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The development of a Mental Health Technology Program at Middlesex Community College is proposed. The 2-year program would train the student to become a middle-level generalist in the field of mental health with special abilities as a communicator. On successfully completing the program, the student would receive an Associate Degree in Mental Health Technology. The sections of the proposal are: 1. Purpose of the Program (Major Objectives, Job Performance, Specific Agencies to Be Served); 2. Need for the Program; 3. Program Design (Curriculum, Course Descriptions); 4. Clinical Affiliations; 5. Potential Enrollment; 6. Similar Programs; 7. Faculty Requirements; 8. Physical Plant and Equipment; 9. Cost Analysis; 10. Cover Letter and Attachments. (DB)

Robotic systems are widely used to interact with humans or to perform critical tasks. As a result, it is imperative to provide guarantees about their behavior. Due to the modularity and complexity of robotic systems, their design and verification are often divided into several layers. However, some system properties can only be investigated by considering multiple layers simultaneously. We propose a cross-layer verification method to verify the expected properties of concrete robotic systems. Our method verifies one layer using abstractions of other layers. We propose two approaches: refining the models of the abstract layers and refining the property under verification. A combination of these two approaches seems to be the most promising to ensure model genericity and to avoid the state-space explosion problem., Comment: In Proceedings FMAS2024, arXiv:2411.13215

Federated Learning (FL) enables multiple clients, such as mobile phones and IoT devices, to collaboratively train a global machine learning model while keeping their data localized. However, recent studies have revealed that the training phase of FL is vulnerable to reconstruction attacks, such as attribute inference attacks (AIA), where adversaries exploit exchanged messages and auxiliary public information to uncover sensitive attributes of targeted clients. While these attacks have been extensively studied in the context of classification tasks, their impact on regression tasks remains largely unexplored. In this paper, we address this gap by proposing novel model-based AIAs specifically designed for regression tasks in FL environments. Our approach considers scenarios where adversaries can either eavesdrop on exchanged messages or directly interfere with the training process. We benchmark our proposed attacks against state-of-the-art methods using real-world datasets. The results demonstrate a significant increase in reconstruction accuracy, particularly in heterogeneous client datasets, a common scenario in FL. The efficacy of our model-based AIAs makes them better candidates for empirically quantifying privacy leakage for federated regression tasks.

AI workloads, particularly those driven by deep learning, are introducing novel usage patterns to high-performance computing (HPC) systems that are not comprehensively captured by standard HPC benchmarks. As one of the largest academic research centers dedicated to deep learning, Mila identified the need to develop a custom benchmarking suite to address the diverse requirements of its community, which consists of over 1,000 researchers. This report introduces Milabench, the resulting benchmarking suite. Its design was informed by an extensive literature review encompassing 867 papers, as well as surveys conducted with Mila researchers. This rigorous process led to the selection of 26 primary benchmarks tailored for procurement evaluations, alongside 16 optional benchmarks for in-depth analysis. We detail the design methodology, the structure of the benchmarking suite, and provide performance evaluations using GPUs from NVIDIA, AMD, and Intel. The Milabench suite is open source and can be accessed at github.com/mila-iqia/milabench.

Transitioning from quantum computation on physical qubits to quantum computation on encoded, logical qubits can improve the error rate of operations, and will be essential for realizing valuable quantum computational advantages. Using a neutral atom quantum processor with 256 qubits, each an individual Ytterbium atom, we demonstrate the entanglement of 24 logical qubits using the distance-two [[4,2,2]] code, simultaneously detecting errors and correcting for lost qubits. We also implement the Bernstein-Vazirani algorithm with up to 28 logical qubits encoded in the [[4,1,2]] code, showing better-than-physical error rates. We demonstrate fault-tolerant quantum computation in our approach, guided by the proposal of Gottesman (2016), by performing repeated loss correction for both structured and random circuits encoded in the [[4,2,2]] code. Finally, since distance-two codes can correct qubit loss, but not other errors, we show repeated loss and error correction using the distance-three [[9,1,3]] Bacon-Shor code. These results begin to clear a path for achieving scientific quantum advantage with a programmable neutral atom quantum processor., Comment: 17 pages, 16 figures

This document summarizes the prospective physics plans of the French QCD and Heavy-Ion community, including the experimental programs at the LHC Run 5 and beyond and future colliders at CERN, within the context of the French contribution to the update of the European Strategy in Particle Physics (ESPPU 2025), as discussed in the workshop on European Strategy for Particle Physics Update 2025 organised by the QCD GdR in Oct. 2024., Comment: 6.5 pages without title page and without references, no figures. Keywords: QCD, Heavy Ions, Quark Gluon Plasma, LHC, FCC

Underwater surveys provide long-term data for informing management strategies, monitoring coral reef health, and estimating blue carbon stocks. Advances in broad-scale survey methods, such as robotic underwater vehicles, have increased the range of marine surveys but generate large volumes of imagery requiring analysis. Computer vision methods such as semantic segmentation aid automated image analysis, but typically rely on fully supervised training with extensive labelled data. While ground truth label masks for tasks like street scene segmentation can be quickly and affordably generated by non-experts through crowdsourcing services like Amazon Mechanical Turk, ecology presents greater challenges. The complexity of underwater images, coupled with the specialist expertise needed to accurately identify species at the pixel level, makes this process costly, time-consuming, and heavily dependent on domain experts. In recent years, some works have performed automated analysis of underwater imagery, and a smaller number of studies have focused on weakly supervised approaches which aim to reduce the expert-provided labelled data required. This survey focuses on approaches which reduce dependency on human expert input, while reviewing the prior and related approaches to position these works in the wider field of underwater perception. Further, we offer an overview of coastal ecosystems and the challenges of underwater imagery. We provide background on weakly and self-supervised deep learning and integrate these elements into a taxonomy that centres on the intersection of underwater monitoring, computer vision, and deep learning, while motivating approaches for weakly supervised deep learning with reduced dependency on domain expert data annotations. Lastly, the survey examines available datasets and platforms, and identifies gaps, barriers, and opportunities for automating underwater surveys., Comment: 70 pages, 20 figures

Models and observations have demonstrated that Twisted Flux Ropes (TFRs) play a significant role in the structure and eruptive dynamics of active regions. Their role in the dynamics of the quiet Sun atmosphere on has remained elusive, their fundamental relevance emerging mainly from theoretical models (Amari et al. 2015), showing that they form and erupt as a result of flux cancellation. Here HINODE high-resolution photospheric vector magnetic field measurements are integrated with advanced environment reconstruction models: TFRs develop on various scales and are associated with the appearance of mesospots. The developing TFRs contain sufficient free magnetic energy to match the requirements of the recently observed "campfires" discovered by Solar Orbiter in the quiet Sun. The free magnetic energy is found to be large enough to trigger eruptions while the magnetic twist large enough to trigger confined eruptions, heating the atmosphere. TFRs are also connected to larger scale magnetic fields such as supergranulation loops, allowing the generation of Alfv\'en waves at the top of the chromosphere that can propagate along them. High-resolution magnetohydrodynamic simulations, incorporating subsurface dynamo activity at an unprecedented 30 km spatial resolution, confirm that TFRs are ubiquitous products of the permanent small scale dynamo engine that feeds their formation, destabilization, eruption via flux emergence, submergence and cancellation of their chromospheric feet, similar to the dynamics driving large scale eruptive events. Future investigations, especially with the Daniel K. Inouye Solar Telescope (DKIST) and Solar Orbiter will deepen our understanding of TFRs in the context of atmospheric heating.

With the aim of understanding Morel's result on the $\mathbb{A}^1$-homotopy sheaves over a field, one extends the theory of unstable spectral sequences of Bousfield and Kan in the $\infty$-categorical setting. With this natural extension, parallel to the classical formalism of 'cohomology theory with supports', we introduce the notion of 'cohomotopy theory with supports'. We extend the Bloch-Ogus-Gabber theorem for Cohomology theory with supports to that of unstable setting, to obtain unstable Gersten (or Cousin) resolutions associated to the coniveau filtration, under suitable assumptions. We apply this theory to motivic homotopy, Nisnevich-local torsors, and Artin-Mazur \'etale homotopy type., Comment: Comments are welcome

A novel concept to enhance the photo-detection efficiency (PDE) of silicon photomultipliers (SiPMs) has been applied and remarkable positive results can be reported. This concept uses arrays of microlenses to cover every second SiPM pixel in a checkerboard arrangement and aims to deflect the light from the dead region of the pixelised structure towards the active region in the center of the pixel. The PDE is improved up to 24%, external cross-talk is reduced by 40% compared to a flat epoxy layer, and single photon time resolution is improved. This detector development is conducted in the context of the next generation LHCb scintillating fibre tracker located in a high radiation environment with a total of 700'000 detector channels. The simulation and measurement results are in good agreement and will be discussed in this work., Comment: 8 pages, 11 figures

We investigate the structure of smooth holomorphic foliations with numerically flat tangent bundles on compact K\"ahler manifolds. Extending earlier results on non-uniruled projective manifolds by the second and fourth authors, we show that such foliations induce a decomposition of the tangent bundle of the ambient manifold, have leaves uniformized by Euclidean spaces, and have torsion canonical bundle. Additionally, we prove that smooth two-dimensional foliations with numerically trivial canonical bundle on projective manifolds are either isotrivial fibrations or have numerically flat tangent bundles. This in turn implies a global Weinstein splitting theorem for rank-two Poisson structures on projective manifolds. We also derive new Hodge-theoretic conditions for the existence of zeros of Poisson structures on compact K\"ahler manifolds., Comment: To Jean-Pierre Demailly, in memoriam. 27 pages, comments welcome

Interpretable Machine Learning faces a recurring challenge of explaining the predictions made by opaque classifiers such as ensemble models, kernel methods, or neural networks in terms that are understandable to humans. When the model is viewed as a black box, the objective is to identify a small set of features that jointly determine the black box response with minimal error. However, finding such model-agnostic explanations is computationally demanding, as the problem is intractable even for binary classifiers. In this paper, the task is framed as a Constraint Optimization Problem, where the constraint solver seeks an explanation of minimum error and bounded size for an input data instance and a set of samples generated by the black box. From a theoretical perspective, this constraint programming approach offers PAC-style guarantees for the output explanation. We evaluate the approach empirically on various datasets and show that it statistically outperforms the state-of-the-art heuristic Anchors method.

We consider the problem of sampling a multimodal distribution with a Markov chain given a small number of samples from the stationary measure. Although mixing can be arbitrarily slow, we show that if the Markov chain has a $k$th order spectral gap, initialization from a set of $\tilde O(k/\varepsilon^2)$ samples from the stationary distribution will, with high probability over the samples, efficiently generate a sample whose conditional law is $\varepsilon$-close in TV distance to the stationary measure. In particular, this applies to mixtures of $k$ distributions satisfying a Poincar\'e inequality, with faster convergence when they satisfy a log-Sobolev inequality. Our bounds are stable to perturbations to the Markov chain, and in particular work for Langevin diffusion over $\mathbb R^d$ with score estimation error, as well as Glauber dynamics combined with approximation error from pseudolikelihood estimation. This justifies the success of data-based initialization for score matching methods despite slow mixing for the data distribution, and improves and generalizes the results of Koehler and Vuong (2023) to have linear, rather than exponential, dependence on $k$ and apply to arbitrary semigroups. As a consequence of our results, we show for the first time that a natural class of low-complexity Ising measures can be efficiently learned from samples.

In this paper, we derive a Chen-Strichartz formula for stochastic differential equations driven by Levy processes, that is, we derive a series expansion of the logarithm of the flowmap of the stochastic differential equation in terms of commutators of vector fields with stochastic coefficients, and we provide an explicit formula for the components in this series. The stochastic components are generated by the Levy processes that drive the stochastic differential equation and their quadratic variation and power jumps; the vector fields are given as linear combinations of commutators of elements in the pre-Lie Magnus expansion generated by the original vector fields governing our stochastic differential equation. In particular, we show the logarithm of the flowmap is a Lie series. These results extend previous results for deterministic differential equations and continuous stochastic differential equations. For these, the Chen-Strichartz series has shown to play a pivotal role in the design of numerical integration schemes that preserve qualitative properties of the solution such as the construction of geometric numerical schemes and in the context of efficient numerical schemes., Comment: 25 pages

Electrodynamic interactions between biomolecules are of potential biological interest for signaling warranting investigation of their activation through various mechanisms in living systems. Here, using as model system a light harvesting protein within the phycobilisome antenna system of red algae, we proved that not only light exposure but also thermal energy alone can trigger attractive electrodynamic interactions up to hundreds of nanometer. The latter are sustained by low frequency collective modes and while the second mode appears only upon illumination, the fundamental one can be activated by temperature alone. Activation of such collective modes and ED interactions might influence conformational rearrangements and energy transport within the phycobilisome antenna system. This is a paradigm-shift that underscores the immense potential of biological systems in exploiting different forms of input energy to achieve optimal energy transfer.

Algorithms that use derivatives of governing equations have accelerated rigid robot simulations and improved their accuracy, enabling the modeling of complex, real-world capabilities. However, extending these methods to soft and hybrid soft-rigid robots is significantly more challenging due to the complexities in modeling continuous deformations inherent in soft bodies. A considerable number of soft robots and the deformable links of hybrid robots can be effectively modeled as slender rods. The Geometric Variable Strain (GVS) model, which employs the screw theory and the strain parameterization of the Cosserat rod, extends the rod theory to model hybrid soft-rigid robots within the same mathematical framework. Using the Recursive Newton-Euler Algorithm, we developed the analytical derivatives of the governing equations of the GVS model. These derivatives facilitate the implicit integration of dynamics and provide the analytical Jacobian of the statics residue, ensuring fast and accurate computations. We applied these derivatives to the mechanical simulations of six common robotic systems: a soft cable-driven manipulator, a hybrid serial robot, a fin-ray finger, a hybrid parallel robot, a contact scenario, and an underwater hybrid mobile robot. Simulation results demonstrate substantial improvements in computational efficiency, with speed-ups of up to three orders of magnitude. We validate the model by comparing simulations done with and without analytical derivatives. Beyond static and dynamic simulations, the techniques discussed in this paper hold the potential to revolutionize the analysis, control, and optimization of hybrid robotic systems for real-world applications., Comment: 27 pages including appendix, 17 figures

This study investigates the influence of Herbig-Haro jets on initiating star formation in dense environments. When molecular clouds are nearing gravitational instability, the impact of a protostellar jet could provide the impetus needed to catalyse star formation. A high-energy-density experiment was carried out at the LULI2000 laser facility, where a supersonic jet generated by a nanosecond laser was used to compress a foam or plastic ball, mimicking the interaction of a Herbig-Haro jet with a molecular cloud. Simulations using the 3D radiation hydrodynamics code TROLL provided comprehensive data for analysing ball compression and calculating jet characteristics. After applying scaling laws, similarities between stellar and experimental jets were explored. Diagnostic simulations, including density gradient, emission and X-ray radiographies, showed strong agreement with experimental data. The results of the experiment, supported by simulations, demonstrated that the impact of a protostellar jet on a molecular cloud could reduce the Bonnor-Ebert mass by approximately 9%, thereby initiating collapse., Comment: Submitted to the Astrophysical Journal

Star formation in galaxies is regulated by dynamical and thermal processes. The photoelectric effect on small dust grains usually dominates the heating of the star-forming neutral atomic gas reservoir in metal-rich galaxies, while the lower dust-to-gas mass ratio and the higher luminosity of X-ray sources in metal-poor galaxies suggest that other heating mechanisms may be at play. We calculate the relative contributions of the photoelectric effect, photoionization by UV and X-ray photons, and ionization by cosmic rays to the total heating in a sample of 37 nearby galaxies reaching down to 3% the Milky Way metallicity. We use the statistical code MULTIGRIS together with a grid of Cloudy models propagating radiation from stellar clusters and X-ray sources to the ionized and neutral gas, each galaxy being described as a statistical distribution of many 1D components. Infrared cooling lines from the interstellar medium (ISM) are used as constraints to evaluate the most likely distributions and parameters. We show that the photoelectric effect heating dominates in high-metallicity galaxies (>1/18 the Milky Way value) while cosmic rays and especially photoionization from X-rays become predominant in low-metallicity galaxies. Our models predict reasonably well the X-ray source fluxes in the 0.3-8 keV band using indirect ISM tracers, illustrating that the adopted strategy makes it possible to recover the global intrinsic radiation field properties when X-ray observations are unavailable, for instance in early universe galaxies. Finally, we show that the photoelectric effect heating efficiency on PAHs may be recovered through the [CII]+[OI] / PAH observational proxy only if the other heating mechanisms are accounted for (abridged)., Comment: Submitted to A&A

Programming a quantum device describes the usage of quantum logic gates, agnostic of hardware specifics, to perform a sequence of operations with (typically) a computing or sensing task in mind. Such programs have been executed on digital quantum computers, which despite their noisy character, have shown the ability to optimize metrological functions, for example in the generation of spin squeezing and optimization of quantum Fisher information for signals manifesting as spin rotations in a quantum register. However, the qubits of these programmable quantum sensors are tightly spatially confined and therefore suboptimal for enclosing the kinds of large spacetime areas required for performing inertial sensing. In this work, we derive a set of quantum logic gates for a cold atom optical lattice interferometer that manipulates the momentum of atoms. Here, the operations are framed in terms of single qubit operations and mappings between qubit subspaces with internal levels given by the Bloch (crystal) eigenstates of the lattice. We describe how the quantum optimal control method of direct collocation is well suited for obtaining the modulation waveforms of the lattice which achieve these operations.

Individual behavioural heterogeneity is key to understanding how natural populations adapt flexibly to environmental changes. In ant species like $\textit{Aphaenogaster senilis}$, two key roles in collective foraging are commonly recognised: scouts, who discover food patches, and recruits, who exploit these patches and transport food back to the nest. These roles involve distinct movement patterns and exploratory behaviours. In this chapter, we develop a correlated random walk model on the bounded honeycomb lattice to interpret and replicate empirical observations of foraging ants in an enclosed arena with honeycomb tiling. We do so by extending the theory of first-passage processes for $\mathcal{N}$ random walkers when individuals belong to a heterogeneous population. We apply this theory to examine how individual behavioural heterogeneity in ants affects collective foraging efficiency, focusing on first-passage time statistics for nest-to-patch and patch-to-patch movements. With the combined use of the mathematical model and the controlled experimental setup we evaluate (i) the impact of distinct movement strategies by scouts and recruits on finding food items and (ii) whether ants practice strict central place foraging or utilise previously discovered patches as starting points for further exploration., Comment: 28 pages, 8 figures

In this paper, we present an adaptive framework designed for the continuous detection, identification and classification of emerging attacks in network traffic. The framework employs a transformer encoder architecture, which captures hidden patterns in a bidirectional manner to differentiate between malicious and legitimate traffic. Initially, the framework focuses on the accurate detection of malicious activities, achieving a perfect recall of 100\% in distinguishing between attack and benign traffic. Subsequently, the system incrementally identifies unknown attack types by leveraging a Gaussian Mixture Model (GMM) to cluster features derived from high-dimensional BERT embeddings. This approach allows the framework to dynamically adjust its identification capabilities as new attack clusters are discovered, maintaining high detection accuracy. Even after integrating additional unknown attack clusters, the framework continues to perform at a high level, achieving 95.6\% in both classification accuracy and recall.The results demonstrate the effectiveness of the proposed framework in adapting to evolving threats while maintaining high accuracy in both detection and identification tasks. Our ultimate goal is to develop a scalable, real-time intrusion detection system that can continuously evolve with the ever-changing network threat landscape., Comment: 8 pages, 11 figures

Integrated population models (IPMs) combine multiple ecological data types such as capture-mark-recapture histories, reproduction surveys, and population counts into a single statistical framework. In such models, each data type is generated by a probabilistic submodel, and an assumption of independence between the different data types is usually made. The fact that the same biological individuals can contribute to multiple data types has been perceived as affecting their independence, and several studies have even investigated IPM robustness in this scenario. However, what matters from a statistical perspective is probabilistic independence: the joint probability of observing all data is equal to the product of the likelihoods of the various datasets. Contrary to a widespread perception, probabilistic non-independence does not automatically result from collecting data on the same physical individuals. Conversely, while there can be good reasons for non-independence of IPM submodels arising from sharing of individuals between data types, these relations do not seem to be included in IPMs whose robustness is being investigated. Furthermore, conditional rather than true independence is sometimes assumed. In this conceptual paper, I survey the various independence concepts used in IPMs, try to make sense of them by getting back to first principles in toy models, and show that it is possible to obtain probabilistic independence (or near-independence) despite two or three data types collected on the same set of biological individuals. I then revisit recommendations pertaining to component data collection and IPM robustness checks, and provide some suggestions to bridge the current gap between individual-level IPMs and their population-level approximations using composite likelihoods.

This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to compute the maximum likelihood estimator (MLE) are proposed: a fixed point algorithm and a Riemannian optimization method based on the derived information geometry of Elliptical Wishart distributions. The existence and uniqueness of the MLE are characterized as well as the convergence of both estimation algorithms. Statistical properties of the MLE are also investigated such as consistency, asymptotic normality and an intrinsic version of Fisher efficiency. On the statistical learning side, novel classification and clustering methods are designed. For the $t$-Wishart distribution, the performance of the MLE and statistical learning algorithms are evaluated on both simulated and real EEG and hyperspectral data, showcasing the interest of our proposed methods.

We explore connections between existence of $\Bbbk$-rational points for Fano varieties defined over $\Bbbk$, a subfield of $\mathbb{C}$, and existence of K\"ahler-Einstein metrics on their geometric models. First, we show that geometric models of del Pezzo surfaces with at worst quotient singularities defined over $\Bbbk\subset\mathbb{C}$ admit (orbifold) K\"ahler--Einstein metrics if they do not have $\Bbbk$-rational points. Then we prove the same result for smooth Fano 3-folds with 8 exceptions. Consequently, we explicitly describe several families of pointless Fano 3-folds whose geometric models admit K\"ahler-Einstein metrics. In particular, we obtain new examples of prime Fano 3-folds of genus $12$ that admit K\"ahler--Einstein metrics. Our result can also be used to prove existence of rational points for certain Fano varieties, for example for any smooth Fano 3-fold over $\Bbbk\subset\mathbb{C}$ whose geometric model is strictly K-semistable., Comment: 44 pages

A class of acyclic digraphs $\mathscr{C}$ is linearly unavoidable if there exists a constant $c$ such that every digraph $D\in \mathscr{C}$ is contained in all tournaments of order $c\cdot |V(D)|$. The class of all acyclic digraphs is not linearly avoidable, and Fox, He, and Widgerson recently showed that this is not even the case for acyclic digraphs with bounded maximum degree. On the positive side, Thomason and H\"aggkvist proved that the class of oriented trees is linearly unavoidable. In this work, we generalize this result to acyclic digraphs obtained from an oriented tree by adding at most $k$ vertices, and $k$-blow-ups of oriented trees, for every fixed integer $k$. More precisely, we show that if $D$ is obtained from an oriented tree $F$ of order $n$ by adding $k$ universal vertices, then $D$ is contained in every tournament of order $2\cdot 3^{(k+1)(2k+1)} \cdot n$; and if $D$ is obtained from $F$ by replacing each vertex $u$ by an independent set $X_u$ of size $k$ and every arc $uv$ by all possible arcs from $X_u$ to $X_v$, then $D$ is contained in every tournament of order $2^{10+18k}k \cdot n$.

Pion-kaon ($\pi K$) final states, often appearing in heavy-particle decays at the precision frontier, are important for Standard-Model tests, to describe crossed channels with exotic states, and for spectroscopy of excited kaon resonances. We construct a representation of the $\pi K$ $S$-wave form factor using the elastic $\pi K$ scattering phase shift via dispersion relations in the elastic region and extend this model into the inelastic region using resonance exchange, while maintaining unitarity and the correct analytic structure. As a first application, we successfully described the $\tau \to K_S \pi \nu_\tau$ spectrum to not only achieve a better distinction between $S$- and $P$-wave contributions, but also to provide an improved estimate of the $CP$ asymmetry produced by a tensor operator as well as the forward-backward asymmetry, both of which can be confronted with future data at Belle II., Comment: 10 pages, 4 figures, Talk given at the 16th International Workshop on Tau Lepton Physics (Tau2021), to be published in SciPost Physics Proceedings, see https://scipost.org/submissions/scipost_202111_00029v1/

We consider the Cops and Robber pursuit-evasion game when the edge-set of the graph is allowed to change in time, possibly at every round. Specifically, the game is played on an infinite periodic sequence $\mathcal{G} = (G_0, \dots, G_{p-1})^*$ of graphs on the same set $V$ of $n$ vertices: in round $t$, the topology of $\mathcal{G}$ is $G_i=(V,E_i)$ where $i\equiv t \pmod{p}$. Concentrating on the case of a single cop, we provide a characterization of copwin periodic temporal graphs, establishing several basic properties on their nature, and extending to the temporal domain classical C&R concepts such as covers and corners. Based on these results, we design an efficient algorithm for determining if a periodic temporal graph is copwin. We also consider the case of $k>1$ cops. By shifting from a representation in terms of directed graphs to one in terms of directed multi-hypergraphs, we prove that all the fundamental properties established for $k=1$ continue to hold, providing a characterization of $k$-copwin periodic graphs, as well as a general strategy to determine if a periodic graph is $k$-copwin. Our results do not rely on any assumption on properties such as connectivity, symmetry, reflexivity held by the individual graphs in the sequence. They are established for a unified version of the game that includes the standard games studied in the literature, both for undirected and directed graphs, and both when the players are fully active and when they are not. They hold also for a variety of settings not considered in the literature.

We first briefly review the adventure of scale invariance in physics, from Galileo Galilei, Weyl, Einstein, and Feynman to the revival by Dirac (1973) and Canuto et al. (1977). We then gather concrete observational evidence that scale-invariant effects are present and measurable in astronomical objects spanning a vast range of masses (0.5 M$_{\odot} <$ M $< 10^{14}$ M$_{\odot}$) and an equally impressive range of spatial scales (0.01 pc $<$ r $<$ 1 Gpc). Scale invariance accounts for the observed excess in velocity in galaxy clusters with respect to the visible mass, the relatively flat/small slope of rotation curves in local galaxies, the observed steep rotation curves of high-redshift galaxies, and the excess of velocity in wide binary stars with separations above 3000 kau found in Gaia DR3. Last but not least, we investigate the effect of scale invariance on gravitational lensing. We show that scale invariance does not affect the geodesics of light rays as they pass in the vicinity of a massive galaxy. However, scale-invariant effects do change the inferred mass-to-light ratio of lens galaxies as compared to GR. As a result, the discrepancies seen in GR between the total lensing mass of galaxies and their stellar mass from photometry may be accounted for. This holds true both for lenses at high redshift like JWST-ER1 and at low redshift like in the SLACS sample. Of note is that none of the above observational tests require dark matter or any adjustable parameter to tweak the theory at any given mass or spatial scale., Comment: 25 pages, 6 figures, accepted in "Symmetry"

Synchrotron X-ray emission has been detected from nearly a dozen young supernova remnants (SNRs). X-rays of synchrotron origin exhibit linear polarization in a regular, non-randomly oriented magnetic field. The significant polarized X-ray emission from four such SNRs has already been reported on the basis of observations with the Imaging X-ray Polarimetry Explorer (IXPE). The magnetic-field structure as derived from IXPE observations is radial for Cassiopeia A, Tycho's SNR, and SN 1006, and tangential for RX J1713.7-3946. The latter together with the recent detection of a tangential magnetic field in SNR 1E 0102.2-7219 by the Australia Telescope Compact Array in the radio band shows that tangential magnetic fields can also be present in young SNRs. Thus, the dichotomy in polarization between young and middle-aged SNRs (radial magnetic fields in young SNRs, but tangential magnetic fields in middle-aged SNRs), previously noticed in the radio band, deserves additional attention. The present analysis of IXPE observations determines, for the first time, a magnetic-field structure in the northwestern rim of Vela Jr, also known as RX J0852.0-4622, and provides a new example of a young SNR with a tangential magnetic field., Comment: Accepted for publication in Astronomy and Astrophysics

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

This work proposes an adaptive sequential Monte Carlo sampling algorithm for solving inverse Bayesian problems in a context where a (costly) likelihood evaluation can be approximated by a surrogate, constructed from previous evaluations of the true likelihood. A rough error estimation of the obtained surrogates is required. The method is based on an adaptive sequential Monte-Carlo (SMC) simulation that jointly adapts the likelihood approximations and a standard tempering scheme of the target posterior distribution. This algorithm is well-suited to cases where the posterior is concentrated in a rare and unknown region of the prior. It is also suitable for solving low-temperature and rare-event simulation problems. The main contribution is to propose an entropy criteria that associates to the accuracy of the current surrogate a maximum inverse temperature for the likelihood approximation. The latter is used to sample a so-called snapshot, perform an exact likelihood evaluation, and update the surrogate and its error quantification. Some consistency results are presented in an idealized framework of the proposed algorithm. Our numerical experiments use in particular a reduced basis approach to construct approximate parametric solutions of a partially observed solution of an elliptic Partial Differential Equation. They demonstrate the convergence of the algorithm and show a significant cost reduction (close to a factor $10$) for comparable accuracy.

Despite being the subject of study for several years, excessive vibration persists in the machining of metal parts. In this context, the Stability Lobe Diagram (SLD) is presented as a viable tool to mitigate this problem as a function of axial depth of cut and spindle speed. However, its accurate construction is subject to the consideration of multiple parameters and models, whose application may be affected by certain inherent uncertainties. In turn, this impacts its accuracy, especially in the stability and instability regions. The present study aims to characterize these uncertainties, analyze their influence on the SLD, and propose strategies for their reduction. Ultimately, the goal is to facilitate the user's decision-making when choosing the trajectory generation parameters.

It has been claimed that graphite hosts superconductivity at room temperature, although all efforts to isolate it have been vain. Here we report a separation method that uses magnetic field gradients to sort the superconducting from normal grains out of industrial graphite powders. We have obtained a concentrate of above room temperature superconducting particles. Electrical resistance measurements on agglomerates of sorted grains of three types of graphite show transition temperatures up to T$_{c{_{onset}}} \sim$ 700K with zero resistance up to $\sim$ 500K. Magnetization measurements confirm these values through jumps at \textit{T$_c$} in the zero field cooled curves, and by the occurrence diamagnetic hysteretic cycles shrinking with temperature. Our results open the door towards the study of above room temperature superconducting ill-stacked graphite phases., Comment: 21 pages, 9 figures

Wearable and locomotive robot designers face multiple challenges when choosing actuation. Traditional fully actuated designs using electric motors are multifunctional but oversized and inefficient for bearing conservative loads and for being backdrivable. Alternatively, quasi-passive and underactuated designs reduce the size of motorization and energy storage, but are often designed for specific tasks. Designers of versatile and stronger wearable robots will face these challenges unless future actuators become very torque-dense, backdrivable and efficient. This paper explores a design paradigm for addressing this issue: reconfigurable hydrostatics. We show that a hydrostatic actuator can integrate a passive force mechanism and a sharing mechanism in the fluid domain and still be multifunctional. First, an analytical study compares how these two mechanisms can relax the motorization requirements in the context of a load-bearing exoskeleton. Then, the hydrostatic concept integrating these two mechanisms using hydraulic components is presented. A case study analysis shows the mass/efficiency/inertia benefits of the concept over a fully actuated one. Then, the feasibility of the concept is partially validated with a proof-of-concept that actuates the knees of an exoskeleton. The experiments show that it can track the vertical ground reaction force (GRF) profiles of walking, running, squatting, and jumping, and that the energy consumption is 6x lower. The transient force behaviors due to switching from one leg to the other are also analyzed along with some mitigation to improve them.

Barocalorics (BCs) are emerging as promising alternatives to vapour-phase refrigerants, which are problematic as they exacerbate climate change when they inevitably leak into the atmosphere. However, the commercialisation of BC refrigerants is significantly hindered by hysteresis in the solid-solid phase transition that would be exploited in a refrigeration cycle. Here, we provide new insight into the hysteresis that is a critical step towards the rational design of viable BCs. By studying the archetypal BC plastic crystal, neopentyl glycol (NPG), we observe for the first time the liberation of the hydroxyl rotational modes that unlock the hydrogen bonding network. We use high-resolution inelastic fixed-window neutron scattering studies in combination with quasielastic neutron scattering (QENS) measurements to build a comprehensive microscopic understanding of the NPG phase transition. Hysteresis previously observed in calorimetric studies of NPG is now observed directly as hysteresis in molecular rotational modes, and hence in the formation and disruption of hydrogen bonding. Furthermore, by tracking the thermal activation of three main reorientation modes, we suggest that their fractional excitations may resolve an outstanding discrepancy between measured and calculated entropy change. These results allow for direct study of the molecular dynamics that govern the thermal hysteresis of small molecule energy materials. They will be broadly applicable, as many promising BC material families possess first-order transitions involving molecular reorientations.

Artificial Water Bodies (AWBs) are human-made and require continuous monitoring due to their artificial biological processes. These systems necessitate regular maintenance to manage their ecosystems effectively. Unmanned Surface Vehicle (USV) offers a collaborative approach for monitoring these environments, working alongside human operators such as boat skippers to identify specific locations. This paper discusses a weed harvesting scenario, demonstrating how human-robot collaboration can be achieved, supported by preliminary results. The USV mainly utilises multibeam SOund NAvigation and Ranging (SONAR) for underwater weed monitoring, showing promising outcomes in these scenarios., Comment: 3 pages, 5 figures. This paper was accepted for poster presentation at IROS 2024 Workshop on Maritime Heteregenous Unmanned Robotic Systems (MHURS)

Plug&Play (PnP) diffusion models are state-of-the-art methods in computed tomography (CT) reconstruction. Such methods usually consider applications where the sinogram contains a sufficient amount of information for the posterior distribution to be peaked, and consequently are evaluated using image-to-image metrics such as PSNR/SSIM. Instead, we are interested in reconstructing compressible flow images from sinograms having a small number of projections, which results in a posterior distribution no longer peaked or even multimodal. Thus, in this paper, we aim at evaluating the approximate posterior of PnP diffusion models and introduce two posterior evaluation criteria. We quantitatively evaluate three PnP diffusion methods on three different datasets for several numbers of projections. We surprisingly find that, for each method, the approximate posterior deviates from the true posterior when the number of projections decreases.

The Multiarmed Bandits (MAB) problem has been extensively studied and has seen many practical applications in a variety of fields. The Survival Multiarmed Bandits (S-MAB) open problem is an extension which constrains an agent to a budget that is directly related to observed rewards. As budget depletion leads to ruin, an agent's objective is to both maximize expected cumulative rewards and minimize the probability of ruin. This paper presents a framework that addresses such a dual goal using an objective function balanced by a ruin aversion component. Action values are estimated through a novel approach which consists of bootstrapping samples from previously observed rewards. In numerical experiments, the policies we present outperform benchmarks from the literature.

A multiphysics simulation model incorporating a sintering model coupled with the MultIHeaTS thermal solver was developed to study the evolution of icy moons' microstructure. The sintering process is highly dependent on temperature, and this study represents the first attempt in planetary science to examine the coupled interaction between heat transfer and sintering. Our approach to ice sintering is based upon the literature while offering a refined description of the matter exchange between grains, bonds, and the pore space. By running the numerical framework, we simulate the evolution of ice microstructure on Galilean satellites, specifically tracking the changes in the ice grain and bond radii over time. LunaIcy, our multiphysics model, was applied to study the evolution of Europa's ice microstructure over 1 million yr along its orbit, with a parameter exploration to investigate the diverse configurations of the icy surface. Our results indicate that effective sintering can take place in regions where daily temperatures briefly surpass 115 K, even during short intervals of the day. Such sintering could not have been detected without the diurnal thermal coupling of LunaIcy due to the cold daily mean temperature. In these regions, sintering occurs within timescales shorter than Europa's ice crust age, suggesting that, in present times, their surface is made of an interconnected ice structure.

A fully implicit scheme is proposed for solving the heat equation in 1D heterogeneous media, available as a computationally efficient open-source Python code. The algorithm uses finite differences on an irregular grid and is unconditionally stable due to the implicit formulation. The thermal solver is validated against a stiff analytical solution, demonstrating its robustness in handling stiff initial conditions. Its general applicability for heterogeneous cases is demonstrated through its use in a planetary surface scenario with nonlinear boundary conditions induced by blackbody thermal emission. MultIHeaTS's advantageous stability allows for computation times up to 100 times faster than Spencer's explicit solver, making it ideal for simulating processes on large timescales. This solver is used to compare the thermal signatures of homogeneous and bilayer profiles on Europa. Results show that homogeneous materials cannot reproduce the thermal signature observed in bilayer profiles, emphasizing the need for multilayer solvers. In order to optimize the scientific return of a space mission, we propose a strategy made of three local time observations that is enough to identify bilayer media, for instance, for the next missions to the Jovian system. A second application of the solver is the estimation of the temperature profile of Europa's near surface (first 10s m) throughout a 1 million yr simulation with varying orbital parameters. The probability distribution of temperature through depth is obtained. Among its various applications, MultIHeaTS serves as the core thermal solver in a multiphysics simulation model detailed in the companion article by C. Mergny & F. Schmidt.

The increasing use of cloud-based speech assistants has heightened the need for effective speech anonymization, which aims to obscure a speaker's identity while retaining critical information for subsequent tasks. One approach to achieving this is through voice conversion. While existing methods often emphasize complex architectures and training techniques, our research underscores the importance of loss functions inspired by the human auditory system. Our proposed loss functions are model-agnostic, incorporating handcrafted and deep learning-based features to effectively capture quality representations. Through objective and subjective evaluations, we demonstrate that a VQVAE-based model, enhanced with our perception-driven losses, surpasses the vanilla model in terms of naturalness, intelligibility, and prosody while maintaining speaker anonymity. These improvements are consistently observed across various datasets, languages, target speakers, and genders., Comment: Accepted in NeurIPS 2024 Workshop (Audio Imagination)

Using an approach developed by Melrose to study the geometry at infinity of the Nakajima metric on the reduced Hilbert scheme of points on $\mathbb{C}^2$, we show that the Nakajima metric on a quiver variety is quasi-asymptotically conical (QAC) whenever its defining parameters satisfy an appropriate genericity assumption. As such, it is of bounded geometry and of maximal volume growth. Being QAC is one of two main ingredients allowing us to use the work of Kottke and the second author to compute its reduced $L^2$-cohomology and prove the Vafa-Witten conjecture. The other is a spectral gap for the Hodge-deRham operator associated with an exact $3$-Sasakian wedge metric that we obtain using the Weitzenb\"ock formula of Semmelmann and Weingart on quaternionic-K\"ahler manifolds. This yields in particular a generalization and a different proof of the vanishing theorem in cohomology originally obtained by Galicki and Salamon for closed $3$-Sasakian manifolds., Comment: 20 pages

Considerable research efforts have recently been made to show that a random neural network $N$ contains subnetworks capable of accurately approximating any given neural network that is sufficiently smaller than $N$, without any training. This line of research, known as the Strong Lottery Ticket Hypothesis (SLTH), was originally motivated by the weaker Lottery Ticket Hypothesis, which states that a sufficiently large random neural network $N$ contains \emph{sparse} subnetworks that can be trained efficiently to achieve performance comparable to that of training the entire network $N$. Despite its original motivation, results on the SLTH have so far not provided any guarantee on the size of subnetworks. Such limitation is due to the nature of the main technical tool leveraged by these results, the Random Subset Sum (RSS) Problem. Informally, the RSS Problem asks how large a random i.i.d. sample $\Omega$ should be so that we are able to approximate any number in $[-1,1]$, up to an error of $ \epsilon$, as the sum of a suitable subset of $\Omega$. We provide the first proof of the SLTH in classical settings, such as dense and equivariant networks, with guarantees on the sparsity of the subnetworks. Central to our results, is the proof of an essentially tight bound on the Random Fixed-Size Subset Sum Problem (RFSS), a variant of the RSS Problem in which we only ask for subsets of a given size, which is of independent interest.

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context understanding to fully comprehend the meetings' content. Previous attempts to address these issues by considering related supplementary resources (e.g., presentation slides) alongside transcripts are hindered by models' limited context sizes and handling the additional complexities of the multi-source tasks, such as identifying relevant information in additional files and seamlessly aligning it with the meeting content. This work explores multi-source meeting summarization considering supplementary materials through a three-stage large language model approach: identifying transcript passages needing additional context, inferring relevant details from supplementary materials and inserting them into the transcript, and generating a summary from this enriched transcript. Our multi-source approach enhances model understanding, increasing summary relevance by ~9% and producing more content-rich outputs. We introduce a personalization protocol that extracts participant characteristics and tailors summaries accordingly, improving informativeness by ~10%. This work further provides insights on performance-cost trade-offs across four leading model families, including edge-device capable options. Our approach can be extended to similar complex generative tasks benefitting from additional resources and personalization, such as dialogue systems and action planning.

lightcurver is a photometric pipeline for time series astronomical imaging data, designed for the semi-automatic extraction of precise light curves from small, blended targets. Such targets include, but are not limited to, lensed quasars, supernovae, or Cepheids in crowded fields. lightcurver leverages STARRED (Michalewicz et al., 2023; Millon et al., 2024) to generate state-of-the-art empirical point spread function (PSF) models for each image. It then determines the relative zeropoints between epochs by combining the PSF-photometry fluxes of several stars in the field of view. Subsequently, STARRED is used again to simultaneously model the calibrated pixels of the region of interest across all epochs. This process yields light curves of the point sources and a high-resolution image model of the region of interest, cumulating the signal from all epochs. lightcurver aims to be maintainable, fast, and incremental in its processing approach. As such, it can enable the daily photometric analysis of a large number of blended targets in the context of the upcoming Rubin Observatory Legacy Survey of Space and Time., Comment: 5 pages

Annotating large datasets can be challenging. However, crowd-sourcing is often expensive and can lack quality, especially for non-trivial tasks. We propose a method of using LLMs as few-shot learners for annotating data in a complex natural language task where we learn a standalone model to predict usage options for products from customer reviews. We also propose a new evaluation metric for this scenario, HAMS4, that can be used to compare a set of strings with multiple reference sets. Learning a custom model offers individual control over energy efficiency and privacy measures compared to using the LLM directly for the sequence-to-sequence task. We compare this data annotation approach with other traditional methods and demonstrate how LLMs can enable considerable cost savings. We find that the quality of the resulting data exceeds the level attained by third-party vendor services and that GPT-4-generated labels even reach the level of domain experts. We make the code and generated labels publicly available., Comment: 9 pages