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Surface porosity has been found to be an important property for small bodies. Some asteroids and comets can exhibit an extremely high surface porosity in the first millimeter layer. This layer may be produced by various processes and maintained by the lack of an atmosphere. However, the influence of porosity on the spectro-photometric properties of small body surfaces is not yet fully understood. In this study, we looked into the effect of porosity on the spectro-photometric properties of Phobos regolith spectroscopic simulants; created by mixing the simulants with ultra-pure water, producing ice-dust particles, and then sublimating the water. The reflectance spectroscopic properties in the visible and near-infrared (0.5-4.2 $\mu$m) show no strong variations between the porous and compact samples. However, one simulant exhibits a bluing of the slope after increasing porosity, providing possible insights into the differences between the blue and red units observed on Phobos. In the mid-infrared range, a contrast increase of the 10-$\mu$m emissivity plateau due to silicates is observed. Photometry reveals a modification in the phase reddening behavior between the compact powder and the sublimation residue for both simulants. However, the observed behavior is different between the simulants, suggesting that the phase reddening may be dependent on the composition of the simulants. The phase curve also appears to be modified by the addition of porosity, with a higher contribution of forward scattering observed for the sublimation residue. The derivation of the Hapke parameters indicates an increase in roughness for the porous sample, but no significant modification of the opposition effect. This study aims to provide new insights into the understanding of porosity by using two Phobos simulants in the context of the upcoming JAXA/Martian Moons eXploration mission., Comment: Submitted to Icarus

Laser-plasma accelerators represent a promising technology for future compact accelerating systems, enabling the acceleration of tens of pC to above $1\,$GeV over just a few centimeters. Nonetheless, these devices currently lack the stability, beam quality and average current of conventional systems. While many efforts have focused on improving acceleration stability and quality, little progress has been made in increasing the beam's average current, which is essential for future laser-plasma-based applications. In this paper, we investigate a laser-plasma acceleration regime aimed at increasing the beam average current with energies up to few-MeVs, efficiently enhancing the beam charge. We present experimental results on configurations that allow reaching charges of $5-30\,$nC and a maximum conversion efficiency of around $14\,$%. Through comprehensive Particle-In-Cell simulations, we interpret the experimental results and present a detailed study on electron dynamics. From our analysis, we show that most electrons are not trapped in a plasma wave; rather, they experience ponderomotive acceleration. Thus, we prove the laser pulse as the main driver of the particles' energy gain process.

This paper proposes a novel approach towards image authentication and tampering detection by using watermarking as a communication channel for semantic information. We modify the HiDDeN deep-learning watermarking architecture to embed and extract high-dimensional real vectors representing image captions. Our method improves significantly robustness on both malign and benign edits. We also introduce a local confidence metric correlated with Message Recovery Rate, enhancing the method's practical applicability. This approach bridges the gap between traditional watermarking and passive forensic methods, offering a robust solution for image integrity verification., Comment: Code will be released

Semiconductor nanowires are believed to play a crucial role for future applications in electronics, spintronics and quantum technologies. A potential candidate is HgTe but its sensitivity to nanofabrication processes restrain its development. A way to circumvent this obstacle is the selective area growth technique. Here, in-plane HgTe nanostructures are grown thanks to selective area molecular beam epitaxy on a semi-insulating CdTe substrate covered with a patterned SiO$_{\mathrm{2}}$ mask. The shape of these nanostructures is defined by the in-plane orientation of the mask aperture along the <$110$>, <$1\bar{\mathrm{1}}0$>, or <$100$> direction, the deposited thickness, and the growth temperature. Several micron long in-plane nanowires can be achieved as well as more complex nanostructures such as networks, diamond structures or rings. A good selectivity is achieved with very little parasitic growth on the mask even for a growth temperature as low as $140${\deg}C and growth rate up to $0.5$ ML/s. For <$110$> oriented nanowires, the center of the nanostructure exhibits a trapezoidal shape with {$111$}B facets and two grains on the sides, while <$1\bar{\mathrm{1}}0$> oriented nanowires show {$111$}A facets with adatoms accumulation on the sides of the top surface. Transmission electron microscopy observations reveal a continuous epitaxial relation between the CdTe substrate and the HgTe nanowire. Measurements of the resistance with fourpoint scanning tunneling microscopy indicates a good electrical homogeneity along the main NW axis and a thermally activated transport. This growth method paves the way toward the fabrication of complex HgTe-based nanostructures for electronic transport measurements., Comment: 18 pages and 8 figures. Submitted to Nanotechnology

This article undertakes a comprehensive examination of two distinct robot morphologies: the PUMA-type arm (Programmable Universal Machine for Assembly) and the UR-type robot (Universal Robots). The primary aim of this comparative analysis is to assess their respective performances within the specialized domain of welding, focusing on predefined industrial application scenarios. These scenarios encompass a range of geometrical components earmarked for welding, along with specified welding paths, spatial constraints, and welding methodologies reflective of real-world scenarios encountered by manual welders. The case studies presented in this research serve as illustrative examples of Weez-U Welding practices, providing insights into the practical implications of employing different robot morphologies. Moreover, this study distinguishes between various base positions for the robot, thereby aiding welders in selecting the optimal base placement aligned with their specific welding objectives. By offering such insights, this research facilitates the selection of the most suitable architecture for this particular range of trajectories, thus optimizing welding efficiency and effectiveness. A departure from conventional methodologies, this study goes beyond merely considering singularities and also delves into the analysis of collisions between the robot and its environment, contingent upon the robot's posture. This holistic approach offers a more nuanced understanding of the challenges and considerations inherent in deploying robotic welding systems, providing valuable insights for practitioners and researchers alike in the field of robotic welding technology., Comment: International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, ASME, Aug 2024, Washignton DC, United States

Circular Rydberg atoms (CRAs), i.e., Rydberg atoms with maximal orbital momentum, ideally combine long coherence times and strong interactions, a key property of quantum systems, in particular for the development of quantum technologies. However, the dipole-dipole interaction between CRAs has not been observed so far. We report the measurement and characterization of the resonant dipole-dipole interaction between two CRAs, individually trapped in optical tweezers, and find excellent agreement with theoretical predictions. We demonstrate a dynamic control over the strength of the interaction by tuning the orientation of an electric field. We use the interaction between the CRAs as a meter for the interatomic distance, and record the relative motion between two atoms in their traps. This motion, that we induce through the interaction between Rydberg levels with permanent electric dipoles, transiently populated during the preparation of the circular states, is a signature of spin-motion coupling.

The simplest Weyl semimetal with broken time-reversal symmetry consists of a pair of Weyl nodes located at wave vectors $\mathbf{K}_{\tau }=\tau \mathbf{b}$ in momentum space with $\tau =\pm 1$ the node index and chirality. The electronic dispersion near each node is linear. In a magnetic field $\mathbf{B}$ along $\mathbf{b}$, this dispersion is modified into a series of positive and negative energy Landau levels $n=\pm 1,\pm 2,\ldots ,$which disperse along the direction of the magnetic field, and a chiral Landau level, $n=0$, with a linear dispersion given by $e_{\tau ,n=0}\left( k_{z}\right) =-\tau \hslash v_{F}k_{z},$ where $k_{z}$ is the component of the wave vector $\mathbf{k}$ along the magnetic field and $v_{F}$ is the Fermi velocity. In the extreme quantum limit, the Fermi level is in the chiral levels near the Dirac point. When Coulomb interaction is considered, a Weyl semimetal may be unstable towards the formation of a condensate of internodal electron-hole pairs. In this article we use the full long-range Coulomb interaction and the self-consistent Hartree-Fock approximation to generate the condensed state. We study its stability with respect to a change in the Fermi velocity, doping and strength of the Coulomb interaction and also consider Weyl nodes with higher Chern number $C=2,3$. We derive the response functions of the excitonic state in the generalized random-phase approximation (GRPA). We show that, in the mean-field gap induced by the internodal coherence, there is, in the GRPA excitonic response, a series of bound electron-hole states with a binding energy that decreases until the Hartree-Fock energy gap is reached. In addition, there is a collective mode gapped at exactly the plasmon frequency: the gapless mode present in the proper excitonic response function is pushed to the plasmon frequency by the long-range Coulomb interaction., Comment: 23 pages, 11 figures

Documents are visually rich structures that convey information through text, as well as tables, figures, page layouts, or fonts. While modern document retrieval systems exhibit strong performance on query-to-text matching, they struggle to exploit visual cues efficiently, hindering their performance on practical document retrieval applications such as Retrieval Augmented Generation. To benchmark current systems on visually rich document retrieval, we introduce the Visual Document Retrieval Benchmark ViDoRe, composed of various page-level retrieving tasks spanning multiple domains, languages, and settings. The inherent shortcomings of modern systems motivate the introduction of a new retrieval model architecture, ColPali, which leverages the document understanding capabilities of recent Vision Language Models to produce high-quality contextualized embeddings solely from images of document pages. Combined with a late interaction matching mechanism, ColPali largely outperforms modern document retrieval pipelines while being drastically faster and end-to-end trainable., Comment: Under Review

We propose a multibody model of a freewing UAV. This model allows obtaining simulations of the UAV's behaviour and, in the future, to design a control law stabilising the entire flight envelope (hovering and forward flight). We also describe the realisation of a prototype and a comparison of possible methods for estimating the UAV's states. With this prototype, we report on experimental hovering flights with a non-linear incremental dynamic inversion controller to stabilise the wing and a proportional derivative controller for the fuselage stabilization.

Gravity measurements provide valuable information on the mass distribution below the earth surface relevant to various areas of geosciences such as hydrology, geodesy, geophysics, volcanology, and natural resources management. During the past decades, the needs for sensitivity, robustness, compactness, and transportability of instruments measuring the gravitational acceleration have constantly increased. Today, applications typically call for 1 $\mu \mathrm{Gal} = 10 \mathrm{nm.s}^{-2}$ ~ $10^{-9}$ g resolution on time scales ranging from minutes to years. Absolute Quantum Gravimeters (AQGs) based on matter-wave interferometry with laser-cooled atoms address all these challenges at once, even in uncontrolled environments. Furthermore, to date, quantum gravimeters are the only technology capable of providing continuous absolute gravity data over long measurement durations (~1 day to months or more). In this paper we recall the AQG working principle and present the reproducible high performance at the {\mu}Gal level on all 16 units fabricated so far. We also describe recent progress on the Differential Quantum Gravimeter (DQG) which measures simultaneously the mean gravitational acceleration and its vertical gradient at the level of $10 \mathrm{nm.s}^{-2}$ and $1$ E (1 E[eotvos] = $10^{-9}$ s$^{-2}$) respectively., Comment: 10 pages, 5 figures

Previous observations of Phobos and Deimos, the moons of Mars, have improved our understanding of these small bodies. However, their formation and composition remain poorly constrained. Physical and spectral properties suggest that Phobos may be a weakly thermal-altered captured asteroid but the dynamical properties of the martian system suggest a formation by giant collision similar to the Earth moon. In 2027, the JAXA's MMX mission aims to address these outstanding questions. We undertook measurements with a new simulant called OPPS (Observatory of Paris Phobos Simulant) which closely matches Phobos spectra in the visible to the mid-infrared range. The simulant was synthesized using a mixture of olivine, saponite, anthracite, and coal. Since observation geometry is a crucial aspect of planetary surface remote sensing exploration, we evaluated the parameters obtained by modeling the phase curves -- obtained through laboratory measurements -- of two different Phobos simulants (UTPS-TB and OPPS) using Hapke IMSA model. Our results show that the photometric properties of Phobos simulants are not fully consistent with those of Tagish Lake, Allende, or the NWA 4766 shergottite. We also investigated the detection of volatiles/organic compounds and hydrated minerals, as the presence of such components is expected on Phobos in the hypothesis of a captured primitive asteroid. The results indicate that a significant amount of organic compounds is required for the detection of C-H bands at 3.4 $\mu$m. In contrast, the 2.7 $\mu$m absorption band, due to hydrated minerals, is much deeper and easier to detect than C-H organic features at the same concentration levels. Posing limits on detectability of some possible key components of Phobos surface will be pivotal to prepare and interpret future observations of the MIRS spectrometer onboard MMX mission., Comment: 58 pages, 19 figures, submitted to Icarus

We investigate the sedimentation of chiral particles in viscous fluid flow. We identify helical ribbons as simple particles with strong translation-rotation coupling whose symmetry ensures that the centers of mass, buoyancy, resistance, and mobility coincide. Experimental measurements of both relevant mobility tensors show excellent agreement with simulations of ribbons made of interacting spheres. We observe quasi-periodic angular dynamics causing complex spatial trajectories. In tilt-spin phase space, orbits are closed due to time-reversal and reflection symmetry. Changing the helical ribbon length reveals a bifurcation at which the stable sedimentation orientations switch., Comment: 7 pages, 6 figures

Hyperspectral Imagery (HSI) has been used in many applications to non-destructively determine the material and/or chemical compositions of samples. There is growing interest in creating 3D hyperspectral reconstructions, which could provide both spatial and spectral information while also mitigating common HSI challenges such as non-Lambertian surfaces and translucent objects. However, traditional 3D reconstruction with HSI is difficult due to technological limitations of hyperspectral cameras. In recent years, Neural Radiance Fields (NeRFs) have seen widespread success in creating high quality volumetric 3D representations of scenes captured by a variety of camera models. Leveraging recent advances in NeRFs, we propose computing a hyperspectral 3D reconstruction in which every point in space and view direction is characterized by wavelength-dependent radiance and transmittance spectra. To evaluate our approach, a dataset containing nearly 2000 hyperspectral images across 8 scenes and 2 cameras was collected. We perform comparisons against traditional RGB NeRF baselines and apply ablation testing with alternative spectra representations. Finally, we demonstrate the potential of hyperspectral NeRFs for hyperspectral super-resolution and imaging sensor simulation. We show that our hyperspectral NeRF approach enables creating fast, accurate volumetric 3D hyperspectral scenes and enables several new applications and areas for future study., Comment: Main paper: 15 pages + 2 pages references. Supplemental/Appendix: 6 pages

We present a framework that combines the adjoint state method together with reverse-time back-propagation to solve prohibitively large open-system quantum control problems. Our approach enables the optimization of arbitrary cost functions with fully general controls applied on large open quantum systems described by a Lindblad master equation. It is scalable, computationally efficient, and has a low memory footprint. We apply this framework to optimize two inherently dissipative operations in superconducting qubits which lag behind in terms of fidelity and duration compared to other unitary operations: the dispersive readout and all-microwave reset of a transmon qubit. Our results show that, given a fixed set of system parameters, shaping the control pulses can yield 2x improvements in the fidelity and duration for both of these operations compared to standard strategies. Our approach can readily be applied to optimize quantum controls in a vast range of applications such as reservoir engineering, autonomous quantum error correction, and leakage-reduction units., Comment: 5 pages, 3 figures

This study aims to analyze Phobos' photometric properties using Mars Express mission observations to support the Martian Moons eXploration mission. We analyzed resolved images of Phobos acquired between 2004 and 2022 by the HRSC and the SRC cameras on board the Mars Express spacecraft. We performed photometric analysis using the Hapke model for both integrated and disk-resolved data. The Phobos phase function has a strong opposition effect due to shadow hiding, with an amplitude and a half-width of the opposition surge of 2.28$\pm$0.03 and 0.0573$\pm$0.0001, respectively. Overall, the surface of Phobos is dark, with a geometric albedo of 6.8 % in the green filter and backscattering. We also found a surface porosity of 87\%, indicating the presence of a thick dust mantle or of fractal aggregates on the top surface. The SSA maps revealed high reflectance variability, with the blue unit area in the northeast Stickney rim being up to 65\% brighter than average, while the Stickney floor is among the darkest regions, with reflectance 10 to 20% lower than average. Photometric modeling of the regions of interest selected in the red and blue units indicates that red unit terrains have a stronger opposition effect and a smaller SSA value than the blue ones, but they have similar porosity and backscattering properties. The HRSC data provide a unique investigation of the Phobos phase function and opposition surge, which is valuable information for the MMX observational planning. The Phobos opposition surge, surface porosity, phase integral, and spectral slope are very similar to the values observed for the comet 67P and for Jupiter family comets in general. Based on these similarities, we formulate a hypothesis that the Mars satellites might be the results of a binary or bilobated comet captured by Mars., Comment: 30 pages, 16 figures, accepted for publication in Astron. & Astroph

We successfully demonstrated a fully-managed, field-deployed, three-node QKD ring network with L1-OTNsec encryption, that employs a hybrid scheme of QKD and classical yet quantum-safe centrally-generated symmetric keys to support point-to-point and relay consumers.

In environments like offices, the duration of a robot's navigation between two locations may vary over time. For instance, reaching a kitchen may take more time during lunchtime since the corridors are crowded with people heading the same way. In this work, we address the problem of routing in such environments with tasks expressed in Metric Interval Temporal Logic (MITL) - a rich robot task specification language that allows us to capture explicit time requirements. Our objective is to find a strategy that maximizes the temporal robustness of the robot's MITL task. As the first step towards a solution, we define a Mixed-integer linear programming approach to solving the task planning problem over a Varying Weighted Transition System, where navigation durations are deterministic but vary depending on the time of day. Then, we apply this planner to optimize for MITL temporal robustness in Markov Decision Processes, where the navigation durations between physical locations are uncertain, but the time-dependent distribution over possible delays is known. Finally, we develop a receding horizon planner for Markov Decision Processes that preserves guarantees over MITL temporal robustness. We show the scalability of our planning algorithms in simulations of robotic tasks., Comment: ICRA 2024

The research field of Artificial Life studies how life-like phenomena such as autopoiesis, agency, or self-regulation can self-organize in computer simulations. In cellular automata (CA), a key open-question has been whether it it is possible to find environment rules that self-organize robust "individuals" from an initial state with no prior existence of things like "bodies", "brain", "perception" or "action". In this paper, we leverage recent advances in machine learning, combining algorithms for diversity search, curriculum learning and gradient descent, to automate the search of such "individuals", i.e. localized structures that move around with the ability to react in a coherent manner to external obstacles and maintain their integrity, hence primitive forms of sensorimotor agency. We show that this approach enables to find systematically environmental conditions in CA leading to self-organization of such basic forms of agency. Through multiple experiments, we show that the discovered agents have surprisingly robust capabilities to move, maintain their body integrity and navigate among various obstacles. They also show strong generalization abilities, with robustness to changes of scale, random updates or perturbations from the environment not seen during training. We discuss how this approach opens new perspectives in AI and synthetic bioengineering.

Tokenization is an understudied and often neglected component of modern LLMs. Most published works use a single tokenizer for all experiments, often borrowed from another model, without performing ablations or analysis to optimize tokenization. Moreover, the tokenizer is generally kept unchanged when fine-tuning a base model. In this paper, we show that the size, pre-tokenization regular expression, and training data of a tokenizer can significantly impact the model's generation speed, effective context size, memory usage, and downstream performance. We train specialized Byte-Pair Encoding code tokenizers, and conduct extensive ablations on the impact of tokenizer design on the performance of LLMs for code generation tasks such as HumanEval and MBPP, and provide recommendations for tokenizer hyper-parameters selection and switching the tokenizer in a pre-trained LLM. We perform our experiments on models trained from scratch and from pre-trained models, verifying their applicability to a wide range of use-cases. We find that when fine-tuning on more than 50 billion tokens, we can specialize the tokenizer of a pre-trained LLM to obtain large gains in generation speed and effective context size.

We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81 % of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.

It is challenging to balance the privacy and accuracy for federated query processing over multiple private data silos. In this work, we will demonstrate an end-to-end workflow for automating an emerging privacy-preserving technique that uses a deep learning model trained using the Differentially-Private Stochastic Gradient Descent (DP-SGD) algorithm to replace portions of actual data to answer a query. Our proposed novel declarative privacy-preserving workflow allows users to specify "what private information to protect" rather than "how to protect". Under the hood, the system automatically chooses query-model transformation plans as well as hyper-parameters. At the same time, the proposed workflow also allows human experts to review and tune the selected privacy-preserving mechanism for audit/compliance, and optimization purposes.

Animals often demonstrate a remarkable ability to adapt to their environments during their lifetime. They do so partly due to the evolution of morphological and neural structures. These structures capture features of environments shared between generations to bias and speed up lifetime learning. In this work, we propose a computational model for studying a mechanism that can enable such a process. We adopt a computational framework based on meta reinforcement learning as a model of the interplay between evolution and development. At the evolutionary scale, we evolve reservoirs, a family of recurrent neural networks that differ from conventional networks in that one optimizes not the synaptic weights, but hyperparameters controlling macro-level properties of the resulting network architecture. At the developmental scale, we employ these evolved reservoirs to facilitate the learning of a behavioral policy through Reinforcement Learning (RL). Within an RL agent, a reservoir encodes the environment state before providing it to an action policy. We evaluate our approach on several 2D and 3D simulated environments. Our results show that the evolution of reservoirs can improve the learning of diverse challenging tasks. We study in particular three hypotheses: the use of an architecture combining reservoirs and reinforcement learning could enable (1) solving tasks with partial observability, (2) generating oscillatory dynamics that facilitate the learning of locomotion tasks, and (3) facilitating the generalization of learned behaviors to new tasks unknown during the evolution phase.

Since two decades, laser-driven neutron emissions are studied as they represent a complementary source to conventional neutron sources, with further more different characteristics (i.e. shorter bunch duration and higher number of neutrons per bunch). We report here a global, thorough characterization of the neutron fields produced at the Apollon laser facility using the secondary laser beam (F2). A Double Plasma Mirror (DPM) was used to improve the temporal contrast of the laser which delivers pulses of 24 fs duration, a mean on-target energy of ~10 J and up to 1 shot/min. The interaction of the laser with thin targets (few tens or hundreds of nm) in ultra-high conditions produced enhanced proton beams (up to 35 MeV), which were then used to generate neutrons via the pitcher-catcher technique. The characterization of these neutron emissions is presented, with results obtained from both simulations and measurements using several diagnostics (activation samples, bubble detectors and Time-of-Flight detectors), leading to a neutron yield of ~$4.10^{7}$ neutrons/shot. Similar neutron emissions were observed during shots with and without DPM, while fewer X-rays are produced when the DPM is used, making this tool interesting to adjust the neutrons/X-rays ratio for some applications like combined neutron/X-ray radiography.

We discuss the use of Low Gain Avalanche (LGAD) silicon detectors for two specific applications, namely measuring cosmic rays in space in collaboration with NASA] and beam properties and received doses for patients undergoing cancer treatment in flash beam therapy., Comment: Proceedings of the ULITIMA 2023 workshop, SLAC, USA, March 13-17 2023, to be published in NIM

This paper presents the official release of the Digital Typhoon dataset, the longest typhoon satellite image dataset for 40+ years aimed at benchmarking machine learning models for long-term spatio-temporal data. To build the dataset, we developed a workflow to create an infrared typhoon-centered image for cropping using Lambert azimuthal equal-area projection referring to the best track data. We also address data quality issues such as inter-satellite calibration to create a homogeneous dataset. To take advantage of the dataset, we organized machine learning tasks by the types and targets of inference, with other tasks for meteorological analysis, societal impact, and climate change. The benchmarking results on the analysis, forecasting, and reanalysis for the intensity suggest that the dataset is challenging for recent deep learning models, due to many choices that affect the performance of various models. This dataset reduces the barrier for machine learning researchers to meet large-scale real-world events called tropical cyclones and develop machine learning models that may contribute to advancing scientific knowledge on tropical cyclones as well as solving societal and sustainability issues such as disaster reduction and climate change. The dataset is publicly available at http://agora.ex.nii.ac.jp/digital-typhoon/dataset/ and https://github.com/kitamoto-lab/digital-typhoon/., Comment: Accepted by NeurIPS 2023 Datasets and Benchmarks Track (Spotlight)

Recent works have proven that intricate cooperative behaviors can emerge in agents trained using meta reinforcement learning on open ended task distributions using self-play. While the results are impressive, we argue that self-play and other centralized training techniques do not accurately reflect how general collective exploration strategies emerge in the natural world: through decentralized training and over an open-ended distribution of tasks. In this work we therefore investigate the emergence of collective exploration strategies, where several agents meta-learn independent recurrent policies on an open ended distribution of tasks. To this end we introduce a novel environment with an open ended procedurally generated task space which dynamically combines multiple subtasks sampled from five diverse task types to form a vast distribution of task trees. We show that decentralized agents trained in our environment exhibit strong generalization abilities when confronted with novel objects at test time. Additionally, despite never being forced to cooperate during training the agents learn collective exploration strategies which allow them to solve novel tasks never encountered during training. We further find that the agents learned collective exploration strategies extend to an open ended task setting, allowing them to solve task trees of twice the depth compared to the ones seen during training. Our open source code as well as videos of the agents can be found on our companion website., Comment: Published at the 2nd Agent Learning in Open-Endedness Workshop at NeurIPS 2023

Instruction Fine-Tuning (IFT) is a powerful paradigm that strengthens the zero-shot capabilities of Large Language Models (LLMs), but in doing so induces new evaluation metric requirements. We show LLM-based metrics to be well adapted to these requirements, and leverage them to conduct an investigation of task-specialization strategies, quantifying the trade-offs that emerge in practical industrial settings. Our findings offer practitioners actionable insights for real-world IFT model deployment., Comment: Short paper accepted at EMNLP 2023

In order to allow a good separation of isotopes in a High Resolution Mass Spectrometer (HRMS), the transverse emittance and the energy spread of the beam should have very low values, for this reason a Beam Cooler (BC) is planned to be located between the ISOL target, i.e. the beam source, and the HRMS in the new project Selective Production of Exotic Species (SPES). In the SPES project the spectrometer resolution must be higher than dm/m=5E-5 and thus the features of the beam at the entrance of the HRMS should be at least emittance (95%, normalized) < 8.3E-3 pi mm mrad and sigma_E < 1.5eV. A new BC has been designed and realized by the Laboratoire de Physique Corpuscolaire (LPC) at Caen, France, for the SPES facility at Laboratori Nazionali di Legnaro (LNL), near Padova, Italy. BCs cool down the beam thanks to a dissipative process in which the thermal energy passes from the beam ions to another medium whose constituent is typically much lighter, Helium gas in our case. This process takes place inside a confinement system that on the one hand to limit the spread of the cooling medium, and on the other to allow the beam to continue along the required trajectory, in the presented device it is a row of radio frequency quadrupoles in an almost closed chamber. This document presents the main features of the new BC together with the results of a preliminary study where the beam dynamic has been simulated. The analysis embeds also the investigation of the gas distribution inside and outside the BC. The beam dynamic simulations are based on the Simion code while the estimation of the gas distribution is computed with MolFlow+. Simulations show that accurately setting the BC leads to a large improvement of the emittance while the energy spread still needs to be improved. Limiting the gas pressure in the acceleration zone seems to allow the required final boost., Comment: 21 pages, 18 figures, submitted to JINST