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Previous research established that the maximal rank of the abstract regular polytopes whose automorphism group is a transitive proper subgroup of $\Sym_n$ is $n/2 + 1$, with only two polytopes attaining this rank, both of which having odd ranks. In this paper, we investigate the case where the rank is equal to $n/2$ ($n\geq 14$). Our analysis reveals that reducing the rank by one results in a substantial increase in the number of regular polytopes ($33$ distinct families are discovered) covering all possible ranks (even and odd).

Planet formation is expected to be severely limited in disks of low metallicity, owing to both the small solid mass reservoir and the low opacity accelerating the disk gas dissipation. While previous studies have found a weak correlation between the occurrence rates of small planets ($\leq$4R$_\oplus$) and stellar metallicity, so far no studies have probed below the metallicity limit beyond which planet formation is predicted to be suppressed. Here, we constructed a large catalog of ~110,000 metal-poor stars observed by the TESS mission with spectroscopically-derived metallicities, and systematically probed planet formation within the metal-poor regime ([Fe/H] $\leq$ -0.5) for the first time. Extrapolating known higher-metallicity trends for small, short-period planets predicts the discovery of ~68 superEarths around these stars (~85,000 stars) after accounting for survey completeness; however, we detect none. As a result, we have placed the most stringent upper limit on super-Earth occurrence rates around metal-poor stars (-0.75 < [Fe/H] $\leq$ -0.5) to date, $\leq$ 1.67%, a statistically significant (p-value=0.000685) deviation from the prediction of metallicity trends derived with Kepler and K2. We find a clear host star metallicity cliff for super-Earths that could indicate the threshold below which planets are unable to grow beyond an Earth-mass at short orbital periods. This finding provides a crucial input to planet formation theories, and has implications for the small planet inventory of the Galaxy and the galactic epoch at which the formation of small planets started., Comment: Accepted to AJ

Electrical signals in excitable cells involve spatially localized ionic fluxes through ion channels and pumps on cellular lipid membranes. Common approaches to understand how these localized fluxes spread assume that the membrane and the surrounding electrolyte comprise an equivalent circuit of capacitors and resistors, which ignores the localized nature of transmembrane ion transport, the resulting ionic gradients and electric fields, and their spatiotemporal relaxation. Here, we consider a model of localized ion pumping across a lipid membrane, and use theory and simulation to investigate how the electrochemical signal propagates spatiotemporally in- and out-of-plane along the membrane. The localized pumping generates long-ranged electric fields with three distinct scaling regimes along the membrane: a constant potential near-field region, an intermediate "monopolar" region, and a far-field "dipolar" region. Upon sustained pumping, the monopolar region expands radially in-plane with a steady speed that is enhanced by the dielectric mismatch and the finite thickness of the lipid membrane. For unmyelinated lipid membranes in physiological settings, we find remarkably fast propagation speeds of $\sim\!40 \, \mathrm{m/s}$, allowing faster ionic reorganization compared to bare diffusion. Together, our work shows that transmembrane ionic fluxes induce transient long-ranged electric fields in electrolyte solutions, which may play hitherto unappreciated roles in biological signaling.

Multi-Source Domain Adaptation (MSDA) is a challenging scenario where multiple related and heterogeneous source datasets must be adapted to an unlabeled target dataset. Conventional MSDA methods often overlook that data holders may have privacy concerns, hindering direct data sharing. In response, decentralized MSDA has emerged as a promising strategy to achieve adaptation without centralizing clients' data. Our work proposes a novel approach, Decentralized Dataset Dictionary Learning, to address this challenge. Our method leverages Wasserstein barycenters to model the distributional shift across multiple clients, enabling effective adaptation while preserving data privacy. Specifically, our algorithm expresses each client's underlying distribution as a Wasserstein barycenter of public atoms, weighted by private barycentric coordinates. Our approach ensures that the barycentric coordinates remain undisclosed throughout the adaptation process. Extensive experimentation across five visual domain adaptation benchmarks demonstrates the superiority of our strategy over existing decentralized MSDA techniques. Moreover, our method exhibits enhanced robustness to client parallelism while maintaining relative resilience compared to conventional decentralized MSDA methodologies., Comment: 17 pages,7 figures

Ultra-high-energy cosmic rays are known to be mainly of extragalactic origin, and their propagation is limited by energy losses, so their arrival directions are expected to correlate with the large-scale structure of the local Universe. In this work, we investigate the possible presence of intermediate-scale excesses in the flux of the most energetic cosmic rays from the direction of the supergalactic plane region using events with energies above 20 EeV recorded with the surface detector array of the Pierre Auger Observatory up to 31 December 2022, with a total exposure of 135,000 km^2 sr yr. The strongest indication for an excess that we find, with a post-trial significance of 3.1{\sigma}, is in the Centaurus region, as in our previous reports, and it extends down to lower energies than previously studied. We do not find any strong hints of excesses from any other region of the supergalactic plane at the same angular scale. In particular, our results do not confirm the reports by the Telescope Array collaboration of excesses from two regions in the Northern Hemisphere at the edge of the field of view of the Pierre Auger Observatory. With a comparable exposure, our results in those regions are in good agreement with the expectations from an isotropic distribution., Comment: submitted to ApJ

In this note we give a presentation for the monoid $IO_n$ of all order-preserving transformations of a $n$-chain whose ranges are intervals. We also consider the submonoid $IO_n^-$ of $IO_n$ consisting of order-decreasing transformations, for which we determine the cardinality, the rank and a presentation.

This paper presents the first public data release of the S-PLUS Ultra-Short Survey (USS), a photometric survey with short exposure times, covering approximately 9300 deg$^{2}$ of the Southern sky. The USS utilizes the Javalambre 12-band magnitude system, including narrow and medium-band and broad-band filters targeting prominent stellar spectral features. The primary objective of the USS is to identify bright, extremely metal-poor (EMP; [Fe/H] $\leq -3$) and ultra metal-poor (UMP; [Fe/H] $\leq -4$) stars for further analysis using medium- and high-resolution spectroscopy.}{This paper provides an overview of the survey observations, calibration method, data quality, and data products. Additionally, it presents the selection of EMP and UMP candidates.}{The data from the USS were reduced and calibrated using the same methods as presented in the S-PLUS DR2. An additional step was introduced, accounting for the offset between the observed magnitudes off the USS and the predicted magnitudes from the very low-resolution Gaia XP spectra.}{This first release contains data for 163 observed fields totaling $\sim$324 deg$^{2}$ along the Celestial Equator. The magnitudes obtained from the USS are well-calibrated, showing a difference of $\sim 15$ mmag compared to the predicted magnitudes by the GaiaXPy toolkit. By combining colors and magnitudes, 140 candidates for EMP or UMP have been identified for follow-up studies.}{The S-PLUS USS DR1 is an important milestone in the search for bright metal-poor stars, with magnitudes in the range 10 $ < r \leq 14$. The USS is an ongoing survey; in the near future, it will provide many more bright metal-poor candidate stars for spectroscopic follow-up., Comment: Accepted for publication in A&A. 17 pages 6 figures. Long table at the end of the paper

Predicting the geometrical evolution of the pore space in geological formations due to fluid-solid interactions has applications in reservoir engineering, oil recovery, and geological storage of carbon dioxide. However, modeling frameworks that combine fluid flow with physical and chemical processes at a rock's pore scale are scarce. Here, we report a method for modeling a rock's pore space as a network of connected capillaries and to simulate the capillary diameter modifications caused by reactive flow processes. Specifically, we model mineral erosion, deposition, dissolution, and precipitation processes by solving the transport equations iteratively, computing diameter changes within each capillary of the network simultaneously. Our automated modeling framework enables simulations on digital rock samples as large as (1.125mm)$^3$ with 125$\times 10^6$ voxels within seconds of CPU time per iteration. As an application of the computational method, we have simulated brine injection and calcium carbonate precipitation in sandstone. For quantitatively comparing simulation results obtained with models predicting either a constant or a flow-rate dependent precipitation, we track the time-dependent capillary diameter distribution as well as the permeability of the connected pore space. For validation and reuse, we have made the automated simulation workflow, the reactive flow model library, and the digital rock samples available in public repositories., Comment: Main article: 12 pages, 1 equation, 2 tables and 4 figures. Supplementary Information: 6 pages, 11 equations and 3 figures. Includes DOI for accompanying digital rock data, URL of github repository for flow simulator code, and of accompanying processing python code for automation of the scientific workflows

The Holstein model on a square lattice at half-filling has a well-established finite temperature phase transition to an insulating state with long range charge density wave (CDW) order. Because this CDW formation suppresses pairing, a superconducting (SC) phase emerges only with doping. In this work, we study the effects of dilution of the local phonon degrees of freedom in the Holstein model while keeping the system at half filling. We find not only that the CDW remains present up to a dilution fraction $f \sim 0.15$, but also that long range pairing is stabilized with increasing $f$, resulting in a {\it supersolid} regime centered at $f \approx 0.10$, where long range diagonal and off-diagonal correlations coexist. Further dilution results in a purely SC phase, and ultimately in a normal metal. Our results provide a new route to the supersolid phase via the introduction of impurities at fixed positions which both increase quantum fluctuations and also are immune to the competing tendency to phase separation often observed in the doped case., Comment: 11 pages and 11 figures. Accepted for publication as a Letter in Physical Review B

Education for sustainable development has evolved to include more constructive approaches and a better understanding of what is needed to align education with the cultural, societal, and pedagogical changes required to avoid the risks posed by an unsustainable society. This evolution aims to lead us toward viable, equitable, and sustainable futures. However, computing education, including software engineering, is not fully aligned with the current understanding of what is needed for transformational learning in light of our current challenges. This is partly because computing is primarily seen as a technical field, focused on industry needs. Until recently, sustainability was not a high priority for most businesses, including the digital sector, nor was it a prominent focus for higher education institutions and society. Given these challenges, we aim to propose a research roadmap to integrate sustainability principles and essential skills into the crowded computing curriculum, nurturing future software engineering professionals with a sustainability mindset. We conducted two extensive studies: a systematic review of academic literature on sustainability in computing education and a survey of industry professionals on their interest in sustainability and desired skills for graduates. Using insights from these studies, we identified key topics for teaching sustainability, including core sustainability principles, values and ethics, systems thinking, impact measurement, soft skills, business value, legal standards, and advocacy. Based on these findings, we will develop recommendations for future computing education programs that emphasise sustainability. The paper is accepted at the 2030 Software Engineering workshop, which is co-located with the FSE'24 conference.

Displacement fields are one of the main tuning knobs employed to engineer flat electronic band dispersions in twisted van der Waals multilayers. Here, we show that electric fields can also be used to tune the phonon dispersion of moir\'e superlattices formed by non-centrosymmetric materials, focusing on twisted transition metal dichalcogenide homobilayers. This effect arises from the intertwining between the local stacking configuration and the formation of polar domains within the moir\'e supercell. For small twist angles, increasing the electric field leads to a universal moir\'e phonon spectrum characterized by a substantially softened longitudinal acoustic phason mode and a flat optical phonon mode, both of which cause a significant enhancement in the vibrational density of states. The phasons also acquire a prominent chiral character, displaying a nonzero angular momentum spread across the Brillouin zone. We discuss how the tunability of the moir\'e phonon spectra may affect electronic properties, focusing on the recently discovered phenomenon of van der Waals ferroelectricity., Comment: 6 pages + SM; comments are welcome

The underlying motivation of the present work lies on a cornerstone question in spectral optimization that consists of determining sharp lower and upper uniform estimates for fundamental frequencies of a set of uniformly elliptic operators on a fixed membrane. We solve completely the problem in the plane for the general class of anisotropic operators in divergence form generated by arbitrary norms, which also includes the computation of optimal constants and the characterization of corresponding anisotropic extremizers (if they exist). Our approach is based on an isoanisotropic optimization formulation which, in turn, demands to be addressed within the broader environment of nonnegative, convex and 1-homogeneous anisotropies. A fine and detailed analysis of least energy levels associated to anisotropies with maximum degeneracy leads to a central connection between shapes and fundamental frequencies of rather degenerate elliptic operators. Such a linking also permits to establish that the supremum of anisotropic fundamental frequencies over all fixed-area membranes is infinite for any nonzero anisotropy. As a by-product, the well-known maximization conjecture for fundamental frequencies of the p-Laplace operator is proved for any p other than 2., Comment: 40 pages, comments are welcome

The ongoing deprecation of third-party cookies by web browser vendors has sparked the proposal of alternative methods to support more privacy-preserving personalized advertising on web browsers and applications. The Topics API is being proposed by Google to provide third-parties with "coarse-grained advertising topics that the page visitor might currently be interested in". In this paper, we analyze the re-identification risks for individual Internet users and the utility provided to advertising companies by the Topics API, i.e. learning the most popular topics and distinguishing between real and random topics. We provide theoretical results dependent only on the API parameters that can be readily applied to evaluate the privacy and utility implications of future API updates, including novel general upper-bounds that account for adversaries with access to unknown, arbitrary side information, the value of the differential privacy parameter $\epsilon$, and experimental results on real-world data that validate our theoretical model., Comment: CCS '24 (to appear)

We conducted extensive experiments on domain adaptation of the Meta-Llama-3-70B-Instruct model on SEC data, exploring its performance on both general and domain-specific benchmarks. Our focus included continual pre-training (CPT) and model merging, aiming to enhance the model's domain-specific capabilities while mitigating catastrophic forgetting. Through this study, we evaluated the impact of integrating financial regulatory data into a robust language model and examined the effectiveness of our model merging techniques in preserving and improving the model's instructive abilities. The model is accessible at hugging face: https://huggingface.co/arcee-ai/Llama-3-SEC-Base, arcee-ai/Llama-3-SEC-Base. This is an intermediate checkpoint of our final model, which has seen 20B tokens so far. The full model is still in the process of training. This is a preprint technical report with thorough evaluations to understand the entire process., Comment: 8 pages, 6 figures

This study investigates the practice of experts aggregating forecasts before informing a decision-maker. The significance of this subject extends to various contexts where experts inform their assessments to a decision-maker following discussions with peers. My findings show that, irrespective of the information structure, aggregation rules introduce no bias to decision-making in expected terms. Nevertheless, the concern revolves around variance. In situations where experts are equally precise, and pair-wise correlation of forecasts is the same across all pairs of experts, the network structure plays a pivotal role in decision-making variance. For classical structures, I show that star networks exhibit the highest variance, contrasting with $d$-regular networks that achieve zero variance, emphasizing their efficiency. Additionally, by employing the Poisson random graph model under the assumptions of a large network size and a small connection probability, the results indicate that both the expected Network Bias and its variance converge to zero as the network size becomes sufficiently large. These insights enhance the understanding of decision-making under different information, network structures and aggregation rules. They enrich the literature on combining forecasts by exploring the effects of prior network communication on decision-making., Comment: WP version 2024-06

Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy preservation in mind. In federated learning, it has been shown that an adversary with knowledge of the machine learning architecture is able to infer the exact value of a training element given an observation of the weight updates performed during stochastic gradient descent. In response to these threats, the privacy community recommends the use of differential privacy in the stochastic gradient descent algorithm, termed DP-SGD. However, DP has not yet been formally established as an effective countermeasure against reconstruction attacks. In this paper, we formalise the reconstruction threat model using the information-theoretic framework of quantitative information flow. We show that the Bayes' capacity, related to the Sibson mutual information of order infinity, represents a tight upper bound on the leakage of the DP-SGD algorithm to an adversary interested in performing a reconstruction attack. We provide empirical results demonstrating the effectiveness of this measure for comparing mechanisms against reconstruction threats.

The demand for strong cyber defence measures grows, especially in countries such as India, where the rate of digitalization far exceeds cybersecurity developments. The increasing amount of cyber threats highlights the urgent need to strengthen cyber defences. The literature review reveals significant shortcomings in India's cyber defence readiness, especially in real-time threat detection and response capabilities. Through simulation models, the study explores network security behaviours and the impact of defences on network security. The next section of this study focuses on implementing a cyber threat detection system that uses machine learning to identify and categorise cyber threats in real time, followed by strategies to integrate it into India's present infrastructure. Also, the study proposes an educational framework for training cyber professionals. The study concludes with a reflection on the implemented defence strategies. It adds to the continuing discussion about national security by providing an in-depth investigation of cyber warfare preparation and recommending a systematic method to improving through both technological and educational solutions., Comment: 8 pages AND 10 figures

Excess chromospheric emissions within deep photospheric lines are effective proxies of stellar magnetism for FGK stars. This emission decays with stellar age and is a potential determinant of this important stellar quantity. We report absolutely calibrated H$\alpha$ chromospheric fluxes for 511 solar-type stars in a wide interval of precisely determined masses, $[$Fe/H$]$, ages, and evolution states from high S/N, moderately high$-$resolution spectra. The comparison of H$\alpha$ and H+K chromospheric fluxes reveals a metallicity bias (absent from H$\alpha$) affecting Ca II H+K fluxes thereby metal-rich stars with deep line profiles mimic low chromospheric flux levels, and vice versa for metal-poor stars. This bias blurs the age-activity relation, precluding age determinations for old, inactive stars unless mass and $[$Fe/H$]$ are calibrated into the relation. The H+K lines being the most widely studied tool to quantify magnetic activity in FGK stars, care should be exercised in its use whenever wide ranges of mass and $[$Fe/H$]$ are involved. The H$\alpha$ age-activity-mass-metallicity calibration appears to be in line with the theoretical expectation that (other parameters being equal) more massive stars possess narrower convective zones and are less active than less massive stars, while more metal-rich stars have deeper convective zones and appear more active than metal-poorer stars. If regarded statistically in tandem with other age diagnostics, H$\alpha$ chromospheric fluxes may be suitable to constrain ages for FGK stars with acceptable precision., Comment: 14 pages, 11 figures, Published on MNRAS

Kagome vanadates {\it A}V$_3$Sb$_5$ display unusual low-temperature electronic properties including charge density waves (CDW), whose microscopic origin remains unsettled. Recently, CDW order has been discovered in a new material ScV$_6$Sn$_6$, providing an opportunity to explore whether the onset of CDW leads to unusual electronic properties. Here, we study this question using angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM). The ARPES measurements show minimal changes to the electronic structure after the onset of CDW. However, STM quasiparticle interference (QPI) measurements show strong dispersing features related to the CDW ordering vectors. A plausible explanation is the presence of a strong momentum-dependent scattering potential peaked at the CDW wavevector, associated with the existence of competing CDW instabilities. Our STM results further indicate that the bands most affected by the CDW are near vHS, analogous to the case of {\it A}V$_3$Sb$_5$ despite very different CDW wavevectors., Comment: 33 pages, 4 figures

We consider the simultaneous control of the relative phase and populations of two-level quantum systems by an external field. We apply a reverse engineering approach, which allows obtaining an analytical expression for the control field depending upon two user-defined functions that dictate the population and the relative phase dynamics. We show that, in general, the prescribed functions for the dynamics cannot be chosen arbitrarily. We implement the reverse engineering technique to reach several target states using different kinds of functions to specify the system dynamics. We show that by adjusting these dynamical functions, we can produce different kinds of control fields. These controls can be easily build, needing, apart from the dynamical function themselves, only their derivatives. The methodology presented here will certainly find many applications that go beyond simple two-level systems.

Condition-Based Maintenance is pivotal in enabling the early detection of potential failures in engineering systems, where precise prediction of the Remaining Useful Life is essential for effective maintenance and operation. However, a predominant focus in the field centers on predicting the Remaining Useful Life using unprocessed or minimally processed data, frequently neglecting the intricate dynamics inherent in the dataset. In this work we introduce a novel methodology grounded in the analysis of statistical similarity within latent data from system components. Leveraging a specifically designed architecture based on a Vector Quantized Variational Autoencoder, we create a sequence of discrete vectors which is used to estimate system-specific priors. We infer the similarity between systems by evaluating the divergence of these priors, offering a nuanced understanding of individual system behaviors. The efficacy of our approach is demonstrated through experiments on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Our validation not only underscores the potential of our method in advancing the study of latent statistical divergence but also demonstrates its superiority over existing techniques., Comment: 16 pages

This paper introduces RoTipBot, a novel robotic system for handling thin, flexible objects. Different from previous works that are limited to singulating them using suction cups or soft grippers, RoTipBot can grasp and count multiple layers simultaneously, emulating human handling in various environments. Specifically, we develop a novel vision-based tactile sensor named RoTip that can rotate and sense contact information around its tip. Equipped with two RoTip sensors, RoTipBot feeds multiple layers of thin, flexible objects into the centre between its fingers, enabling effective grasping and counting. RoTip's tactile sensing ensures both fingers maintain good contact with the object, and an adjustment approach is designed to allow the gripper to adapt to changes in the object. Extensive experiments demonstrate the efficacy of the RoTip sensor and the RoTipBot approach. The results show that RoTipBot not only achieves a higher success rate but also grasps and counts multiple layers simultaneously -- capabilities not possible with previous methods. Furthermore, RoTipBot operates up to three times faster than state-of-the-art methods. The success of RoTipBot paves the way for future research in object manipulation using mobilised tactile sensors. All the materials used in this paper are available at \url{https://sites.google.com/view/rotipbot}., Comment: 20 pages, 21 figures

Scientific Machine Learning is a new class of approaches that integrate physical knowledge and mechanistic models with data-driven techniques for uncovering governing equations of complex processes. Among the available approaches, Universal Differential Equations (UDEs) are used to combine prior knowledge in the form of mechanistic formulations with universal function approximators, like neural networks. Integral to the efficacy of UDEs is the joint estimation of parameters within mechanistic formulations and the universal function approximators using empirical data. The robustness and applicability of resultant models, however, hinge upon the rigorous quantification of uncertainties associated with these parameters, as well as the predictive capabilities of the overall model or its constituent components. With this work, we provide a formalisation of uncertainty quantification (UQ) for UDEs and investigate important frequentist and Bayesian methods. By analysing three synthetic examples of varying complexity, we evaluate the validity and efficiency of ensembles, variational inference and Markov chain Monte Carlo sampling as epistemic UQ methods for UDEs., Comment: Shared last authorship between W.W. and J.H

The Pierre Auger Observatory is the most sensitive instrument to detect photons with energies above $10^{17}$ eV. It measures extensive air showers generated by ultra high energy cosmic rays using a hybrid technique that exploits the combination of a fluorescence detector with a ground array of particle detectors. The signatures of a photon-induced air shower are a larger atmospheric depth of the shower maximum ($X_{max}$) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced cascades. In this work, a new analysis technique in the energy interval between 1 and 30 EeV (1 EeV = $10^{18}$ eV) has been developed by combining the fluorescence detector-based measurement of $X_{max}$ with the specific features of the surface detector signal through a parameter related to the air shower muon content, derived from the universality of the air shower development. No evidence of a statistically significant signal due to photon primaries was found using data collected in about 12 years of operation. Thus, upper bounds to the integral photon flux have been set using a detailed calculation of the detector exposure, in combination with a data-driven background estimation. The derived 95% confidence level upper limits are 0.0403, 0.01113, 0.0035, 0.0023, and 0.0021 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above 1, 2, 3, 5, and 10 EeV, respectively, leading to the most stringent upper limits on the photon flux in the EeV range. Compared with past results, the upper limits were improved by about 40% for the lowest energy threshold and by a factor 3 above 3 EeV, where no candidates were found and the expected background is negligible. The presented limits can be used to probe the assumptions on chemical composition of ultra-high energy cosmic rays and allow for the constraint of the mass and lifetime phase space of super-heavy dark matter particles., Comment: 19 pages, 22 figures

We report an investigation of the mass composition of cosmic rays with energies from 3 to 100 EeV (1 EeV=$10^{18}$ eV) using the distributions of the depth of shower maximum $X_\mathrm{max}$. The analysis relies on ${\sim}50,000$ events recorded by the Surface Detector of the Pierre Auger Observatory and a deep-learning-based reconstruction algorithm. Above energies of 5 EeV, the data set offers a 10-fold increase in statistics with respect to fluorescence measurements at the Observatory. After cross-calibration using the Fluorescence Detector, this enables the first measurement of the evolution of the mean and the standard deviation of the $X_\mathrm{max}$ distributions up to 100 EeV. Our findings are threefold: (1.) The evolution of the mean logarithmic mass towards a heavier composition with increasing energy can be confirmed and is extended to 100 EeV. (2.) The evolution of the fluctuations of $X_\mathrm{max}$ towards a heavier and purer composition with increasing energy can be confirmed with high statistics. We report a rather heavy composition and small fluctuations in $X_\mathrm{max}$ at the highest energies. (3.) We find indications for a characteristic structure beyond a constant change in the mean logarithmic mass, featuring three breaks that are observed in proximity to the ankle, instep, and suppression features in the energy spectrum., Comment: Version accepted for publication in Phys. Rev. D, 29 pages, 19 figures, 5 tables

We present measurements of the atmospheric depth of the shower maximum $X_\mathrm{max}$, inferred for the first time on an event-by-event level using the Surface Detector of the Pierre Auger Observatory. Using deep learning, we were able to extend measurements of the $X_\mathrm{max}$ distributions up to energies of 100 EeV ($10^{20}$ eV), not yet revealed by current measurements, providing new insights into the mass composition of cosmic rays at extreme energies. Gaining a 10-fold increase in statistics compared to the Fluorescence Detector data, we find evidence that the rate of change of the average $X_\mathrm{max}$ with the logarithm of energy features three breaks at $6.5\pm0.6~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, $11\pm 2~(\mathrm{stat})\pm1~(\mathrm{sys})$ EeV, and $31\pm5~(\mathrm{stat})\pm3~(\mathrm{sys})$ EeV, in the vicinity to the three prominent features (ankle, instep, suppression) of the cosmic-ray flux. The energy evolution of the mean and standard deviation of the measured $X_\mathrm{max}$ distributions indicates that the mass composition becomes increasingly heavier and purer, thus being incompatible with a large fraction of light nuclei between 50 EeV and 100 EeV., Comment: Version accepted for publication in Phys. Rev. Lett., 9 pages, 3 figures, 1 table

The use of e-learning systems has a long tradition, where students can study online helped by a system. In this context, the use of recommender systems is relatively new. In our research project, we investigated various ways to create a recommender system. They all aim at facilitating the learning and understanding of a student. We present a common concept of the learning path and its learning indicators and embed 5 different recommenders in this context.

Efficiently post-training large language models remains a challenging task due to the vast computational resources required. We present Spectrum, a method that accelerates LLM training by selectively targeting layer modules based on their signal-to-noise ratio (SNR), and freezing the remaining modules. Our approach, which utilizes an algorithm to compute module SNRs prior to training, has shown to effectively match the performance of full fine-tuning while reducing GPU memory usage. Experiments comparing Spectrum to existing methods such as QLoRA demonstrate its effectiveness in terms of model quality and VRAM efficiency in distributed environments.

In the context of planar conformal gauge theory, we study five-point correlation functions between the interaction Lagrangian and four of the lightest single-trace, gauge-invariant scalar primaries. After performing two light-cone OPEs, we express this correlator in terms of the three-point functions between two leading-twist spinning operators and the Lagrangian. For finite values of spin, we compute these structure constants in perturbation theory up to two loops in $\mathcal{N}=4$ Super Yang-Mills theory. Large values of spin are captured by null polygon kinematics, where we use dualities with null polygon Wilson loops as well as factorization properties to bootstrap the universal behavior of the structure constants at all loops. We find explicit maps that relate the Lagrangian structure constants with the leading-twist anomalous dimension. From the large-spin map, we recover the cusp anomalous dimension at strong and weak coupling, including genus-one terms., Comment: 10 pages, 1 figure, 1 mathematica notebook

Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems. However, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest, highlighting a need for methods to effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach's real-time performance on real-world datasets. They show that it outperforms state-of-the-art denoising and restoration performance at over 30fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications., Comment: 13pp, 5 figures, 1 table

Automatically creating a computer program using input-output examples can be a challenging task, especially when trying to synthesize computer programs that require loops or recursion. Even though the use of recursion can make the algorithmic description more succinct and declarative, this concept creates additional barriers to program synthesis algorithms such as the creation and the (tentative) evaluation of non-terminating programs. One reason is that the recursive function must define how to traverse (or generate) the data structure and, at the same time, how to process it. In functional programming, the concept of recursion schemes decouples these two tasks by putting a major focus on the latter. This can also help to avoid some of the pitfalls of recursive functions during program synthesis, as argued in a previous work where we introduced the Origami technique. In our previous paper, we showed how this technique was effective in finding solutions for programs that require folding lists. In this work, we incorporate other recursion schemes into Origami, such as accumulated folding, unfolding, and the combination of unfolding and folding. We evaluated Origami on the 29 problems of the standard General Program Synthesis Benchmark Suite 1, obtaining favorable results against other well-known algorithms. Overall, Origami achieves the best result in 25% more problems than its predecessor (HOTGP) and an even higher increase when compared to other approaches. Not only that, but it can also consistently find a solution to problems that many algorithms report a low success rate.

Commercially impactful quantum algorithms such as quantum chemistry and Shor's algorithm require a number of qubits and gates far beyond the capacity of any existing quantum processor. Distributed architectures, which scale horizontally by networking modules, provide a route to commercial utility and will eventually surpass the capability of any single quantum computing module. Such processors consume remote entanglement distributed between modules to realize distributed quantum logic. Networked quantum computers will therefore require the capability to rapidly distribute high fidelity entanglement between modules. Here we present preliminary demonstrations of some key distributed quantum computing protocols on silicon T centres in isotopically-enriched silicon. We demonstrate the distribution of entanglement between modules and consume it to apply a teleported gate sequence, establishing a proof-of-concept for T centres as a distributed quantum computing and networking platform., Comment: 14 pages, 13 figures

Each generation of cellular networks is characterized by its distinct capabilities and innovations, which reflect the significant milestones reached with each new release. 5G has made substantial progress through the deployment of advanced encoding and modulation techniques, nearly reaching the Shannon physical capacity limit. In light of the requirements of Beyond-5G technologies, there is the need of a paradigm shift in the development of communication systems. Recent developments in the realm of Artificial Intelligence (AI) have enabled the deployment of tools with high abstraction capabilities, relevant for feature extraction processes and End-to-End system optimization tasks. In this context, Semantic Communications has emerged as a novel information transmission system, with AI as one of the core components in its implementation. This communication paradigm relies on the extraction and transmission of the "semantic meaning" of the source information using AI techniques, diverging from the conventional systems that primarily focus on ensuring the successful reception of the transmitted bits. The purpose of this survey is to provide a comprehensive overview of the fundamental concepts underlying Semantic Communications, including Shannon's Information Theory, classical and modern theories of semantic information, and an examination of the framework and system design of Semantic Communications. Additionally, recent implementations are reviwed, including the analysis of Semantic Communications systems according to the information object transmitted and the objective of the information transmission. Moreover, an in-depth study of prototypes and demonstrations are presented, supporting the viability of the Semantic Communications systems. Finally, some of the most relevant open challenges are detailed, highlighting open research questions to be pursued in Semantic Communications.