and the inherent variability and complexity of satellite images. In order to mitigate those issues, our study explores the effectiveness of a Cut-and-Paste augmentation technique for semantic segmentation in satellite images. We adapt this augmentation, which usually requires labeled instances, to the case of semantic segmentation. By leveraging the connected components in the semantic segmentation labels, we extract instances that are then randomly pasted during training. Using the DynamicEarthNet dataset and a U-Net model for evaluation, we found that this augmentation significantly enhances the mIoU score on the test set from 37.9 to 44.1. This finding highlights the potential of the Cut-and-Paste augmentation to improve the generalization capabilities of semantic segmentation models in satellite imagery., Comment: Accepted for publication in IEEE 2024 International Geoscience & Remote Sensing Symposium (IGARSS 2024)

Resonant positron annihilation on atomic electrons provides a powerful method to search for light new particles coupled to $e^+e^-$. Reliable estimates of production rates require a detailed characterization of electron momentum distributions. We describe a general method that harnesses the target material Compton profile to properly include electron velocity effects in resonant annihilation cross-sections. We additionally find that high $Z$ atoms can efficiently act as particle physics accelerators, providing a density of relativistic electrons that allows to extend by several times the experimental mass reach., Comment: 11 pages, 2 figures. One error corrected, mainly affecting only Fig. 2 right (erratum to appear in PRL). Conclusions unchanged

The aim of the present paper is to establish a Bialy-Mironov type rigidity for centrally symmetric symplectic billiards. For a centrally symmetric $C^2$ strongly-convex domain $D$ with boundary $\partial D$, assume that the symplectic billiard map has a (simple) continuous invariant curve $\delta \subset \mathcal{P}$ of rotation number $1/4$ (winding once around $\partial D$) and consisting only of $4$-periodic orbits. If one of the parts between $\delta$ and each boundary of the phase-space is entirely foliated by continuous invariant closed (not null-homotopic) curves, then $\partial D$ is an ellipse. The differences with Birkhoff billiards are essentially two: it is possible to assume the existence of the foliation in one of the parts of the phase-space detected by the curve $\delta$, and the result is obtained by tracing back the problem directly to the totally integrable case., Comment: 11 pages, 3 figures

In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse. This complexity arises from the need to handle a wide range of document types, often characterized by semi-structured forms. Semi-structured documents present a fixed set of data without a fixed format. As a consequence, a template-based solution cannot be used, as understanding a document requires the extraction of the data structure. The recent introduction of Large Language Models (LLMs) has enabled the creation of customized text output satisfying user requests. In this work, we propose a novel approach that combines the LLMs with prompt engineering and multi-agent systems for generating new documents compliant with a desired structure. The main contribution of this work concerns replacing the commonly used manual prompting with a task description generated by semantic retrieval from an LLM. The potential of this approach is demonstrated through a series of experiments and case studies, showcasing its effectiveness in real-world PA scenarios., Comment: Accepted at HCI INTERNATIONAL 2024 - 26th International Conference on Human-Computer Interaction. Washington Hilton Hotel, Washington DC, USA, 29 June - 4 July 2024

We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data including high frequency globally aligned 3D trajectories, scene point cloud, per-frame 3D eye gaze vector and time aligned speech transcription. In this paper, we demonstrate a few exemplar research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation. AEA is an open source dataset that can be downloaded from https://www.projectaria.com/datasets/aea/. We are also providing open-source implementations and examples of how to use the dataset in Project Aria Tools https://github.com/facebookresearch/projectaria_tools., Comment: Dataset website: https://www.projectaria.com/datasets/aea/

Most of the intrusion detection datasets to research machine learning-based intrusion detection systems (IDSs) are devoted to cyber-only systems, and they typically collect data from one architectural layer. Additionally, often the attacks are generated in dedicated attack sessions, without reproducing the realistic alternation and overlap of normal and attack actions. We present a dataset for intrusion detection by performing penetration testing on an embedded cyber-physical system built over Robot Operating System 2 (ROS2). Features are monitored from three architectural layers: the Linux operating system, the network, and the ROS2 services. The dataset is structured as a time series and describes the expected behavior of the system and its response to ROS2-specific attacks: it repeatedly alternates periods of attack-free operation with periods when a specific attack is being performed. Noteworthy, this allows measuring the time to detect an attacker and the number of malicious activities performed before detection. Also, it allows training an intrusion detector to minimize both, by taking advantage of the numerous alternating periods of normal and attack operations., Comment: 18 pages

Online discussion platforms are a vital part of the public discourse in a deliberative democracy. However, how to interpret the outcomes of the discussions on these platforms is often unclear. In this paper, we propose a novel and explainable method for selecting a set of most representative, consistent points of view by combining methods from computational social choice and abstract argumentation. Specifically, we model online discussions as abstract argumentation frameworks combined with information regarding which arguments voters approve of. Based on ideas from approval-based multiwinner voting, we introduce several voting rules for selecting a set of preferred extensions that represents voters' points of view. We compare the proposed methods across several dimensions, theoretically and in numerical simulations, and give clear suggestions on which methods to use depending on the specific situation., Comment: 33 pages. Extended version of an accepted AAMAS-24 paper

High-dimensional problems have long been considered the Achilles' heel of Bayesian optimization algorithms. Spurred by the curse of dimensionality, a large collection of algorithms aim to make it more performant in this setting, commonly by imposing various simplifying assumptions on the objective. In this paper, we identify the degeneracies that make vanilla Bayesian optimization poorly suited to high-dimensional tasks, and further show how existing algorithms address these degeneracies through the lens of lowering the model complexity. Moreover, we propose an enhancement to the prior assumptions that are typical to vanilla Bayesian optimization algorithms, which reduces the complexity to manageable levels without imposing structural restrictions on the objective. Our modification - a simple scaling of the Gaussian process lengthscale prior with the dimensionality - reveals that standard Bayesian optimization works drastically better than previously thought in high dimensions, clearly outperforming existing state-of-the-art algorithms on multiple commonly considered real-world high-dimensional tasks.

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings., Comment: International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) - Italian Workshop on Artificial Intelligence and Robotics (AIRO) Rome, 6 - 9 November, 2023

Molecular ions that are generated by chemical reactions with trapped atomic ions can serve as an accessible testbed for developing molecular quantum technologies. On the other hand, they are also a hindrance to scaling up quantum computers based on atomic ions as unavoidable reactions with background gas destroy the information carriers. Here, we investigate the single- and two-photon dissociation processes of single $\text{CaOH}^+$ molecular ions co-trapped in $\text{Ca}^+$ ion crystals using a femtosecond laser system. We report the photodissociation cross section spectra of $\text{CaOH}^+$ for single-photon processes at $\lambda=$245 - 275$\,$nm and for two-photon processes at $\lambda=$500 - 540$\,$nm. Measurements are interpreted with quantum-chemical calculations, which predict the photodissociation threshold for $\text{CaOH}^+\to \text{Ca}^++\text{OH}$ at 265$\,$nm. This result can serve as a basis for dissociation-based spectroscopy for studying the internal structure of $\text{CaOH}^+$. The result also gives a prescription for recycling $\text{Ca}^+$ ions in large-scale trapped $\text{Ca}^+$ quantum experiments from undesired $\text{CaOH}^+$ ions formed in the presence of background water vapor., Comment: 13 pages, 18 figures

Human-Robot Interaction (HRI) has become increasingly important as robots are being integrated into various aspects of daily life. One key aspect of HRI is gesture recognition, which allows robots to interpret and respond to human gestures in real-time. Gesture recognition plays an important role in non-verbal communication in HRI. To this aim, there is ongoing research on how such non-verbal communication can strengthen verbal communication and improve the system's overall efficiency, thereby enhancing the user experience with the robot. However, several challenges need to be addressed in gesture recognition systems, which include data generation, transferability, scalability, generalizability, standardization, and lack of benchmarking of the gestural systems. In this preliminary paper, we want to address the challenges of data generation using virtual reality simulations and standardization issues by presenting gestures to some commands that can be used as a standard in ground robots., Comment: 4 Pages, 3 Figures, Workshop YOUR Study Design!(WYSD 2023) Participatory critique and refinement of participants' studies. Workshop at the ACM/IEEE International Conference on Human-Robot Interaction (HRI 2023). March 13-16, 2023, Stockholm, SE

In recent years, an increasing number of Human-Robot Interaction (HRI) approaches have been implemented and evaluated in Virtual Reality (VR), as it allows to speed-up design iterations and makes it safer for the final user to evaluate and master the HRI primitives. However, identifying the most suitable VR experience is not straightforward. In this work, we evaluate how, in a smart agriculture scenario, immersive and non-immersive VR are perceived by users with respect to a speech act understanding task. In particular, we collect opinions and suggestions from the 81 participants involved in both experiments to highlight the strengths and weaknesses of these different experiences., Comment: 8 Pages, 3 Figures, 6th International Workshop on Virtual, Augmented, and Mixed-Reality for Human-Robot Interactions VAM-HRI 2023. ACM/IEEE International Conference on Human-Robot Interaction. HRI 2023. March 13-16, 2023 Stockholm, SE