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Monte Carlo particle transport codes are well established on classical hardware and are considered as the reference tool for nuclear applications. In a growing number of domains, the design of algorithms is progressively shifting towards the field of quantum computing, where theoretical speedups over their classical counterparts are expected. In some of these domains, Monte Carlo methods have already been converted to a quantum computing friendly setup where the expected and observed gain in complexity is quadratic. Surprisingly, particle transport has been left aside along the path and lacks an implementation on these new architectures. In this work, a numerical scheme for particle transport based on discrete-time quantum walks and its combination with the amplitude amplification routine are proposed to catch up with the promised speedups.

Similar to Distributed Acoustic Sensing (DAS), phase transmission fibre optics allows for large bandwidth seismic data measurements using fibre-optic cables. However, while the application range of DAS is limited to tens of kilometres, phase transmission fibre optics has an application range that can go up to thousands of kilometres. This new method has been shown as an effective method to record earthquakes, but its ability to record ambient seismic noise that can be used for seismic imaging and tomography is still up for question, and will be analysed in this work. We provide the theoretical foundation for the interpretation of seismic noise autocorrelations and interferometry from phase transmission fibre optics. Further, we test the model on actual phase transmission data sourced from a phase-stabilised optical frequency network in Switzerland. There, the phase stabilisation scheme measures and compensates noise on the optical phase caused by distortions of the fibre. We analyse the autocorrelation of the measured phase noise correction and explore potential interpretations by comparing it with the autocorrelation of a synthetically computed phase noise correction. This comparison is challenging due to two factors: the intricate cable geometry increases the computational cost of generating synthetic data, and the precise location and geometry of the cable are uncertain. Despite these difficulties, we believe that when applied to a different dataset, this approach could enable seismic tomography with ambient noise interferometry using a long-range fibre-optic sensing device.

We report the existence of hydrostatic equilibrium states for a composite body made of two rigidly rotating, homogeneous layers bounded by spheroidal surfaces, where the core has a prolate shape. These new configurations require an oblate envelope that spins faster than the core. No solution with a prolate envelope is found. For some parameters, the prolate core can even be at rest. Numerical experiments based on the self-consistent field method support this result in the case of heterogeneous layers with polytropic equations of state. The possible cancellation of the first gravitational moment, $J_2$, is discussed., Comment: Accepted for publication in A&A. 7 pages, 4 figures, 4 tables

In the Milky Way and other main-sequence galaxies, stars form exclusively in molecular gas, which is traced by CO emission. However, low metallicity dwarf galaxies are often `CO-dark' in the sense that CO emission is not observable even at the high resolution and sensitivities of modern observing facilities. In this work we use ultra high-resolution simulations of four low-metalicity dwarf galaxies (which resolve star formation down to the scale of star-forming cores, 0.01 pc) combined with a time-dependent treatment of the chemistry of the interstellar medium, to investigate the star formation environment in this previously hidden regime. By generating synthetic observations of our models we show that the galaxies have high to extremely high dark gas fractions (0.13 to 1.00 dependent on beam size and conditions), yet despite this form stars. However, when examined on smaller scales, we find that the stars still form in regions dominated by molecular gas, it is simply that these are far smaller than the scale of the beam (1.5"). Thus, while stars in CO-dark dwarf galaxies form in small molecular cores like larger galaxies, their cloud-scale environment is very different., Comment: Re-submitted to MNRAS after referee report, comments and questions welcome, 23 pages, 16 figures

Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large space of trajectories. For settings where the dynamical system of interest follows a Brownian motion with known drift, the question of conditioning the process to reach a given endpoint or desired rare event is definitively answered by Doob's h-transform. However, the naive estimation of this transform is infeasible, as it requires simulating sufficiently many forward trajectories to estimate rare event probabilities. In this work, we propose a variational formulation of Doob's h-transform as an optimization problem over trajectories between a given initial point and the desired ending point. To solve this optimization, we propose a simulation-free training objective with a model parameterization that imposes the desired boundary conditions by design. Our approach significantly reduces the search space over trajectories and avoids expensive trajectory simulation and inefficient importance sampling estimators which are required in existing methods. We demonstrate the ability of our method to find feasible transition paths on real-world molecular simulation and protein folding tasks., Comment: Accepted as Spotlight at Conference on Neural Information Processing Systems (NeurIPS 2024); Alanine dipeptide results updated after fixing unphysical parameterization

Graphs are ubiquitous for modeling complex systems involving structured data and relationships. Consequently, graph representation learning, which aims to automatically learn low-dimensional representations of graphs, has drawn a lot of attention in recent years. The overwhelming majority of existing methods handle unsigned graphs. However, signed graphs appear in an increasing number of application domains to model systems involving two types of opposed relationships. Several authors took an interest in signed graphs and proposed methods for providing vertex-level representations, but only one exists for whole-graph representations, and it can handle only fully connected graphs. In this article, we tackle this issue by proposing two approaches to learning whole-graph representations of general signed graphs. The first is a SG2V, a signed generalization of the whole-graph embedding method Graph2vec that relies on a modification of the Weisfeiler--Lehman relabelling procedure. The second one is WSGCN, a whole-graph generalization of the signed vertex embedding method SGCN that relies on the introduction of master nodes into the GCN. We propose several variants of both these approaches. A bottleneck in the development of whole-graph-oriented methods is the lack of data. We constitute a benchmark composed of three collections of signed graphs with corresponding ground truths. We assess our methods on this benchmark, and our results show that the signed whole-graph methods learn better representations for this task. Overall, the baseline obtains an F-measure score of 58.57, when SG2V and WSGCN reach 73.01 and 81.20, respectively. Our source code and benchmark dataset are both publicly available online.

This study presents a novel quantitative estimation method for total retinal arterial blood flow utilizing real-time Doppler holography at an unprecedented frame rate of 33,000 frames per second. This technique, leveraging high-speed digital holography, enables non-invasive angiographic imaging of the retina, providing detailed blood flow contrasts essential for assessing retinal health. The proposed quantitative analysis method consists of segmenting primary in-plane retinal arteries and calculating local blood velocity using Doppler frequency broadening. The analysis integrates a forward scattering model to achieve blood flow estimation. Our findings highlight the potential of Doppler holography as a powerful tool for diagnosing and monitoring the treatment of retinal vascular conditions, complementary to existing imaging methods.

Uranus and Neptune have atmospheres dominated by molecular hydrogen and helium. In the upper troposphere, methane is the third main molecule and condenses, yielding a vertical gradient in CH4. This condensable species being heavier than H2 and He, the resulting change in mean molecular weight due to condensation comes as a factor countering dry and moist convection. As observations also show latitudinal variations in methane abundance, one can expect different vertical gradients from one latitude to another. In this paper, we investigate the impact of this methane vertical gradient on the atmospheric regimes, especially on the formation and inhibition of moist convective storms in the troposphere of ice giants. We develop a 3D cloud-resolving model to simulate convective processes. Using our simulations, we conclude that typical velocities of dry convection in the deep atmosphere are rather low (of the order of 1 m/s) but sufficient to sustain upward methane transport, and that moist convection at methane condensation level is strongly inhibited. Previous studies derived an analytical criterion on the methane vapor amount above which moist convection should be inhibited. We first validate this analytical criterion numerically. We then show that the critical methane abundance governs the inhibition and formation of moist convective storms, and we conclude that the intensity and intermittency of these storms should depend on the methane abundance and saturation. In ice giants, dry convection is weak, and moist convection is strongly inhibited. However, when enough methane is transported upwards, through dry convection and turbulent diffusion, sporadic moist convective storms can form. These storms should be more frequent on Neptune than on Uranus, because of Neptune's internal heat flow. Our results can explain the observed sporadicity of clouds in ice giants.

Variational ab-initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows in principle straightforward extraction of any other observable of interest, besides the energy, but in practice this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation. We use variational quantum Monte Carlo with deep-learning ans\"atze (deep QMC) to obtain highly accurate wave functions free of basis set errors, and from them, using our novel method, correspondingly accurate electron densities, which we demonstrate by calculating dipole moments, nuclear forces, contact densities, and other density-based properties., Comment: 12 pages, 9 figures in the main text

Feedback control of open quantum systems is of fundamental importance for practical applications in various contexts, ranging from quantum computation to quantum error correction and quantum metrology. Its use in the context of thermodynamics further enables the study of the interplay between information and energy. However, deriving optimal feedback control strategies is highly challenging, as it involves the optimal control of open quantum systems, the stochastic nature of quantum measurement, and the inclusion of policies that maximize a long-term time- and trajectory-averaged goal. In this work, we employ a reinforcement learning approach to automate and capture the role of a quantum Maxwell's demon: the agent takes the literal role of discovering optimal feedback control strategies in qubit-based systems that maximize a trade-off between measurement-powered cooling and measurement efficiency. Considering weak or projective quantum measurements, we explore different regimes based on the ordering between the thermalization, the measurement, and the unitary feedback timescales, finding different and highly non-intuitive, yet interpretable, strategies. In the thermalization-dominated regime, we find strategies with elaborate finite-time thermalization protocols conditioned on measurement outcomes. In the measurement-dominated regime, we find that optimal strategies involve adaptively measuring different qubit observables reflecting the acquired information, and repeating multiple weak measurements until the quantum state is "sufficiently pure", leading to random walks in state space. Finally, we study the case when all timescales are comparable, finding new feedback control strategies that considerably outperform more intuitive ones. We discuss a two-qubit example where we explore the role of entanglement and conclude discussing the scaling of our results to quantum many-body systems., Comment: 16+10 pages, 21 figures

We characterize separable Banach spaces having $G_\delta$ isometry classes in the Polish codings $\mathcal{P}$, $\mathcal{P}_\infty$ and $\mathcal{B}$ introduced by C\'uth-Dole\v{z}al-Doucha-Kurka [13] as those being guarded Fra\"iss\'e, a weakening of the notion of Fra\"iss\'e Banach spaces defined by Ferenczi-Lopez-Abad-Mbombo-Todorcevic [18]. We prove a Fra\"iss\'e correspondence for those spaces and make links with the notion of $\omega$-categoricity from continuous logic, showing that $\omega$-categorical Banach spaces are a natural source of guarded Fra\"iss\'e Banach spaces. Using those results, we prove that for many values of $(p, q)$, the Banach space $L_p(L_q)$ has a $G_\delta$ isometry class; we precisely characterize those values., Comment: 55 pages

We prove the Gromov-Lawson-Rosenberg Conjecture for the group Z/4xZ/4 by computing the connective real k-homology of the classifying space with the Adams spectral sequence and two types of detection theorems for the kernel of the alpha invariant: one based on eta-invariants, closely following work of Botvinnik-Gilkey-Stolz, and a second one based on homological methods. Along the way, we determine differentials of the Adams spectral sequence for classifying spaces involved in the computation, and we study the cap structure of the Adams spectral sequence for sub-hopf algebras of the Steenrod algebra relevant to the computation of connective real and complex k-homology.

Originating in game theory, Shapley values are widely used for explaining a machine learning model's prediction by quantifying the contribution of each feature's value to the prediction. This requires a scalar prediction as in binary classification, whereas a multiclass probabilistic prediction is a discrete probability distribution, living on a multidimensional simplex. In such a multiclass setting the Shapley values are typically computed separately on each class in a one-vs-rest manner, ignoring the compositional nature of the output distribution. In this paper, we introduce Shapley compositions as a well-founded way to properly explain a multiclass probabilistic prediction, using the Aitchison geometry from compositional data analysis. We prove that the Shapley composition is the unique quantity satisfying linearity, symmetry and efficiency on the Aitchison simplex, extending the corresponding axiomatic properties of the standard Shapley value. We demonstrate this proper multiclass treatment in a range of scenarios., Comment: To be published in ECAI2024's proceedings

The relation between magnetic field strength B and gas density n in the interstellar medium is of fundamental importance to many areas of astrophysics, from protostellar disks to galaxy evolution. We present and compare Bayesian analyses of the B - n relation for a comprehensive observational data set, as well as a large body of numerical MHD simulations. We extend the original Zeeman relation of Crutcher et al. (2010) with a large body of magnetic data that includes 700 observations with the Davis-Chandrasekhar-Fermi method. By using a new multiparameter Bayesian analysis we present a new, more general, time-averaged observational relation: B \propto n^{0.27 \pm 0.017} for n \leq n_0 and B \propto n^{0.54 \pm 0.18} for n \geq n_0, with n_0 = 924^(+145-144) cm^-3. We perform a separate analysis on 19 numerical magnetohydrodynamics simulations that cover a wide range of scales, resolutions, initial conditions, and completed with a variety of codes: arepo, flash, pencil, and ramses. The power law exponents derived from the simulations depend on several physical factors including: dynamo effects, time scales, turbulence, and the initial seed field strength. In particular, early-time simulations where the density, velocity and magnetic fields are unevolved do not match the observational scalings. The simulations that trace the observed density range best, the evolved dwarf galaxy and Milky Way like galaxy simulations, settle into a near consistent exponent of = 0.5 in the dense gas, with variability in the diffuse gas exponent., Comment: 23 figures, 28 pages, submitted to MNRAS, Comments welcome

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a database that allows findability, accessibility, interoperability, and reusability of molecular dynamics simulation data. Such a development would democratize the field and significantly improve the impact of MD simulations on life science research. This will transform our working paradigm, pushing the field to a new frontier. We invite you to support our initiative at the MDDB community (https://mddbr.eu/community/)

Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for modeling biomolecules. However, MLCG models currently require large amounts of data from reference atomistic molecular dynamics or substantial computation for training. Denoising score matching -- the technology behind the widely popular diffusion models -- has simultaneously emerged as a machine-learning framework for creating samples from noise. Models in the first category are often trained using atomistic forces, while those in the second category extract the data distribution by reverting noise-based corruption. We unify these approaches to improve the training of MLCG force-fields, reducing data requirements by a factor of 100 while maintaining advantages typical to force-based parameterization. The methods are demonstrated on proteins Trp-Cage and NTL9 and published as open-source code., Comment: 25 pages, 8 figures

This work introduces the Elliptica pseudo-spectral code for generating initial data of binary neutron star systems. Building upon the recent Elliptica code update, we can now construct initial data using not only piecewise polytropic equations of state, but also tabulated equations of state for these binary systems. Furthermore, the code allows us to endow neutron stars within the binary system with spins. These spins can have a magnitude close to the mass shedding limit and can point in any direction., Comment: Elliptica code is now open-source; please see https://github.com/rashti-alireza/Elliptica

The generation of equilibrium samples of molecular systems has been a long-standing problem in statistical physics. Boltzmann Generators are a generative machine learning method that addresses this issue by learning a transformation via a normalizing flow from a simple prior distribution to the target Boltzmann distribution of interest. Recently, flow matching has been employed to train Boltzmann Generators for small molecular systems in Cartesian coordinates. We extend this work and propose a first framework for Boltzmann Generators that are transferable across chemical space, such that they predict zero-shot Boltzmann distributions for test molecules without being retrained for these systems. These transferable Boltzmann Generators allow approximate sampling from the target distribution of unseen systems, as well as efficient reweighting to the target Boltzmann distribution. The transferability of the proposed framework is evaluated on dipeptides, where we show that it generalizes efficiently to unseen systems. Furthermore, we demonstrate that our proposed architecture enhances the efficiency of Boltzmann Generators trained on single molecular systems.

The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences. However, the computational prediction of the relative stability between phases based on their free energy is a daunting task, as traditional free energy estimators require a large amount of simulation data to obtain uncorrelated equilibrium samples over a grid of thermodynamic states. In this work, we develop deep generative machine learning models based on the Boltzmann Generator approach for entire phase diagrams, employing normalizing flows conditioned on the thermodynamic states, e.g., temperature and pressure, that they map to. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples across the entire phase diagram. Using a permutation-equivariant architecture allows us, thereby, to treat solid and liquid phases on the same footing. We demonstrate our approach by predicting the solid-liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods while significantly reducing the number of energy evaluations needed.

Fusing outputs from automatic speaker verification (ASV) and spoofing countermeasure (CM) is expected to make an integrated system robust to zero-effort imposters and synthesized spoofing attacks. Many score-level fusion methods have been proposed, but many remain heuristic. This paper revisits score-level fusion using tools from decision theory and presents three main findings. First, fusion by summing the ASV and CM scores can be interpreted on the basis of compositional data analysis, and score calibration before fusion is essential. Second, the interpretation leads to an improved fusion method that linearly combines the log-likelihood ratios of ASV and CM. However, as the third finding reveals, this linear combination is inferior to a non-linear one in making optimal decisions. The outcomes of these findings, namely, the score calibration before fusion, improved linear fusion, and better non-linear fusion, were found to be effective on the SASV challenge database., Comment: Proceedings of Interspeech, DOI: 10.21437/Interspeech.2024-422. Code: https://github.com/nii-yamagishilab/SpeechSPC-mini

We set up, at the abstract Hilbert space setting, the general question on when an inverse linear problem induced by an operator of Friedrichs type admits solutions belonging to (the closure of) the Krylov subspace associated to such operator. Such Krylov solvability of abstract Friedrichs systems allows to predict when, for concrete differential inverse problems, truncation algorithms can or cannot reproduce the exact solutions in terms of approximants from the Krylov subspace., Comment: To appear in Springer INdAM Series "Singularities, Asymptotics, and Limiting Models" (2024)

We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle $\textit{et al.}$ [M. T. Entwistle $\textit{et al.}$, Nat. Commun. $\textbf{14}$, 274 (2023)], and demonstrate that the accuracy of the updated method is competitive with other available excited-state VMC approaches. A theoretical comparison of the computational aspects of these algorithms is presented, where several benefits of the penalty-based method are identified. Our main contributions include an automatic mechanism for tuning the scale of the penalty terms, an updated form of the overlap penalty with proven convergence properties, and a new term that penalizes the spin of the wave function, enabling the selective computation of states with a given spin. With these improvements, along with the use of the latest self-attention-based ansatz, the penalty-based method achieves a mean absolute error below 1 kcal/mol for the vertical excitation energies of a set of 26 atoms and molecules, without relying on variance matching schemes. Considering excited states along the dissociation of the carbon dimer, the accuracy of the penalty-based method is on par with that of natural-excited-state (NES) VMC, while also providing results for larger sections of the potential energy surface. Additionally, the accuracy of the original penalty-based method is improved for a conical intersection of ethylene, with the predicted angle of the intersection agreeing well with both NES-VMC and multi-reference configuration interaction., Comment: 18 pages, 7 figures

The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de novo}$ generation of 3D ligand structures using the equivariant diffusion model PILOT, combining pocket conditioning with a large-scale pre-training and property guidance. Its multi-objective trajectory-based importance sampling strategy is designed to direct the model towards molecules that not only exhibit desired characteristics such as increased binding affinity for a given protein pocket but also maintains high synthetic accessibility. This ensures the practicality of sampled molecules, thus maximizing their potential for the drug discovery pipeline. PILOT significantly outperforms existing methods across various metrics on the common benchmark dataset CrossDocked2020. Moreover, we employ PILOT to generate novel ligands for unseen protein pockets from the Kinodata-3D dataset, which encompasses a substantial portion of the human kinome. The generated structures exhibit predicted $IC_{50}$ values indicative of potent biological activity, which highlights the potential of PILOT as a powerful tool for structure-based drug design.

The entropic associative memory (EAM) is a computational model of natural memory incorporating some of its putative properties of being associative, distributed, declarative, abstractive and constructive. Previous experiments satisfactorily tested the model on structured, homogeneous and conventional data: images of manuscripts digits and letters, images of clothing, and phone representations. In this work we show that EAM appropriately stores, recognizes and retrieves complex and unconventional images of animals and vehicles. Additionally, the memory system generates meaningful retrieval association chains for such complex images. The retrieved objects can be seen as proper memories, associated recollections or products of imagination.

In this paper, we present a novel approach for text independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. Our method leverages a self-supervised model (wav2vec2) fine-tuned for phoneme recognition using a Connectionist Temporal Classification (CTC) loss, a dimension reduction model and a frame-level phoneme classifier trained thanks to forced-alignment labels (using Montreal Forced Aligner) to produce multi-lingual phonetic representations, thus requiring minimal additional training. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English, respectively. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems. We leave experiments on other languages for future work but the design of the system makes it easily adaptable to other languages.

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) does not directly optimize a given multi-objective function $f$, but instead optimizes $N + 1$ single-objective subproblems of $f$ in a co-evolutionary manner. It maintains an archive of all non-dominated solutions found and outputs it as approximation to the Pareto front. Once the MOEA/D found all optima of the subproblems (the $g$-optima), it may still miss Pareto optima of $f$. The algorithm is then tasked to find the remaining Pareto optima directly by mutating the $g$-optima. In this work, we analyze for the first time how the MOEA/D with only standard mutation operators computes the whole Pareto front of the OneMinMax benchmark when the $g$-optima are a strict subset of the Pareto front. For standard bit mutation, we prove an expected runtime of $O(n N \log n + n^{n/(2N)} N \log n)$ function evaluations. Especially for the second, more interesting phase when the algorithm start with all $g$-optima, we prove an $\Omega(n^{(1/2)(n/N + 1)} \sqrt{N} 2^{-n/N})$ expected runtime. This runtime is super-polynomial if $N = o(n)$, since this leaves large gaps between the $g$-optima, which require costly mutations to cover. For power-law mutation with exponent $\beta \in (1, 2)$, we prove an expected runtime of $O\left(n N \log n + n^{\beta} \log n\right)$ function evaluations. The $O\left(n^{\beta} \log n\right)$ term stems from the second phase of starting with all $g$-optima, and it is independent of the number of subproblems $N$. This leads to a huge speedup compared to the lower bound for standard bit mutation. In general, our overall bound for power-law suggests that the MOEA/D performs best for $N = O(n^{\beta - 1})$, resulting in an $O(n^\beta \log n)$ bound. In contrast to standard bit mutation, smaller values of $N$ are better for power-law mutation, as it is capable of easily creating missing solutions.

This paper presents a study of the inherent structural properties of Krylov subspaces, in particular for the self-adjoint class of operators, and how they relate with the important phenomenon of `Krylov solvability' of linear inverse problems. Owing to the complexity of the problem in the unbounded setting, recently developed perturbative techniques are used that exploit the use of the weak topology on $\mathcal{H}$. We also make a strong connection between the approximation properties of the Krylov subspace and the famous Hamburger problem of moments, in particular the determinacy condition., Comment: Accepted to the Banach Journal of Mathematical Analysis

Predicting the star formation rate (SFR) in galaxies is crucial to understand their evolution and morphology. To do so requires a fine understanding of how dense structures of gas are created and collapse. In that, turbulence and gravity play a major role. Within the gravo-turbulent framework, we assume that turbulence shapes the ISM, creating density fluctuations that, if gravitationally unstable, will collapse and form stars. The goal of this work is to quantify how different regimes of turbulence, characterized by the strength and compressibility of the driving, shape the density field. We are interested in the outcome in terms of SFR and how it compares with existing analytical models for the SFR. We run a series of hydrodynamical simulations of turbulent gas. The simulations are first conducted without gravity, so that the density and velocity are shaped by the turbulence driving. Gravity is then switched on, and the SFR is measured and compared with analytical models. The physics included in these simulations is very close to the one assumed in the classical gravo-turbulent SFR analytical models, which makes the comparison straightforward. We found that the existing analytical models convincingly agree with simulations at low Mach number, but we measure a much lower SFR in the simulation with a high Mach number. We develop, in a companion paper, an updated physically-motivated SFR model that reproduces well the inefficient high Mach regime of the simulations. Our work demonstrates that accurate estimations of the turbulent-driven replenishment time of dense structures and the dense gas spatial distribution are necessary to correctly predict the SFR in the high Mach regime. The inefficient high-Mach regime is a possible explanation for the low SFR found in dense and turbulent environments such as the centers of our Milky Way and other galaxies., Comment: 13 pages, 14 figures, accepted for publication in A&A

The star formation rate (SFR), the number of stars formed per unit of time, is a fundamental quantity in the evolution of the Universe. While turbulence is believed to play a crucial role in setting the SFR, the exact mechanism remains unclear. Turbulence promotes star formation by compressing the gas, but also slows it down by stabilizing the gas against gravity. Most widely-used analytical models rely on questionable assumptions, including: $i)$ integrating over the density PDF, a one-point statistical description that ignores spatial correlation, $ii)$ selecting self-gravitating gas based on a density threshold that often ignores turbulent dispersion, $iii)$ assuming the freefall time as the timescale for estimating SFR without considering the need to rejuvenate the density PDF, $iv)$ assuming the density PDF to be lognormal. Improving upon the only existing model that incorporates the spatial correlation of the density field, we present a new analytical model. We calculate the time needed to rejuvenate density fluctuations of a given density and spatial scale, revealing that it is generally much longer than the freefall time, rendering the latter inappropriate for use. We make specific predictions regarding the role of the Mach number, $ M $, and the driving scale of turbulence divided by the mean Jeans length. At low to moderate Mach numbers, turbulence does not reduce and may even slightly promote star formation by broadening the PDF. However, at higher Mach numbers, most density fluctuations are stabilized by turbulent dispersion, leading to a steep drop in the SFR as the Mach number increases., Comment: 16 pages, 7 figures, Accepted for publication in A&A

Human-aware navigation is a complex task for mobile robots, requiring an autonomous navigation system capable of achieving efficient path planning together with socially compliant behaviors. Social planners usually add costs or constraints to the objective function, leading to intricate tuning processes or tailoring the solution to the specific social scenario. Machine Learning can enhance planners' versatility and help them learn complex social behaviors from data. This work proposes an adaptive social planner, using a Deep Reinforcement Learning agent to dynamically adjust the weighting parameters of the cost function used to evaluate trajectories. The resulting planner combines the robustness of the classic Dynamic Window Approach, integrated with a social cost based on the Social Force Model, and the flexibility of learning methods to boost the overall performance on social navigation tasks. Our extensive experimentation on different environments demonstrates the general advantage of the proposed method over static cost planners.

Obtaining reliable distance estimates to gas clouds within the Milky Way is challenging in the absence of certain tracers. The kinematic distance approach has been used as an alternative, derived from the assumption of circular trajectories around the Galactic centre. Consequently, significant errors are expected in regions where gas flow deviates from purely circular motions. We aim to quantify the systematic errors that arise from the kinematic distance method in the presence of a Galactic potential that is non-axisymmetric. We investigate how these errors differ in certain regions of the Galaxy and how they relate to the underlying dynamics. We perform 2D hydrodynamical simulation of the gas disk with the moving-mesh code Arepo, adding the capability of using an external potential provided by the Agama library for galactic dynamics. We introduce a new analytic potential of the Milky Way, taking elements from existing models and adjusting parameters to match recent observational constraints. In line with results of previous studies, we report significant errors in the kinematic distance estimate for gas close to the Sun, along sight lines towards the Galactic centre and anti-centre, and associated with the Galactic bar. Kinematic distance errors are low within the spiral arms as gas resides close to local potential minima and the resulting LOS velocity is similar to what is expected for an axisymmetric potential. Interarm regions exhibit large deviations at any given Galactic radius. This is caused by the gas being sped up or slowed down as it travels into or out of spiral arms. In addition, we identify 'zones of avoidance' in the lv-diagram, where the kinematic distance method is particularly unreliable and should only be used with caution, and we find a power law relation between the kinematic distance error and the deviation of the projected LOS velocity from circular motion., Comment: 23 pages, 20 figures, 6 tables, accepted for publication in A&A

For robots to interact socially, they must interpret human intentions and anticipate their potential outcomes accurately. This is particularly important for social robots designed for human care, which may face potentially dangerous situations for people, such as unseen obstacles in their way, that should be avoided. This paper explores the Artificial Theory of Mind (ATM) approach to inferring and interpreting human intentions. We propose an algorithm that detects risky situations for humans, selecting a robot action that removes the danger in real time. We use the simulation-based approach to ATM and adopt the 'like-me' policy to assign intentions and actions to people. Using this strategy, the robot can detect and act with a high rate of success under time-constrained situations. The algorithm has been implemented as part of an existing robotics cognitive architecture and tested in simulation scenarios. Three experiments have been conducted to test the implementation's robustness, precision and real-time response, including a simulated scenario, a human-in-the-loop hybrid configuration and a real-world scenario., Comment: Associated mpeg file see https://youtu.be/87UEB8P97KY

With the support of the national program on measurements, standards, and evaluation of quantum technologies MetriQs-France, a part of the French national quantum strategy, the BACQ project is dedicated to application-oriented benchmarks for quantum computing. The consortium gathering THALES, EVIDEN, an Atos business, CEA, CNRS, TERATEC, and LNE aims at establishing performance evaluation criteria of reference, meaningful for industry users.

We present an executable, proven-safe, faithful, and future-proof Coq mechanization of JavaScript regular expression (regex) matching, as specified by the latest published edition of ECMA-262 section 22.2. This is, to our knowledge, the first time that an industrial-strength regex language has been faithfully mechanized in an interactive theorem prover. We highlight interesting challenges that arose in the process (including issues of encoding, corner cases, and executability), and we document the steps that we took to ensure that the result is straightforwardly auditable and that our understanding of the specification aligns with existing implementations. We demonstrate the usability and versatility of the mechanization through a broad collection of analyses, case studies, and experiments: we prove that JavaScript regex matching always terminates and is safe (no assertion failures); we identify subtle corner cases that led to mistakes in previous publications; we verify an optimization extracted from a state-of-the-art regex engine; we show that some classic properties described in automata textbooks and used in derivatives-based matchers do not hold in JavaScript regexes; and we demonstrate that the cost of updating the mechanization to account for changes in the original specification is reasonably low. Our mechanization can be extracted to OCaml and JavaScript and linked with Unicode libraries to produce an executable regex engine that passes the relevant parts of the official Test262 conformance test suite.

We present a statistical investigation of the radial evolution of 28 interplanetary coronal mass ejections (ICMEs), measured in situ by the Parker Solar Probe (PSP) spacecraft from 2018 October to 2022 August. First, by analyzing the radial distribution of ICME classification based on magnetic hodograms, we find that coherent configurations are more likely to be observed close to the Sun. In contrast, more complex configurations are observed farther out. We also notice that the post-ICME magnetic field is more impacted following an ICME passage at larger heliocentric distances. Second, with a multi-linear robust regression, we derive a slower magnetic ejecta (ME) expansion rate within 1~au compared to previous statistical estimates. Then, investigating the magnetic field fluctuations within ICME sheaths, we see that these fluctuations are strongly coupled to the relative magnetic field strength gradient from the upstream solar wind to the ME. Third, we identify ME expansion as an important factor in forming sheaths. Finally, we determine the distortion parameter (DiP) which is a measure of magnetic field asymmetry in an ME. We discover lower overall asymmetries within MEs. We reveal that even for expanding MEs, the time duration over which an ME is sampled does not correlate with DiP values, indicating that the aging effect is not the sole contributor to the observed ME asymmetries.

User demand on network communication infrastructure has never been greater with applications such as extended reality, holographic telepresence, and wireless brain-computer interfaces challenging current networking capabilities. Open RAN (O-RAN) is critical to supporting new and anticipated uses of 6G and beyond. It promotes openness and standardisation, increased flexibility through the disaggregation of Radio Access Network (RAN) components, supports programmability, flexibility, and scalability with technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and cloud, and brings automation through the RAN Intelligent Controller (RIC). Furthermore, the use of xApps, rApps, and Artificial Intelligence/Machine Learning (AI/ML) within the RIC enables efficient management of complex RAN operations. However, due to the open nature of O-RAN and its support for heterogeneous systems, the possibility of misconfiguration problems becomes critical. In this paper, we present a thorough analysis of the potential misconfiguration issues in O-RAN with respect to integration and operation, the use of SDN and NFV, and, specifically, the use of AI/ML. The opportunity for AI/ML to be used to identify these misconfigurations is investigated. A case study is presented to illustrate the direct impact on the end user of conflicting policies amongst xApps along with a potential AI/ML-based solution to this problem. This research presents a first analysis of the impact of AI/ML on misconfiguration challenges in O-RAN.

Molecular clouds (MC) are structures of dense gas in the interstellar medium (ISM), that extend from ten to a few hundred parsecs and form the main gas reservoir available for star formation. Hydrodynamical simulations of varying complexity are a promising way to investigate MC evolution and their properties. However, each simulation typically has a limited range in resolution and different cloud extraction algorithms are used, which complicates the comparison between simulations. In this work, we aim to extract clouds from different simulations covering a wide range of spatial scales. We compare their properties, such as size, shape, mass, internal velocity dispersion and virial state. We apply the Hop cloud detection algorithm on (M)HD numerical simulations of stratified ISM boxes and isolated galactic disk simulations that were produced using Flash Ramses and Arepo We find that the extracted clouds are complex in shape ranging from round objects to complex filamentary networks in all setups. Despite the wide range of scales, resolution, and sub-grid physics, we observe surprisingly robust trends in the investigated metrics. The mass spectrum matches in the overlap between simulations without rescaling and with a high-mass slope of $\mathrm{d} N/\mathrm{d}\ln M\propto-1$ in accordance with theoretical predictions. The internal velocity dispersion scales with the size of the cloud as $\sigma\propto R^{0.75}$ for large clouds ($R\gtrsim3\,\mathrm{pc}$). For small clouds we find larger sigma compared to the power-law scaling, as seen in observations, which is due to supernova-driven turbulence. Almost all clouds are gravitationally unbound with the virial parameter scaling as $\alpha_\mathrm{vir}\propto M^{-0.4}$, which is slightly flatter compared to observed scaling, but in agreement given the large scatter., Comment: 22 pages, 16 figures, proposed for acceptance in A&A

New computational strategies, such as molecular docking, are emerging to speed up the drug discovery process. This method predicts the activity of molecules at the binding site of proteins, helping to select the ones that exhibit desirable behavior and rejecting the rest. However, for large chemical libraries, it is essential to search and score configurations using fewer computational resources while maintaining high precision. In this work, we map the molecular docking problem to a graph problem, a maximum-weight independent set problem on a unit-disk graph in a physical neutral atom quantum processor. Here, each vertex represents an atom trapped by optical tweezers. The Variational Quantum Adiabatic Algorithm (VQAA) approach is used to solve the generic graph problem with two optimization methods, Scipy and Hyperopt. Additionally, a machine learning method is explored using the adiabatic algorithm. Results for multiple graphs are presented, and a small instance of the molecular docking problem is solved, demonstrating the potential for near-term quantum applications., Comment: arXiv admin note: text overlap with arXiv:2209.03965 by other authors

Classic Delphi and Fuzzy Delphi methods are used to test content validity of data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solves it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution., Comment: 47 pages, 7 figures