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The research of machine learning (ML) algorithms for human activity recognition (HAR) has made significant progress with publicly available datasets. However, most research prioritizes statistical metrics over examining negative sample details. While recent models like transformers have been applied to HAR datasets with limited success from the benchmark metrics, their counterparts have effectively solved problems on similar levels with near 100% accuracy. This raises questions about the limitations of current approaches. This paper aims to address these open questions by conducting a fine-grained inspection of six popular HAR benchmark datasets. We identified for some parts of the data, none of the six chosen state-of-the-art ML methods can correctly classify, denoted as the intersect of false classifications (IFC). Analysis of the IFC reveals several underlying problems, including ambiguous annotations, irregularities during recording execution, and misaligned transition periods. We contribute to the field by quantifying and characterizing annotated data ambiguities, providing a trinary categorization mask for dataset patching, and stressing potential improvements for future data collections.

Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of neighboring nodes are likely to be the same. In recent years, an increasing body of work has been devoted to the development of GNN architectures for heterophilic datasets, where labels do not exhibit this low-pass behavior. In this work, we create a new graph in which nodes are connected if they share structural characteristics, meaning a higher chance of sharing their labels, and then use this new graph in the GNN architecture. To do this, we compute the k-nearest neighbors graph according to distances between structural features, which are either (i) role-based, such as degree, or (ii) global, such as centrality measures. Experiments show that the labels are smoother in this newly defined graph and that the performance of GNN architectures improves when using this alternative structure., Comment: Presented as a conference paper in the Asilomar Conference on Signals, Systems, and Computers 2024

KM3NeT/ORCA is an underwater neutrino telescope under construction in the Mediterranean Sea. Its primary scientific goal is to measure the atmospheric neutrino oscillation parameters and to determine the neutrino mass ordering. ORCA can constrain the oscillation parameters $\Delta m^{2}_{31}$ and $\theta_{23}$ by reconstructing the arrival direction and energy of multi-GeV neutrinos crossing the Earth. Searches for deviations from the Standard Model of particle physics in the forward scattering of neutrinos inside Earth matter, produced by Non-Standard Interactions, can be conducted by investigating distortions of the standard oscillation pattern of neutrinos of all flavours. This work reports on the results of the search for non-standard neutrino interactions using the first six detection units of ORCA and 433 kton-years of exposure. No significant deviation from standard interactions was found in a sample of 5828 events reconstructed in the 1 GeV$-$1 TeV energy range. The flavour structure of the non-standard coupling was constrained at 90\% confidence level to be $|\varepsilon_{\mu\tau} | \leq 5.4 \times 10^{-3}$, $|\varepsilon_{e\tau} | \leq 7.4 \times 10^{-2}$, $|\varepsilon_{e\mu} | \leq 5.6 \times 10^{-2}$ and $-0.015 \leq \varepsilon_{\tau\tau} - \varepsilon_{\mu\mu} \leq 0.017$. The results are comparable to the current most stringent limits placed on the parameters by other experiments.

The Affleck-Kennedy-Lieb-Tasaki (AKLT) point of the bilinear-biquadratic spin-1 chain is a cornerstone example of a disorder point where short-range correlations become incommensurate, and correlation lengths and momenta are non-analytic. While the presence of singularities appears to be generic for AKLT points, we show that for a family of SU(N) models, the AKLT point is not a disorder point: It occurs entirely within an incommensurate phase yet the wave vector remains singular on both sides of the AKLT point. We conjecture that this new possibility is generic for models where the representation is not self-conjugate and the transfer matrix non-Hermitian, while for self-conjugate representations the AKLT points remain disorder points., Comment: 4.5p + Appendices, comments welcome

This document presents a new C++ Automatic Differentiation (AD) tool, AD-HOC (Automatic Differentiation for High-Order Calculations). This tool aims to have the following features: -Calculation of user specified derivatives of arbitrary order -To be able to run with similar speeds as handwritten code -All derivatives calculations are computed in a single backpropagation tree pass -No source code generation is used, relying heavily on the C++ compiler to statically build the computation tree before runtime -A simple interface -The ability to be used \textit{in conjunction} with other established, general-purpose dynamic AD tools -Header-only library, with no external dependencies -Open source, with a business-friendly license

We present a study on the evolution of transitional dwarf galaxies, specifically dwarf lenticulars (dS0s) and early-type dwarfs with blue cores (ETdG(bc)s), driven by environmental processes in the Virgo cluster utilizing the Extended Virgo Cluster Catalog. We investigated the morphological fraction and stellar mass of transitional dwarf galaxies in relation to the clustercentric distance, compared to dwarf elliptical galaxies (dEs) and dwarf irregular galaxies (dIrrs). We found that dS0s beyond 0.7R_vir exhibit a similar trend in the morphology-clustercentric distance relation to dEs, demonstrating a decreasing fraction with clustercentric distance, whereas ETdG(bc)s display an opposite trend to dS0s. The spatial distributions of transitional dwarf galaxies and dEs correlate with the mass, in which fractions of bright, massive galaxies increase towards the central region of the Virgo cluster. In the mass-clustercentric distance plane, dS0s exhibit a skewed distribution that favors more massive galaxies than dEs at a given clustercentric distance. In the projected phase-space diagram, dS0s are scarce in the stripped region, whereas ETdG(bc)s are absent in both the stripped and virialized regions. In addition, the dS0s in the virialized region are predominantly brighter and more massive than the dEs, indicating that the transformation of dS0s into dEs depends on the stellar mass. We propose that the majority of observed dS0s constitute a population that has settled into the Virgo cluster, whereas ETdG(bc)s represent a recently accreted population. We discuss the impact of ram pressure stripping effects on mass-dependent morphological evolution., Comment: 17 pages, 6 figures. Accepted for publication in ApJ

We consider Betti numbers of the excursion of a smooth Euclidean Gaussian field restricted to a rectangular window, in the asymptotics where the window grows to R^d . With motivations coming from Topological Data Analysis, we derive a functional Central Limit Theorem where the varying argument is the thresholding parameter, under assumptions of regularity and covariance decay for the field and its derivatives. We also show fixed-level CLTs coming from martingale based techniques inspired from the theory of geometric stabilisation, and limiting non-degenerate variance.

Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of neutrinos is searched for in nearby dark matter reservoirs, such as the Sun and the Galactic Centre, which could potentially produce a sizeable flux of Standard Model particles. The KM3NeT infrastructure, currently under construction, comprises the ARCA and ORCA undersea \v{C}erenkov neutrino detectors located at two different sites in the Mediterranean Sea, offshore of Italy and France, respectively. The two detector configurations are optimised for the detection of neutrinos of different energies, enabling the search for dark matter particles with masses ranging from a few GeV/c$^2$ to hundreds of TeV/c$^2$. In this work, searches for dark matter annihilations in the Galactic Centre and the Sun with data samples taken with the first configurations of both detectors are presented. No significant excess over the expected background was found in either of the two analyses. Limits on the velocity-averaged self-annihilation cross section of dark matter particles are computed for five different primary annihilation channels in the Galactic Centre. For the Sun, limits on the spin-dependent and spin-independent scattering cross sections of dark matter with nucleons are given for three annihilation channels.

The vertical plane transverse emittance of accelerated electron bunches at the AWAKE experiment at CERN has been determined, using three different methods of data analysis. This is a proof-of-principle measurement using the existing AWAKE electron spectrometer to validate the measurement technique. Large values of the geometric emittance, compared to that of the injection beam, are observed ($\sim \SI{0.5}{\milli\metre\milli\radian}$ compared with $\sim \SI{0.08}{\milli\metre\milli\radian}$), which is in line with expectations of emittance growth arising from plasma density ramps and large injection beam bunch size. Future iterations of AWAKE are anticipated to operate in conditions where emittance growth is better controlled, and the effects of the imaging systems of the existing and future spectrometer designs on the ability to measure the emittance are discussed. Good performance of the instrument down to geometric emittances of approximately $\SI{1e-4}{\milli\metre\milli\radian}$ is required, which may be possible with improved electron optics and imaging., Comment: 20 pages, 9 figures

We show that the anisotropies in the spectrum of gravitational waves induced by scalar modes after the end of inflation in canonical, single-field models are completely determined by the tilt of the scalar and tensor power spectra. The latter contains information about anisotropies produced due to the propagation of the tensor modes in an inhomogeneous Universe, whereas the former represents the anisotropies generated at the time of production and arise only when non-Gaussian corrections to the angular power spectrum are considered. Our proof takes into account all scalar interactions in the cubic inflaton Lagrangian., Comment: 8 pages

We introduce MEGATRON, a new galaxy formation model for cosmological radiation hydrodynamics simulations of high-redshift galaxies. The model accounts for the non-equilibrium chemistry and heating/cooling processes of $\geq 80$ atoms, ions, and molecules, coupled to on-the-fly radiation transfer. We apply the model in a cosmological setting to the formation of a $10^9\ {\rm M_{\odot}}$ halo at $z=6$, and run 25 realizations at pc-scale resolution, varying numerous parameters associated with our state-of-the-art star formation, stellar feedback, and chemical enrichment models. We show that the overall budget of feedback energy is the key parameter that controls star formation regulation at high redshift, with other numerical parameters (e.g. supernova clustering, star formation conditions) having a more limited impact. As a similar feedback model has been shown to produce realistic $z=0$ galaxies, our work demonstrates that calibration at $z=0$ does not guarantee strong regulation of star formation at high-redshift. Interestingly, we find that subgrid model variations that have little impact on the final $z=6$ stellar mass can lead to substantial changes on the observable properties of high-redshift galaxies. For example, different star formation models based on, e.g. density thresholds or turbulence inspired criteria, lead to fundamentally distinct nebular emission line ratios across the interstellar medium (ISM). These results highlight the ISM as an important resource for constraining models of star formation, feedback, and galaxy formation in the JWST era, where emission line measurements for $>1,000$ high-redshift galaxies are now available., Comment: 25 pages, 16 Figures, submitted to the Open Journal of Astrophysics

Optoacoustic tomography image reconstruction has been a problem of interest in recent years. By exploiting the exceptional generative power of the recently proposed diffusion models we consider a scheme which is based on a conditional diffusion process. Using a simple initial image reconstruction method such as Delay and Sum, we consider a specially designed autoencoder architecture which generates a latent representation which is used as conditional information in the generative diffusion process. Numerical results show the merits of our proposal in terms of quality metrics such as PSNR and SSIM, showing that the conditional information generated in terms of the initial reconstructed image is able to bias the generative process of the diffusion model in order to enhance the image, correct artifacts and even recover some finer details that the initial reconstruction method is not able to obtain., Comment: Published in 2024 IEEE Biennial Congress of Argentina. arXiv admin note: substantial text overlap with arXiv:2404.10239

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures, submitted to Computer Physics Communications

A granular gas is a collection of macroscopic particles that interact through energy-dissipating collisions, also known as inelastic collisions. This inelasticity is characterized by a collision mechanics in which mass and momentum are conserved and kinetic energy is dissipated. Such a system can be described by a kinetic equation of the Boltzmann type. Nevertheless, due to the macroscopic aspect of the particles, any realistic description of a granular gas should be written as a mixture model composed of M different species, each with its own mass. We propose in this work such a granular multi-species model and analyse it, providing Povzner-type inequalities, and a Cauchy theory in general Orlicz spaces. We also analyse its large time behavior, showing that it exhibits a mixture analogue of the seminal Haff's Law., Comment: 34 pages, 1 figure

Medical procedures such as venipuncture and cannulation are essential for nurses and require precise skills. Learning this skill, in turn, is a challenge for educators due to the number of teachers per class and the complexity of the task. The study aims to help students with skill acquisition and alleviate the educator's workload by integrating generative AI methods to provide real-time feedback on medical procedures such as venipuncture and cannulation., Comment: 2 pages, 2 figures

Paradigmatic spin Hamiltonians in condensed matter and quantum sensing typically utilize pair-wise or 2-body interactions between constituents in the material or ensemble. However, there is growing interest in exploring more general $n$-body interactions for $n >2$, with examples including more efficient quantum gates or the realization of exotic many-body fracton states. Here we realize an effective $n=3$-body Hamiltonian interaction using an ensemble of laser-cooled atoms in a high finesse optical cavity with the pseudo-spin 1/2 encoded by two atomic momentum states. To realize this interaction, we apply two dressing tones that coax the atoms to exchange photons via the cavity to realize a virtual 6-photon process, while the lower-order interactions destructively interfere. The resulting photon mediated interactions are not only $n>2$-body but also all-to-all(-to-all) and therefore of great interest for fast entanglement generation and quantum simulation of exotic phases such as the long sought but not yet observed charge-Qe superconductors, with $Q=2n$ . The versatility of our experimental system can also allow for extending to 3-body interactions in multi-level systems or to higher-order interactions, such as the signature of a $n=4$-body interaction mediated by a virtual eight photon process that we also observe.

Convection-diffusion-reaction equations are a class of second-order partial differential equations widely used to model phenomena involving the change of concentration/population of one or more substances/species distributed in space. Understanding and preserving their stability properties in numerical simulation is crucial for accurate predictions, system analysis, and decision-making. This work presents a comprehensive framework for constructing fully discrete Lyapunov-consistent discretizations of any order for convection-diffusion-reaction models. We introduce a systematic methodology for constructing discretizations that mimic the stability analysis of the continuous model using Lyapunov's direct method. The spatial algorithms are based on collocated discontinuous Galerkin methods with the summation-by-parts property and the simultaneous approximation terms approach for imposing interface coupling and boundary conditions. Relaxation Runge-Kutta schemes are used to integrate in time and achieve fully discrete Lyapunov consistency. To verify the properties of the new schemes, we numerically solve a system of convection-diffusion-reaction partial differential equations governing the dynamic evolution of monomer and dimer concentrations during the dimerization process. Numerical results demonstrated the accuracy and consistency of the proposed discretizations. The new framework can enable further advancements in the analysis, control, and understanding of general convection-diffusion-reaction systems.

We compute the non-Gaussian corrections to the energy density and anisotropies of gravitational waves induced after an ultra-slow-roll phase of inflation by using a diagrammatic approach, and present the corresponding Feynman rules. Our two-loop calculation includes both the intrinsic non-Gaussianity of the inflaton perturbation $\delta\phi$ and the non-Gaussianity arising from the nonlinear relation between the latter and the curvature perturbation $\mathcal{R}$. We apply our formalism to an analytical model in which the ultra-slow-roll phase is followed by a constant-roll stage with a nonvanishing second slow-roll parameter $\eta$, and address the renormalization of the one-loop scalar power spectrum in this scenario., Comment: 30 pages, 3 figures

A key challenge in observing measurement-induced phase transitions is the mitigation of the post-selection barrier, which causes the reproducibility of specific sequences of measurement readouts--the trajectory--to be exponentially small in system size. Recent studies suggest that some classes of monitored infinite-range systems alleviate this problem by exhibiting a fast saturation of entanglement, resulting in only a polynomial post-selection overhead. This paper answers whether this feature is inherent in infinite-range systems, due to their underlying semiclassical dynamics. We consider three experimentally relevant monitored models: a Tavis-Cummings model, a Superradiance model, and a Bose-Hubbard dimer, each exhibiting non-trivial monitored dynamics. We unveil the occurrence of entanglement phase transitions in these models, showing how the saturation time is strongly affected by bistability regions, which also prevent the mitigation of the post-selection barrier. Finally, we propose experimental realizations of these models, providing a discussion of post selection from an experimental perspective., Comment: 31 pages, 27 figures

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

The number rigidity of a point process $P$ entails that for a bounded set $A$ the knowledge of $P$ on $A^{c}$ a.s. determines $P(A)$; the $k$-order rigidity means we can recover the moments of $P1_{A}$ up to order $k$. We show that there is $k$-rigidity if the continuous component $s$ of $P$'s structure factor has a zero of order $k$ in 0, by exploiting a connection with Schwartz' Paley-Wiener theorem for analytic functions of exponential type; these results apply to any random $L^2$ wide sense stationary measure on $ \mathbb{R}^{d}$ or $\mathbb{Z}^{d}$. In the continuous setting, these local conditions are also necessary if $s$ has finitely many zeros, or is isotropic, or at the opposite separable. This explains why no model seems to exhibit rigidity in dimension $d \ge 3$, and allows to efficiently recover many recent rigidity results about point processes. In the discrete setting, these results hold provided $\# A >2k$. We derive new results about models of cluster lattices and give the first example of a stationary point process $P \subset \mathbb{R}^{d}$ exhibiting arbitrary low decay of the structure factor in 0, hence arbitrary high order of rigidity. For a continuous Determinantal point process with kernel $\kappa$, $k$-rigidity is equivalent to $(1-\widehat {\kappa^{2}})^{-1}$ having a zero of order $2k$ in 0, which answers questions on completeness and number rigidity. We also explore the consequences of these statements in the less tractable realm of Riesz gases.

We consider two different versions of the double dimer model on a planar domain, where we either fold a single dimer cover on a symmetric domain onto itself across the line of symmetry, or we superimpose two independent dimer covers on two, almost identical, domains that differ only on a certain portion of the boundary. This results in a collection of loops and doubled edges that, unlike in the classical double dimer case of Kenyon, are accompanied by arcs emanating from the line of symmetry or the chosen portion of the boundary. We argue that these arcs together with the associated height function satisfy a discrete version of the coupling of Qian and Werner between the Arc loop ensemble (ALE) and two different variants of the Gaussian free field (with Dirichlet and Neumann boundary conditions). We also show that certain statistics of the arcs (when the loops are disregarded from the picture) converge to conformally invariant quantities in the small-mesh scaling limit, and moreover the limits are the same for the two versions of the model, and equal to the corresponding statistics of the arc loop ensemble (ALE). This gives evidence to the conjecture of [7] (that concerns one of these models)., Comment: 44 pages, 15 figures

The miniaturization of mechanical machines is critical for advancing nanotechnology and reducing device footprints. Traditional efforts to downsize gears and micromotors have faced limitations at around 0.1 mm for over thirty years due to the complexities of constructing drives and coupling systems at such scales. Here, we present an alternative approach utilizing optical metasurfaces to locally drive microscopic machines, which can then be fabricated using standard lithography techniques and seamlessly integrated on the chip, achieving sizes down to tens of micrometers with movements precise to the sub-micrometer scale. As a proof of principle, we demonstrate the construction of microscopic gear trains powered by a single driving gear with a metasurface activated by a plane light wave. Additionally, we develop a versatile pinion and rack micromachine capable of transducing rotational motion, performing periodic motion, and controlling microscopic mirrors for light deflection. Our on-chip fabrication process allows for straightforward parallelization and integration. Using light as a widely available and easily controllable energy source, these miniaturized metamachines offer precise control and movement, unlocking new possibilities for micro- and nanoscale systems.

Quantum simulation and metrology with atoms, ions, and molecules often rely on using light fields to manipulate their internal states. The absorbed momentum from the light fields can induce spin-orbit coupling and associated motional-induced (Doppler) dephasing, which may limit the coherence time available for metrology and simulation. We experimentally demonstrate the suppression of Doppler dephasing on a strontium optical clock transition by enabling atomic interactions through a shared mode in a high-finesse optical ring cavity. The interactions create a many-body energy gap that increases with atom number, suppressing motional dephasing when it surpasses the dephasing energy scale. This collective approach offers an alternative to traditional methods, like Lamb-Dicke confinement or M\"ossbauer spectroscopy, for advancing optical quantum sensors and simulations., Comment: Main Text with Supplementary Material, 8 pages, 4 figures

Disordered Quantum many-body Systems (DQS) and Quantum Neural Networks (QNN) have many structural features in common. However, a DQS is essentially an initialized QNN with random weights, often leading to non-random outcomes. In this work, we emphasize the possibilities of random processes being a deceptive quantum-generating model effectively hidden in a QNN. In contrast to classical noisy maps, quantum maps have the possibility of ergodicity breaking leading to memory effects and thus multiple consequences on the learnability and trainability of QNN. This phenomenon may lead to a fundamental misunderstanding of the capabilities of common quantum generative models, where the generation of new samples is essentially averaging over random outputs. While we suggest that DQS can be effectively used for tasks like image augmentation, we draw the attention that overly simple datasets are often used to show the generative capabilities of quantum models, potentially leading to overestimation of their effectiveness., Comment: 5 pages, 4 figures

In a randomised clinical trial, when the result of the primary endpoint shows a significant benefit, the secondary endpoints are scrutinised to identify additional effects of the treatment. However, this approach entails a risk of concluding that there is a benefit for one of these endpoints when such benefit does not exist (inflation of type I error risk). There are mainly two methods used to control the risk of drawing erroneous conclusions for secondary endpoints. The first method consists of distributing the risk over several co-primary endpoints, so as to maintain an overall risk of 5%. The second is the hierarchical test procedure, which consists of first establishing a hierarchy of the endpoints, then evaluating each endpoint in succession according to this hierarchy while the endpoints continue to show statistical significance. This simple method makes it possible to show the additional advantages of treatments and to identify the factors that differentiate them.

The aquaculture industry is constantly making efforts to improve fish welfare while maintaining the ethically sustainable farming practises. This work presents an enhanced tank environment designed for testing and developing novel combinations of technologies for analyzing and detecting behavioral responses in fish shoals/groups. Regular cameras are combined with event cameras and a scanning sonar to comprise a sensor suite that offers a more detailed and complex way of fish observation. The modified tank environment is designed to simulate the prevailing conditions on-site at cage based farms, particularly in terms of lighting conditions, while all tank systems and sensors are hidden behind specially designed enclosures, providing a "clean" environment (open arena) less likely to impact the fish behavior. The proposed sensor suite will be tested and demonstrated in the modified tank environment to benchmark its ability in monitoring fish, after which it will be adapted for use in a more industrially relevant situation with open cages.

Exploiting symmetries in the numerical renormalization group (NRG) method significantly enhances performance by improving accuracy, increasing computational speed, and optimizing memory efficiency. Published codes focus on continuous rotations and unitary groups, which generally are not applicable to systems with strong crystal-field effects. The PointGroupNRG code implements symmetries related to discrete rotation groups, which are defined by the user in terms of Clebsch-Gordan coefficients, together with particle conservation and spin rotation symmetries. In this paper we present a new version of the code that extends the available finite groups, previously limited to simply reducible point groups, in a way that all point and double groups become accessible. It also includes the full spin-orbital rotation group. Moreover, to improve the code's flexibility for impurities with complex interactions, this new version allows to choose between a standard Anderson Hamiltonian for the impurity or, as another novel feature, an ionic model that requires only the spectrum and the impurity Lehmann amplitudes., Comment: 19 pages, 2 figures

Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneously but also that all nodes can be observed. However, in many real-world scenarios, data can neither be known completely nor obtained all at once. We present a novel method for online graph estimation that accounts for the presence of hidden nodes. We consider signals that are stationary on the underlying graph, which provides a model for the unknown connections to hidden nodes. We then formulate a convex optimization problem for graph learning from streaming, incomplete graph signals. We solve the proposed problem through an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Additionally, we provide theoretical conditions under which our online algorithm is similar to batch-wise solutions. Through experimental results on synthetic and real-world data, we demonstrate the viability of our approach for online graph learning in the presence of missing observations.

Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is represented by a homophilic graph. However, recent works have revealed that many relevant applications involve heterophilic data where the performance of GNNs can be notably compromised. To address this challenge, we present a simple yet effective architecture designed to mitigate the limitations of the homophily assumption. The proposed architecture reinterprets the role of graph filters in convolutional GNNs, resulting in a more general architecture while incorporating a stronger inductive bias than GNNs based on filter banks. The proposed convolutional layer enhances the expressive capacity of the architecture enabling it to learn from both homophilic and heterophilic data and preventing the issue of oversmoothing. From a theoretical standpoint, we show that the proposed architecture is permutation equivariant. Finally, we show that the proposed GNNs compares favorably relative to several state-of-the-art baselines in both homophilic and heterophilic datasets, showcasing its promising potential.

This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample covariance matrix is a key aspect of our approach. We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes. To provide further insights into the proposed method, we specialize it in Gaussian Markov random field settings, where we analyze the computational complexity and characterize the dynamic cumulative regret. Finally, we demonstrate the effectiveness of the proposed approach using both controlled experiments and real-world datasets from epidemic and financial networks.

Interpretation of data from faint dwarf galaxies is made challenging by observations limited to only the brightest stars. We present a major improvement to tackle this challenge by undertaking zoomed cosmological simulations that resolve the evolution of all individual stars more massive than $0.5\,{\rm M}_{\odot}$, thereby explicitly tracking all observable stars for the Hubble time. For the first time, we predict observable color-magnitude diagrams and the spatial distribution of $\approx 100,000$ stars within four faint ($M_{\star} \approx 10^5 \, \,{\rm M}_{\odot}$) dwarf galaxies directly from their cosmological initial conditions. In all cases, simulations predict complex light profiles with multiple components, implying that typical observational measures of structural parameters can make total V-band magnitudes appear up to 0.5 mag dimmer compared to estimates from simulations. Furthermore, when only small ($\lessapprox100$) numbers of stars are observable, shot noise from realizations of the color-magnitude diagram introduces uncertainties comparable to the population scatter in, e.g., total magnitude, half-light radius, and mean iron abundance measurements. Estimating these uncertainties with fully self-consistent mass growth, star formation and chemical enrichment histories paves the way for more robust interpretation of dwarf galaxy data., Comment: Submitted to ApJL, comments welcome

We address the problem of learning the topology of directed acyclic graphs (DAGs) from nodal observations, which adhere to a linear structural equation model. Recent advances framed the combinatorial DAG structure learning task as a continuous optimization problem, yet existing methods must contend with the complexities of non-convex optimization. To overcome this limitation, we assume that the latent DAG contains only non-negative edge weights. Leveraging this additional structure, we argue that cycles can be effectively characterized (and prevented) using a convex acyclicity function based on the log-determinant of the adjacency matrix. This convexity allows us to relax the task of learning the non-negative weighted DAG as an abstract convex optimization problem. We propose a DAG recovery algorithm based on the method of multipliers, that is guaranteed to return a global minimizer. Furthermore, we prove that in the infinite sample size regime, the convexity of our approach ensures the recovery of the true DAG structure. We empirically validate the performance of our algorithm in several reproducible synthetic-data test cases, showing that it outperforms state-of-the-art alternatives.

Acoustic levitation is a container-free method for examining novel crystallization effects, though liquid-to-solid phase change has seen little investigation for levitated nanofluids. Recent developments have allowed for examining the morphological and temperature evolution of multiple levitated nanofluid droplets freezing simultaneously. The fundamental effect of adding nanoparticles to a levitated crystallization system is crystal growth rate enhancement from improved mass transfer at the growing solid front. Nucleation times are unaffected as freezing is initiated by secondary ice nucleation particles (INPs). Instead, the enhancement produces higher instantaneous nucleation pressures and more cracking in the primary ice shell. In turn, more INPs are ejected, resulting in faster protrusion formation on the droplet surface (hastened further in systems containing adjacent droplets). The crystal matrix also includes more defects, resulting in liquid escaping and forming beads at the droplet base and optical clarity loss. During crystal decomposition, thermal gradients create convective currents dampened by the same transport phenomena that enhance crystal growth. Suspension loss after a crystallization-decomposition cycle reduced opacity and light absorbance such that the droplets were 62% closer in appearance to water. However, the non-isobaric, sample-encompassing cooling process resulted in smaller particle clusters than if the droplets were frozen on a solid surface.

Ellipsoidal particles confined at liquid interfaces exhibit complex self-assembly behaviour due to quadrupolar capillary interactions induced by meniscus deformation. These interactions cause particles to attract each other in either tip-to-tip or side-to-side configurations. However, controlling their interfacial self-assembly is challenging because it is difficult to predict which of these two states will be preferred. In this study, we demonstrate that introducing a soft shell around hard ellipsoidal particles provides a means to control the self-assembly process, allowing us to switch the preferred configuration between these states. We study their interfacial self-assembly and find that pure ellipsoids without a shell consistently form a "chain-like" side-to-side assembly, regardless of aspect ratio. In contrast, core-shell ellipsoids transition from "flower-like" tip-to-tip to "chain-like" side-to-side arrangements as their aspect ratios increase. The critical aspect ratio for transitioning between these structures increases with shell-to-core ratios. Our experimental findings are corroborated by theoretical calculations and Monte Carlo simulations, which map out the phase diagram of thermodynamically preferred self-assembly structures for core-shell ellipsoids as a function of aspect ratio and shell-to-core ratios. This study shows how to program the self-assembly of anisotropic particles by tuning their physicochemical properties, allowing the deterministic realization of distinct structural configurations.

We investigate the quantum many-body dynamics of bosonic atoms hopping in a two-leg ladder with strong on-site contact interactions. We observe that when the atoms are prepared in a staggered pattern with pairs of atoms on every other rung, singlon defects, i.e.~rungs with only one atom, can localize due to an emergent topological model, even though the underlying model in the absence of interactions admits only topologically trivial states. This emergent topological localization results from the formation of a zero-energy edge mode in an effective lattice formed by two adjacent chains with alternating strong and weak hoping links (Su-Schrieffer-Heeger chains) and opposite staggering which interface at the defect position. Our findings open the opportunity to dynamically generate non-trivial topological behaviors without the need for complex Hamiltonian engineering., Comment: 4+6.5 pages, 4+6 figures

The stellar-mass--halo-mass (SMHM) relation is central to our understanding of galaxy formation and the nature of dark matter. However, its normalisation, slope, and scatter are highly uncertain at dwarf galaxy scales. In this paper, we present DarkLight, a new semi-empirical dwarf galaxy formation model designed to robustly predict the SMHM relation for the smallest galaxies. DarkLight harnesses a correlation between the mean star formation rate of dwarfs and their peak rotation speed -- the $\langle$SFR$\rangle$-$v_{\rm max}$ relation -- that we derive from simulations and observations. Given the sparsity of data for isolated dwarfs with $v_{\rm max} \lesssim 20$ km/s, we fit the $\langle$SFR$\rangle$-$v_{\rm max}$ relation to observational data for dwarfs above this velocity scale and to the high-resolution EDGE cosmological simulations below. Reionisation quenching is implemented via distinct $\langle$SFR$\rangle$-$v_{\rm max}$ relations before and after reionisation. We find that the SMHM scatter is small at reionisation, $\sim$0.2 dex, but rises to $\sim$0.5 dex ($1\sigma$) at a halo mass of $\sim$10$^9$ M$_\odot$ as star formation is quenched by reionisation but dark matter halo masses continue to grow. While we do not find a significant break in the slope of the SMHM relation, one can be introduced if reionisation occurs early ($z_{\rm quench} \gtrsim 5$). Finally, we find that dwarfs can be star forming today down to a halo mass of $\sim$2 $\times 10^9$ M$_\odot$. We predict that the lowest mass star forming dwarf irregulars in the nearby universe are the tip of the iceberg of a much larger population of quiescent isolated dwarfs., Comment: 15 pages, 13 figures. Key results are summarized in Figures 3-6. To be submitted to MNRAS. Comments welcome!

We use an ensemble of laser-cooled strontium atoms in a high-finesse cavity to cleanly emulate the technique of rf spectroscopy employed in studies of BEC-BCS physics in fermionic superfluids of degenerate cold gases. Here, we leverage the multilevel internal structure of the atoms to study the physics of Cooper pair breaking in this system. In doing so, we observe and distinguish the properties of two distinct many-body gaps, the BCS pairing gap and the spectral gap, using nondestructive readout techniques. The latter is found to depend on the populations of the internal atomic states, reflecting the chemical potential dependence predicted in fermionic superfluids. This work opens the path for more fully exploiting the rich internal structure of atoms in cavity QED emulators to study both analogous systems and also more exotic states yet to be realized., Comment: Main Text with Supplementary Material, 15 pages, 5 figures

In cavity quantum electrodynamics (QED), emitters and a resonator are coupled together to enable precise studies of quantum light-matter interactions. Over the past few decades, this has led to a variety of quantum technologies such as more precise inertial sensors, clocks, memories, controllable qubits, and quantum simulators. Furthermore, the intrinsically dissipative nature of cavity QED platforms makes them a natural testbed for exploring driven-dissipative phenomena in open quantum systems as well as equilibrium and non-equilibrium phase transitions in quantum optics. One such model, the so-called cooperative resonance fluorescence (CRF) model, concerns the behavior of coherently driven emitters in the presence of collective dissipation (superradiance). Despite tremendous interest, this model has yet to be realized in a clean experimental system. Here we provide an observation of the continuous superradiant phase transition predicted in the CRF model using an ensemble of ultracold $^{88}$Sr atoms coupled to a driven high-finesse optical cavity on a long-lived optical transition. Below a critical drive, atoms quickly reach a steady state determined by the self-balancing of the drive and the collective dissipation. The steady state possesses a macroscopic dipole moment and corresponds to a superradiant phase. Above a critical drive strength, the atoms undergo persistent Rabi-like oscillations until other decoherence processes kick in. In fact, our platform also allows us to witness the change of this phase transition from second to first order induced by single-particle spontaneous emission, which pushes the system towards a different steady state. Our observations are a first step towards finer control of driven-dissipative systems, which have been predicted to generate quantum states that can be harnessed for quantum information processing and in particular quantum sensing., Comment: 12 pages, 7 figures (main text and methods); 11 pages, 4 figures (supplementary information); typos corrected

Research on multi-modal contrastive learning strategies for audio and text has rapidly gained interest. Contrastively trained Audio-Language Models (ALMs), such as CLAP, which establish a unified representation across audio and language modalities, have enhanced the efficacy in various subsequent tasks by providing good text aligned audio encoders and vice versa. These improvements are evident in areas like zero-shot audio classification and audio retrieval, among others. However, the ability of these models to understand natural language and temporal relations is still a largely unexplored and open field for research. In this paper, we propose to equip the multi-modal ALMs with temporal understanding without loosing their inherent prior capabilities of audio-language tasks with a temporal instillation method TeminAL. We implement a two-stage training scheme TeminAL A $\&$ B, where the model first learns to differentiate between multiple sounds in TeminAL A, followed by a phase that instills a sense of time, thereby enhancing its temporal understanding in TeminAL B. This approach results in an average performance gain of $5.28\%$ in temporal understanding on the ESC-50 dataset, while the model remains competitive in zero-shot retrieval and classification tasks on the AudioCap/Clotho datasets. We also note the lack of proper evaluation techniques for contrastive ALMs and propose a strategy for evaluating ALMs in zero-shot settings. The general-purpose zero-shot model evaluation strategy ZSTE, is used to evaluate various prior models. ZSTE demonstrates a general strategy to evaluate all ZS contrastive models. The model trained with TeminAL successfully outperforms current models on most downstream tasks., Comment: 31 pages, 11 figures

KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.18^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 29 pages, 12 figures

Starting with the overall wave function expression for the electron in an Aharonov-Bohm potential, we derive a version of the Kuramoto Model describing the phase dynamics of the bound state of the quantum mechanical system., Comment: 14 pages, 6 figures, 1 table

We construct the Feynman integral for the Schr\"odinger propagator in the polar conjugate momentum space, which describes the bound state Aharonov-Bohm effect, as a well-defined white noise functional., Comment: 28 pages, 1 figure

We study feedback-driven cold dark matter core creation in the EDGE suite of radiation-hydrodynamical dwarf galaxy simulations. Understanding this process is crucial when using observed dwarf galaxies to constrain the particle nature of dark matter. While previous studies have shown the stellar-mass to halo-mass ratio $(M_{\star} / M_{200})$ determines the extent of core creation, we find that in low-mass dwarfs there is a crucial additional effect, namely the timing of star formation relative to reionisation. Sustained post-reionisation star formation decreases central dark matter density through potential fluctuations; conversely, pre-reionisation star formation is too short-lived to have such an effect. In fact, large stellar masses accrued prior to reionisation are a strong indicator of early collapse, and therefore indicative of an increased central dark matter density. We parameterise this differentiated effect by considering $M_{\star,\mathrm{post}}/M_{\star,\mathrm{pre}}$, where the numerator and denominator represent the amount of star formation after and before $z\sim6.5$, respectively. Our study covers the halo mass range $10^9 < M_{200} < 10^{10} M_\odot$ (stellar masses between $10^4 < M_{\star} < 10^8 M_\odot$), spanning both ultra-faint and classical dwarfs. In this regime, $M_{\star,\mathrm{post}}/M_{\star,\mathrm{pre}}$ correlates almost perfectly with the central dark matter density at $z=0$, even when including simulations with a substantially different variant of feedback and cooling. We provide fitting formulae to describe the newfound dependence., Comment: 11 pages, 8 figures, 1 appendix. Accepted for publication in MNRAS

We formulate well-posed continuous-time generative flows for learning distributions that are supported on low-dimensional manifolds through Wasserstein proximal regularizations of $f$-divergences. Wasserstein-1 proximal operators regularize $f$-divergences so that singular distributions can be compared. Meanwhile, Wasserstein-2 proximal operators regularize the paths of the generative flows by adding an optimal transport cost, i.e., a kinetic energy penalization. Via mean-field game theory, we show that the combination of the two proximals is critical for formulating well-posed generative flows. Generative flows can be analyzed through optimality conditions of a mean-field game (MFG), a system of a backward Hamilton-Jacobi (HJ) and a forward continuity partial differential equations (PDEs) whose solution characterizes the optimal generative flow. For learning distributions that are supported on low-dimensional manifolds, the MFG theory shows that the Wasserstein-1 proximal, which addresses the HJ terminal condition, and the Wasserstein-2 proximal, which addresses the HJ dynamics, are both necessary for the corresponding backward-forward PDE system to be well-defined and have a unique solution with provably linear flow trajectories. This implies that the corresponding generative flow is also unique and can therefore be learned in a robust manner even for learning high-dimensional distributions supported on low-dimensional manifolds. The generative flows are learned through adversarial training of continuous-time flows, which bypasses the need for reverse simulation. We demonstrate the efficacy of our approach for generating high-dimensional images without the need to resort to autoencoders or specialized architectures.

Atmospheric states derived from reanalysis comprise a substantial portion of weather and climate simulation outputs. Many stakeholders -- such as researchers, policy makers, and insurers -- use this data to better understand the earth system and guide policy decisions. Atmospheric states have also received increased interest as machine learning approaches to weather prediction have shown promising results. A key issue for all audiences is that dense time series of these high-dimensional states comprise an enormous amount of data, precluding all but the most well resourced groups from accessing and using historical data and future projections. To address this problem, we propose a method for compressing atmospheric states using methods from the neural network literature, adapting spherical data to processing by conventional neural architectures through the use of the area-preserving HEALPix projection. We investigate two model classes for building neural compressors: the hyperprior model from the neural image compression literature and recent vector-quantised models. We show that both families of models satisfy the desiderata of small average error, a small number of high-error reconstructed pixels, faithful reproduction of extreme events such as hurricanes and heatwaves, preservation of the spectral power distribution across spatial scales. We demonstrate compression ratios in excess of 1000x, with compression and decompression at a rate of approximately one second per global atmospheric state., Comment: 44 pages, 25 figures